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Licenses
Licensed to the Apache Software Foundation (ASF) under one or more contributor
license agreements. See the NOTICE file distributed with this work for additional
information regarding copyright ownership. The ASF licenses this file to you under
the Apache License, Version 2.0 (the "License"); you may not use this file except in
compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0.
Unless required by applicable law or agreed to in writing, software distributed under the License is
distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied. See the License for the specific language governing permissions and limitations under the License.
Apache and the Apache feather logo are trademarks of The Apache Software Foundation. Apache Lucene,
Apache Solr and their respective logos are trademarks of the Apache Software Foundation. Please see the
Apache Trademark Policy for more information.
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Apache Solr Reference Guide
This reference guide describes Apache Solr, the open source solution for search. You can download Apache
Solr from the Solr website at http://lucene.apache.org/solr/.
This Guide contains the following sections:
Getting Started: This section guides you through the installation and setup of Solr.
Using the Solr Administration User Interface: This section introduces the Solr Web-based user interface.
From your browser you can view configuration files, submit queries, view logfile settings and Java
environment settings, and monitor and control distributed configurations.
Documents, Fields, and Schema Design: This section describes how Solr organizes its data for indexing. It
explains how a Solr schema defines the fields and field types which Solr uses to organize data within the
document files it indexes.
Understanding Analyzers, Tokenizers, and Filters: This section explains how Solr prepares text for
indexing and searching. Analyzers parse text and produce a stream of tokens, lexical units used for indexing
and searching. Tokenizers break field data down into tokens. Filters perform other transformational or
selective work on token streams.
Indexing and Basic Data Operations: This section describes the indexing process and basic index
operations, such as commit, optimize, and rollback.
Searching: This section presents an overview of the search process in Solr. It describes the main
components used in searches, including request handlers, query parsers, and response writers. It lists the
query parameters that can be passed to Solr, and it describes features such as boosting and faceting, which
can be used to fine-tune search results.
The Well-Configured Solr Instance: This section discusses performance tuning for Solr. It begins with an
overview of the solrconfig.xml file, then tells you how to configure cores with solr.xml , how to configure
the Lucene index writer, and more.
Managing Solr: This section discusses important topics for running and monitoring Solr. Other topics
include how to back up a Solr instance, and how to run Solr with Java Management Extensions (JMX).
SolrCloud: This section describes the newest and most exciting of Solr’s new features, SolrCloud, which
provides comprehensive distributed capabilities.
Legacy Scaling and Distribution: This section tells you how to grow a Solr distribution by dividing a large
index into sections called shards, which are then distributed across multiple servers, or by replicating a
single index across multiple services.
Client APIs: This section tells you how to access Solr through various client APIs, including JavaScript, JSON,
and Ruby.
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About This Guide
This guide describes all of the important features and functions of Apache Solr.
Solr is free to download from http://lucene.apache.org/solr/.
Designed to provide high-level documentation, this guide is intended to be more encyclopedic and less of a
cookbook. It is structured to address a broad spectrum of needs, ranging from new developers getting
started to well-experienced developers extending their application or troubleshooting. It will be of use at
any point in the application life cycle, for whenever you need authoritative information about Solr.
The material as presented assumes that you are familiar with some basic search concepts and that you can
read XML. It does not assume that you are a Java programmer, although knowledge of Java is helpful when
working directly with Lucene or when developing custom extensions to a Lucene/Solr installation.
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Special Inline Notes
Special notes are included throughout these pages. There are several types of notes:
Information blocks
?
These provide additional information that’s useful for you to know.
Important
?
These provide information that is critical for you to know.
Tip
?
These provide helpful tips.
Caution
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These provide details on scenarios or configurations you should be careful with.
Warning
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These are meant to warn you from a possibly dangerous change or action.
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Hosts and Port Examples
The default port when running Solr is 8983. The samples, URLs and screenshots in this guide may show
different ports, because the port number that Solr uses is configurable. If you have not customized your
installation of Solr, please make sure that you use port 8983 when following the examples, or configure your
own installation to use the port numbers shown in the examples. For information about configuring port
numbers, see the section Managing Solr.
Similarly, URL examples use ‘localhost‘ throughout; if you are accessing Solr from a location remote to the
server hosting Solr, replace ‘localhost‘ with the proper domain or IP where Solr is running.
For example, we might provide a sample query like:
http://localhost:8983/solr/gettingstarted/select?q=brown+cow
There are several items in this URL you might need to change locally. First, if your server is running at
"www.example.com", you’ll replace "localhost" with the proper domain. If you aren’t using port 8983, you’ll
replace that also. Finally, you’ll want to replace "gettingstarted" (the collection or core name) with the
proper one in use in your implementation. The URL would then become:
http://www.example.com/solr/mycollection/select?q=brown+cow
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Paths
Path information is given relative to solr.home , which is the location under the main Solr installation where
Solr’s collections and their conf and data directories are stored. When running the various examples
mentioned through out this tutorial (i.e., bin/solr -e techproducts ) the solr.home will be a sub-directory
of example/ created for you automatically.
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Getting Started
Solr makes it easy for programmers to develop sophisticated, high-performance search applications with
advanced features such as faceting (arranging search results in columns with numerical counts of key
terms).
Solr builds on another open source search technology: Lucene, a Java library that provides indexing and
search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
Both Solr and Lucene are managed by the Apache Software Foundation (www.apache.org).
The Lucene search library currently ranks among the top 15 open source projects and is one of the top 5
Apache projects, with installations at over 4,000 companies. Lucene/Solr downloads have grown nearly ten
times over the past three years, with a current run-rate of over 6,000 downloads a day. The Solr search
server, which provides application builders a ready-to-use search platform on top of the Lucene search
library, is the fastest growing Lucene sub-project. Apache Lucene/Solr offers an attractive alternative to the
proprietary licensed search and discovery software vendors.
This section helps you get Solr up and running quickly, and introduces you to the basic Solr architecture and
features. It covers the following topics:
Installing Solr: A walkthrough of the Solr installation process.
Running Solr: An introduction to running Solr. Includes information on starting up the servers, adding
documents, and running queries.
A Quick Overview: A high-level overview of how Solr works.
A Step Closer: An introduction to Solr’s home directory and configuration options.
Solr Control Script Reference: a complete reference of all of the commands and options available with the
bin/solr script.
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Solr includes a Quick Start tutorial which will be helpful if you are just starting out with Solr.
You can find it online at http://lucene.apache.org/solr/quickstart.html, or in your Solr
installation at $SOLR_INSTALL_DIR/docs/quickstart.html .
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Installing Solr
This section describes how to install Solr.
You can install Solr in any system where a suitable Java Runtime Environment (JRE) is available, as detailed
below. Currently this includes Linux, OS X, and Microsoft Windows. The instructions in this section should
work for any platform, with a few exceptions for Windows as noted.
Got Java?
You will need the Java Runtime Environment (JRE) version 1.8 or higher. At a command line, check your Java
version like this:
$ java -version
java version "1.8.0_60"
Java(TM) SE Runtime Environment (build 1.8.0_60-b27)
Java HotSpot(TM) 64-Bit Server VM (build 25.60-b23, mixed mode)
The exact output will vary, but you need to make sure you meet the minimum version requirement. We also
recommend choosing a version that is not end-of-life from its vendor. If you don’t have the required version,
or if the java command is not found, download and install the latest version from Oracle at
http://www.oracle.com/technetwork/java/javase/downloads/index.html.
Installing Solr
Solr is available from the Solr website at http://lucene.apache.org/solr/.
For Linux/Unix/OSX systems, download the .tgz file. For Microsoft Windows systems, download the .zip
file.
When getting started, all you need to do is extract the Solr distribution archive to a directory of your
choosing. When you’re ready to setup Solr for a production environment, please refer to the instructions
provided on the Taking Solr to Production page.
To keep things simple for now, extract the Solr distribution archive to your local home directory, for instance
on Linux, do:
cd ~/
tar zxf solr-x.y.z.tgz
Once extracted, you are now ready to run Solr using the instructions provided in the Running Solr section.
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Running Solr
This section describes how to run Solr with an example schema, how to add documents, and how to run
queries.
Start the Server
If you didn’t start Solr after installing it, you can start it by running bin/solr from the Solr directory.
bin/solr start
If you are running Windows, you can start Solr by running bin\solr.cmd instead.
bin\solr.cmd start
This will start Solr in the background, listening on port 8983.
When you start Solr in the background, the script will wait to make sure Solr starts correctly before returning
to the command line prompt.
The bin/solr and bin\solr.cmd scripts allow you to customize how you start Solr. Let’s work through a few
examples of using the bin/solr script (if you’re running Solr on Windows, the bin\solr.cmd works the
same as what is shown in the examples below):
Solr Script Options
The bin/solr script has several options.
Script Help
To see how to use the bin/solr script, execute:
bin/solr -help
For specific usage instructions for the start command, do:
bin/solr start -help
Start Solr in the Foreground
Since Solr is a server, it is more common to run it in the background, especially on Unix/Linux. However, to
start Solr in the foreground, simply do:
bin/solr start -f
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If you are running Windows, you can run:
bin\solr.cmd start -f
Start Solr with a Different Port
To change the port Solr listens on, you can use the -p parameter when starting, such as:
bin/solr start -p 8984
Stop Solr
When running Solr in the foreground (using -f), then you can stop it using Ctrl-c . However, when running
in the background, you should use the stop command, such as:
bin/solr stop -p 8983
The stop command requires you to specify the port Solr is listening on or you can use the -all parameter to
stop all running Solr instances.
Start Solr with a Specific Bundled Example
Solr also provides a number of useful examples to help you learn about key features. You can launch the
examples using the -e flag. For instance, to launch the "techproducts" example, you would do:
bin/solr -e techproducts
Currently, the available examples you can run are: techproducts, dih, schemaless, and cloud. See the section
Running with Example Configurations for details on each example.
?
Getting Started with SolrCloud
Running the cloud example starts Solr in SolrCloud mode. For more information on
starting Solr in cloud mode, see the section Getting Started with SolrCloud.
Check if Solr is Running
If you’re not sure if Solr is running locally, you can use the status command:
bin/solr status
This will search for running Solr instances on your computer and then gather basic information about them,
such as the version and memory usage.
That’s it! Solr is running. If you need convincing, use a Web browser to see the Admin Console.
http://localhost:8983/solr/
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The Solr Admin interface.
If Solr is not running, your browser will complain that it cannot connect to the server. Check your port
number and try again.
Create a Core
If you did not start Solr with an example configuration, you would need to create a core in order to be able
to index and search. You can do so by running:
bin/solr create -c <name>
This will create a core that uses a data-driven schema which tries to guess the correct field type when you
add documents to the index.
To see all available options for creating a new core, execute:
bin/solr create -help
Add Documents
Solr is built to find documents that match queries. Solr’s schema provides an idea of how content is
structured (more on the schema later), but without documents there is nothing to find. Solr needs input
before it can do much.
You may want to add a few sample documents before trying to index your own content. The Solr installation
comes with different types of example documents located under the sub-directories of the example/
directory of your installation.
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In the bin/ directory is the post script, a command line tool which can be used to index different types of
documents. Do not worry too much about the details for now. The Indexing and Basic Data Operations
section has all the details on indexing.
To see some information about the usage of bin/post , use the -help option. Windows users, see the section
for Post Tool on Windows.
bin/post can post various types of content to Solr, including files in Solr’s native XML and JSON formats, CSV
files, a directory tree of rich documents, or even a simple short web crawl. See the examples at the end of
bin/post -help for various commands to easily get started posting your content into Solr.
Go ahead and add all the documents in some example XML files:
$ bin/post -c gettingstarted example/exampledocs/*.xml
SimplePostTool version 5.0.0
Posting files to [base] url http://localhost:8983/solr/gettingstarted/update...
Entering auto mode. File endings considered are
xml,json,csv,pdf,doc,docx,ppt,pptx,xls,xlsx,odt,odp,ods,ott,otp,ots,rtf,htm,html,txt,log
POSTing file gb18030-example.xml (application/xml) to [base]
POSTing file hd.xml (application/xml) to [base]
POSTing file ipod_other.xml (application/xml) to [base]
POSTing file ipod_video.xml (application/xml) to [base]
POSTing file manufacturers.xml (application/xml) to [base]
POSTing file mem.xml (application/xml) to [base]
POSTing file money.xml (application/xml) to [base]
POSTing file monitor.xml (application/xml) to [base]
POSTing file monitor2.xml (application/xml) to [base]
POSTing file mp500.xml (application/xml) to [base]
POSTing file sd500.xml (application/xml) to [base]
POSTing file solr.xml (application/xml) to [base]
POSTing file utf8-example.xml (application/xml) to [base]
POSTing file vidcard.xml (application/xml) to [base]
14 files indexed.
COMMITting Solr index changes to http://localhost:8983/solr/gettingstarted/update...
Time spent: 0:00:00.153
That’s it! Solr has indexed the documents contained in those files.
Ask Questions
Now that you have indexed documents, you can perform queries. The simplest way is by building a URL that
includes the query parameters. This is exactly the same as building any other HTTP URL.
For example, the following query searches all document fields for "video":
http://localhost:8983/solr/gettingstarted/select?q=video
Notice how the URL includes the host name ( localhost ), the port number where the server is listening (
8983 ), the application name ( solr ), the request handler for queries ( select ), and finally, the query itself
( q=video ).
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The results are contained in an XML document, which you can examine directly by clicking on the link above.
The document contains two parts. The first part is the responseHeader , which contains information about
the response itself. The main part of the reply is in the result tag, which contains one or more doc tags, each
of which contains fields from documents that match the query. You can use standard XML transformation
techniques to mold Solr’s results into a form that is suitable for displaying to users. Alternatively, Solr can
output the results in JSON, PHP, Ruby and even user-defined formats.
Just in case you are not running Solr as you read, the following screen shot shows the result of a query (the
next example, actually) as viewed in Mozilla Firefox. The top-level response contains a lst named
responseHeader and a result named response. Inside result, you can see the three docs that represent the
search results.
An XML response to a query.
Once you have mastered the basic idea of a query, it is easy to add enhancements to explore the query
syntax. This one is the same as before but the results only contain the ID, name, and price for each returned
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document. If you don’t specify which fields you want, all of them are returned.
http://localhost:8983/solr/gettingstarted/select?q=video&fl=id,name,price
Here is another example which searches for "black" in the name field only. If you do not tell Solr which field
to search, it will search default fields, as specified in the schema.
http://localhost:8983/solr/gettingstarted/select?q=name:black
You can provide ranges for fields. The following query finds every document whose price is between $0 and
$400.
http://localhost:8983/solr/gettingstarted/select?q=price:0%20TO%20400&fl=id,name,price
Faceted browsing is one of Solr’s key features. It allows users to narrow search results in ways that are
meaningful to your application. For example, a shopping site could provide facets to narrow search results
by manufacturer or price.
Faceting information is returned as a third part of Solr’s query response. To get a taste of this power, take a
look at the following query. It adds facet=true and facet.field=cat .
http://localhost:8983/solr/gettingstarted/select?q=price:0%20TO%20400&fl=id,name,price&facet
=true&facet.field=cat
In addition to the familiar responseHeader and response from Solr, a facet_counts element is also present.
Here is a view with the responseHeader and response collapsed so you can see the faceting information
clearly.
An XML Response with faceting
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<response>
<lst name="responseHeader">
...
</lst>
<result name="response" numFound="9" start="0">
<doc>
<str name="id">SOLR1000</str>
<str name="name">Solr, the Enterprise Search Server</str>
<float name="price">0.0</float></doc>
...
</result>
<lst name="facet_counts">
<lst name="facet_queries"/>
<lst name="facet_fields">
<lst name="cat">
<int name="electronics">6</int>
<int name="memory">3</int>
<int name="search">2</int>
<int name="software">2</int>
<int name="camera">1</int>
<int name="copier">1</int>
<int name="multifunction printer">1</int>
<int name="music">1</int>
<int name="printer">1</int>
<int name="scanner">1</int>
<int name="connector">0</int>
<int name="currency">0</int>
<int name="graphics card">0</int>
<int name="hard drive">0</int>
<int name="monitor">0</int>
</lst>
</lst>
<lst name="facet_dates"/>
<lst name="facet_ranges"/>
</lst>
</response>
The facet information shows how many of the query results have each possible value of the cat field. You
could easily use this information to provide users with a quick way to narrow their query results. You can
filter results by adding one or more filter queries to the Solr request. This request constrains documents
with a category of "software".
http://localhost:8983/solr/gettingstarted/select?q=price:0%20TO%20400&fl=id,name,price&facet
=true&facet.field=cat&fq=cat:software
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A Quick Overview
Having had some fun with Solr, you will now learn about all the cool things it can do.
Here is a example of how Solr might be integrated into an application:
Solr integration with applications
In the scenario above, Solr runs along side other server applications. For example, an online store
application would provide a user interface, a shopping cart, and a way to make purchases for end users;
while an inventory management application would allow store employees to edit product information. The
product metadata would be kept in some kind of database, as well as in Solr.
Solr makes it easy to add the capability to search through the online store through the following steps:
1. Define a schema. The schema tells Solr about the contents of documents it will be indexing. In the online
store example, the schema would define fields for the product name, description, price, manufacturer,
and so on. Solr’s schema is powerful and flexible and allows you to tailor Solr’s behavior to your
application. See Documents, Fields, and Schema Design for all the details.
2. Deploy Solr.
3. Feed Solr documents for which your users will search.
4. Expose search functionality in your application.
Because Solr is based on open standards, it is highly extensible. Solr queries are RESTful, which means, in
essence, that a query is a simple HTTP request URL and the response is a structured document: mainly XML,
but it could also be JSON, CSV, or some other format. This means that a wide variety of clients will be able to
use Solr, from other web applications to browser clients, rich client applications, and mobile devices. Any
platform capable of HTTP can talk to Solr. See Client APIs for details on client APIs.
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Solr is based on the Apache Lucene project, a high-performance, full-featured search engine. Solr offers
support for the simplest keyword searching through to complex queries on multiple fields and faceted
search results. Searching has more information about searching and queries.
If Solr’s capabilities are not impressive enough, its ability to handle very high-volume applications should do
the trick.
A relatively common scenario is that you have so much data, or so many queries, that a single Solr server is
unable to handle your entire workload. In this case, you can scale up the capabilities of your application
using SolrCloud to better distribute the data, and the processing of requests, across many servers. Multiple
options can be mixed and matched depending on the type of scalability you need.
For example: "Sharding" is a scaling technique in which a collection is split into multiple logical pieces called
"shards" in order to scale up the number of documents in a collection beyond what could physically fit on a
single server. Incoming queries are distributed to every shard in the collection, which respond with merged
results. Another technique available is to increase the "Replication Factor" of your collection, which allows
you to add servers with additional copies of your collection to handle higher concurrent query load by
spreading the requests around to multiple machines. Sharding and Replication are not mutually exclusive,
and together make Solr an extremely powerful and scalable platform.
Best of all, this talk about high-volume applications is not just hypothetical: some of the famous Internet
sites that use Solr today are Macy’s, EBay, and Zappo’s.
For more information, take a look at https://wiki.apache.org/solr/PublicServers.
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A Step Closer
You already have some idea of Solr’s schema. This section describes Solr’s home directory and other
configuration options.
When Solr runs in an application server, it needs access to a home directory. The home directory contains
important configuration information and is the place where Solr will store its index. The layout of the home
directory will look a little different when you are running Solr in standalone mode vs when you are running
in SolrCloud mode.
The crucial parts of the Solr home directory are shown in these examples:
Standalone Mode
<solr-home-directory>/
solr.xml
core_name1/
core.properties
conf/
solrconfig.xml
managed-schema
data/
core_name2/
core.properties
conf/
solrconfig.xml
managed-schema
data/
SolrCloud Mode
<solr-home-directory>/
solr.xml
core_name1/
core.properties
data/
core_name2/
core.properties
data/
You may see other files, but the main ones you need to know are:
? solr.xml specifies configuration options for your Solr server instance. For more information on
solr.xml see Solr Cores and solr.xml.
? Per Solr Core:
? core.properties defines specific properties for each core such as its name, the collection the core
belongs to, the location of the schema, and other parameters. For more details on core.properties ,
see the section Defining core.properties.
? solrconfig.xml controls high-level behavior. You can, for example, specify an alternate location for
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the data directory. For more information on solrconfig.xml , see Configuring solrconfig.xml.
? managed-schema (or schema.xml instead) describes the documents you will ask Solr to index. The
Schema define a document as a collection of fields. You get to define both the field types and the
fields themselves. Field type definitions are powerful and include information about how Solr
processes incoming field values and query values. For more information on Solr Schemas, see
Documents, Fields, and Schema Design and the Schema API.
? data/ The directory containing the low level index files.
Note that the SolrCloud example does not include a conf directory for each Solr Core (so there is no
solrconfig.xml or Schema file). This is because the configuration files usually found in the conf directory
are stored in ZooKeeper so they can be propagated across the cluster.
If you are using SolrCloud with the embedded ZooKeeper instance, you may also see zoo.cfg and zoo.data
which are ZooKeeper configuration and data files. However, if you are running your own ZooKeeper
ensemble, you would supply your own ZooKeeper configuration file when you start it and the copies in Solr
would be unused. For more information about ZooKeeper and SolrCloud, see the section SolrCloud.
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Solr Control Script Reference
Solr includes a script known as “bin/solr” that allows you to perform many common operations on your Solr
installation or cluster.
You can start and stop Solr, create and delete collections or cores, perform operations on ZooKeeper and
check the status of Solr and configured shards.
You can find the script in the bin/ directory of your Solr installation. The bin/solr script makes Solr easier
to work with by providing simple commands and options to quickly accomplish common goals.
More examples of bin/solr in use are available throughout the Solr Reference Guide, but particularly in the
sections Running Solr and Getting Started with SolrCloud.
Starting and Stopping
Start and Restart
The start command starts Solr. The restart command allows you to restart Solr while it is already running
or if it has been stopped already.
The start and restart commands have several options to allow you to run in SolrCloud mode, use an
example configuration set, start with a hostname or port that is not the default and point to a local
ZooKeeper ensemble.
bin/solr start [options]
bin/solr start -help
bin/solr restart [options]
bin/solr restart -help
When using the restart command, you must pass all of the parameters you initially passed when you
started Solr. Behind the scenes, a stop request is initiated, so Solr will be stopped before being started again.
If no nodes are already running, restart will skip the step to stop and proceed to starting Solr.
Available Parameters
The`bin/solr` script provides many options to allow you to customize the server in common ways, such as
changing the listening port. However, most of the defaults are adequate for most Solr installations,
especially when just getting started.
Parameter Description Example
-a "<string>" Start Solr with additional JVM parameters,
such as those starting with -X. If you are
passing JVM parameters that begin with "-
D", you can omit the -a option.
bin/solr start -a "-Xdebug
-Xrunjdwp:transport=dt_socket,
server=y,suspend=n,address=1044"
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Parameter Description Example
-cloud Start Solr in SolrCloud mode, which will
also launch the embedded ZooKeeper
instance included with Solr.
This option can be shortened to simply -
c .
If you are already running a ZooKeeper
ensemble that you want to use instead of
the embedded (single-node) ZooKeeper,
you should also pass the -z parameter.
For more details, see the section
SolrCloud Mode below.
bin/solr start -c
-d <dir> Define a server directory, defaults to
server (as in, $SOLR_HOME/server ). It is
uncommon to override this option. When
running multiple instances of Solr on the
same host, it is more common to use the
same server directory for each instance
and use a unique Solr home directory
using the -s option.
bin/solr start -d newServerDir
-e <name> Start Solr with an example configuration.
These examples are provided to help you
get started faster with Solr generally, or
just try a specific feature.
The available options are:
? cloud
? techproducts
? dih
? schemaless
See the section Running with Example
Configurations below for more details on
the example configurations.
bin/solr start -e schemaless
-f Start Solr in the foreground; you cannot
use this option when running examples
with the -e option.
bin/solr start -f
-h <hostname> Start Solr with the defined hostname. If
this is not specified, ‘localhost‘ will be
assumed.
bin/solr start -h search.mysolr.com
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Parameter Description Example
-m <memory> Start Solr with the defined value as the
min (-Xms) and max (-Xmx) heap size for
the JVM.
bin/solr start -m 1g
-noprompt Start Solr and suppress any prompts that
may be seen with another option. This
would have the side effect of accepting all
defaults implicitly.
For example, when using the "cloud"
example, an interactive session guides
you through several options for your
SolrCloud cluster. If you want to accept all
of the defaults, you can simply add the
-noprompt option to your request.
bin/solr start -e cloud -noprompt
-p <port> Start Solr on the defined port. If this is not
specified, ‘8983‘ will be used.
bin/solr start -p 8655
-s <dir> Sets the solr.solr.home system property;
Solr will create core directories under this
directory. This allows you to run multiple
Solr instances on the same host while
reusing the same server directory set
using the -d parameter. If set, the
specified directory should contain a
solr.xml file, unless solr.xml exists in
ZooKeeper. The default value is
server/solr .
This parameter is ignored when running
examples (-e), as the solr.solr.home
depends on which example is run.
bin/solr start -s newHome
-v Be more verbose. This changes the
logging level of log4j from INFO to DEBUG ,
having the same effect as if you edited
log4j.properties accordingly.
bin/solr start -f -v
-q Be more quiet. This changes the logging
level of log4j from INFO to WARN , having
the same effect as if you edited
log4j.properties accordingly. This can
be useful in a production setting where
you want to limit logging to warnings and
errors.
bin/solr start -f -q
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Parameter Description Example
-V Start Solr with verbose messages from
the start script.
bin/solr start -V
-z <zkHost> Start Solr with the defined ZooKeeper
connection string. This option is only used
with the -c option, to start Solr in
SolrCloud mode. If this option is not
provided, Solr will start the embedded
ZooKeeper instance and use that instance
for SolrCloud operations.
bin/solr start -c -z
server1:2181,server2:2181
-force If attempting to start Solr as the root user,
the script will exit with a warning that
running Solr as "root" can cause
problems. It is possible to override this
warning with the -force parameter.
sudo bin/solr start -force
To emphasize how the default settings work take a moment to understand that the following commands are
equivalent:
bin/solr start
bin/solr start -h localhost -p 8983 -d server -s solr -m 512m
It is not necessary to define all of the options when starting if the defaults are fine for your needs.
Setting Java System Properties
The bin/solr script will pass any additional parameters that begin with -D to the JVM, which allows you to
set arbitrary Java system properties.
For example, to set the auto soft-commit frequency to 3 seconds, you can do:
bin/solr start -Dsolr.autoSoftCommit.maxTime=3000
SolrCloud Mode
The -c and -cloud options are equivalent:
bin/solr start -c
bin/solr start -cloud
If you specify a ZooKeeper connection string, such as -z 192.168.1.4:2181 , then Solr will connect to
ZooKeeper and join the cluster.
If you do not specify the -z option when starting Solr in cloud mode, then Solr will launch an embedded
ZooKeeper server listening on the Solr port + 1000, i.e., if Solr is running on port 8983, then the embedded
ZooKeeper will be listening on port 9983.
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?
If your ZooKeeper connection string uses a chroot, such as localhost:2181/solr , then you
need to create the /solr znode before launching SolrCloud using the bin/solr script.
+ To do this use the mkroot command outlined below, for example: bin/solr zk mkroot
/solr -z 192.168.1.4:2181
When starting in SolrCloud mode, the interactive script session will prompt you to choose a configset to use.
For more information about starting Solr in SolrCloud mode, see also the section Getting Started with
SolrCloud.
Running with Example Configurations
bin/solr start -e <name>
The example configurations allow you to get started quickly with a configuration that mirrors what you hope
to accomplish with Solr.
Each example launches Solr with a managed schema, which allows use of the Schema API to make schema
edits, but does not allow manual editing of a Schema file If you would prefer to manually modify a
schema.xml file directly, you can change this default as described in the section Schema Factory Definition in
SolrConfig.
Unless otherwise noted in the descriptions below, the examples do not enable SolrCloud nor schemaless
mode.
The following examples are provided:
? cloud: This example starts a 1-4 node SolrCloud cluster on a single machine. When chosen, an interactive
session will start to guide you through options to select the initial configset to use, the number of nodes
for your example cluster, the ports to use, and name of the collection to be created. When using this
example, you can choose from any of the available configsets found in
$SOLR_HOME/server/solr/configsets .
? techproducts: This example starts Solr in standalone mode with a schema designed for the sample
documents included in the $SOLR_HOME/example/exampledocs directory. The configset used can be
found in $SOLR_HOME/server/solr/configsets/sample_techproducts_configs .
? dih: This example starts Solr in standalone mode with the DataImportHandler (DIH) enabled and several
example dataconfig.xml files pre-configured for different types of data supported with DIH (such as,
database contents, email, RSS feeds, etc.). The configset used is customized for DIH, and is found in
$SOLR_HOME/example/example-DIH/solr/conf . For more information about DIH, see the section
Uploading Structured Data Store Data with the Data Import Handler.
? schemaless: This example starts Solr in standalone mode using a managed schema, as described in the
section Schema Factory Definition in SolrConfig, and provides a very minimal pre-defined schema. Solr
will run in Schemaless Mode with this configuration, where Solr will create fields in the schema on the fly
and will guess field types used in incoming documents. The configset used can be found in
$SOLR_HOME/server/solr/configsets/data_driven_schema_configs .
?
The run in-foreground option ( -f ) is not compatible with the -e option since the script
needs to perform additional tasks after starting the Solr server.
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Stop
The stop command sends a STOP request to a running Solr node, which allows it to shutdown gracefully.
The command will wait up to 5 seconds for Solr to stop gracefully and then will forcefully kill the process (kill
-9).
bin/solr stop [options]
bin/solr stop -help
Available Parameters
Parameter Description Example
-p <port> Stop Solr running on the given port. If you
are running more than one instance, or
are running in SolrCloud mode, you either
need to specify the ports in separate
requests or use the -all option.
bin/solr stop -p 8983
-all Stop all running Solr instances that have a
valid PID.
bin/solr stop -all
-k <key> Stop key used to protect from stopping
Solr inadvertently; default is "solrrocks".
bin/solr stop -k solrrocks
System Information
Version
The version command simply returns the version of Solr currently installed and immediately exists.
$ bin/solr version
X.Y.0
Status
The status command displays basic JSON-formatted information for any Solr nodes found running on the
local system.
The status command uses the SOLR_PID_DIR environment variable to locate Solr process ID files to find
running Solr instances, which defaults to the bin directory.
bin/solr status
The output will include a status of each node of the cluster, as in this example:
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Found 2 Solr nodes:
Solr process 39920 running on port 7574
{
"solr_home":"/Applications/Solr/example/cloud/node2/solr/",
"version":"X.Y.0",
"startTime":"2015-02-10T17:19:54.739Z",
"uptime":"1 days, 23 hours, 55 minutes, 48 seconds",
"memory":"77.2 MB (%15.7) of 490.7 MB",
"cloud":{
"ZooKeeper":"localhost:9865",
"liveNodes":"2",
"collections":"2"}}
Solr process 39827 running on port 8865
{
"solr_home":"/Applications/Solr/example/cloud/node1/solr/",
"version":"X.Y.0",
"startTime":"2015-02-10T17:19:49.057Z",
"uptime":"1 days, 23 hours, 55 minutes, 54 seconds",
"memory":"94.2 MB (%19.2) of 490.7 MB",
"cloud":{
"ZooKeeper":"localhost:9865",
"liveNodes":"2",
"collections":"2"}}
Healthcheck
The healthcheck command generates a JSON-formatted health report for a collection when running in
SolrCloud mode. The health report provides information about the state of every replica for all shards in a
collection, including the number of committed documents and its current state.
bin/solr healthcheck [options]
bin/solr healthcheck -help
Available Parameters
Parameter Description Example
-c <collection> Name of the collection to run a
healthcheck against (required).
bin/solr healthcheck -c
gettingstarted
-z <zkhost> ZooKeeper connection string, defaults to
localhost:9983. If you are running Solr on
a port other than 8983, you will have to
specify the ZooKeeper connection string.
By default, this will be the Solr port + 1000.
bin/solr healthcheck -z
localhost:2181
Below is an example healthcheck request and response using a non-standard ZooKeeper connect string,
with 2 nodes running:
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$ bin/solr healthcheck -c gettingstarted -z localhost:9865
{
"collection":"gettingstarted",
"status":"healthy",
"numDocs":0,
"numShards":2,
"shards":[
{
"shard":"shard1",
"status":"healthy",
"replicas":[
{
"name":"core_node1",
"url":"http://10.0.1.10:8865/solr/gettingstarted_shard1_replica2/",
"numDocs":0,
"status":"active",
"uptime":"2 days, 1 hours, 18 minutes, 48 seconds",
"memory":"25.6 MB (%5.2) of 490.7 MB",
"leader":true},
{
"name":"core_node4",
"url":"http://10.0.1.10:7574/solr/gettingstarted_shard1_replica1/",
"numDocs":0,
"status":"active",
"uptime":"2 days, 1 hours, 18 minutes, 42 seconds",
"memory":"95.3 MB (%19.4) of 490.7 MB"}]},
{
"shard":"shard2",
"status":"healthy",
"replicas":[
{
"name":"core_node2",
"url":"http://10.0.1.10:8865/solr/gettingstarted_shard2_replica2/",
"numDocs":0,
"status":"active",
"uptime":"2 days, 1 hours, 18 minutes, 48 seconds",
"memory":"25.8 MB (%5.3) of 490.7 MB"},
{
"name":"core_node3",
"url":"http://10.0.1.10:7574/solr/gettingstarted_shard2_replica1/",
"numDocs":0,
"status":"active",
"uptime":"2 days, 1 hours, 18 minutes, 42 seconds",
"memory":"95.4 MB (%19.4) of 490.7 MB",
"leader":true}]}]}
Collections and Cores
The bin/solr script can also help you create new collections (in SolrCloud mode) or cores (in standalone
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mode), or delete collections.
Create
The create command detects the mode that Solr is running in (standalone or SolrCloud) and then creates a
core or collection depending on the mode.
bin/solr create [options]
bin/solr create -help
Available Parameters
Parameter Description Example
-c <name> Name of the core or collection to create
(required).
bin/solr create -c mycollection
-d <confdir> The configuration directory. This defaults
to data_driven_schema_configs .
See the section Configuration Directories
and SolrCloud below for more details
about this option when running in
SolrCloud mode.
bin/solr create -d basic_configs
-n <configName> The configuration name. This defaults to
the same name as the core or collection.
bin/solr create -n basic
-p <port> Port of a local Solr instance to send the
create command to; by default the script
tries to detect the port by looking for
running Solr instances.
This option is useful if you are running
multiple standalone Solr instances on the
same host, thus requiring you to be
specific about which instance to create
the core in.
bin/solr create -p 8983
-s <shards>
-shards
Number of shards to split a collection into,
default is 1; only applies when Solr is
running in SolrCloud mode.
bin/solr create -s 2
-rf <replicas>
-replicationFactor
Number of copies of each document in
the collection. The default is 1 (no
replication).
bin/solr create -rf 2
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Parameter Description Example
-force If attempting to run create as "root" user,
the script will exit with a warning that
running Solr or actions against Solr as
"root" can cause problems. It is possible
to override this warning with the -force
parameter.
bin/solr create -c foo -force
Configuration Directories and SolrCloud
Before creating a collection in SolrCloud, the configuration directory used by the collection must be
uploaded to ZooKeeper. The create command supports several use cases for how collections and
configuration directories work. The main decision you need to make is whether a configuration directory in
ZooKeeper should be shared across multiple collections.
Let’s work through a few examples to illustrate how configuration directories work in SolrCloud.
First, if you don’t provide the -d or -n options, then the default configuration
( $SOLR_HOME/server/solr/configsets/data_driven_schema_configs/conf ) is uploaded to ZooKeeper
using the same name as the collection. For example, the following command will result in the
data_driven_schema_configs configuration being uploaded to /configs/contacts in ZooKeeper: bin/solr
create -c contacts . If you create another collection, by doing bin/solr create -c contacts2 , then
another copy of the data_driven_schema_configs directory will be uploaded to ZooKeeper under
/configs/contacts2 . Any changes you make to the configuration for the contacts collection will not affect
the contacts2 collection. Put simply, the default behavior creates a unique copy of the configuration
directory for each collection you create.
You can override the name given to the configuration directory in ZooKeeper by using the -n option. For
instance, the command bin/solr create -c logs -d basic_configs -n basic will upload the
server/solr/configsets/basic_configs/conf directory to ZooKeeper as /configs/basic .
Notice that we used the -d option to specify a different configuration than the default. Solr provides several
built-in configurations under server/solr/configsets . However you can also provide the path to your own
configuration directory using the -d option. For instance, the command bin/solr create -c mycoll -d
/tmp/myconfigs , will upload /tmp/myconfigs into ZooKeeper under /configs/mycoll . To reiterate, the
configuration directory is named after the collection unless you override it using the -n option.
Other collections can share the same configuration by specifying the name of the shared configuration
using the -n option. For instance, the following command will create a new collection that shares the basic
configuration created previously: bin/solr create -c logs2 -n basic .
Data-driven Schema and Shared Configurations
The data_driven_schema_configs schema can mutate as data is indexed. Consequently, we recommend
that you do not share data-driven configurations between collections unless you are certain that all
collections should inherit the changes made when indexing data into one of the collections.
Delete
The delete command detects the mode that Solr is running in (standalone or SolrCloud) and then deletes
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the specified core (standalone) or collection (SolrCloud) as appropriate.
bin/solr delete [options]
bin/solr delete -help
If running in SolrCloud mode, the delete command checks if the configuration directory used by the
collection you are deleting is being used by other collections. If not, then the configuration directory is also
deleted from ZooKeeper. For example, if you created a collection by doing bin/solr create -c contacts ,
then the delete command bin/solr delete -c contacts will check to see if the /configs/contacts
configuration directory is being used by any other collections. If not, then the /configs/contacts directory
is removed from ZooKeeper.
Available Parameters
Parameter Description Example
-c <name> Name of the core / collection to delete
(required).
bin/solr delete -c mycoll
-deleteConfig
<true|false>
Delete the configuration directory from
ZooKeeper. The default is true.
If the configuration directory is being
used by another collection, then it will not
be deleted even if you pass
-deleteConfig as true.
bin/solr delete -deleteConfig false
-p <port> The port of a local Solr instance to send
the delete command to. By default the
script tries to detect the port by looking
for running Solr instances.
This option is useful if you are running
multiple standalone Solr instances on the
same host, thus requiring you to be
specific about which instance to delete
the core from.
bin/solr delete -p 8983
Authentication
The bin/solr script allows enabling or disabling Basic Authentication, allowing you to configure
authentication from the command line.
Currently, this script only enables Basic Authentication, and is only available when using SolrCloud mode.
Enabling Basic Authentication
The command bin/solr auth enable configures Solr to use Basic Authentication when accessing the User
Interface, using bin/solr and any API requests.
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?
For more information about Solr’s authentication plugins, see the section Securing Solr. For
more information on Basic Authentication support specifically, see the section Basic
Authentication Plugin.
The bin/solr auth enable command makes several changes to enable Basic Authentication:
? Creates a security.json file and uploads it to ZooKeeper. The security.json file will look similar to:
{
"authentication":{
"blockUnknown": false,
"class":"solr.BasicAuthPlugin",
"credentials":{"user":"vgGVo69YJeUg/O6AcFiowWsdyOUdqfQvOLsrpIPMCzk=
7iTnaKOWe+Uj5ZfGoKKK2G6hrcF10h6xezMQK+LBvpI="}
},
"authorization":{
"class":"solr.RuleBasedAuthorizationPlugin",
"permissions":[
{"name":"security-edit", "role":"admin"},
{"name":"collection-admin-edit", "role":"admin"},
{"name":"core-admin-edit", "role":"admin"}
],
"user-role":{"user":"admin"}
}
}
? Adds two lines to bin/solr.in.sh or bin\solr.in.cmd to set the authentication type, and the path to
basicAuth.conf :
# The following lines added by ./solr for enabling BasicAuth
SOLR_AUTH_TYPE="basic"
SOLR_AUTHENTICATION_OPTS="-Dsolr.httpclient.config=/path/to/solr-
6.6.0/server/solr/basicAuth.conf"
? Creates the file server/solr/basicAuth.conf to store the credential information that is used with
bin/solr commands.
The command takes the following parameters:
-credentials
The username and password in the format of username:password of the initial user.
If you prefer not to pass the username and password as an argument to the script, you can choose the
-prompt option. Either -credentials or -prompt must be specified.
-prompt
If prompt is preferred, pass true as a parameter to request the script to prompt the user to enter a
username and password.
Either -credentials or -prompt must be specified.
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-blockUnknown
When true, blocks all unauthenticated users from accessing Solr. This defaults to false, which means
unauthenticated users will still be able to access Solr.
-updateIncludeFileOnly
When true, only the settings in bin/solr.in.sh or bin\solr.in.cmd will be updated, and security.json
will not be created.
-z
Defines the ZooKeeper connect string. This is useful if you want to enable authentication before all your
Solr nodes have come up.
-d
Defines the Solr server directory, by default $SOLR_HOME/server . It is not common to need to override the
default, and is only needed if you have customized the $SOLR_HOME directory path.
-s
Defines the location of solr.solr.home , which by default is server/solr . If you have multiple instances
of Solr on the same host, or if you have customized the $SOLR_HOME directory path, you likely need to
define this.
Disabling Basic Authentication
You can disable Basic Authentication with bin/solr auth disable .
If the -updateIncludeFileOnly option is set to true, then only the settings in bin/solr.in.sh or
bin\solr.in.cmd will be updated, and security.json will not be removed.
If the -updateIncludeFileOnly option is set to false, then the settings in bin/solr.in.sh or
bin\solr.in.cmd will be updated, and security.json will be removed. However, the basicAuth.conf file is
not removed with either option.
ZooKeeper Operations
The bin/solr script allows certain operations affecting ZooKeeper. These operations are for SolrCloud mode
only. The operations are available as sub-commands, which each have their own set of options.
bin/solr zk [sub-command] [options]
bin/solr zk -help
?
Solr should have been started at least once before issuing these commands to initialize
ZooKeeper with the znodes Solr expects. Once ZooKeeper is initialized, Solr doesn’t need to
be running on any node to use these commands.
Upload a Configuration Set
Use the zk upconfig command to upload one of the pre-configured configuration set or a customized
configuration set to ZooKeeper.
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Available Parameters (all parameters are required)
Parameter Description Example
-n <name> Name of the configuration set in
ZooKeeper. This command will upload the
configuration set to the "configs"
ZooKeeper node giving it the name
specified.
You can see all uploaded configuration
sets in the Admin UI via the Cloud
screens. Choose Cloud → Tree → configs
to see them.
If a pre-existing configuration set is
specified, it will be overwritten in
ZooKeeper.
-n myconfig
-d <configset dir> The path of the configuration set to
upload. It should have a "conf" directory
immediately below it that in turn contains
solrconfig.xml etc.
If just a name is supplied,
$SOLR_HOME/server/solr/configsets
will be checked for this name. An absolute
path may be supplied instead.
-d directory_under_configsets
-d /path/to/configset/source
-z <zkHost> The ZooKeeper connection string.
Unnecessary if ZK_HOST is defined in
solr.in.sh or solr.in.cmd .
-z 123.321.23.43:2181
An example of this command with these parameters is:
bin/solr zk upconfig -z 111.222.333.444:2181 -n mynewconfig -d /path/to/configset
?
Reload Collections When Changing Configurations
This command does not automatically make changes effective! It simply uploads the
configuration sets to ZooKeeper. You can use the Collection API’s RELOAD command to
reload any collections that uses this configuration set.
Download a Configuration Set
Use the zk downconfig command to download a configuration set from ZooKeeper to the local filesystem.
Available Parameters (all parameters are required)
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Parameter Description Example
-n <name> Name of config set in ZooKeeper to
download. The Admin UI Cloud → Tree →
configs node lists all available
configuration sets.
-n myconfig
-d <configset dir> The path to write the downloaded
configuration set into. If just a name is
supplied,
$SOLR_HOME/server/solr/configsets
will be the parent. An absolute path may
be supplied as well.
In either case, pre-existing configurations
at the destination will be overwritten!
-d directory_under_configsets
-d /path/to/configset/destination
-z <zkHost> The ZooKeeper connection string.
Unnecessary if ZK_HOST is defined in
solr.in.sh or solr.in.cmd .
-z 123.321.23.43:2181
An example of this command with the parameters is:
bin/solr zk downconfig -z 111.222.333.444:2181 -n mynewconfig -d /path/to/configset
A "best practice" is to keep your configuration sets in some form of version control as the system-of-record.
In that scenario, downconfig should rarely be used.
Copy between Local Files and ZooKeeper znodes
Use the zk cp command for transferring files and directories between ZooKeeper znodes and your local
drive. This command will copy from the local drive to ZooKeeper, from ZooKeeper to the local drive or from
ZooKeeper to ZooKeeper.
Available Parameters
Parameter Description Example
-r Optional. Do a recursive copy. The
command will fail if the <src> has children
unless ‘-r‘ is specified.
-r
<src> The file or path to copy from. If prefixed
with zk: then the source is presumed to
be ZooKeeper. If no prefix or the prefix is
‘file:‘ this is the local drive. At least one of
<src> or <dest> must be prefixed by ‘zk:’
or the command will fail.
zk:/configs/myconfigs/solrconfig.xm
l
file:/Users/apache/configs/src
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Parameter Description Example
<dest> The file or path to copy to. If prefixed with
zk: then the source is presumed to be
ZooKeeper. If no prefix or the prefix is
‘file:‘ this is the local drive. At least one of
<src> or <dest> must be prefixed by zk:
or the command will fail. If <dest> ends in
a slash character it names a directory.
zk:/configs/myconfigs/solrconfig.xml
file:/Users/apache/configs/src
-z <zkHost> The ZooKeeper connection string.
Unnecessary if ZK_HOST is defined in
solr.in.sh or solr.in.cmd .
-z 123.321.23.43:2181
An example of this command with the parameters is:
Recursively copy a directory from local to ZooKeeper.
bin/solr zk cp -r file:/apache/confgs/whatever/conf zk:/configs/myconf -z
111.222.333.444:2181
Copy a single file from ZooKeeper to local.
bin/solr zk cp zk:/configs/myconf/managed_schema /configs/myconf/managed_schema -z
111.222.333.444:2181
Remove a znode from ZooKeeper
Use the zk rm command to remove a znode (and optionally all child nodes) from ZooKeeper
Available Parameters
Parameter Description Example
-r Optional. Do a recursive removal. The
command will fail if the <path> has
children unless ‘-r‘ is specified.
-r
<path> The path to remove from ZooKeeper,
either a parent or leaf node.
There are limited safety checks, you
cannot remove ‘/‘ or ‘/zookeeper‘ nodes.
The path is assumed to be a ZooKeeper
node, no zk: prefix is necessary.
/configs
/configs/myconfigset
/configs/myconfigset/solrconfig.xml
-z <zkHost> The ZooKeeper connection string.
Unnecessary if ZK_HOST is defined in
solr.in.sh or solr.in.cmd .
-z 123.321.23.43:2181
An example of this command with the parameters is:
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bin/solr zk rm -r /configs
bin/solr zk rm /configs/myconfigset/schema.xml
Move One ZooKeeper znode to Another (Rename)
Use the zk mv command to move (rename) a ZooKeeper znode
Available Parameters
Parameter Description Example
<src> The znode to rename. The zk: prefix is
assumed.
/configs/oldconfigset
<dest> The new name of the znode. The zk:
prefix is assumed.
/configs/newconfigset
-z <zkHost> The ZooKeeper connection string.
Unnecessary if ZK_HOST is defined in
solr.in.sh or solr.in.cmd .
-z 123.321.23.43:2181
An example of this command is:
bin/solr zk mv /configs/oldconfigset /configs/newconfigset
List a ZooKeeper znode’s Children
Use the zk ls command to see the children of a znode.
Available Parameters
Parameter Description Example
-r Optional. Recursively list all descendants
of a znode.
-r
<path> The path on ZooKeeper to list.
/collections/mycollection
-z <zkHost> The ZooKeeper connection string.
Unnecessary if ZK_HOST is defined in
solr.in.sh or solr.in.cmd .
-z 123.321.23.43:2181
An example of this command with the parameters is:
bin/solr zk ls -r /collections/mycollection
bin/solr zk ls /collections
Create a znode (supports chroot)
Use the zk mkroot command to create a znode. The primary use-case for this command to support
ZooKeeper’s "chroot" concept. However, it can also be used to create arbitrary paths.
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Available Parameters
Parameter Description Example
<path> The path on ZooKeeper to create.
Intermediate znodes will be created if
necessary. A leading slash is assumed
even if not specified.
/solr
-z <zkHost> The ZooKeeper connection string.
Unnecessary if ZK_HOST is defined in
solr.in.sh or solr.in.cmd .
-z 123.321.23.43:2181
Examples of this command:
bin/solr zk mkroot /solr -z 123.321.23.43:2181
bin/solr zk mkroot /solr/production
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Upgrading Solr
If you are already using Solr 6.5, Solr 6.6 should not present any major problems. However, you should
review the CHANGES.txt file found in your Solr package for changes and updates that may effect your
existing implementation. Detailed steps for upgrading a Solr cluster can be found in the appendix:
Upgrading a Solr Cluster.
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Upgrading from 6.5.x
? Solr contribs map-reduce, morphlines-core and morphlines-cell have been removed.
? JSON Facet API now uses hyper-log-log for numBuckets cardinality calculation and calculates cardinality
before filtering buckets by any mincount greater than 1.
? ZooKeeper dependency has been upgraded from 3.4.6 to 3.4.10.
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Upgrading from earlier 6.x versions
? If you use historical dates, specifically on or before the year 1582, you should re-index after upgrading to
this version.
? If you use the JSON Facet API (json.facet) with method=stream , you must now set sort=‘index asc‘ to
get the streaming behavior; otherwise it won’t stream. Reminder: "method" is a hint that doesn’t
change defaults of other parameters.
? If you use the JSON Facet API (json.facet) to facet on a numeric field and if you use mincount=0 or if you
set the prefix, then you will now get an error as these options are incompatible with numeric faceting.
? Solr’s logging verbosity at the INFO level has been greatly reduced, and you may need to update the log
configs to use the DEBUG level to see all the logging messages you used to see at INFO level before.
? We are no longer backing up solr.log and solr_gc.log files in date-stamped copies forever. If you
relied on the solr_log_<date> or solr_gc_log_<date> being in the logs folder that will no longer be the
case. See the section Configuring Logging for details on how log rotation works as of Solr 6.3.
? The create/deleteCollection methods on MiniSolrCloudCluster have been deprecated. Clients should
instead use the CollectionAdminRequest API. In addition,
MiniSolrCloudCluster#uploadConfigDir(File, String) has been deprecated in favour of
#uploadConfigSet(Path, String) .
? The bin/solr.in.sh ( bin/solr.in.cmd on Windows) is now completely commented by default.
Previously, this wasn’t so, which had the effect of masking existing environment variables.
? The _version_ field is no longer indexed and is now defined with indexed=false by default, because the
field has DocValues enabled.
? The /export handler has been changed so it no longer returns zero (0) for numeric fields that are not in
the original document. One consequence of this change is that you must be aware that some tuples will
not have values if there were none in the original document.
? Metrics-related classes in org.apache.solr.util.stats have been removed in favor of the Dropwizard
metrics library. Any custom plugins using these classes should be changed to use the equivalent classes
from the metrics library. As part of this, the following changes were made to the output of Overseer
Status API:
? The "totalTime" metric has been removed because it is no longer supported.
? The metrics "75thPctlRequestTime", "95thPctlRequestTime", "99thPctlRequestTime" and
"999thPctlRequestTime" in Overseer Status API have been renamed to "75thPcRequestTime",
"95thPcRequestTime" and so on for consistency with stats output in other parts of Solr.
? The metrics "avgRequestsPerMinute", "5minRateRequestsPerMinute" and
"15minRateRequestsPerMinute" have been replaced by corresponding per-second rates viz.
"avgRequestsPerSecond", "5minRateRequestsPerSecond" and "15minRateRequestsPerSecond" for
consistency with stats output in other parts of Solr.
? A new highlighter named UnifiedHighlighter has been added. You are encouraged to try out the
UnifiedHighlighter by setting hl.method=unified and report feedback. It might become the default in
7.0. It’s more efficient/faster than the other highlighters, especially compared to the original Highlighter.
That said, some options aren’t supported yet. It will get more features in time, especially with your input.
See HighlightParams.java for a listing of highlight parameters annotated with which highlighters use
them. hl.useFastVectorHighlighter is now considered deprecated in lieu of hl.method=fastVector .
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? The maxWarmingSearchers parameter now defaults to 1, and more importantly commits will now block if
this limit is exceeded instead of throwing an exception (a good thing). Consequently there is no longer a
risk in overlapping commits. Nonetheless users should continue to avoid excessive committing. Users
are advised to remove any pre-existing maxWarmingSearchers entries from their solrconfig.xml files.
? The Complex Phrase query parser now supports leading wildcards. Beware of its possible heaviness,
users are encouraged to use ReversedWildcardFilter in index time analysis.
? The JMX metric "avgTimePerRequest" (and the corresponding metric in the metrics API for each handler)
used to be a simple non-decaying average based on total cumulative time and the number of requests.
New Codahale Metrics implementation applies exponential decay to this value, which heavily biases the
average towards the last 5 minutes.
? Index-time boosts are now deprecated. As a replacement, index-time scoring factors should be indexed
in a separate field and combined with the query score using a function query. These boosts will be
removed in Solr 7.0.
? Parallel SQL now uses Apache Calcite as its SQL framework. As part of this change the default
aggregation mode has been changed to facet rather than map_reduce. There have also been changes to
the SQL aggregate response and some SQL syntax changes. Consult the Parallel SQL Interface
documentation for full details.
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Upgrading from 5.5.x
? The deprecated SolrServer and subclasses have been removed, use SolrClient instead.
? The deprecated <nrtMode> configuration in solrconfig.xml has been removed. Please remove it from
solrconfig.xml .
? SolrClient.shutdown() has been removed, use SolrClient.close() instead.
? The deprecated zkCredientialsProvider element in solrcloud section of solr.xml is now removed.
Use the correct spelling ( zkCredentialsProvider ) instead.
? Internal/expert - ResultContext was significantly changed and expanded to allow for multiple full query
results ( DocLists ) per Solr request. TransformContext was rendered redundant and was removed. See
SOLR-7957 for details.
? Several changes have been made regarding the " Similarity " used in Solr, in order to provide better
default behavior for new users. There are 3 key impacts of these changes on existing users who upgrade:
? DefaultSimilarityFactory has been removed. If you currently have DefaultSimilarityFactory
explicitly referenced in your schema.xml , edit your config to use the functionally identical
ClassicSimilarityFactory . See SOLR-8239 for more details.
? The implicit default Similarity used when no <similarity/> is configured in schema.xml has been
changed to SchemaSimilarityFactory . Users who wish to preserve back-compatible behavior should
either explicitly configure ClassicSimilarityFactory , or ensure that the luceneMatchVersion for
the collection is less then 6.0. See SOLR-8270 + SOLR-8271 for details.
? SchemaSimilarityFactory has been modified to use BM25Similarity as the default for fieldTypes
that do not explicitly declare a Similarity. The legacy behavior of using ClassicSimilarity as the
default will occur if the luceneMatchVersion for the collection is less then 6.0, or the
‘defaultSimFromFieldType’ configuration option may be used to specify any default of your choosing.
See SOLR-8261 + SOLR-8329 for more details.
? If your solrconfig.xml file doesn’t explicitly mention the schemaFactory to use then Solr will choose the
ManagedIndexSchemaFactory by default. Previously it would have chosen ClassicIndexSchemaFactory .
This means that the Schema APIs ( /<collection>/schema ) are enabled and the schema is mutable.
When Solr starts your schema.xml file will be renamed to managed-schema . If you want to retain the old
behaviour then please ensure that the solrconfig.xml explicitly uses the ClassicIndexSchemaFactory
or your luceneMatchVersion in the solrconfig.xml is less than 6.0. See the Schema Factory Definition in
SolrConfig section for more details
? SolrIndexSearcher.QueryCommand and QueryResult were moved to their own classes. If you reference
them in your code, you should import them under o.a.s.search (or use your IDE’s "Organize Imports").
? The ‘ useParams ‘ attribute specified in request handler cannot be overridden from request params. See
SOLR-8698 for more details.
? When requesting stats in date fields, "sum" is now returned as a double value instead of a date. See
SOLR-8671 for more details.
? The deprecated GET methods for schema are now accessible through the bulk API. These methods now
accept fewer request parameters, and output less information. See SOLR-8736 for more details. Some of
the removed functionality will likely be restored in a future version of Solr - see SOLR-8992.
? In the past, Solr guaranteed that retrieval of multi-valued fields would preserve the order of values.
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Because values may now be retrieved from column-stored fields ( docValues="true" ), in conjunction with
the fact that DocValues do not currently preserve order, means that users should set
useDocValuesAsStored="false" to prevent future optimizations from using the column-stored values
over the row-stored values when fields have both stored="true" and docValues="true" .
? Formatted date-times from Solr have some differences. If the year is more than 4 digits, there is a
leading ‘+‘. When there is a non-zero number of milliseconds, it is padded with zeros to 3 digits. Negative
year (BC) dates are now possible. Parsing: It is now an error to supply a portion of the date out of its,
range, like 67 seconds.
? SolrJ no longer includes DateUtil . If for some reason you need to format or parse dates, simply use
Instant.format() and Instant.parse() .
? If you are using spatial4j, please upgrade to 0.6 and edit your spatialContextFactory to replace
com.spatial4j.core with org.locationtech.spatial4j .
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Upgrading from Older Versions of Solr
Users upgrading from older versions are strongly encouraged to consult CHANGES.txt for the details of all
changes since the version they are upgrading from.
A summary of the significant changes between Solr 5.x and Solr 6.0 can be found in the Major Changes from
Solr 5 to Solr 6 section.
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Using the Solr Administration User Interface
This section discusses the Solr Administration User Interface ("Admin UI").
The Overview of the Solr Admin UI explains the basic features of the user interface, what’s on the initial
Admin UI page, and how to configure the interface. In addition, there are pages describing each screen of
the Admin UI:
? Getting Assistance shows you how to get more information about the UI.
? Logging >shows recent messages logged by this Solr node and provides a way to change logging levels
for specific classes.
? Cloud Screens display information about nodes when running in SolrCloud mode.
? Collections / Core Admin explains how to get management information about each core.
? Java Properties shows the Java information about each core.
? Thread Dump lets you see detailed information about each thread, along with state information.
? Collection-Specific Tools is a section explaining additional screens available for each collection.
? Analysis - lets you analyze the data found in specific fields.
? Dataimport - shows you information about the current status of the Data Import Handler.
? Documents - provides a simple form allowing you to execute various Solr indexing commands
directly from the browser.
? Files - shows the current core configuration files such as solrconfig.xml .
? Query - lets you submit a structured query about various elements of a core.
? Stream - allows you to submit streaming expressions and see results and parsing explanations.
? Schema Browser - displays schema data in a browser window.
? Core-Specific Tools is a section explaining additional screens available for each named core.
? Ping - lets you ping a named core and determine whether the core is active.
? Plugins/Stats - shows statistics for plugins and other installed components.
? Replication - shows you the current replication status for the core, and lets you enable/disable
replication.
? Segments Info - Provides a visualization of the underlying Lucene index segments.
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Overview of the Solr Admin UI
Solr features a Web interface that makes it easy for Solr administrators and programmers to view Solr
configuration details, run queries and analyze document fields in order to fine-tune a Solr configuration and
access online documentation and other help.
Solr Dashboard
Accessing the URL http://hostname:8983/solr/ will show the main dashboard, which is divided into two
parts.
A left-side of the screen is a menu under the Solr logo that provides the navigation through the screens of
the UI. The first set of links are for system-level information and configuration and provide access to
Logging, Collection/Core Administration, and Java Properties, among other things. At the end of this
information is at least one pulldown listing Solr cores configured for this instance. On SolrCloud nodes, an
additional pulldown list shows all collections in this cluster. Clicking on a collection or core name shows
secondary menus of information for the specified collection or core, such as a Schema Browser, Config Files,
Plugins & Statistics, and an ability to perform Queries on indexed data.
The center of the screen shows the detail of the option selected. This may include a sub-navigation for the
option or text or graphical representation of the requested data. See the sections in this guide for each
screen for more details.
Under the covers, the Solr Admin UI re-uses the same HTTP APIs available to all clients to access Solr-related
data to drive an external interface.
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?
The path to the Solr Admin UI given above is http://hostname:port/solr , which redirects
to http://hostname:port/solr/#/ in the current version. A convenience redirect is also
supported, so simply accessing the Admin UI at http://hostname:port/ will also redirect
to http://hostname:port/solr/#/ .
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Getting Assistance
At the bottom of each screen of the Admin UI is a set of links that can be used to get more assistance with
configuring and using Solr.
Assistance icons
These icons include the following links.
Link Description
Documentation Navigates to the Apache Solr documentation hosted on
https://lucene.apache.org/solr/.
Issue Tracker Navigates to the JIRA issue tracking server for the Apache Solr project. This
server resides at https://issues.apache.org/jira/browse/SOLR.
IRC Channel Navigates to Solr’s IRC live-chat room: http://webchat.freenode.net/?
channels=#solr.
Community forum Navigates to the Apache Wiki page which has further information about ways to
engage in the Solr User community mailing lists: https://wiki.apache.org/solr/
UsingMailingLists.
Solr Query Syntax Navigates to the section Query Syntax and Parsing in this Reference Guide.
These links cannot be modified without editing the index.html in the server/solr/solr-webapp directory
that contains the Admin UI files.
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Logging
The Logging page shows recent messages logged by this Solr node.
When you click the link for "Logging", a page similar to the one below will be displayed:
The Main Logging Screen, including an example of an error due to a bad document sent by a client
While this example shows logged messages for only one core, if you have multiple cores in a single instance,
they will each be listed, with the level for each.
Selecting a Logging Level
When you select the Level link on the left, you see the hierarchy of classpaths and classnames for your
instance. A row highlighted in yellow indicates that the class has logging capabilities. Click on a highlighted
row, and a menu will appear to allow you to change the log level for that class. Characters in boldface
indicate that the class will not be affected by level changes to root.
Log level selection
For an explanation of the various logging levels, see Configuring Logging.
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Cloud Screens
When running in SolrCloud mode, a "Cloud" option will appear in the Admin UI between Logging and
Collections/Core Admin.
This screen provides status information about each collection & node in your cluster, as well as access to the
low level data being stored in ZooKeeper.
?
Only Visible When using SolrCloud
The "Cloud" menu option is only available on Solr instances running in SolrCloud mode.
Single node or master/slave replication instances of Solr will not display this option.
Click on the Cloud option in the left-hand navigation, and a small sub-menu appears with options called
"Tree", "Graph", "Graph (Radial)" and "Dump". The default view ("Graph") shows a graph of each collection,
the shards that make up those collections, and the addresses of each replica for each shard.
This example shows the very simple two-node cluster created using the bin/solr -e cloud -noprompt
example command. In addition to the 2 shard, 2 replica "gettingstarted" collection, there is an additional
"films" collection consisting of a single shard/replica:
The "Graph (Radial)" option provides a different visual view of each node. Using the same example cluster,
the radial graph view looks like:
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The "Tree" option shows a directory structure of the data in ZooKeeper, including cluster wide information
regarding the live_nodes and overseer status, as well as collection specific information such as the
state.json , current shard leaders, and configuration files in use. In this example, we see the state.json file
definition for the "films" collection:
The final option is "Dump", which returns a JSON document containing all nodes, their contents and their
children (recursively). This can be used to export a snapshot of all the data that Solr has kept inside
ZooKeeper and can aid in debugging SolrCloud problems.
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Collections / Core Admin
The Collections screen provides some basic functionality for managing your Collections, powered by the
Collections API.
?
If you are running a single node Solr instance, you will not see a Collections option in the
left nav menu of the Admin UI.
You will instead see a "Core Admin" screen that supports some comparable Core level
information & manipulation via the CoreAdmin API instead.
The main display of this page provides a list of collections that exist in your cluster. Clicking on a collection
name provides some basic metadata about how the collection is defined, and its current shards & replicas,
with options for adding and deleting individual replicas.
The buttons at the top of the screen let you make various collection level changes to your cluster, from add
new collections or aliases to reloading or deleting a single collection.
Replicas can be deleted by clicking the red "X" next to the replica name.
If the shard is inactive, for example after a SPLITSHARD action, an option to delete the shard will appear as a
red "X" next to the shard name.
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Java Properties
The Java Properties screen provides easy access to one of the most essential components of a top-
performing Solr systems. With the Java Properties screen, you can see all the properties of the JVM running
Solr, including the class paths, file encodings, JVM memory settings, operating system, and more.
Java Properties Screen
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Thread Dump
The Thread Dump screen lets you inspect the currently active threads on your server.
Each thread is listed and access to the stacktraces is available where applicable. Icons to the left indicate the
state of the thread: for example, threads with a green check-mark in a green circle are in a "RUNNABLE"
state. On the right of the thread name, a down-arrow means you can expand to see the stacktrace for that
thread.
List of Threads
When you move your cursor over a thread name, a box floats over the name with the state for that thread.
Thread states can be:
State Meaning
NEW A thread that has not yet started.
RUNNABLE A thread executing in the Java virtual machine.
BLOCKED A thread that is blocked waiting for a monitor lock.
WAITING A thread that is waiting indefinitely for another thread to perform a particular
action.
TIMED_WAITING A thread that is waiting for another thread to perform an action for up to a
specified waiting time.
TERMINATED A thread that has exited.
When you click on one of the threads that can be expanded, you’ll see the stacktrace, as in the example
below:
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Inspecting a Thread
You can also check the Show all Stacktraces button to automatically enable expansion for all threads.
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Collection-Specific Tools
In the left-hand navigation bar, you will see a pull-down menu titled "Collection Selector" that can be used to
access collection specific administration screens.
?
Only Visible When Using SolrCloud
The "Collection Selector" pull-down menu is only available on Solr instances running in
SolrCloud mode.
Single node or master/slave replication instances of Solr will not display this menu, instead
the Collection specific UI pages described in this section will be available in the Core
Selector pull-down menu.
Clicking on the Collection Selector pull-down menu will show a list of the collections in your Solr cluster, with
a search box that can be used to find a specific collection by name. When you select a collection from the
pull-down, the main display of the page will display some basic metadata about the collection, and a
secondary menu will appear in the left nav with links to additional collection specific administration screens.
The collection-specific UI screens are listed below, with a link to the section of this guide to find out more:
? Analysis - lets you analyze the data found in specific fields.
? Dataimport - shows you information about the current status of the Data Import Handler.
? Documents - provides a simple form allowing you to execute various Solr indexing commands directly
from the browser.
? Files - shows the current core configuration files such as solrconfig.xml .
? Query - lets you submit a structured query about various elements of a core.
? Stream - allows you to submit streaming expressions and see results and parsing explanations.
? Schema Browser - displays schema data in a browser window.
Analysis Screen
The Analysis screen lets you inspect how data will be handled according to the field, field type and dynamic
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field configurations found in your Schema. You can analyze how content would be handled during indexing
or during query processing and view the results separately or at the same time. Ideally, you would want
content to be handled consistently, and this screen allows you to validate the settings in the field type or
field analysis chains.
Enter content in one or both boxes at the top of the screen, and then choose the field or field type
definitions to use for analysis.
If you click the Verbose Output check box, you see more information, including more details on the
transformations to the input (such as, convert to lower case, strip extra characters, etc.) including the raw
bytes, type and detailed position information at each stage. The information displayed will vary depending
on the settings of the field or field type. Each step of the process is displayed in a separate section, with an
abbreviation for the tokenizer or filter that is applied in that step. Hover or click on the abbreviation, and
you’ll see the name and path of the tokenizer or filter.
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In the example screenshot above, several transformations are applied to the input "Running is a sport." The
words "is" and "a" have been removed and the word "running" has been changed to its basic form, "run".
This is because we are using the field type text_en in this scenario, which is configured to remove stop
words (small words that usually do not provide a great deal of context) and "stem" terms when possible to
find more possible matches (this is particularly helpful with plural forms of words). If you click the question
mark next to the Analyze Fieldname/Field Type pull-down menu, the Schema Browser window will open,
showing you the settings for the field specified.
The section Understanding Analyzers, Tokenizers, and Filters describes in detail what each option is and how
it may transform your data and the section Running Your Analyzer has specific examples for using the
Analysis screen.
Dataimport Screen
The Dataimport screen shows the configuration of the DataImportHandler (DIH) and allows you start, and
monitor the status of, import commands as defined by the options selected on the screen and defined in the
configuration file.
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The Dataimport Screen
This screen also lets you adjust various options to control how the data is imported to Solr, and view the
data import configuration file that controls the import.
For more information about data importing with DIH, see the section on Uploading Structured Data Store
Data with the Data Import Handler.
Documents Screen
The Documents screen provides a simple form allowing you to execute various Solr indexing commands in a
variety of formats directly from the browser.
The Documents Screen
The screen allows you to:
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? Copy documents in JSON, CSV or XML and submit them to the index
? Upload documents (in JSON, CSV or XML)
? Construct documents by selecting fields and field values
?
There are other ways to load data, see also these sections:
? Uploading Data with Index Handlers
? Uploading Data with Solr Cell using Apache Tika
The first step is to define the RequestHandler to use (aka, ‘qt‘). By default /update will be defined. To use
Solr Cell, for example, change the request handler to /update/extract .
Then choose the Document Type to define the type of document to load. The remaining parameters will
change depending on the document type selected.
JSON
When using the JSON document type, the functionality is similar to using a requestHandler on the command
line. Instead of putting the documents in a curl command, they can instead be input into the Document
entry box. The document structure should still be in proper JSON format.
Then you can choose when documents should be added to the index (Commit Within), & whether existing
documents should be overwritten with incoming documents with the same id (if this is not true, then the
incoming documents will be dropped).
This option will only add or overwrite documents to the index; for other update tasks, see the Solr Command
option.
CSV
When using the CSV document type, the functionality is similar to using a requestHandler on the command
line. Instead of putting the documents in a curl command, they can instead be input into the Document
entry box. The document structure should still be in proper CSV format, with columns delimited and one row
per document.
Then you can choose when documents should be added to the index (Commit Within), and whether existing
documents should be overwritten with incoming documents with the same id (if this is not true, then the
incoming documents will be dropped).
Document Builder
The Document Builder provides a wizard-like interface to enter fields of a document
File Upload
The File Upload option allows choosing a prepared file and uploading it. If using only /update for the
Request-Handler option, you will be limited to XML, CSV, and JSON.
However, to use the ExtractingRequestHandler (aka Solr Cell), you can modify the Request-Handler to
/update/extract . You must have this defined in your solrconfig.xml file, with your desired defaults. You
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should also update the &literal.id shown in the Extracting Req. Handler Params so the file chosen is given
a unique id.
Then you can choose when documents should be added to the index (Commit Within), and whether existing
documents should be overwritten with incoming documents with the same id (if this is not true, then the
incoming documents will be dropped).
Solr Command
The Solr Command option allows you use XML or JSON to perform specific actions on documents, such as
defining documents to be added or deleted, updating only certain fields of documents, or commit and
optimize commands on the index.
The documents should be structured as they would be if using /update on the command line.
XML
When using the XML document type, the functionality is similar to using a requestHandler on the command
line. Instead of putting the documents in a curl command, they can instead be input into the Document
entry box. The document structure should still be in proper Solr XML format, with each document separated
by <doc> tags and each field defined.
Then you can choose when documents should be added to the index (Commit Within), and whether existing
documents should be overwritten with incoming documents with the same id (if this is not true, then the
incoming documents will be dropped).
This option will only add or overwrite documents to the index; for other update tasks, see the Solr Command
option.
Files Screen
The Files screen lets you browse & view the various configuration files (such solrconfig.xml and the
schema file) for the collection you selected.
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The Files Screen
If you are using SolrCloud, the files displayed are the configuration files for this collection stored in
ZooKeeper. In a standalone Solr installations, all files in the conf directory are displayed.
While solrconfig.xml defines the behavior of Solr as it indexes content and responds to queries, the
Schema allows you to define the types of data in your content (field types), the fields your documents will be
broken into, and any dynamic fields that should be generated based on patterns of field names in the
incoming documents. Any other configuration files are used depending on how they are referenced in either
solrconfig.xml or your schema.
Configuration files cannot be edited with this screen, so a text editor of some kind must be used.
This screen is related to the Schema Browser Screen, in that they both can display information from the
schema, but the Schema Browser provides a way to drill into the analysis chain and displays linkages
between field types, fields, and dynamic field rules.
Many of the options defined in these configuration files are described throughout the rest of this Guide. In
particular, you will want to review these sections:
? Indexing and Basic Data Operations
? Searching
? The Well-Configured Solr Instance
? Documents, Fields, and Schema Design
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Query Screen
You can use the Query screen to submit a search query to a Solr collection and analyze the results.
In the example in the screenshot, a query has been submitted, and the screen shows the query results sent
to the browser as JSON.
JSON Results of a Query
In this example, a query for genre:Fantasy was sent to a "films" collection. Defaults were used for all other
options in the form, which are explained briefly in the table below, and covered in detail in later parts of this
Guide.
The response is shown to the right of the form. Requests to Solr are simply HTTP requests, and the query
submitted is shown in light type above the results; if you click on this it will open a new browser window with
just this request and response (without the rest of the Solr Admin UI). The rest of the response is shown in
JSON, which is part of the request (see the wt=json part at the end).
The response has at least two sections, but may have several more depending on the options chosen. The
two sections it always has are the responseHeader and the response . The responseHeader includes the
status of the search ( status ), the processing time ( QTime ), and the parameters ( params ) that were used to
process the query.
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The response includes the documents that matched the query, in doc sub-sections. The fields return depend
on the parameters of the query (and the defaults of the request handler used). The number of results is also
included in this section.
This screen allows you to experiment with different query options, and inspect how your documents were
indexed. The query parameters available on the form are some basic options that most users want to have
available, but there are dozens more available which could be simply added to the basic request by hand (if
opened in a browser). The table below explains the parameters available:
Field Description
Request-handler
(qt)
Specifies the query handler for the request. If a query handler is not specified, Solr
processes the response with the standard query handler.
q The query event. See Searching for an explanation of this parameter.
fq The filter queries. See Common Query Parameters for more information on this
parameter.
sort Sorts the response to a query in either ascending or descending order based on the
response’s score or another specified characteristic.
start, rows start is the offset into the query result starting at which documents should be
returned. The default value is 0, meaning that the query should return results starting
with the first document that matches. This field accepts the same syntax as the start
query parameter, which is described in Searching. rows is the number of rows to
return.
fl Defines the fields to return for each document. You can explicitly list the stored fields,
functions, and doc transformers you want to have returned by separating them with
either a comma or a space.
wt Specifies the Response Writer to be used to format the query response. Defaults to
XML if not specified.
indent Click this button to request that the Response Writer use indentation to make the
responses more readable.
debugQuery Click this button to augment the query response with debugging information,
including "explain info" for each document returned. This debugging information is
intended to be intelligible to the administrator or programmer.
dismax Click this button to enable the Dismax query parser. See The DisMax Query Parser for
further information.
edismax Click this button to enable the Extended query parser. See The Extended DisMax
Query Parser for further information.
hl Click this button to enable highlighting in the query response. See Highlighting for
more information.
facet Enables faceting, the arrangement of search results into categories based on indexed
terms. See Faceting for more information.
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Field Description
spatial Click to enable using location data for use in spatial or geospatial searches. See
Spatial Search for more information.
spellcheck Click this button to enable the Spellchecker, which provides inline query suggestions
based on other, similar, terms. See Spell Checking for more information.
Stream Screen
The Stream screen allows you to enter a streaming expression and see the results. It is very similar to the
Query Screen, except the input box is at the top and all options must be declared in the expression.
The screen will insert everything up to the streaming expression itself, so you do not need to enter the full
URI with the hostname, port, collection, etc. Simply input the expression after the expr= part, and the URL
will be constructed dynamically as appropriate.
Under the input box, the Execute button will run the expression. An option "with explanation" will show the
parts of the streaming expression that were executed. Under this, the streamed results are shown. A URL to
be able to view the output in a browser is also available.
Stream Screen with query and results
Schema Browser Screen
The Schema Browser screen lets you review schema data in a browser window.
If you have accessed this window from the Analysis screen, it will be opened to a specific field, dynamic field
rule or field type. If there is nothing chosen, use the pull-down menu to choose the field or field type.
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Schema Browser Screen
The screen provides a great deal of useful information about each particular field and fieldtype in the
Schema, and provides a quick UI for adding fields or fieldtypes using the Schema API (if enabled). In the
example above, we have chosen the cat field. On the left side of the main view window, we see the field
name, that it is copied to the _text_ (because of a copyField rule) and that it use the strings fieldtype. Click
on one of those field or fieldtype names, and you can see the corresponding definitions.
In the right part of the main view, we see the specific properties of how the cat field is defined – either
explicitly or implicitly via its fieldtype, as well as how many documents have populated this field. Then we see
the analyzer used for indexing and query processing. Click the icon to the left of either of those, and you’ll
see the definitions for the tokenizers and/or filters that are used. The output of these processes is the
information you see when testing how content is handled for a particular field with the Analysis Screen.
Under the analyzer information is a button to Load Term Info. Clicking that button will show the top N
terms that are in a sample shard for that field, as well as a histogram showing the number of terms with
various frequencies. Click on a term, and you will be taken to the Query Screen to see the results of a query
of that term in that field. If you want to always see the term information for a field, choose Autoload and it
will always appear when there are terms for a field. A histogram shows the number of terms with a given
frequency in the field.
?
Term Information is loaded from single arbitrarily selected core from the collection, to
provide a representative sample for the collection. Full Field Facet query results are needed
to see precise term counts across the entire collection.
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Core-Specific Tools
The Core-Specific tools are a group of UI screens that allow you to see core-level information.
In the left-hand navigation bar, you will see a pull-down menu titled "Core Selector". Clicking on the menu
will show a list of Solr cores hosted on this Solr node, with a search box that can be used to find a specific
core by name.
When you select a core from the pull-down, the main display of the page will show some basic metadata
about the core, and a secondary menu will appear in the left nav with links to additional core specific
administration screens.
You can also define a configuration file called admin-extra.html that includes links or other information you
would like to display in the "Admin Extra" part of this main screen.
Core overview screen
The core-specific UI screens are listed below, with a link to the section of this guide to find out more:
? Ping - lets you ping a named core and determine whether the core is active.
? Plugins/Stats - shows statistics for plugins and other installed components.
? Replication - shows you the current replication status for the core, and lets you enable/disable
replication.
? Segments Info - Provides a visualization of the underlying Lucene index segments.
If you are running a single node instance of Solr, additional UI screens normally displayed on a per-
collection bases will also be listed:
? Analysis - lets you analyze the data found in specific fields.
? Dataimport - shows you information about the current status of the Data Import Handler.
? Documents - provides a simple form allowing you to execute various Solr indexing commands directly
from the browser.
? Files - shows the current core configuration files such as solrconfig.xml .
? Query - lets you submit a structured query about various elements of a core.
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? Stream - allows you to submit streaming expressions and see results and parsing explanations.
? Schema Browser - displays schema data in a browser window.
Ping
Choosing Ping under a core name issues a ping request to check whether the core is up and responding to
requests.
Ping Option in Core Dropdown
The search executed by a Ping is configured with the Request Parameters API. See Implicit RequestHandlers
for the paramset to use for the /admin/ping endpoint.
The Ping option doesn’t open a page, but the status of the request can be seen on the core overview page
shown when clicking on a collection name. The length of time the request has taken is displayed next to the
Ping option, in milliseconds.
API Examples
While the UI screen makes it easy to see the ping response time, the underlying ping command can be more
useful when executed by remote monitoring tools:
Input
http://localhost:8983/solr/<core-name>/admin/ping
This command will ping the core name for a response.
Input
http://localhost:8983/solr/<collection-name>admin/ping?wt=json&distrib=true&indent=true
This command will ping all replicas of the given collection name for a response
Sample Output
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<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">13</int>
<lst name="params">
<str name="q">{!lucene}*:*</str>
<str name="distrib">false</str>
<str name="df">_text_</str>
<str name="rows">10</str>
<str name="echoParams">all</str>
</lst>
</lst>
<str name="status">OK</str>
</response>
Both API calls have the same output. A status=OK indicates that the nodes are responding.
SolrJ Example
SolrPing ping = new SolrPing();
ping.getParams().add("distrib", "true"); //To make it a distributed request against a collection
rsp = ping.process(solrClient, collectionName);
int status = rsp.getStatus();
Plugins & Stats Screen
The Plugins screen shows information and statistics about the status and performance of various plugins
running in each Solr core. You can find information about the performance of the Solr caches, the state of
Solr’s searchers, and the configuration of Request Handlers and Search Components.
Choose an area of interest on the right, and then drill down into more specifics by clicking on one of the
names that appear in the central part of the window. In this example, we’ve chosen to look at the Searcher
stats, from the Core area:
Searcher Statistics
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The display is a snapshot taken when the page is loaded. You can get updated status by choosing to either
Watch Changes or Refresh Values. Watching the changes will highlight those areas that have changed,
while refreshing the values will reload the page with updated information.
Replication Screen
The Replication screen shows you the current replication state for the core you have specified. SolrCloud has
supplanted much of this functionality, but if you are still using Master-Slave index replication, you can use
this screen to:
1. View the replicatable index state. (on a master node)
2. View the current replication status (on a slave node)
3. Disable replication. (on a master node)
?
Caution When Using SolrCloud
When using SolrCloud, do not attempt to disable replication via this screen.
More details on how to configure replication is available in the section called Index Replication.
Segments Info
The Segments Info screen lets you see a visualization of the various segments in the underlying Lucene
index for this core, with information about the size of each segment – both bytes and in number of
documents – as well as other basic metadata about those segments. Most visible is the the number of
deleted documents, but you can hover your mouse over the segments to see additional numeric details.
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This information may be useful for people to help make decisions about the optimal merge settings for their
data.
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Documents, Fields, and Schema Design
This section discusses how Solr organizes its data into documents and fields, as well as how to work with a
schema in Solr.
This section includes the following topics:
Overview of Documents, Fields, and Schema Design: An introduction to the concepts covered in this section.
Solr Field Types: Detailed information about field types in Solr, including the field types in the default Solr
schema.
Defining Fields: Describes how to define fields in Solr.
Copying Fields: Describes how to populate fields with data copied from another field.
Dynamic Fields: Information about using dynamic fields in order to catch and index fields that do not exactly
conform to other field definitions in your schema.
Schema API: Use curl commands to read various parts of a schema or create new fields and copyField rules.
Other Schema Elements: Describes other important elements in the Solr schema.
Putting the Pieces Together: A higher-level view of the Solr schema and how its elements work together.
DocValues: Describes how to create a docValues index for faster lookups.
Schemaless Mode: Automatically add previously unknown schema fields using value-based field type
guessing.
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Overview of Documents, Fields, and Schema Design
The fundamental premise of Solr is simple. You give it a lot of information, then later you can ask it
questions and find the piece of information you want. The part where you feed in all the information is
called indexing or updating. When you ask a question, it’s called a query.
One way to understand how Solr works is to think of a loose-leaf book of recipes. Every time you add a
recipe to the book, you update the index at the back. You list each ingredient and the page number of the
recipe you just added. Suppose you add one hundred recipes. Using the index, you can very quickly find all
the recipes that use garbanzo beans, or artichokes, or coffee, as an ingredient. Using the index is much
faster than looking through each recipe one by one. Imagine a book of one thousand recipes, or one million.
Solr allows you to build an index with many different fields, or types of entries. The example above shows
how to build an index with just one field, ingredients . You could have other fields in the index for the
recipe’s cooking style, like Asian , Cajun , or vegan , and you could have an index field for preparation times.
Solr can answer questions like "What Cajun-style recipes that have blood oranges as an ingredient can be
prepared in fewer than 30 minutes?"
The schema is the place where you tell Solr how it should build indexes from input documents.
How Solr Sees the World
Solr’s basic unit of information is a document, which is a set of data that describes something. A recipe
document would contain the ingredients, the instructions, the preparation time, the cooking time, the tools
needed, and so on. A document about a person, for example, might contain the person’s name, biography,
favorite color, and shoe size. A document about a book could contain the title, author, year of publication,
number of pages, and so on.
In the Solr universe, documents are composed of fields, which are more specific pieces of information. Shoe
size could be a field. First name and last name could be fields.
Fields can contain different kinds of data. A name field, for example, is text (character data). A shoe size field
might be a floating point number so that it could contain values like 6 and 9.5. Obviously, the definition of
fields is flexible (you could define a shoe size field as a text field rather than a floating point number, for
example), but if you define your fields correctly, Solr will be able to interpret them correctly and your users
will get better results when they perform a query.
You can tell Solr about the kind of data a field contains by specifying its field type. The field type tells Solr how
to interpret the field and how it can be queried.
When you add a document, Solr takes the information in the document’s fields and adds that information to
an index. When you perform a query, Solr can quickly consult the index and return the matching documents.
Field Analysis
Field analysis tells Solr what to do with incoming data when building an index. A more accurate name for this
process would be processing or even digestion, but the official name is analysis.
Consider, for example, a biography field in a person document. Every word of the biography must be
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indexed so that you can quickly find people whose lives have had anything to do with ketchup, or
dragonflies, or cryptography.
However, a biography will likely contains lots of words you don’t care about and don’t want clogging up
your index—words like "the", "a", "to", and so forth. Furthermore, suppose the biography contains the word
"Ketchup", capitalized at the beginning of a sentence. If a user makes a query for "ketchup", you want Solr
to tell you about the person even though the biography contains the capitalized word.
The solution to both these problems is field analysis. For the biography field, you can tell Solr how to break
apart the biography into words. You can tell Solr that you want to make all the words lower case, and you
can tell Solr to remove accents marks.
Field analysis is an important part of a field type. Understanding Analyzers, Tokenizers, and Filters is a
detailed description of field analysis.
Solr’s Schema File
Solr stores details about the field types and fields it is expected to understand in a schema file. The name
and location of this file may vary depending on how you initially configured Solr or if you modified it later.
? managed-schema is the name for the schema file Solr uses by default to support making Schema changes
at runtime via the Schema API, or Schemaless Mode features. You may explicitly configure the managed
schema features to use an alternative filename if you choose, but the contents of the files are still
updated automatically by Solr.
? schema.xml is the traditional name for a schema file which can be edited manually by users who use the
ClassicIndexSchemaFactory .
? If you are using SolrCloud you may not be able to find any file by these names on the local filesystem.
You will only be able to see the schema through the Schema API (if enabled) or through the Solr Admin
UI’s Cloud Screens.
Whichever name of the file in use in your installation, the structure of the file is not changed. However, the
way you interact with the file will change. If you are using the managed schema, it is expected that you only
interact with the file with the Schema API, and never make manual edits. If you do not use the managed
schema, you will only be able to make manual edits to the file, the Schema API will not support any
modifications.
Note that if you are not using the Schema API yet you do use SolrCloud, you will need to interact with
schema.xml through ZooKeeper using upconfig and downconfig commands to make a local copy and upload
your changes. The options for doing this are described in Solr Control Script Reference and Using ZooKeeper
to Manage Configuration Files.
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Solr Field Types
The field type defines how Solr should interpret data in a field and how the field can be queried. There are
many field types included with Solr by default, and they can also be defined locally.
Topics covered in this section:
? Field Type Definitions and Properties
? Field Types Included with Solr
? Working with Currencies and Exchange Rates
? Working with Dates
? Working with Enum Fields
? Working with External Files and Processes
? Field Properties by Use Case
?
See also the FieldType Javadoc.
Field Type Definitions and Properties
A field type defines the analysis that will occur on a field when documents are indexed or queries are sent to
the index.
A field type definition can include four types of information:
? The name of the field type (mandatory).
? An implementation class name (mandatory).
? If the field type is TextField , a description of the field analysis for the field type.
? Field type properties - depending on the implementation class, some properties may be mandatory.
Field Type Definitions in schema.xml
Field types are defined in schema.xml . Each field type is defined between fieldType elements. They can
optionally be grouped within a types element. Here is an example of a field type definition for a type called
text_general :
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<fieldType name="text_general" class="solr.TextField" positionIncrementGap="100"> ①
<analyzer type="index"> ②
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.StopFilterFactory" ignoreCase="true" words="stopwords.txt" />
<!-- in this example, we will only use synonyms at query time
<filter class="solr.SynonymFilterFactory" synonyms="index_synonyms.txt" ignoreCase="true"
expand="false"/>
-->
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
<analyzer type="query">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.StopFilterFactory" ignoreCase="true" words="stopwords.txt" />
<filter class="solr.SynonymFilterFactory" synonyms="synonyms.txt" ignoreCase="true" expand=
"true"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
</fieldType>
①
The first line in the example above contains the field type name, text_general , and the name of the
implementing class, solr.TextField .
②
The rest of the definition is about field analysis, described in Understanding Analyzers, Tokenizers, and
Filters.
The implementing class is responsible for making sure the field is handled correctly. In the class names in
schema.xml , the string solr is shorthand for org.apache.solr.schema or org.apache.solr.analysis .
Therefore, solr.TextField is really org.apache.solr.schema.TextField .
Field Type Properties
The field type class determines most of the behavior of a field type, but optional properties can also be
defined. For example, the following definition of a date field type defines two properties, sortMissingLast
and omitNorms .
<fieldType name="date" class="solr.TrieDateField"
sortMissingLast="true" omitNorms="true"/>
The properties that can be specified for a given field type fall into three major categories:
? Properties specific to the field type’s class.
? General Properties Solr supports for any field type.
? Field Default Properties that can be specified on the field type that will be inherited by fields that use this
type instead of the default behavior.
General Properties
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Property Description Values
name The name of the fieldType. This value gets
used in field definitions, in the "type"
attribute. It is strongly recommended that
names consist of alphanumeric or
underscore characters only and not start
with a digit. This is not currently strictly
enforced.
class The class name that gets used to store
and index the data for this type. Note that
you may prefix included class names with
"solr." and Solr will automatically figure
out which packages to search for the class
- so solr.TextField will work. If you are
using a third-party class, you will probably
need to have a fully qualified class name.
The fully qualified equivalent for
solr.TextField is
org.apache.solr.schema.TextField .
positionIncrementGap For multivalued fields, specifies a distance
between multiple values, which prevents
spurious phrase matches
integer
autoGeneratePhraseQueries For text fields. If true, Solr automatically
generates phrase queries for adjacent
terms. If false, terms must be enclosed in
double-quotes to be treated as phrases.
true or false
enableGraphQueries For text fields, applicable when querying
with sow=false . Use true (the default) for
field types with query analyzers including
graph-aware filters, e.g. Synonym Graph
Filter and Word Delimiter Graph Filter.
Use false for field types with query
analyzers including filters that can match
docs when some tokens are missing, e.g.,
Shingle Filter.
true or false
docValuesFormat Defines a custom DocValuesFormat to use
for fields of this type. This requires that a
schema-aware codec, such as the
SchemaCodecFactory has been configured
in solrconfig.xml.
n/a
postingsFormat Defines a custom PostingsFormat to use
for fields of this type. This requires that a
schema-aware codec, such as the
SchemaCodecFactory has been configured
in solrconfig.xml.
n/a
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?
Lucene index back-compatibility is only supported for the default codec. If you choose to
customize the postingsFormat or docValuesFormat in your schema.xml, upgrading to a
future version of Solr may require you to either switch back to the default codec and
optimize your index to rewrite it into the default codec before upgrading, or re-build your
entire index from scratch after upgrading.
Field Default Properties
These are properties that can be specified either on the field types, or on individual fields to override the
values provided by the field types.
The default values for each property depend on the underlying FieldType class, which in turn may depend
on the version attribute of the <schema/> . The table below includes the default value for most FieldType
implementations provided by Solr, assuming a schema.xml that declares version="1.6" .
Property Description Values Implicit Default
indexed If true, the value of the field can be used
in queries to retrieve matching
documents.
true or false true
stored If true, the actual value of the field can be
retrieved by queries.
true or false true
docValues If true, the value of the field will be put in
a column-oriented DocValues structure.
true or false false
sortMissingFirst
sortMissingLast
Control the placement of documents
when a sort field is not present.
true or false false
multiValued If true, indicates that a single document
might contain multiple values for this field
type.
true or false false
omitNorms If true, omits the norms associated with
this field (this disables length
normalization for the field, and saves
some memory). Defaults to true for all
primitive (non-analyzed) field types,
such as int, float, data, bool, and string.
Only full-text fields or fields need norms.
true or false *
omitTermFreqAndP
ositions
If true, omits term frequency, positions,
and payloads from postings for this field.
This can be a performance boost for fields
that don’t require that information. It also
reduces the storage space required for
the index. Queries that rely on position
that are issued on a field with this option
will silently fail to find documents. This
property defaults to true for all field
types that are not text fields.
true or false *
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Property Description Values Implicit Default
omitPositions Similar to omitTermFreqAndPositions but
preserves term frequency information.
true or false *
termVectors
termPositions
termOffsets
termPayloads
These options instruct Solr to maintain full
term vectors for each document,
optionally including position, offset and
payload information for each term
occurrence in those vectors. These can be
used to accelerate highlighting and other
ancillary functionality, but impose a
substantial cost in terms of index size.
They are not necessary for typical uses of
Solr.
true or false false
required Instructs Solr to reject any attempts to
add a document which does not have a
value for this field. This property defaults
to false.
true or false false
useDocValuesAsStor
ed
If the field has docValues enabled, setting
this to true would allow the field to be
returned as if it were a stored field (even
if it has stored=false ) when matching
“*” in an fl parameter.
true or false true
large Large fields are always lazy loaded and
will only take up space in the document
cache if the actual value is < 512KB. This
option requires stored="true" and
multiValued="false" . It’s intended for
fields that might have very large values so
that they don’t get cached in memory.
true or false false
Field Type Similarity
A field type may optionally specify a <similarity/> that will be used when scoring documents that refer to
fields with this type, as long as the "global" similarity for the collection allows it.
By default, any field type which does not define a similarity, uses BM25Similarity . For more details, and
examples of configuring both global & per-type Similarities, please see Other Schema Elements.
Field Types Included with Solr
The following table lists the field types that are available in Solr. The org.apache.solr.schema package
includes all the classes listed in this table.
Class Description
BinaryField Binary data.
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Class Description
BoolField Contains either true or false. Values of "1", "t", or "T" in the first character are
interpreted as true. Any other values in the first character are interpreted as
false.
CollationField Supports Unicode collation for sorting and range queries. ICUCollationField is a
better choice if you can use ICU4J. See the section Unicode Collation.
CurrencyField Supports currencies and exchange rates. See the section Working with
Currencies and Exchange Rates.
DateRangeField Supports indexing date ranges, to include point in time date instances as well
(single-millisecond durations). See the section Working with Dates for more
detail on using this field type. Consider using this field type even if it’s just for
date instances, particularly when the queries typically fall on UTC
year/month/day/hour, etc., boundaries.
ExternalFileField Pulls values from a file on disk. See the section Working with External Files and
Processes.
EnumField Allows defining an enumerated set of values which may not be easily sorted by
either alphabetic or numeric order (such as a list of severities, for example). This
field type takes a configuration file, which lists the proper order of the field
values. See the section Working with Enum Fields for more information.
ICUCollationField Supports Unicode collation for sorting and range queries. See the section
Unicode Collation.
LatLonPointSpatialField Spatial Search: a latitude/longitude coordinate pair; possibly multi-valued for
multiple points. Usually it’s specified as "lat,lon" order with a comma.
LatLonType (deprecated) Spatial Search: a single-valued latitude/longitude coordinate pair.
Usually it’s specified as "lat,lon" order with a comma.
PointType Spatial Search: A single-valued n-dimensional point. It’s both for sorting spatial
data that is not lat-lon, and for some more rare use-cases. (NOTE: this is not
related to the "Point" based numeric fields)
PreAnalyzedField Provides a way to send to Solr serialized token streams, optionally with
independent stored values of a field, and have this information stored and
indexed without any additional text processing. Configuration and usage of
PreAnalyzedField is documented on the Working with External Files and
Processes page.
RandomSortField Does not contain a value. Queries that sort on this field type will return results in
random order. Use a dynamic field to use this feature.
SpatialRecursivePrefixTre
eFieldType
(RPT for short) Spatial Search: Accepts latitude comma longitude strings or
other shapes in WKT format.
StrField String (UTF-8 encoded string or Unicode). Strings are intended for small fields
and are not tokenized or analyzed in any way. They have a hard limit of slightly
less than 32K.
TextField Text, usually multiple words or tokens.
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Class Description
TrieDateField Date field. Represents a point in time with millisecond precision. See the section
Working with Dates. precisionStep="0" minimizes index size;
precisionStep="8" (the default) enables more efficient range queries. For
single valued fields, use docValues="true" for efficient sorting.
TrieDoubleField Double field (64-bit IEEE floating point). precisionStep="0" minimizes index
size; precisionStep="8" (the default) enables more efficient range queries. For
single valued fields, use docValues="true" for efficient sorting.
TrieFloatField Floating point field (32-bit IEEE floating point) . precisionStep="0" enables
efficient numeric sorting and minimizes index size; precisionStep="8" (the
default) enables efficient range queries. Use docValues="true" for efficient
sorting. For single valued fields, use docValues="true" for efficient sorting.
TrieIntField Integer field (32-bit signed integer). precisionStep="0" enables efficient
numeric sorting and minimizes index size; precisionStep="8" (the default)
enables efficient range queries. For single valued fields, use docValues="true"
for efficient sorting.
TrieLongField Long field (64-bit signed integer). precisionStep="0" minimizes index size;
precisionStep="8" (the default) enables more efficient range queries. For
single valued fields, use docValues="true" for efficient sorting.
TrieField If this field type is used, a "type" attribute must also be specified, valid values
are: integer , long , float , double , date . Using this field is the same as using any
of the Trie fields mentioned above
DatePointField Date field. Represents a point in time with millisecond precision. See the section
Working with Dates. This class functions similarly to TrieDateField, but using a
"Dimensional Points" based data structure instead of indexed terms, and
doesn’t require configuration of a precision step. For single valued fields,
docValues="true" must be used to enable sorting.
DoublePointField Double field (64-bit IEEE floating point). This class functions similarly to
TrieDoubleField, but using a "Dimensional Points" based data structure instead
of indexed terms, and doesn’t require configuration of a precision step. For
single valued fields, docValues="true" must be used to enable sorting.
FloatPointField Floating point field (32-bit IEEE floating point). This class functions similarly to
TrieFloatField, but using a "Dimensional Points" based data structure instead of
indexed terms, and doesn’t require configuration of a precision step. For single
valued fields, docValues="true" must be used to enable sorting.
IntPointField Integer field (32-bit signed integer). This class functions similarly to TrieIntField,
but using a "Dimensional Points" based data structure instead of indexed
terms, and doesn’t require configuration of a precision step. For single valued
fields, docValues="true" must be used to enable sorting.
LongPointField Long field (64-bit signed integer). This class functions similarly to TrieLongField,
but using a "Dimensional Points" based data structure instead of indexed
terms, and doesn’t require configuration of a precision step. For single valued
fields, docValues="true" must be used to enable sorting.
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Class Description
UUIDField Universally Unique Identifier (UUID). Pass in a value of "NEW" and Solr will
create a new UUID. Note: configuring a UUIDField instance with a default value
of "NEW" is not advisable for most users when using SolrCloud (and not
possible if the UUID value is configured as the unique key field) since the result
will be that each replica of each document will get a unique UUID value. Using
UUIDUpdateProcessorFactory to generate UUID values when documents are
added is recommended instead.
Working with Currencies and Exchange Rates
The currency FieldType provides support for monetary values to Solr/Lucene with query-time currency
conversion and exchange rates. The following features are supported:
? Point queries
? Range queries
? Function range queries
? Sorting
? Currency parsing by either currency code or symbol
? Symmetric & asymmetric exchange rates (asymmetric exchange rates are useful if there are fees
associated with exchanging the currency)
Configuring Currencies
The currency field type is defined in schema.xml . This is the default configuration of this type:
<fieldType name="currency" class="solr.CurrencyField" precisionStep="8"
defaultCurrency="USD" currencyConfig="currency.xml" />
In this example, we have defined the name and class of the field type, and defined the defaultCurrency as
"USD", for U.S. Dollars. We have also defined a currencyConfig to use a file called "currency.xml". This is a
file of exchange rates between our default currency to other currencies. There is an alternate
implementation that would allow regular downloading of currency data. See Exchange Rates below for
more.
Many of the example schemas that ship with Solr include a dynamic field that uses this type, such as this
example:
<dynamicField name="*_c" type="currency" indexed="true" stored="true"/>
This dynamic field would match any field that ends in _c and make it a currency typed field.
At indexing time, money fields can be indexed in a native currency. For example, if a product on an e-
commerce site is listed in Euros, indexing the price field as "1000,EUR" will index it appropriately. The price
should be separated from the currency by a comma, and the price must be encoded with a floating point
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value (a decimal point).
During query processing, range and point queries are both supported.
Exchange Rates
You configure exchange rates by specifying a provider. Natively, two provider types are supported:
FileExchangeRateProvider or OpenExchangeRatesOrgProvider .
FileExchangeRateProvider
This provider requires you to provide a file of exchange rates. It is the default, meaning that to use this
provider you only need to specify the file path and name as a value for currencyConfig in the definition for
this type.
There is a sample currency.xml file included with Solr, found in the same directory as the schema.xml file.
Here is a small snippet from this file:
<currencyConfig version="1.0">
<rates>
<!-- Updated from http://www.exchangerate.com/ at 2011-09-27 -->
<rate from="USD" to="ARS" rate="4.333871" comment="ARGENTINA Peso" />
<rate from="USD" to="AUD" rate="1.025768" comment="AUSTRALIA Dollar" />
<rate from="USD" to="EUR" rate="0.743676" comment="European Euro" />
<rate from="USD" to="CAD" rate="1.030815" comment="CANADA Dollar" />
<!-- Cross-rates for some common currencies -->
<rate from="EUR" to="GBP" rate="0.869914" />
<rate from="EUR" to="NOK" rate="7.800095" />
<rate from="GBP" to="NOK" rate="8.966508" />
<!-- Asymmetrical rates -->
<rate from="EUR" to="USD" rate="0.5" />
</rates>
</currencyConfig>
OpenExchangeRatesOrgProvider
You can configure Solr to download exchange rates from OpenExchangeRates.Org, with updates rates
between USD and 170 currencies hourly. These rates are symmetrical only.
In this case, you need to specify the providerClass in the definitions for the field type and sign up for an API
key. Here is an example:
<fieldType name="currency" class="solr.CurrencyField" precisionStep="8"
providerClass="solr.OpenExchangeRatesOrgProvider"
refreshInterval="60"
ratesFileLocation=
"http://www.openexchangerates.org/api/latest.json?app_id=yourPersonalAppIdKey"/>
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The refreshInterval is minutes, so the above example will download the newest rates every 60 minutes.
The refresh interval may be increased, but not decreased.
Working with Dates
Date Formatting
Solr’s date fields ( TrieDateField , DatePointField and DateRangeField ) represent "dates" as a point in
time with millisecond precision. The format used is a restricted form of the canonical representation of
dateTime in the XML Schema specification – a restricted subset of ISO-8601. For those familiar with Java 8,
Solr uses DateTimeFormatter.ISO_INSTANT for formatting, and parsing too with "leniency".
YYYY-MM-DDThh:mm:ssZ
? YYYY is the year.
? MM is the month.
? DD is the day of the month.
? hh is the hour of the day as on a 24-hour clock.
? mm is minutes.
? ss is seconds.
? Z is a literal ‘Z‘ character indicating that this string representation of the date is in UTC
Note that no time zone can be specified; the String representations of dates is always expressed in
Coordinated Universal Time (UTC). Here is an example value:
1972-05-20T17:33:18Z
You can optionally include fractional seconds if you wish, although any precision beyond milliseconds will be
ignored. Here are example values with sub-seconds:
? 1972-05-20T17:33:18.772Z
? 1972-05-20T17:33:18.77Z
? 1972-05-20T17:33:18.7Z
There must be a leading ‘-’ for dates prior to year 0000, and Solr will format dates with a leading ‘+’ for years
after 9999. Year 0000 is considered year 1 BC; there is no such thing as year 0 AD or BC.
?
Query escaping may be required
As you can see, the date format includes colon characters separating the hours, minutes,
and seconds. Because the colon is a special character to Solr’s most common query
parsers, escaping is sometimes required, depending on exactly what you are trying to do.
This is normally an invalid query: datefield:1972-05-20T17:33:18.772Z
These are valid queries:
datefield:1972-05-20T17\:33\:18.772Z
datefield:"1972-05-20T17:33:18.772Z"
datefield:[1972-05-20T17:33:18.772Z TO *]
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Date Range Formatting
Solr’s DateRangeField supports the same point in time date syntax described above (with date math
described below) and more to express date ranges. One class of examples is truncated dates, which
represent the entire date span to the precision indicated. The other class uses the range syntax ( [ TO ] ).
Here are some examples:
? 2000-11 – The entire month of November, 2000.
? 2000-11T13 – Likewise but for an hour of the day (1300 to before 1400, i.e. 1pm to 2pm).
? -0009 – The year 10 BC. A 0 in the year position is 0 AD, and is also considered 1 BC.
? [2000-11-01 TO 2014-12-01] – The specified date range at a day resolution.
? [2014 TO 2014-12-01] – From the start of 2014 till the end of the first day of December.
? [* TO 2014-12-01] – From the earliest representable time thru till the end of the day on 2014-12-01.
Limitations: The range syntax doesn’t support embedded date math. If you specify a date instance
supported by TrieDateField with date math truncating it, like NOW/DAY , you still get the first millisecond of
that day, not the entire day’s range. Exclusive ranges (using { & } ) work in queries but not for indexing
ranges.
Date Math
Solr’s date field types also supports date math expressions, which makes it easy to create times relative to
fixed moments in time, include the current time which can be represented using the special value of “ NOW ”.
Date Math Syntax
Date math expressions consist either adding some quantity of time in a specified unit, or rounding the
current time by a specified unit. expressions can be chained and are evaluated left to right.
For example: this represents a point in time two months from now:
NOW+2MONTHS
This is one day ago:
NOW-1DAY
A slash is used to indicate rounding. This represents the beginning of the current hour:
NOW/HOUR
The following example computes (with millisecond precision) the point in time six months and three days
into the future and then rounds that time to the beginning of that day:
NOW+6MONTHS+3DAYS/DAY
Note that while date math is most commonly used relative to NOW it can be applied to any fixed moment in
time as well:
1972-05-20T17:33:18.772Z+6MONTHS+3DAYS/DAY
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Request Parameters That Affect Date Math
NOW
The NOW parameter is used internally by Solr to ensure consistent date math expression parsing across
multiple nodes in a distributed request. But it can be specified to instruct Solr to use an arbitrary moment in
time (past or future) to override for all situations where the the special value of “ NOW ” would impact date
math expressions.
It must be specified as a (long valued) milliseconds since epoch
Example:
q=solr&fq=start_date:[* TO NOW]&NOW=1384387200000
TZ
By default, all date math expressions are evaluated relative to the UTC TimeZone, but the TZ parameter can
be specified to override this behaviour, by forcing all date based addition and rounding to be relative to the
specified time zone.
For example, the following request will use range faceting to facet over the current month, "per day"
relative UTC:
http://localhost:8983/solr/my_collection/select?q=*:*&facet.range=my_date_field&facet=true&facet.
range.start=NOW/MONTH&facet.range.end=NOW/MONTH%2B1MONTH&facet.range.gap=%2B1DAY
<int name="2013-11-01T00:00:00Z">0</int>
<int name="2013-11-02T00:00:00Z">0</int>
<int name="2013-11-03T00:00:00Z">0</int>
<int name="2013-11-04T00:00:00Z">0</int>
<int name="2013-11-05T00:00:00Z">0</int>
<int name="2013-11-06T00:00:00Z">0</int>
<int name="2013-11-07T00:00:00Z">0</int>
...
While in this example, the "days" will be computed relative to the specified time zone - including any
applicable Daylight Savings Time adjustments:
http://localhost:8983/solr/my_collection/select?q=*:*&facet.range=my_date_field&facet=true&facet.
range.start=NOW/MONTH&facet.range.end=NOW/MONTH%2B1MONTH&facet.range.gap=%2B1DAY&TZ=America/Los_A
ngeles
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<int name="2013-11-01T07:00:00Z">0</int>
<int name="2013-11-02T07:00:00Z">0</int>
<int name="2013-11-03T07:00:00Z">0</int>
<int name="2013-11-04T08:00:00Z">0</int>
<int name="2013-11-05T08:00:00Z">0</int>
<int name="2013-11-06T08:00:00Z">0</int>
<int name="2013-11-07T08:00:00Z">0</int>
...
More DateRangeField Details
DateRangeField is almost a drop-in replacement for places where TrieDateField is used. The only
difference is that Solr’s XML or SolrJ response formats will expose the stored data as a String instead of a
Date. The underlying index data for this field will be a bit larger. Queries that align to units of time a second
on up should be faster than TrieDateField, especially if it’s in UTC. But the main point of DateRangeField as
its name suggests is to allow indexing date ranges. To do that, simply supply strings in the format shown
above. It also supports specifying 3 different relational predicates between the indexed data, and the query
range: Intersects (default), Contains , Within . You can specify the predicate by querying using the op local-
params parameter like so:
fq={!field f=dateRange op=Contains}[2013 TO 2018]
Unlike most/all local-params, op is actually not defined by any query parser ( field ), it is defined by the field
type – DateRangeField . In that example, it would find documents with indexed ranges that contain (or
equals) the range 2013 thru 2018. Multi-valued overlapping indexed ranges in a document are effectively
coalesced.
For a DateRangeField example use-case and possibly other information, see Solr’s community wiki.
Working with Enum Fields
The EnumField type allows defining a field whose values are a closed set, and the sort order is pre-
determined but is not alphabetic nor numeric. Examples of this are severity lists, or risk definitions.
Defining an EnumField in schema.xml
The EnumField type definition is quite simple, as in this example defining field types for "priorityLevel" and
"riskLevel" enumerations:
<fieldType name="priorityLevel" class="solr.EnumField" enumsConfig="enumsConfig.xml" enumName=
"priority"/>
<fieldType name="riskLevel" class="solr.EnumField" enumsConfig="enumsConfig.xml" enumName=
"risk" />
Besides the name and the class , which are common to all field types, this type also takes two additional
parameters:
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? enumsConfig : the name of a configuration file that contains the <enum/> list of field values and their
order that you wish to use with this field type. If a path to the file is not defined specified, the file should
be in the conf directory for the collection.
? enumName : the name of the specific enumeration in the enumsConfig file to use for this type.
Defining the EnumField configuration file
The file named with the enumsConfig parameter can contain multiple enumeration value lists with different
names if there are multiple uses for enumerations in your Solr schema.
In this example, there are two value lists defined. Each list is between enum opening and closing tags:
<?xml version="1.0" ?>
<enumsConfig>
<enum name="priority">
<value>Not Available</value>
<value>Low</value>
<value>Medium</value>
<value>High</value>
<value>Urgent</value>
</enum>
<enum name="risk">
<value>Unknown</value>
<value>Very Low</value>
<value>Low</value>
<value>Medium</value>
<value>High</value>
<value>Critical</value>
</enum>
</enumsConfig>
?
Changing Values
You cannot change the order, or remove, existing values in an <enum/> without reindexing.
You can however add new values to the end.
Working with External Files and Processes
The ExternalFileField Type
The ExternalFileField type makes it possible to specify the values for a field in a file outside the Solr index.
For such a field, the file contains mappings from a key field to the field value. Another way to think of this is
that, instead of specifying the field in documents as they are indexed, Solr finds values for this field in the
external file.
?
External fields are not searchable. They can be used only for function queries or display.
For more information on function queries, see the section on Function Queries.
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The ExternalFileField type is handy for cases where you want to update a particular field in many
documents more often than you want to update the rest of the documents. For example, suppose you have
implemented a document rank based on the number of views. You might want to update the rank of all the
documents daily or hourly, while the rest of the contents of the documents might be updated much less
frequently. Without ExternalFileField , you would need to update each document just to change the rank.
Using ExternalFileField is much more efficient because all document values for a particular field are
stored in an external file that can be updated as frequently as you wish.
In schema.xml , the definition of this field type might look like this:
<fieldType name="entryRankFile" keyField="pkId" defVal="0" stored="false" indexed="false" class=
"solr.ExternalFileField" valType="pfloat"/>
The keyField attribute defines the key that will be defined in the external file. It is usually the unique key for
the index, but it doesn’t need to be as long as the keyField can be used to identify documents in the index.
A defVal defines a default value that will be used if there is no entry in the external file for a particular
document.
The valType attribute specifies the actual type of values that will be found in the file. The type specified must
be either a float field type, so valid values for this attribute are pfloat , float or tfloat . This attribute can be
omitted.
Format of the External File
The file itself is located in Solr’s index directory, which by default is $SOLR_HOME/data . The name of the file
should be external_fieldname or external_fieldname.* . For the example above, then, the file could be
named external_entryRankFile or external_entryRankFile.txt .
?
If any files using the name pattern .* (such as .txt ) appear, the last (after being sorted by
name) will be used and previous versions will be deleted. This behavior supports
implementations on systems where one may not be able to overwrite a file (for example,
on Windows, if the file is in use).
The file contains entries that map a key field, on the left of the equals sign, to a value, on the right. Here are
a few example entries:
doc33=1.414
doc34=3.14159
doc40=42
The keys listed in this file do not need to be unique. The file does not need to be sorted, but Solr will be able
to perform the lookup faster if it is.
Reloading an External File
It’s possible to define an event listener to reload an external file when either a searcher is reloaded or when
a new searcher is started. See the section Query-Related Listeners for more information, but a sample
definition in solrconfig.xml might look like this:
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<listener event="newSearcher" class="org.apache.solr.schema.ExternalFileFieldReloader"/>
<listener event="firstSearcher" class="org.apache.solr.schema.ExternalFileFieldReloader"/>
The PreAnalyzedField Type
The PreAnalyzedField type provides a way to send to Solr serialized token streams, optionally with
independent stored values of a field, and have this information stored and indexed without any additional
text processing applied in Solr. This is useful if user wants to submit field content that was already processed
by some existing external text processing pipeline (e.g., it has been tokenized, annotated, stemmed,
synonyms inserted, etc.), while using all the rich attributes that Lucene’s TokenStream provides (per-token
attributes).
The serialization format is pluggable using implementations of PreAnalyzedParser interface. There are two
out-of-the-box implementations:
? JsonPreAnalyzedParser: as the name suggests, it parses content that uses JSON to represent field’s
content. This is the default parser to use if the field type is not configured otherwise.
? SimplePreAnalyzedParser: uses a simple strict plain text format, which in some situations may be easier
to create than JSON.
There is only one configuration parameter, parserImpl . The value of this parameter should be a fully
qualified class name of a class that implements PreAnalyzedParser interface. The default value of this
parameter is org.apache.solr.schema.JsonPreAnalyzedParser .
By default, the query-time analyzer for fields of this type will be the same as the index-time analyzer, which
expects serialized pre-analyzed text. You must add a query type analyzer to your fieldType in order to
perform analysis on non-pre-analyzed queries. In the example below, the index-time analyzer expects the
default JSON serialization format, and the query-time analyzer will employ
StandardTokenizer/LowerCaseFilter:
<fieldType name="pre_with_query_analyzer" class="solr.PreAnalyzedField">
<analyzer type="query">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
</fieldType>
JsonPreAnalyzedParser
This is the default serialization format used by PreAnalyzedField type. It uses a top-level JSON map with the
following keys:
Key Description Required
v
Version key. Currently the supported version is 1 . required
str
Stored string value of a field. You can use at most one of str or
bin .
optional
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Key Description Required
bin
Stored binary value of a field. The binary value has to be Base64
encoded.
optional
tokens
serialized token stream. This is a JSON list. optional
Any other top-level key is silently ignored.
Token stream serialization
The token stream is expressed as a JSON list of JSON maps. The map for each token consists of the following
keys and values:
Key Description Lucene Attribute Value Required?
t
token CharTermAttribute UTF-8 string representing the
current token
required
s
start offset OffsetAttribute Non-negative integer optional
e
end offset OffsetAttribute Non-negative integer optional
i
position increment PositionIncrementAt
tribute
Non-negative integer - default
is 1
optional
p
payload PayloadAttribute Base64 encoded payload optional
y
lexical type TypeAttribute UTF-8 string optional
f
flags FlagsAttribute String representing an integer
value in hexadecimal format
optional
Any other key is silently ignored.
Example
{
"v":"1",
"str":"test ????ńó???",
"tokens": [
{"t":"one","s":123,"e":128,"i":22,"p":"DQ4KDQsODg8=","y":"word"},
{"t":"two","s":5,"e":8,"i":1,"y":"word"},
{"t":"three","s":20,"e":22,"i":1,"y":"foobar"}
]
}
SimplePreAnalyzedParser
The fully qualified class name to use when specifying this format via the parserImpl configuration
parameter is org.apache.solr.schema.SimplePreAnalyzedParser .
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Syntax
The serialization format supported by this parser is as follows:
Serialization format
content ::= version (stored)? tokens
version ::= digit+ " "
; stored field value - any "=" inside must be escaped!
stored ::= "=" text "="
tokens ::= (token ((" ") + token)*)*
token ::= text ("," attrib)*
attrib ::= name ‘=‘ value
name ::= text
value ::= text
Special characters in "text" values can be escaped using the escape character \ . The following escape
sequences are recognized:
EscapeSequence Description
\
literal space character
\,
literal , character
\=
literal = character
\\
literal \ character
\n
newline
\r
carriage return
\t
horizontal tab
Please note that Unicode sequences (e.g. \u0001 ) are not supported.
Supported attribute names
The following token attributes are supported, and identified with short symbolic names:
Name Description Lucene attribute Value format
i
position increment PositionIncrementAttribute integer
s
start offset OffsetAttribute integer
e
end offset OffsetAttribute integer
y
lexical type TypeAttribute string
f
flags FlagsAttribute hexadecimal integer
p
payload PayloadAttribute bytes in hexadecimal format;
whitespace is ignored
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Token positions are tracked and implicitly added to the token stream - the start and end offsets consider
only the term text and whitespace, and exclude the space taken by token attributes.
Example token streams
1 one two three
? version: 1
? stored: null
? token: (term= one ,startOffset=0,endOffset=3)
? token: (term= two ,startOffset=4,endOffset=7)
? token: (term= three ,startOffset=8,endOffset=13)
1 one two three
? version: 1
? stored: null
? token: (term= one ,startOffset=0,endOffset=3)
? token: (term= two ,startOffset=5,endOffset=8)
? token: (term= three ,startOffset=11,endOffset=16)
1 one,s=123,e=128,i=22 two three,s=20,e=22
? version: 1
? stored: null
? token: (term= one ,positionIncrement=22,startOffset=123,endOffset=128)
? token: (term= two ,positionIncrement=1,startOffset=5,endOffset=8)
? token: (term= three ,positionIncrement=1,startOffset=20,endOffset=22)
1 \ one\ \,,i=22,a=\, two\=
\n,\ =\ \
? version: 1
? stored: null
? token: (term= one , ,positionIncrement=22,startOffset=0,endOffset=6)
? token: (term= two= ,positionIncrement=1,startOffset=7,endOffset=15)
? token: (term= \ ,positionIncrement=1,startOffset=17,endOffset=18)
Note that unknown attributes and their values are ignored, so in this example, the “ a ” attribute on the first
token and the " " (escaped space) attribute on the second token are ignored, along with their values,
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because they are not among the supported attribute names.
1 ,i=22 ,i=33,s=2,e=20 ,
? version: 1
? stored: null
? token: (term=,positionIncrement=22,startOffset=0,endOffset=0)
? token: (term=,positionIncrement=33,startOffset=2,endOffset=20)
? token: (term=,positionIncrement=1,startOffset=2,endOffset=2)
1 =This is the stored part with \=
\n \t escapes.=one two three
? version: 1
? stored: This is the stored part with = \t escapes.
? token: (term= one ,startOffset=0,endOffset=3)
? token: (term= two ,startOffset=4,endOffset=7)
? token: (term= three ,startOffset=8,endOffset=13)
Note that the \t in the above stored value is not literal; it’s shown that way to visually indicate the actual tab
char that is in the stored value.
1 ==
? version: 1
? stored: ""
? (no tokens)
1 =this is a test.=
? version: 1
? stored: this is a test.
? (no tokens)
Field Properties by Use Case
Here is a summary of common use cases, and the attributes the fields or field types should have to support
the case. An entry of true or false in the table indicates that the option must be set to the given value for the
use case to function correctly. If no entry is provided, the setting of that attribute has no impact on the case.
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Use Case indexed stored multiValue
d
omitNorm
s
termVecto
rs
termPositi
ons
docValues
search within
field
true
retrieve
contents
true
8
true
8
use as unique
key
true false
sort on field true
7
false true
1
true
7
highlighting true
4
true true
2
true
3
faceting
5
true
7
true
7
add multiple
values,
maintaining
order
true
field length
affects doc
score
false
MoreLikeThis
5
true
6
Notes:
1. Recommended but not necessary.
2. Will be used if present, but not necessary.
3. (if termVectors=true)
4. A tokenizer must be defined for the field, but it doesn’t need to be indexed.
5. Described in Understanding Analyzers, Tokenizers, and Filters.
6. Term vectors are not mandatory here. If not true, then a stored field is analyzed. So term vectors are
recommended, but only required if stored=false .
7. For most field types, either indexed or docValues must be true, but both are not required. DocValues can
be more efficient in many cases. For [Int/Long/Float/Double/Date]PointFields , docValues=true is
required.
8. Stored content will be used by default, but docValues can alternatively be used. See DocValues.
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Defining Fields
Fields are defined in the fields element of schema.xml . Once you have the field types set up, defining the
fields themselves is simple.
Example
The following example defines a field named price with a type named float and a default value of 0.0 ; the
indexed and stored properties are explicitly set to true , while any other properties specified on the float
field type are inherited.
<field name="price" type="float" default="0.0" indexed="true" stored="true"/>
Field Properties
Property Description
name The name of the field. Field names should consist of alphanumeric or
underscore characters only and not start with a digit. This is not currently
strictly enforced, but other field names will not have first class support
from all components and back compatibility is not guaranteed. Names
with both leading and trailing underscores (e.g., _version_ ) are reserved.
Every field must have a name .
type The name of the fieldType for this field. This will be found in the name
attribute on the fieldType definition. Every field must have a type .
default A default value that will be added automatically to any document that does
not have a value in this field when it is indexed. If this property is not
specified, there is no default.
Optional Field Type Override Properties
Fields can have many of the same properties as field types. Properties from the table below which are
specified on an individual field will override any explicit value for that property specified on the the
fieldType of the field, or any implicit default property value provided by the underlying fieldType
implementation. The table below is reproduced from Field Type Definitions and Properties, which has more
details:
Property Description Values Implicit Default
indexed If true, the value of the field can be used
in queries to retrieve matching
documents.
true or false true
stored If true, the actual value of the field can be
retrieved by queries.
true or false true
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Property Description Values Implicit Default
docValues If true, the value of the field will be put in
a column-oriented DocValues structure.
true or false false
sortMissingFirst
sortMissingLast
Control the placement of documents
when a sort field is not present.
true or false false
multiValued If true, indicates that a single document
might contain multiple values for this field
type.
true or false false
omitNorms If true, omits the norms associated with
this field (this disables length
normalization for the field, and saves
some memory). Defaults to true for all
primitive (non-analyzed) field types,
such as int, float, data, bool, and string.
Only full-text fields or fields need norms.
true or false *
omitTermFreqAndP
ositions
If true, omits term frequency, positions,
and payloads from postings for this field.
This can be a performance boost for fields
that don’t require that information. It also
reduces the storage space required for
the index. Queries that rely on position
that are issued on a field with this option
will silently fail to find documents. This
property defaults to true for all field
types that are not text fields.
true or false *
omitPositions Similar to omitTermFreqAndPositions but
preserves term frequency information.
true or false *
termVectors
termPositions
termOffsets
termPayloads
These options instruct Solr to maintain full
term vectors for each document,
optionally including position, offset and
payload information for each term
occurrence in those vectors. These can be
used to accelerate highlighting and other
ancillary functionality, but impose a
substantial cost in terms of index size.
They are not necessary for typical uses of
Solr.
true or false false
required Instructs Solr to reject any attempts to
add a document which does not have a
value for this field. This property defaults
to false.
true or false false
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Property Description Values Implicit Default
useDocValuesAsStor
ed
If the field has docValues enabled, setting
this to true would allow the field to be
returned as if it were a stored field (even
if it has stored=false ) when matching
“*” in an fl parameter.
true or false true
large Large fields are always lazy loaded and
will only take up space in the document
cache if the actual value is < 512KB. This
option requires stored="true" and
multiValued="false" . It’s intended for
fields that might have very large values so
that they don’t get cached in memory.
true or false false
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Copying Fields
You might want to interpret some document fields in more than one way. Solr has a mechanism for making
copies of fields so that you can apply several distinct field types to a single piece of incoming information.
The name of the field you want to copy is the source, and the name of the copy is the destination. In
schema.xml , it’s very simple to make copies of fields:
<copyField source="cat" dest="text" maxChars="30000" />
In this example, we want Solr to copy the cat field to a field named text . Fields are copied before analysis is
done, meaning you can have two fields with identical original content, but which use different analysis
chains and are stored in the index differently.
In the example above, if the text destination field has data of its own in the input documents, the contents
of the cat field will be added as additional values – just as if all of the values had originally been specified by
the client. Remember to configure your fields as multivalued="true" if they will ultimately get multiple
values (either from a multivalued source or from multiple copyField directives).
A common usage for this functionality is to create a single "search" field that will serve as the default query
field when users or clients do not specify a field to query. For example, title , author , keywords , and body
may all be fields that should be searched by default, with copy field rules for each field to copy to a catchall
field (for example, it could be named anything). Later you can set a rule in solrconfig.xml to search the
catchall field by default. One caveat to this is your index will grow when using copy fields. However,
whether this becomes problematic for you and the final size will depend on the number of fields being
copied, the number of destination fields being copied to, the analysis in use, and the available disk space.
The maxChars parameter, an int parameter, establishes an upper limit for the number of characters to be
copied from the source value when constructing the value added to the destination field. This limit is useful
for situations in which you want to copy some data from the source field, but also control the size of index
files.
Both the source and the destination of copyField can contain either leading or trailing asterisks, which will
match anything. For example, the following line will copy the contents of all incoming fields that match the
wildcard pattern *_t to the text field.:
<copyField source="*_t" dest="text" maxChars="25000" />
?
The copyField command can use a wildcard (*) character in the dest parameter only if the
source parameter contains one as well. copyField uses the matching glob from the source
field for the dest field name into which the source content is copied.
Copying is done at the stream source level and no copy feeds into another copy. This means that copy fields
cannot be chained i.e. you cannot copy from here to there and then from there to elsewhere . However, the
same source field can be copied to multiple destination fields:
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<copyField source="here" dest="there"/>
<copyField source="here" dest="elsewhere"/>
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Dynamic Fields
Dynamic fields allow Solr to index fields that you did not explicitly define in your schema.
This is useful if you discover you have forgotten to define one or more fields. Dynamic fields can make your
application less brittle by providing some flexibility in the documents you can add to Solr.
A dynamic field is just like a regular field except it has a name with a wildcard in it. When you are indexing
documents, a field that does not match any explicitly defined fields can be matched with a dynamic field.
For example, suppose your schema includes a dynamic field with a name of *_i . If you attempt to index a
document with a cost_i field, but no explicit cost_i field is defined in the schema, then the cost_i field will
have the field type and analysis defined for *_i .
Like regular fields, dynamic fields have a name, a field type, and options.
<dynamicField name="*_i" type="int" indexed="true" stored="true"/>
It is recommended that you include basic dynamic field mappings (like that shown above) in your
schema.xml . The mappings can be very useful.
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Other Schema Elements
This section describes several other important elements of schema.xml not covered in earlier sections.
Unique Key
The uniqueKey element specifies which field is a unique identifier for documents. Although uniqueKey is not
required, it is nearly always warranted by your application design. For example, uniqueKey should be used if
you will ever update a document in the index.
You can define the unique key field by naming it:
<uniqueKey>id</uniqueKey>
Schema defaults and copyFields cannot be used to populate the uniqueKey field. The fieldType of
uniqueKey must not be analyzed. You can use UUIDUpdateProcessorFactory to have uniqueKey values
generated automatically.
Further, the operation will fail if the uniqueKey field is used, but is multivalued (or inherits the multivalue-
ness from the fieldtype ). However, uniqueKey will continue to work, as long as the field is properly used.
Default Search Field & Query Operator
Although they have been deprecated for quite some time, Solr still has support for Schema based
configuration of a <defaultSearchField/> (which is superseded by the df parameter ) and
<solrQueryParser defaultOperator="OR"/> (which is superseded by the q.op parameter.
If you have these options specified in your Schema, you are strongly encouraged to replace them with
request parameters (or request parameter defaults) as support for them may be removed from future Solr
release.
Similarity
Similarity is a Lucene class used to score a document in searching.
Each collection has one "global" Similarity, and by default Solr uses an implicit SchemaSimilarityFactory
which allows individual field types to be configured with a "per-type" specific Similarity and implicitly uses
BM25Similarity for any field type which does not have an explicit Similarity.
This default behavior can be overridden by declaring a top level <similarity/> element in your schema.xml ,
outside of any single field type. This similarity declaration can either refer directly to the name of a class with
a no-argument constructor, such as in this example showing BM25Similarity :
<similarity class="solr.BM25SimilarityFactory"/>
or by referencing a SimilarityFactory implementation, which may take optional initialization parameters:
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<similarity class="solr.DFRSimilarityFactory">
<str name="basicModel">P</str>
<str name="afterEffect">L</str>
<str name="normalization">H2</str>
<float name="c">7</float>
</similarity>
In most cases, specifying global level similarity like this will cause an error if your schema.xml also includes
field type specific <similarity/> declarations. One key exception to this is that you may explicitly declare a
SchemaSimilarityFactory and specify what that default behavior will be for all field types that do not
declare an explicit Similarity using the name of field type (specified by defaultSimFromFieldType ) that is
configured with a specific similarity:
<similarity class="solr.SchemaSimilarityFactory">
<str name="defaultSimFromFieldType">text_dfr</str>
</similarity>
<fieldType name="text_dfr" class="solr.TextField">
<analyzer ... />
<similarity class="solr.DFRSimilarityFactory">
<str name="basicModel">I(F)</str>
<str name="afterEffect">B</str>
<str name="normalization">H3</str>
<float name="mu">900</float>
</similarity>
</fieldType>
<fieldType name="text_ib" class="solr.TextField">
<analyzer ... />
<similarity class="solr.IBSimilarityFactory">
<str name="distribution">SPL</str>
<str name="lambda">DF</str>
<str name="normalization">H2</str>
</similarity>
</fieldType>
<fieldType name="text_other" class="solr.TextField">
<analyzer ... />
</fieldType>
In the example above IBSimilarityFactory (using the Information-Based model) will be used for any fields
of type text_ib , while DFRSimilarityFactory (divergence from random) will be used for any fields of type
text_dfr , as well as any fields using a type that does not explicitly specify a <similarity/> .
If SchemaSimilarityFactory is explicitly declared with out configuring a defaultSimFromFieldType , then
BM25Similarity is implicitly used as the default.
In addition to the various factories mentioned on this page, there are several other similarity
implementations that can be used such as the SweetSpotSimilarityFactory , ClassicSimilarityFactory ,
etc…. For details, see the Solr Javadocs for the similarity factories.
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Schema API
The Schema API allows you to use an HTTP API to manage many of the elements of your schema.
The Schema API utilizes the ManagedIndexSchemaFactory class, which is the default schema factory in
modern Solr versions. See the section Schema Factory Definition in SolrConfig for more information about
choosing a schema factory for your index.
This API provides read and write access to the Solr schema for each collection (or core, when using
standalone Solr). Read access to all schema elements is supported. Fields, dynamic fields, field types and
copyField rules may be added, removed or replaced. Future Solr releases will extend write access to allow
more schema elements to be modified.
?
Why is hand editing of the managed schema discouraged?
The file named "managed-schema" in the example configurations may include a note that
recommends never hand-editing the file. Before the Schema API existed, such edits were
the only way to make changes to the schema, and users may have a strong desire to
continue making changes this way.
The reason that this is discouraged is because hand-edits of the schema may be lost if the
Schema API described here is later used to make a change, unless the core or collection is
reloaded or Solr is restarted before using the Schema API. If care is taken to always reload
or restart after a manual edit, then there is no problem at all with doing those edits.
The API allows two output modes for all calls: JSON or XML. When requesting the complete schema, there is
another output mode which is XML modeled after the managed-schema file itself, which is in XML format.
When modifying the schema with the API, a core reload will automatically occur in order for the changes to
be available immediately for documents indexed thereafter. Previously indexed documents will not be
automatically updated - they must be re-indexed if existing index data uses schema elements that you
changed.
?
Re-index after schema modifications!
If you modify your schema, you will likely need to re-index all documents. If you do not, you
may lose access to documents, or not be able to interpret them properly, e.g. after
replacing a field type.
Modifying your schema will never modify any documents that are already indexed. You
must re-index documents in order to apply schema changes to them. Queries and updates
made after the change may encounter errors that were not present before the change.
Completely deleting the index and rebuilding it is usually the only option to fix such errors.
The base address for the API is http://<host>:<port>/solr/<collection_name> . If, for example, you run
Solr’s “ cloud ” example (via the bin/solr command shown below), which creates a “ gettingstarted ”
collection, then the base URL for that collection (as in all the sample URLs in this section) would be:
http://localhost:8983/solr/gettingstarted .
bin/solr -e cloud -noprompt
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API Entry Points
? /schema : retrieve the schema, or modify the schema to add, remove, or replace fields, dynamic fields,
copy fields, or field types
? /schema/fields : retrieve information about all defined fields or a specific named field
? /schema/dynamicfields : retrieve information about all dynamic field rules or a specific named dynamic
rule
? /schema/fieldtypes : retrieve information about all field types or a specific field type
? /schema/copyfields : retrieve information about copy fields
? /schema/name : retrieve the schema name
? /schema/version : retrieve the schema version
? /schema/uniquekey : retrieve the defined uniqueKey
? /schema/similarity : retrieve the global similarity definition
? /schema/solrqueryparser/defaultoperator : retrieve the default operator
Modify the Schema
POST /collection/schema
To add, remove or replace fields, dynamic field rules, copy field rules, or new field types, you can send a
POST request to the /collection/schema/ endpoint with a sequence of commands to perform the
requested actions. The following commands are supported:
? add-field : add a new field with parameters you provide.
? delete-field : delete a field.
? replace-field : replace an existing field with one that is differently configured.
? add-dynamic-field : add a new dynamic field rule with parameters you provide.
? delete-dynamic-field : delete a dynamic field rule.
? replace-dynamic-field : replace an existing dynamic field rule with one that is differently configured.
? add-field-type : add a new field type with parameters you provide.
? delete-field-type : delete a field type.
? replace-field-type : replace an existing field type with one that is differently configured.
? add-copy-field : add a new copy field rule.
? delete-copy-field : delete a copy field rule.
These commands can be issued in separate POST requests or in the same POST request. Commands are
executed in the order in which they are specified.
In each case, the response will include the status and the time to process the request, but will not include
the entire schema.
When modifying the schema with the API, a core reload will automatically occur in order for the changes to
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be available immediately for documents indexed thereafter. Previously indexed documents will not be
automatically handled - they must be re-indexed if they used schema elements that you changed.
Add a New Field
The add-field command adds a new field definition to your schema. If a field with the same name exists an
error is thrown.
All of the properties available when defining a field with manual schema.xml edits can be passed via the API.
These request attributes are described in detail in the section Defining Fields.
For example, to define a new stored field named "sell-by", of type "tdate", you would POST the following
request:
curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"add-field":{
"name":"sell-by",
"type":"tdate",
"stored":true }
}‘ http://localhost:8983/solr/gettingstarted/schema
Delete a Field
The delete-field command removes a field definition from your schema. If the field does not exist in the
schema, or if the field is the source or destination of a copy field rule, an error is thrown.
For example, to delete a field named "sell-by", you would POST the following request:
curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"delete-field" : { "name":"sell-by" }
}‘ http://localhost:8983/solr/gettingstarted/schema
Replace a Field
The replace-field command replaces a field’s definition. Note that you must supply the full definition for a
field - this command will not partially modify a field’s definition. If the field does not exist in the schema an
error is thrown.
All of the properties available when defining a field with manual schema.xml edits can be passed via the API.
These request attributes are described in detail in the section Defining Fields.
For example, to replace the definition of an existing field "sell-by", to make it be of type "date" and to not be
stored, you would POST the following request:
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curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"replace-field":{
"name":"sell-by",
"type":"date",
"stored":false }
}‘ http://localhost:8983/solr/gettingstarted/schema
Add a Dynamic Field Rule
The add-dynamic-field command adds a new dynamic field rule to your schema.
All of the properties available when editing schema.xml can be passed with the POST request. The section
Dynamic Fields has details on all of the attributes that can be defined for a dynamic field rule.
For example, to create a new dynamic field rule where all incoming fields ending with "_s" would be stored
and have field type "string", you can POST a request like this:
curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"add-dynamic-field":{
"name":"*_s",
"type":"string",
"stored":true }
}‘ http://localhost:8983/solr/gettingstarted/schema
Delete a Dynamic Field Rule
The delete-dynamic-field command deletes a dynamic field rule from your schema. If the dynamic field
rule does not exist in the schema, or if the schema contains a copy field rule with a target or destination that
matches only this dynamic field rule, an error is thrown.
For example, to delete a dynamic field rule matching "*_s", you can POST a request like this:
curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"delete-dynamic-field":{ "name":"*_s" }
}‘ http://localhost:8983/solr/gettingstarted/schema
Replace a Dynamic Field Rule
The replace-dynamic-field command replaces a dynamic field rule in your schema. Note that you must
supply the full definition for a dynamic field rule - this command will not partially modify a dynamic field
rule’s definition. If the dynamic field rule does not exist in the schema an error is thrown.
All of the properties available when editing schema.xml can be passed with the POST request. The section
Dynamic Fields has details on all of the attributes that can be defined for a dynamic field rule.
For example, to replace the definition of the "*_s" dynamic field rule with one where the field type is
"text_general" and it’s not stored, you can POST a request like this:
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curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"replace-dynamic-field":{
"name":"*_s",
"type":"text_general",
"stored":false }
}‘ http://localhost:8983/solr/gettingstarted/schema
Add a New Field Type
The add-field-type command adds a new field type to your schema.
All of the field type properties available when editing schema.xml by hand are available for use in a POST
request. The structure of the command is a json mapping of the standard field type definition, including the
name, class, index and query analyzer definitions, etc. Details of all of the available options are described in
the section Solr Field Types.
For example, to create a new field type named "myNewTxtField", you can POST a request as follows:
curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"add-field-type" : {
"name":"myNewTxtField",
"class":"solr.TextField",
"positionIncrementGap":"100",
"analyzer" : {
"charFilters":[{
"class":"solr.PatternReplaceCharFilterFactory",
"replacement":"$1$1",
"pattern":"([a-zA-Z])\\\\1+" }],
"tokenizer":{
"class":"solr.WhitespaceTokenizerFactory" },
"filters":[{
"class":"solr.WordDelimiterFilterFactory",
"preserveOriginal":"0" }]}}
}‘ http://localhost:8983/solr/gettingstarted/schema
Note in this example that we have only defined a single analyzer section that will apply to index analysis and
query analysis. If we wanted to define separate analysis, we would replace the analyzer section in the above
example with separate sections for indexAnalyzer and queryAnalyzer . As in this example:
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curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"add-field-type":{
"name":"myNewTextField",
"class":"solr.TextField",
"indexAnalyzer":{
"tokenizer":{
"class":"solr.PathHierarchyTokenizerFactory",
"delimiter":"/" }},
"queryAnalyzer":{
"tokenizer":{
"class":"solr.KeywordTokenizerFactory" }}}
}‘ http://localhost:8983/solr/gettingstarted/schema
Delete a Field Type
The delete-field-type command removes a field type from your schema. If the field type does not exist in
the schema, or if any field or dynamic field rule in the schema uses the field type, an error is thrown.
For example, to delete the field type named "myNewTxtField", you can make a POST request as follows:
curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"delete-field-type":{ "name":"myNewTxtField" }
}‘ http://localhost:8983/solr/gettingstarted/schema
Replace a Field Type
The replace-field-type command replaces a field type in your schema. Note that you must supply the full
definition for a field type - this command will not partially modify a field type’s definition. If the field type
does not exist in the schema an error is thrown.
All of the field type properties available when editing schema.xml by hand are available for use in a POST
request. The structure of the command is a json mapping of the standard field type definition, including the
name, class, index and query analyzer definitions, etc. Details of all of the available options are described in
the section Solr Field Types.
For example, to replace the definition of a field type named "myNewTxtField", you can make a POST request
as follows:
curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"replace-field-type":{
"name":"myNewTxtField",
"class":"solr.TextField",
"positionIncrementGap":"100",
"analyzer":{
"tokenizer":{
"class":"solr.StandardTokenizerFactory" }}}
}‘ http://localhost:8983/solr/gettingstarted/schema
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Add a New Copy Field Rule
The add-copy-field command adds a new copy field rule to your schema.
The attributes supported by the command are the same as when creating copy field rules by manually
editing the schema.xml , as below:
Name Required Description
source Yes The source field.
dest Yes A field or an array of fields to which the source field will be
copied.
maxChars No The upper limit for the number of characters to be copied. The
section Copying Fields has more details.
For example, to define a rule to copy the field "shelf" to the "location" and "catchall" fields, you would POST
the following request:
curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"add-copy-field":{
"source":"shelf",
"dest":[ "location", "catchall" ]}
}‘ http://localhost:8983/solr/gettingstarted/schema
Delete a Copy Field Rule
The delete-copy-field command deletes a copy field rule from your schema. If the copy field rule does not
exist in the schema an error is thrown.
The source and dest attributes are required by this command.
For example, to delete a rule to copy the field "shelf" to the "location" field, you would POST the following
request:
curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"delete-copy-field":{ "source":"shelf", "dest":"location" }
}‘ http://localhost:8983/solr/gettingstarted/schema
Multiple Commands in a Single POST
It is possible to perform one or more add requests in a single command. The API is transactional and all
commands in a single call either succeed or fail together.
The commands are executed in the order in which they are specified. This means that if you want to create a
new field type and in the same request use the field type on a new field, the section of the request that
creates the field type must come before the section that creates the new field. Similarly, since a field must
exist for it to be used in a copy field rule, a request to add a field must come before a request for the field to
be used as either the source or the destination for a copy field rule.
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The syntax for making multiple requests supports several approaches. First, the commands can simply be
made serially, as in this request to create a new field type and then a field that uses that type:
curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"add-field-type":{
"name":"myNewTxtField",
"class":"solr.TextField",
"positionIncrementGap":"100",
"analyzer":{
"charFilters":[{
"class":"solr.PatternReplaceCharFilterFactory",
"replacement":"$1$1",
"pattern":"([a-zA-Z])\\\\1+" }],
"tokenizer":{
"class":"solr.WhitespaceTokenizerFactory" },
"filters":[{
"class":"solr.WordDelimiterFilterFactory",
"preserveOriginal":"0" }]}},
"add-field" : {
"name":"sell-by",
"type":"myNewTxtField",
"stored":true }
}‘ http://localhost:8983/solr/gettingstarted/schema
Or, the same command can be repeated, as in this example:
curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"add-field":{
"name":"shelf",
"type":"myNewTxtField",
"stored":true },
"add-field":{
"name":"location",
"type":"myNewTxtField",
"stored":true },
"add-copy-field":{
"source":"shelf",
"dest":[ "location", "catchall" ]}
}‘ http://localhost:8983/solr/gettingstarted/schema
Finally, repeated commands can be sent as an array:
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curl -X POST -H ‘Content-type:application/json‘ --data-binary ‘{
"add-field":[
{ "name":"shelf",
"type":"myNewTxtField",
"stored":true },
{ "name":"location",
"type":"myNewTxtField",
"stored":true }]
}‘ http://localhost:8983/solr/gettingstarted/schema
Schema Changes among Replicas
When running in SolrCloud mode, changes made to the schema on one node will propagate to all replicas in
the collection.
You can pass the updateTimeoutSecs parameter with your request to set the number of seconds to wait
until all replicas confirm they applied the schema updates. This helps your client application be more robust
in that you can be sure that all replicas have a given schema change within a defined amount of time.
If agreement is not reached by all replicas in the specified time, then the request fails and the error message
will include information about which replicas had trouble. In most cases, the only option is to re-try the
change after waiting a brief amount of time. If the problem persists, then you’ll likely need to investigate the
server logs on the replicas that had trouble applying the changes.
If you do not supply an updateTimeoutSecs parameter, the default behavior is for the receiving node to
return immediately after persisting the updates to ZooKeeper. All other replicas will apply the updates
asynchronously. Consequently, without supplying a timeout, your client application cannot be sure that all
replicas have applied the changes.
Retrieve Schema Information
The following endpoints allow you to read how your schema has been defined. You can GET the entire
schema, or only portions of it as needed.
To modify the schema, see the previous section Modify the Schema.
Retrieve the Entire Schema
GET /collection/schema
INPUT
Path Parameters
Key Description
collection The collection (or core) name.
Query Parameters
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The query parameters should be added to the API request after ‘?‘.
Key Type Required Default Description
wt string No json Defines the format of the response. The options are
json, xml or schema.xml. If not specified, JSON will
be returned by default.
OUTPUT
Output Content
The output will include all fields, field types, dynamic rules and copy field rules, in the format requested
(JSON or XML). The schema name and version are also included.
EXAMPLES
Get the entire schema in JSON.
curl http://localhost:8983/solr/gettingstarted/schema?wt=json
{
"responseHeader":{
"status":0,
"QTime":5},
"schema":{
"name":"example",
"version":1.5,
"uniqueKey":"id",
"fieldTypes":[{
"name":"alphaOnlySort",
"class":"solr.TextField",
"sortMissingLast":true,
"omitNorms":true,
"analyzer":{
"tokenizer":{
"class":"solr.KeywordTokenizerFactory"},
"filters":[{
"class":"solr.LowerCaseFilterFactory"},
{
"class":"solr.TrimFilterFactory"},
{
"class":"solr.PatternReplaceFilterFactory",
"replace":"all",
"replacement":"",
"pattern":"([^a-z])"}]}}],
"fields":[{
"name":"_version_",
"type":"long",
"indexed":true,
"stored":true},
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{
"name":"author",
"type":"text_general",
"indexed":true,
"stored":true},
{
"name":"cat",
"type":"string",
"multiValued":true,
"indexed":true,
"stored":true}],
"copyFields":[{
"source":"author",
"dest":"text"},
{
"source":"cat",
"dest":"text"},
{
"source":"content",
"dest":"text"},
{
"source":"author",
"dest":"author_s"}]}}
Get the entire schema in XML.
curl http://localhost:8983/solr/gettingstarted/schema?wt=xml
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<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">5</int>
</lst>
<lst name="schema">
<str name="name">example</str>
<float name="version">1.5</float>
<str name="uniqueKey">id</str>
<arr name="fieldTypes">
<lst>
<str name="name">alphaOnlySort</str>
<str name="class">solr.TextField</str>
<bool name="sortMissingLast">true</bool>
<bool name="omitNorms">true</bool>
<lst name="analyzer">
<lst name="tokenizer">
<str name="class">solr.KeywordTokenizerFactory</str>
</lst>
<arr name="filters">
<lst>
<str name="class">solr.LowerCaseFilterFactory</str>
</lst>
<lst>
<str name="class">solr.TrimFilterFactory</str>
</lst>
<lst>
<str name="class">solr.PatternReplaceFilterFactory</str>
<str name="replace">all</str>
<str name="replacement"/>
<str name="pattern">([^a-z])</str>
</lst>
</arr>
</lst>
</lst>
...
<lst>
<str name="source">author</str>
<str name="dest">author_s</str>
</lst>
</arr>
</lst>
</response>
Get the entire schema in "schema.xml" format.
curl http://localhost:8983/solr/gettingstarted/schema?wt=schema.xml
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<schema name="example" version="1.5">
<uniqueKey>id</uniqueKey>
<types>
<fieldType name="alphaOnlySort" class="solr.TextField" sortMissingLast="true" omitNorms="
true">
<analyzer>
<tokenizer class="solr.KeywordTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.TrimFilterFactory"/>
<filter class="solr.PatternReplaceFilterFactory" replace="all" replacement="" pattern=
"([^a-z])"/>
</analyzer>
</fieldType>
...
<copyField source="url" dest="text"/>
<copyField source="price" dest="price_c"/>
<copyField source="author" dest="author_s"/>
</schema>
List Fields
GET /collection/schema/fields
GET /collection/schema/fields/fieldname
INPUT
Path Parameters
Key Description
collection The collection (or core) name.
fieldname The specific fieldname (if limiting request to a single field).
Query Parameters
The query parameters can be added to the API request after a ‘?‘.
Key Type Required Default Description
wt string No json Defines the format of the response. The
options are json or xml. If not specified,
JSON will be returned by default.
fl string No (all fields) Comma- or space-separated list of one or
more fields to return. If not specified, all
fields will be returned by default.
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Key Type Required Default Description
includeDynamic boolean No false If true, and if the fl query parameter is
specified or the fieldname path
parameter is used, matching dynamic
fields are included in the response and
identified with the dynamicBase
property. If neither the fl query
parameter nor the fieldname path
parameter is specified, the
includeDynamic query parameter is
ignored. If false, matching dynamic fields
will not be returned.
showDefaults boolean No false If true, all default field properties from
each field’s field type will be included in
the response (e.g. tokenized for
solr.TextField ). If false, only explicitly
specified field properties will be included.
OUTPUT
Output Content
The output will include each field and any defined configuration for each field. The defined configuration can
vary for each field, but will minimally include the field name , the type , if it is indexed and if it is stored . If
multiValued is defined as either true or false (most likely true), that will also be shown. See the section
Defining Fields for more information about each parameter.
EXAMPLES
Get a list of all fields.
curl http://localhost:8983/solr/gettingstarted/schema/fields?wt=json
The sample output below has been truncated to only show a few fields.
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{
"fields": [
{
"indexed": true,
"name": "_version_",
"stored": true,
"type": "long"
},
{
"indexed": true,
"name": "author",
"stored": true,
"type": "text_general"
},
{
"indexed": true,
"multiValued": true,
"name": "cat",
"stored": true,
"type": "string"
},
"..."
],
"responseHeader": {
"QTime": 1,
"status": 0
}
}
List Dynamic Fields
GET /collection/schema/dynamicfields
GET /collection/schema/dynamicfields/name
INPUT
Path Parameters
Key Description
collection The collection (or core) name.
name The name of the dynamic field rule (if limiting
request to a single dynamic field rule).
Query Parameters
The query parameters can be added to the API request after a ‘?‘.
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Key Type Required Default Description
wt string No json Defines the format of the response. The options are json,
xml. If not specified, JSON will be returned by default.
showDefaults boolean No false If true, all default field properties from each dynamic
field’s field type will be included in the response (e.g.
tokenized for solr.TextField ). If false, only explicitly
specified field properties will be included.
OUTPUT
Output Content
The output will include each dynamic field rule and the defined configuration for each rule. The defined
configuration can vary for each rule, but will minimally include the dynamic field name , the type , if it is
indexed and if it is stored . See the section Dynamic Fields for more information about each parameter.
EXAMPLES
Get a list of all dynamic field declarations:
curl http://localhost:8983/solr/gettingstarted/schema/dynamicfields?wt=json
The sample output below has been truncated.
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{
"dynamicFields": [
{
"indexed": true,
"name": "*_coordinate",
"stored": false,
"type": "tdouble"
},
{
"multiValued": true,
"name": "ignored_*",
"type": "ignored"
},
{
"name": "random_*",
"type": "random"
},
{
"indexed": true,
"multiValued": true,
"name": "attr_*",
"stored": true,
"type": "text_general"
},
{
"indexed": true,
"multiValued": true,
"name": "*_txt",
"stored": true,
"type": "text_general"
}
"..."
],
"responseHeader": {
"QTime": 1,
"status": 0
}
}
List Field Types
GET /collection/schema/fieldtypes
GET /collection/schema/fieldtypes/name
INPUT
Path Parameters
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Key Description
collection The collection (or core) name.
name The name of the field type (if limiting request to a single field type).
Query Parameters
The query parameters can be added to the API request after a ‘?‘.
Key Type Required Default Description
wt string No json Defines the format of the response. The options are json
or xml. If not specified, JSON will be returned by default.
showDefaults boolean No false If true, all default field properties from each field type will
be included in the response (e.g. tokenized for
solr.TextField ). If false, only explicitly specified field
properties will be included.
OUTPUT
Output Content
The output will include each field type and any defined configuration for the type. The defined configuration
can vary for each type, but will minimally include the field type name and the class . If query or index
analyzers, tokenizers, or filters are defined, those will also be shown with other defined parameters. See the
section Solr Field Types for more information about how to configure various types of fields.
EXAMPLES
Get a list of all field types.
curl http://localhost:8983/solr/gettingstarted/schema/fieldtypes?wt=json
The sample output below has been truncated to show a few different field types from different parts of the
list.
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{
"fieldTypes": [
{
"analyzer": {
"class": "solr.TokenizerChain",
"filters": [
{
"class": "solr.LowerCaseFilterFactory"
},
{
"class": "solr.TrimFilterFactory"
},
{
"class": "solr.PatternReplaceFilterFactory",
"pattern": "([^a-z])",
"replace": "all",
"replacement": ""
}
],
"tokenizer": {
"class": "solr.KeywordTokenizerFactory"
}
},
"class": "solr.TextField",
"dynamicFields": [],
"fields": [],
"name": "alphaOnlySort",
"omitNorms": true,
"sortMissingLast": true
},
{
"class": "solr.TrieFloatField",
"dynamicFields": [
"*_fs",
"*_f"
],
"fields": [
"price",
"weight"
],
"name": "float",
"positionIncrementGap": "0",
"precisionStep": "0"
}]
}
List Copy Fields
GET /collection/schema/copyfields
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INPUT
Path Parameters
Key Description
collection The collection (or core) name.
Query Parameters
The query parameters can be added to the API request after a ‘?‘.
Key Type Required Default Description
wt string No json Defines the format of the response. The options are json
or xml. If not specified, JSON will be returned by default.
source.fl string No (all
source
fields)
Comma- or space-separated list of one or more copyField
source fields to include in the response - copyField
directives with all other source fields will be excluded from
the response. If not specified, all copyField-s will be
included in the response.
dest.fl string No (all dest
fields)
Comma- or space-separated list of one or more copyField
dest fields to include in the response - copyField directives
with all other dest fields will be excluded. If not specified,
all copyField-s will be included in the response.
OUTPUT
Output Content
The output will include the source and dest`ination of each copy field rule defined in
`schema.xml . For more information about copying fields, see the section Copying Fields.
EXAMPLES
Get a list of all copyfields.
curl http://localhost:8983/solr/gettingstarted/schema/copyfields?wt=json
The sample output below has been truncated to the first few copy definitions.
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{
"copyFields": [
{
"dest": "text",
"source": "author"
},
{
"dest": "text",
"source": "cat"
},
{
"dest": "text",
"source": "content"
},
{
"dest": "text",
"source": "content_type"
},
],
"responseHeader": {
"QTime": 3,
"status": 0
}
}
Show Schema Name
GET /collection/schema/name
INPUT
Path Parameters
Key Description
collection The collection (or core) name.
Query Parameters
The query parameters can be added to the API request after a ‘?‘.
Key Type Required Default Description
wt string No json Defines the format of the response. The options are json
or xml. If not specified, JSON will be returned by default.
OUTPUT
Output Content The output will be simply the name given to the schema.
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EXAMPLES
Get the schema name.
curl http://localhost:8983/solr/gettingstarted/schema/name?wt=json
{
"responseHeader":{
"status":0,
"QTime":1},
"name":"example"}
Show the Schema Version
GET /collection/schema/version
INPUT
Path Parameters
Key Description
collection The collection (or core) name.
Query Parameters
The query parameters can be added to the API request after a ‘?‘.
Key Type Required Default Description
wt string No json Defines the format of the response. The options are json
or xml. If not specified, JSON will be returned by default.
OUTPUT
Output Content
The output will simply be the schema version in use.
EXAMPLES
Get the schema version
curl http://localhost:8983/solr/gettingstarted/schema/version?wt=json
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{
"responseHeader":{
"status":0,
"QTime":2},
"version":1.5}
List UniqueKey
GET /collection/schema/uniquekey
INPUT
Path Parameters
Key Description
collection The collection (or core) name.
Query Parameters
The query parameters can be added to the API request after a ‘?‘.
Key Type Required Default Description
wt string No json Defines the format of the response. The options are json
or xml. If not specified, JSON will be returned by default.
OUTPUT
Output Content
The output will include simply the field name that is defined as the uniqueKey for the index.
EXAMPLES
List the uniqueKey.
curl http://localhost:8983/solr/gettingstarted/schema/uniquekey?wt=json
{
"responseHeader":{
"status":0,
"QTime":2},
"uniqueKey":"id"}
Show Global Similarity
GET /collection/schema/similarity
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INPUT
Path Parameters
Key Description
collection The collection (or core) name.
Query Parameters
The query parameters can be added to the API request after a ‘?‘.
Key Type Required Default Description
wt string No json Defines the format of the response. The options are json
or xml. If not specified, JSON will be returned by default.
OUTPUT
Output Content
The output will include the class name of the global similarity defined (if any).
EXAMPLES
Get the similarity implementation.
curl http://localhost:8983/solr/gettingstarted/schema/similarity?wt=json
{
"responseHeader":{
"status":0,
"QTime":1},
"similarity":{
"class":"org.apache.solr.search.similarities.DefaultSimilarityFactory"}}
Get the Default Query Operator
GET /collection/schema/solrqueryparser/defaultoperator
INPUT
Path Parameters
Key Description
collection The collection (or core) name.
Query Parameters
The query parameters can be added to the API request after a ‘?‘.
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Key Type Required Default Description
wt string No json Defines the format of the response. The options are json
or xml. If not specified, JSON will be returned by default.
OUTPUT
Output Content
The output will include simply the default operator if none is defined by the user.
EXAMPLES
Get the default operator.
curl http://localhost:8983/solr/gettingstarted/schema/solrqueryparser/defaultoperator?wt=json
{
"responseHeader":{
"status":0,
"QTime":2},
"defaultOperator":"OR"}
Manage Resource Data
The Managed Resources REST API provides a mechanism for any Solr plugin to expose resources that should
support CRUD (Create, Read, Update, Delete) operations. Depending on what Field Types and Analyzers are
configured in your Schema, additional /schema/ REST API paths may exist. See the Managed Resources
section for more information and examples.
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Putting the Pieces Together
At the highest level, schema.xml is structured as follows.
This example is not real XML, but it gives you an idea of the structure of the file.
<schema>
<types>
<fields>
<uniqueKey>
<copyField>
</schema>
Obviously, most of the excitement is in types and fields , where the field types and the actual field
definitions live.
These are supplemented by copyFields .
The uniqueKey must always be defined.
In older Solr versions you would find defaultSearchField and solrQueryParser tags as well, but although
these still work they are deprecated and discouraged, see Other Schema Elements.
?
Types and fields are optional tags
Note that the types and fields sections are optional, meaning you are free to mix field ,
dynamicField , copyField and fieldType definitions on the top level. This allows for a more
logical grouping of related tags in your schema.
Choosing Appropriate Numeric Types
For general numeric needs, consider using one of the` IntPointField`, LongPointField , FloatPointField , or
DoublePointField classes, depending on the specific values you expect. These "Dimensional Point" based
numeric classes use specially encoded data structures to support efficient range queries regardless of the
size of the ranges used. Enable DocValues on these fields as needed for sorting and/or faceting.
Some Solr features may not yet work with "Dimensional Points", in which case you may want to consider the
equivalent TrieIntField , TrieLongField , TrieFloatField , and TrieDoubleField classes. Configure a
precisionStep="0" if you wish to minimize index size, but if you expect users to make frequent range
queries on numeric types, use the default precisionStep (by not specifying it) or specify it as
precisionStep="8" (which is the default). This offers faster speed for range queries at the expense of
increasing index size.
Working With Text
Handling text properly will make your users happy by providing them with the best possible results for text
searches.
One technique is using a text field as a catch-all for keyword searching. Most users are not sophisticated
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about their searches and the most common search is likely to be a simple keyword search. You can use
copyField to take a variety of fields and funnel them all into a single text field for keyword searches.
In the schema.xml file for the “ techproducts ” example included with Solr, copyField declarations are used
to dump the contents of cat , name , manu , features , and includes into a single field, text . In addition, it
could be a good idea to copy ID into text in case users wanted to search for a particular product by passing
its product number to a keyword search.
Another technique is using copyField to use the same field in different ways. Suppose you have a field that
is a list of authors, like this:
Schildt, Herbert; Wolpert, Lewis; Davies, P.
For searching by author, you could tokenize the field, convert to lower case, and strip out punctuation:
schildt / herbert / wolpert / lewis / davies / p
For sorting, just use an untokenized field, converted to lower case, with punctuation stripped:
schildt herbert wolpert lewis davies p
Finally, for faceting, use the primary author only via a StrField :
Schildt, Herbert
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DocValues
DocValues are a way of recording field values internally that is more efficient for some purposes, such as
sorting and faceting, than traditional indexing.
Why DocValues?
The standard way that Solr builds the index is with an inverted index. This style builds a list of terms found in
all the documents in the index and next to each term is a list of documents that the term appears in (as well
as how many times the term appears in that document). This makes search very fast - since users search by
terms, having a ready list of term-to-document values makes the query process faster.
For other features that we now commonly associate with search, such as sorting, faceting, and highlighting,
this approach is not very efficient. The faceting engine, for example, must look up each term that appears in
each document that will make up the result set and pull the document IDs in order to build the facet list. In
Solr, this is maintained in memory, and can be slow to load (depending on the number of documents, terms,
etc.).
In Lucene 4.0, a new approach was introduced. DocValue fields are now column-oriented fields with a
document-to-value mapping built at index time. This approach promises to relieve some of the memory
requirements of the fieldCache and make lookups for faceting, sorting, and grouping much faster.
Enabling DocValues
To use docValues, you only need to enable it for a field that you will use it with. As with all schema design,
you need to define a field type and then define fields of that type with docValues enabled. All of these
actions are done in schema.xml .
Enabling a field for docValues only requires adding docValues="true" to the field (or field type) definition,
as in this example from the schema.xml of Solr’s sample_techproducts_configs config set:
<field name="manu_exact" type="string" indexed="false" stored="false" docValues="true" />
?
If you have already indexed data into your Solr index, you will need to completely re-index
your content after changing your field definitions in schema.xml in order to successfully use
docValues.
DocValues are only available for specific field types. The types chosen determine the underlying Lucene
docValue type that will be used. The available Solr field types are:
? StrField and UUIDField .
? If the field is single-valued (i.e., multi-valued is false), Lucene will use the SORTED type.
? If the field is multi-valued, Lucene will use the SORTED_SET type.
? Any Trie* numeric fields, date fields and EnumField .
? If the field is single-valued (i.e., multi-valued is false), Lucene will use the NUMERIC type.
? If the field is multi-valued, Lucene will use the SORTED_SET type.
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? Boolean fields
? Int|Long|Float|Double|Date PointField
? If the field is single-valued (i.e., multi-valued is false), Lucene will use the NUMERIC type.
? If the field is multi-valued, Lucene will use the SORTED_NUMERIC type.
These Lucene types are related to how the values are sorted and stored.
There is an additional configuration option available, which is to modify the docValuesFormat used by the
field type. The default implementation employs a mixture of loading some things into memory and keeping
some on disk. In some cases, however, you may choose to specify an alternative DocValuesFormat
implementation. For example, you could choose to keep everything in memory by specifying
docValuesFormat="Memory" on a field type:
<fieldType name="string_in_mem_dv" class="solr.StrField" docValues="true" docValuesFormat="
Memory" />
Please note that the docValuesFormat option may change in future releases.
?
Lucene index back-compatibility is only supported for the default codec. If you choose to
customize the docValuesFormat in your schema.xml, upgrading to a future version of Solr
may require you to either switch back to the default codec and optimize your index to
rewrite it into the default codec before upgrading, or re-build your entire index from
scratch after upgrading.
Using DocValues
Sorting, Faceting & Functions
If docValues="true" for a field, then DocValues will automatically be used any time the field is used for
sorting, faceting or function queries.
Retrieving DocValues During Search
Field values retrieved during search queries are typically returned from stored values. However, non-stored
docValues fields will be also returned along with other stored fields when all fields (or pattern matching
globs) are specified to be returned (e.g. “fl=*”) for search queries depending on the effective value of the
useDocValuesAsStored parameter for each field. For schema versions >= 1.6, the implicit default is
useDocValuesAsStored="true" . See Field Type Definitions and Properties & Defining Fields for more details.
When useDocValuesAsStored="false" , non-stored DocValues fields can still be explicitly requested by name
in the fl param, but will not match glob patterns ( "*" ). Note that returning DocValues along with "regular"
stored fields at query time has performance implications that stored fields may not because DocValues are
column-oriented and may therefore incur additional cost to retrieve for each returned document. Also note
that while returning non-stored fields from DocValues, the values of a multi-valued field are returned in
sorted order (and not insertion order). If you require the multi-valued fields to be returned in the original
insertion order, then make your multi-valued field as stored (such a change requires re-indexing).
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In cases where the query is returning only docValues fields performance may improve since returning stored
fields requires disk reads and decompression whereas returning docValues fields in the fl list only requires
memory access.
When retrieving fields from their docValues form (using the /export handler, streaming expressions or if the
field is requested in the fl parameter), two important differences between regular stored fields and
docValues fields must be understood:
1. Order is not preserved. For simply retrieving stored fields, the insertion order is the return order. For
docValues, it is the sorted order.
2. Multiple identical entries are collapsed into a single value. Thus if I insert values 4, 5, 2, 4, 1, my return will
be 1, 2, 4, 5.
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Schemaless Mode
Schemaless Mode is a set of Solr features that, when used together, allow users to rapidly construct an
effective schema by simply indexing sample data, without having to manually edit the schema.
These Solr features, all controlled via solrconfig.xml , are:
1. Managed schema: Schema modifications are made at runtime through Solr APIs, which requires the use
of schemaFactory that supports these changes - see Schema Factory Definition in SolrConfig for more
details.
2. Field value class guessing: Previously unseen fields are run through a cascading set of value-based
parsers, which guess the Java class of field values - parsers for Boolean, Integer, Long, Float, Double, and
Date are currently available.
3. Automatic schema field addition, based on field value class(es): Previously unseen fields are added to the
schema, based on field value Java classes, which are mapped to schema field types - see Solr Field Types.
Using the Schemaless Example
The three features of schemaless mode are pre-configured in the data_driven_schema_configs config set
in the Solr distribution. To start an example instance of Solr using these configs, run the following
command:
bin/solr start -e schemaless
This will launch a Solr server, and automatically create a collection (named “ gettingstarted ”) that contains
only three fields in the initial schema: id , _version_ , and _text_ .
You can use the /schema/fields Schema API to confirm this: curl
http://localhost:8983/solr/gettingstarted/schema/fields will output:
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{
"responseHeader":{
"status":0,
"QTime":1},
"fields":[{
"name":"_text_",
"type":"text_general",
"multiValued":true,
"indexed":true,
"stored":false},
{
"name":"_version_",
"type":"long",
"indexed":true,
"stored":true},
{
"name":"id",
"type":"string",
"multiValued":false,
"indexed":true,
"required":true,
"stored":true,
"uniqueKey":true}]}
?
The data_driven_schema_configs configset includes a copyField directive that causes all
content to be indexed in a predefined "catch-all" _text_ field, which is used to enable
single-field search that includes all fields‘ content. This will cause the index to be larger
than it would be without this "catch-all" copyField . When you nail down your schema,
consider removing the _text_ field and the corresponding copyField directive if you don’t
need it.
Configuring Schemaless Mode
As described above, there are three configuration elements that need to be in place to use Solr in
schemaless mode. In the data_driven_schema_configs config set included with Solr these are already
configured. If, however, you would like to implement schemaless on your own, you should make the
following changes.
Enable Managed Schema
As described in the section Schema Factory Definition in SolrConfig, Managed Schema support is enabled by
default, unless your configuration specifies that ClassicIndexSchemaFactory should be used.
You can configure the ManagedIndexSchemaFactory (and control the resource file used, or disable future
modifications) by adding an explicit <schemaFactory/> like the one below, please see Schema Factory
Definition in SolrConfig for more details on the options available.
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<schemaFactory class="ManagedIndexSchemaFactory">
<bool name="mutable">true</bool>
<str name="managedSchemaResourceName">managed-schema</str>
</schemaFactory>
Define an UpdateRequestProcessorChain
The UpdateRequestProcessorChain allows Solr to guess field types, and you can define the default field type
classes to use. To start, you should define it as follows (see the javadoc links below for update processor
factory documentation):
<updateRequestProcessorChain name="add-unknown-fields-to-the-schema">
<!-- UUIDUpdateProcessorFactory will generate an id if none is present in the incoming document
-->
<processor class="solr.UUIDUpdateProcessorFactory" />
<processor class="solr.RemoveBlankFieldUpdateProcessorFactory"/>
<processor class="solr.FieldNameMutatingUpdateProcessorFactory">
<str name="pattern">[^\w-\.]</str>
<str name="replacement">_</str>
</processor>
<processor class="solr.ParseBooleanFieldUpdateProcessorFactory"/>
<processor class="solr.ParseLongFieldUpdateProcessorFactory"/>
<processor class="solr.ParseDoubleFieldUpdateProcessorFactory"/>
<processor class="solr.ParseDateFieldUpdateProcessorFactory">
<arr name="format">
<str>yyyy-MM-dd‘T‘HH:mm:ss.SSSZ</str>
<str>yyyy-MM-dd‘T‘HH:mm:ss,SSSZ</str>
<str>yyyy-MM-dd‘T‘HH:mm:ss.SSS</str>
<str>yyyy-MM-dd‘T‘HH:mm:ss,SSS</str>
<str>yyyy-MM-dd‘T‘HH:mm:ssZ</str>
<str>yyyy-MM-dd‘T‘HH:mm:ss</str>
<str>yyyy-MM-dd‘T‘HH:mmZ</str>
<str>yyyy-MM-dd‘T‘HH:mm</str>
<str>yyyy-MM-dd HH:mm:ss.SSSZ</str>
<str>yyyy-MM-dd HH:mm:ss,SSSZ</str>
<str>yyyy-MM-dd HH:mm:ss.SSS</str>
<str>yyyy-MM-dd HH:mm:ss,SSS</str>
<str>yyyy-MM-dd HH:mm:ssZ</str>
<str>yyyy-MM-dd HH:mm:ss</str>
<str>yyyy-MM-dd HH:mmZ</str>
<str>yyyy-MM-dd HH:mm</str>
<str>yyyy-MM-dd</str>
</arr>
</processor>
<processor class="solr.AddSchemaFieldsUpdateProcessorFactory">
<str name="defaultFieldType">strings</str>
<lst name="typeMapping">
<str name="valueClass">java.lang.Boolean</str>
<str name="fieldType">booleans</str>
</lst>
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<lst name="typeMapping">
<str name="valueClass">java.util.Date</str>
<str name="fieldType">tdates</str>
</lst>
<lst name="typeMapping">
<str name="valueClass">java.lang.Long</str>
<str name="valueClass">java.lang.Integer</str>
<str name="fieldType">tlongs</str>
</lst>
<lst name="typeMapping">
<str name="valueClass">java.lang.Number</str>
<str name="fieldType">tdoubles</str>
</lst>
</processor>
<processor class="solr.LogUpdateProcessorFactory"/>
<processor class="solr.DistributedUpdateProcessorFactory"/>
<processor class="solr.RunUpdateProcessorFactory"/>
</updateRequestProcessorChain>
Javadocs for update processor factories mentioned above:
? UUIDUpdateProcessorFactory
? RemoveBlankFieldUpdateProcessorFactory
? FieldNameMutatingUpdateProcessorFactory
? ParseBooleanFieldUpdateProcessorFactory
? ParseLongFieldUpdateProcessorFactory
? ParseDoubleFieldUpdateProcessorFactory
? ParseDateFieldUpdateProcessorFactory
? AddSchemaFieldsUpdateProcessorFactory
Make the UpdateRequestProcessorChain the Default for the
UpdateRequestHandler
Once the UpdateRequestProcessorChain has been defined, you must instruct your UpdateRequestHandlers
to use it when working with index updates (i.e., adding, removing, replacing documents). Here is an example
using InitParams to set the defaults on all /update request handlers:
<initParams path="/update/**">
<lst name="defaults">
<str name="update.chain">add-unknown-fields-to-the-schema</str>
</lst>
</initParams>
?
After each of these changes have been made, Solr should be restarted (or, you can reload
the cores to load the new solrconfig.xml definitions).
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Examples of Indexed Documents
Once the schemaless mode has been enabled (whether you configured it manually or are using
data_driven_schema_configs ), documents that include fields that are not defined in your schema should
be added to the index, and the new fields added to the schema.
For example, adding a CSV document will cause its fields that are not in the schema to be added, with
fieldTypes based on values:
curl "http://localhost:8983/solr/gettingstarted/update?commit=true" -H "Content-
type:application/csv" -d ‘
id,Artist,Album,Released,Rating,FromDistributor,Sold
44C,Old Shews,Mead for Walking,1988-08-13,0.01,14,0‘
Output indicating success:
<response>
<lst name="responseHeader"><int name="status">0</int><int name="QTime">106</int></lst>
</response>
The fields now in the schema (output from curl
http://localhost:8983/solr/gettingstarted/schema/fields ):
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{
"responseHeader":{
"status":0,
"QTime":1},
"fields":[{
"name":"Album",
"type":"strings"}, // Field value guessed as String -> strings fieldType
{
"name":"Artist",
"type":"strings"}, // Field value guessed as String -> strings fieldType
{
"name":"FromDistributor",
"type":"tlongs"}, // Field value guessed as Long -> tlongs fieldType
{
"name":"Rating",
"type":"tdoubles"}, // Field value guessed as Double -> tdoubles fieldType
{
"name":"Released",
"type":"tdates"}, // Field value guessed as Date -> tdates fieldType
{
"name":"Sold",
"type":"tlongs"}, // Field value guessed as Long -> tlongs fieldType
{
"name":"_text_",
...
},
{
"name":"_version_",
...
},
{
"name":"id",
...
}]}
?
You Can Still Be Explicit
Even if you want to use schemaless mode for most fields, you can still use the Schema API
to pre-emptively create some fields, with explicit types, before you index documents that
use them.
Internally, the Schema API and the Schemaless Update Processors both use the same
Managed Schema functionality.
Once a field has been added to the schema, its field type is fixed. As a consequence, adding documents with
field value(s) that conflict with the previously guessed field type will fail. For example, after adding the above
document, the “ Sold ” field has the fieldType tlongs , but the document below has a non-integral decimal
value in this field:
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curl "http://localhost:8983/solr/gettingstarted/update?commit=true" -H "Content-
type:application/csv" -d ‘
id,Description,Sold
19F,Cassettes by the pound,4.93‘
This document will fail, as shown in this output:
<response>
<lst name="responseHeader">
<int name="status">400</int>
<int name="QTime">7</int>
</lst>
<lst name="error">
<str name="msg">ERROR: [doc=19F] Error adding field ‘Sold‘=‘4.93‘ msg=For input string:
"4.93"</str>
<int name="code">400</int>
</lst>
</response>
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Understanding Analyzers, Tokenizers, and
Filters
The following sections describe how Solr breaks down and works with textual data. There are three main
concepts to understand: analyzers, tokenizers, and filters.
? Field analyzers are used both during ingestion, when a document is indexed, and at query time. An
analyzer examines the text of fields and generates a token stream. Analyzers may be a single class or
they may be composed of a series of tokenizer and filter classes.
? Tokenizers break field data into lexical units, or tokens.
? Filters examine a stream of tokens and keep them, transform or discard them, or create new ones.
Tokenizers and filters may be combined to form pipelines, or chains, where the output of one is input to
the next. Such a sequence of tokenizers and filters is called an analyzer and the resulting output of an
analyzer is used to match query results or build indices.
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Using Analyzers, Tokenizers, and Filters
Although the analysis process is used for both indexing and querying, the same analysis process need not
be used for both operations. For indexing, you often want to simplify, or normalize, words. For example,
setting all letters to lowercase, eliminating punctuation and accents, mapping words to their stems, and so
on. Doing so can increase recall because, for example, "ram", "Ram" and "RAM" would all match a query for
"ram". To increase query-time precision, a filter could be employed to narrow the matches by, for example,
ignoring all-cap acronyms if you’re interested in male sheep, but not Random Access Memory.
The tokens output by the analysis process define the values, or terms, of that field and are used either to
build an index of those terms when a new document is added, or to identify which documents contain the
terms you are querying for.
For More Information
These sections will show you how to configure field analyzers and also serves as a reference for the details
of configuring each of the available tokenizer and filter classes. It also serves as a guide so that you can
configure your own analysis classes if you have special needs that cannot be met with the included filters or
tokenizers.
For Analyzers, see:
? Analyzers: Detailed conceptual information about Solr analyzers.
? Running Your Analyzer: Detailed information about testing and running your Solr analyzer.
For Tokenizers, see:
? About Tokenizers: Detailed conceptual information about Solr tokenizers.
? Tokenizers: Information about configuring tokenizers, and about the tokenizer factory classes included
in this distribution of Solr.
For Filters, see:
? About Filters: Detailed conceptual information about Solr filters.
? Filter Descriptions: Information about configuring filters, and about the filter factory classes included in
this distribution of Solr.
? CharFilterFactories: Information about filters for pre-processing input characters.
To find out how to use Tokenizers and Filters with various languages, see:
? Language Analysis: Information about tokenizers and filters for character set conversion or for use with
specific languages.
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Analyzers
An analyzer examines the text of fields and generates a token stream.
Analyzers are specified as a child of the <fieldType> element in the schema.xml configuration file (in the
same conf/ directory as solrconfig.xml ).
In normal usage, only fields of type solr.TextField will specify an analyzer. The simplest way to configure
an analyzer is with a single <analyzer> element whose class attribute is a fully qualified Java class name. The
named class must derive from org.apache.lucene.analysis.Analyzer . For example:
<fieldType name="nametext" class="solr.TextField">
<analyzer class="org.apache.lucene.analysis.core.WhitespaceAnalyzer"/>
</fieldType>
In this case a single class, WhitespaceAnalyzer , is responsible for analyzing the content of the named text
field and emitting the corresponding tokens. For simple cases, such as plain English prose, a single analyzer
class like this may be sufficient. But it’s often necessary to do more complex analysis of the field content.
Even the most complex analysis requirements can usually be decomposed into a series of discrete, relatively
simple processing steps. As you will soon discover, the Solr distribution comes with a large selection of
tokenizers and filters that covers most scenarios you are likely to encounter. Setting up an analyzer chain is
very straightforward; you specify a simple <analyzer> element (no class attribute) with child elements that
name factory classes for the tokenizer and filters to use, in the order you want them to run.
For example:
<fieldType name="nametext" class="solr.TextField">
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.StandardFilterFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.StopFilterFactory"/>
<filter class="solr.EnglishPorterFilterFactory"/>
</analyzer>
</fieldType>
Note that classes in the org.apache.solr.analysis package may be referred to here with the shorthand
solr. prefix.
In this case, no Analyzer class was specified on the <analyzer> element. Rather, a sequence of more
specialized classes are wired together and collectively act as the Analyzer for the field. The text of the field is
passed to the first item in the list ( solr.StandardTokenizerFactory ), and the tokens that emerge from the
last one ( solr.EnglishPorterFilterFactory ) are the terms that are used for indexing or querying any
fields that use the "nametext" fieldType .
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?
Field Values versus Indexed Terms
The output of an Analyzer affects the terms indexed in a given field (and the terms used
when parsing queries against those fields) but it has no impact on the stored value for the
fields. For example: an analyzer might split "Brown Cow" into two indexed terms "brown"
and "cow", but the stored value will still be a single String: "Brown Cow"
Analysis Phases
Analysis takes place in two contexts. At index time, when a field is being created, the token stream that
results from analysis is added to an index and defines the set of terms (including positions, sizes, and so on)
for the field. At query time, the values being searched for are analyzed and the terms that result are
matched against those that are stored in the field’s index.
In many cases, the same analysis should be applied to both phases. This is desirable when you want to
query for exact string matches, possibly with case-insensitivity, for example. In other cases, you may want to
apply slightly different analysis steps during indexing than those used at query time.
If you provide a simple <analyzer> definition for a field type, as in the examples above, then it will be used
for both indexing and queries. If you want distinct analyzers for each phase, you may include two
<analyzer> definitions distinguished with a type attribute. For example:
<fieldType name="nametext" class="solr.TextField">
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.KeepWordFilterFactory" words="keepwords.txt"/>
<filter class="solr.SynonymFilterFactory" synonyms="syns.txt"/>
</analyzer>
<analyzer type="query">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
</fieldType>
In this theoretical example, at index time the text is tokenized, the tokens are set to lowercase, any that are
not listed in keepwords.txt are discarded and those that remain are mapped to alternate values as defined
by the synonym rules in the file syns.txt . This essentially builds an index from a restricted set of possible
values and then normalizes them to values that may not even occur in the original text.
At query time, the only normalization that happens is to convert the query terms to lowercase. The filtering
and mapping steps that occur at index time are not applied to the query terms. Queries must then, in this
example, be very precise, using only the normalized terms that were stored at index time.
Analysis for Multi-Term Expansion
In some types of queries (ie: Prefix, Wildcard, Regex, etc…) the input provided by the user is not natural
language intended for Analysis. Things like Synonyms or Stop word filtering do not work in a logical way in
these types of Queries.
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The analysis factories that can work in these types of queries (such as Lowercasing, or Normalizing
Factories) are known as MultiTermAwareComponents . When Solr needs to perform analysis for a query that
results in Multi-Term expansion, only the MultiTermAwareComponents used in the query analyzer are used,
Factory that is not Multi-Term aware will be skipped.
For most use cases, this provides the best possible behavior, but if you wish for absolute control over the
analysis performed on these types of queries, you may explicitly define a multiterm analyzer to use, such as
in the following example:
<fieldType name="nametext" class="solr.TextField">
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.KeepWordFilterFactory" words="keepwords.txt"/>
<filter class="solr.SynonymFilterFactory" synonyms="syns.txt"/>
</analyzer>
<analyzer type="query">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
<!-- No analysis at all when doing queries that involved Multi-Term expansion -->
<analyzer type="multiterm">
<tokenizer class="solr.KeywordTokenizerFactory" />
</analyzer>
</fieldType>
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About Tokenizers
The job of a tokenizer is to break up a stream of text into tokens, where each token is (usually) a sub-
sequence of the characters in the text. An analyzer is aware of the field it is configured for, but a tokenizer is
not. Tokenizers read from a character stream (a Reader) and produce a sequence of Token objects (a
TokenStream).
Characters in the input stream may be discarded, such as whitespace or other delimiters. They may also be
added to or replaced, such as mapping aliases or abbreviations to normalized forms. A token contains
various metadata in addition to its text value, such as the location at which the token occurs in the field.
Because a tokenizer may produce tokens that diverge from the input text, you should not assume that the
text of the token is the same text that occurs in the field, or that its length is the same as the original text.
It’s also possible for more than one token to have the same position or refer to the same offset in the
original text. Keep this in mind if you use token metadata for things like highlighting search results in the
field text.
<fieldType name="text" class="solr.TextField">
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
</analyzer>
</fieldType>
The class named in the tokenizer element is not the actual tokenizer, but rather a class that implements the
TokenizerFactory API. This factory class will be called upon to create new tokenizer instances as needed.
Objects created by the factory must derive from Tokenizer , which indicates that they produce sequences of
tokens. If the tokenizer produces tokens that are usable as is, it may be the only component of the analyzer.
Otherwise, the tokenizer’s output tokens will serve as input to the first filter stage in the pipeline.
A TypeTokenFilterFactory is available that creates a TypeTokenFilter that filters tokens based on their
TypeAttribute, which is set in factory.getStopTypes .
For a complete list of the available TokenFilters, see the section Tokenizers.
When To use a CharFilter vs. a TokenFilter
There are several pairs of CharFilters and TokenFilters that have related (ie: MappingCharFilter and
ASCIIFoldingFilter ) or nearly identical (ie: PatternReplaceCharFilterFactory and
PatternReplaceFilterFactory ) functionality and it may not always be obvious which is the best choice.
The decision about which to use depends largely on which Tokenizer you are using, and whether you need
to preprocess the stream of characters.
For example, suppose you have a tokenizer such as StandardTokenizer and although you are pretty happy
with how it works overall, you want to customize how some specific characters behave. You could modify the
rules and re-build your own tokenizer with JFlex, but it might be easier to simply map some of the characters
before tokenization with a CharFilter .
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About Filters
Like tokenizers, filters consume input and produce a stream of tokens. Filters also derive from
org.apache.lucene.analysis.TokenStream . Unlike tokenizers, a filter’s input is another TokenStream. The
job of a filter is usually easier than that of a tokenizer since in most cases a filter looks at each token in the
stream sequentially and decides whether to pass it along, replace it or discard it.
A filter may also do more complex analysis by looking ahead to consider multiple tokens at once, although
this is less common. One hypothetical use for such a filter might be to normalize state names that would be
tokenized as two words. For example, the single token "california" would be replaced with "CA", while the
token pair "rhode" followed by "island" would become the single token "RI".
Because filters consume one TokenStream and produce a new TokenStream , they can be chained one after
another indefinitely. Each filter in the chain in turn processes the tokens produced by its predecessor. The
order in which you specify the filters is therefore significant. Typically, the most general filtering is done first,
and later filtering stages are more specialized.
<fieldType name="text" class="solr.TextField">
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.StandardFilterFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.EnglishPorterFilterFactory"/>
</analyzer>
</fieldType>
This example starts with Solr’s standard tokenizer, which breaks the field’s text into tokens. Those tokens
then pass through Solr’s standard filter, which removes dots from acronyms, and performs a few other
common operations. All the tokens are then set to lowercase, which will facilitate case-insensitive matching
at query time.
The last filter in the above example is a stemmer filter that uses the Porter stemming algorithm. A stemmer
is basically a set of mapping rules that maps the various forms of a word back to the base, or stem, word
from which they derive. For example, in English the words "hugs", "hugging" and "hugged" are all forms of
the stem word "hug". The stemmer will replace all of these terms with "hug", which is what will be indexed.
This means that a query for "hug" will match the term "hugged", but not "huge".
Conversely, applying a stemmer to your query terms will allow queries containing non stem terms, like
"hugging", to match documents with different variations of the same stem word, such as "hugged". This
works because both the indexer and the query will map to the same stem ("hug").
Word stemming is, obviously, very language specific. Solr includes several language-specific stemmers
created by the Snowball generator that are based on the Porter stemming algorithm. The generic Snowball
Porter Stemmer Filter can be used to configure any of these language stemmers. Solr also includes a
convenience wrapper for the English Snowball stemmer. There are also several purpose-built stemmers for
non-English languages. These stemmers are described in Language Analysis.
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Tokenizers
Tokenizers are responsible for breaking field data into lexical units, or tokens.
You configure the tokenizer for a text field type in schema.xml with a <tokenizer> element, as a child of
<analyzer> :
<fieldType name="text" class="solr.TextField">
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.StandardFilterFactory"/>
</analyzer>
</fieldType>
The class attribute names a factory class that will instantiate a tokenizer object when needed. Tokenizer
factory classes implement the org.apache.solr.analysis.TokenizerFactory . A TokenizerFactory’s
create() method accepts a Reader and returns a TokenStream. When Solr creates the tokenizer it passes a
Reader object that provides the content of the text field.
Arguments may be passed to tokenizer factories by setting attributes on the <tokenizer> element.
<fieldType name="semicolonDelimited" class="solr.TextField">
<analyzer type="query">
<tokenizer class="solr.PatternTokenizerFactory" pattern="; "/>
</analyzer>
</fieldType>
The following sections describe the tokenizer factory classes included in this release of Solr.
For user tips about Solr’s tokenizers, see http://wiki.apache.org/solr/AnalyzersTokenizersTokenFilters.
Standard Tokenizer
This tokenizer splits the text field into tokens, treating whitespace and punctuation as delimiters. Delimiter
characters are discarded, with the following exceptions:
? Periods (dots) that are not followed by whitespace are kept as part of the token, including Internet
domain names.
? The "@" character is among the set of token-splitting punctuation, so email addresses are not preserved
as single tokens.
Note that words are split at hyphens.
The Standard Tokenizer supports Unicode standard annex UAX#29 word boundaries with the following
token types: <ALPHANUM> , <NUM> , <SOUTHEAST_ASIAN> , <IDEOGRAPHIC> , and <HIRAGANA> .
Factory class: solr.StandardTokenizerFactory
Arguments:
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maxTokenLength : (integer, default 255) Solr ignores tokens that exceed the number of characters specified
by maxTokenLength .
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
</analyzer>
In: "Please, email john.doe@foo.com by 03-09, re: m37-xq."
Out: "Please", "email", "john.doe", "foo.com", "by", "03", "09", "re", "m37", "xq"
Classic Tokenizer
The Classic Tokenizer preserves the same behavior as the Standard Tokenizer of Solr versions 3.1 and
previous. It does not use the Unicode standard annex UAX#29 word boundary rules that the Standard
Tokenizer uses. This tokenizer splits the text field into tokens, treating whitespace and punctuation as
delimiters. Delimiter characters are discarded, with the following exceptions:
? Periods (dots) that are not followed by whitespace are kept as part of the token.
? Words are split at hyphens, unless there is a number in the word, in which case the token is not split and
the numbers and hyphen(s) are preserved.
? Recognizes Internet domain names and email addresses and preserves them as a single token.
Factory class: solr.ClassicTokenizerFactory
Arguments:
maxTokenLength : (integer, default 255) Solr ignores tokens that exceed the number of characters specified
by maxTokenLength .
Example:
<analyzer>
<tokenizer class="solr.ClassicTokenizerFactory"/>
</analyzer>
In: "Please, email john.doe@foo.com by 03-09, re: m37-xq."
Out: "Please", "email", "john.doe@foo.com", "by", "03-09", "re", "m37-xq"
Keyword Tokenizer
This tokenizer treats the entire text field as a single token.
Factory class: solr.KeywordTokenizerFactory
Arguments: None
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Example:
<analyzer>
<tokenizer class="solr.KeywordTokenizerFactory"/>
</analyzer>
In: "Please, email john.doe@foo.com by 03-09, re: m37-xq."
Out: "Please, email john.doe@foo.com by 03-09, re: m37-xq."
Letter Tokenizer
This tokenizer creates tokens from strings of contiguous letters, discarding all non-letter characters.
Factory class: solr.LetterTokenizerFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.LetterTokenizerFactory"/>
</analyzer>
In: "I can’t."
Out: "I", "can", "t"
Lower Case Tokenizer
Tokenizes the input stream by delimiting at non-letters and then converting all letters to lowercase.
Whitespace and non-letters are discarded.
Factory class: solr.LowerCaseTokenizerFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.LowerCaseTokenizerFactory"/>
</analyzer>
In: "I just *LOVE* my iPhone!"
Out: "i", "just", "love", "my", "iphone"
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N-Gram Tokenizer
Reads the field text and generates n-gram tokens of sizes in the given range.
Factory class: solr.NGramTokenizerFactory
Arguments:
minGramSize : (integer, default 1) The minimum n-gram size, must be > 0.
maxGramSize : (integer, default 2) The maximum n-gram size, must be >= minGramSize .
Example:
Default behavior. Note that this tokenizer operates over the whole field. It does not break the field at
whitespace. As a result, the space character is included in the encoding.
<analyzer>
<tokenizer class="solr.NGramTokenizerFactory"/>
</analyzer>
In: "hey man"
Out: "h", "e", "y", " ", "m", "a", "n", "he", "ey", "y ", " m", "ma", "an"
Example:
With an n-gram size range of 4 to 5:
<analyzer>
<tokenizer class="solr.NGramTokenizerFactory" minGramSize="4" maxGramSize="5"/>
</analyzer>
In: "bicycle"
Out: "bicy", "bicyc", "icyc", "icycl", "cycl", "cycle", "ycle"
Edge N-Gram Tokenizer
Reads the field text and generates edge n-gram tokens of sizes in the given range.
Factory class: solr.EdgeNGramTokenizerFactory
Arguments:
minGramSize : (integer, default is 1) The minimum n-gram size, must be > 0.
maxGramSize : (integer, default is 1) The maximum n-gram size, must be >= minGramSize .
side : ("front" or "back", default is "front") Whether to compute the n-grams from the beginning (front) of
the text or from the end (back).
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Example:
Default behavior (min and max default to 1):
<analyzer>
<tokenizer class="solr.EdgeNGramTokenizerFactory"/>
</analyzer>
In: "babaloo"
Out: "b"
Example:
Edge n-gram range of 2 to 5
<analyzer>
<tokenizer class="solr.EdgeNGramTokenizerFactory" minGramSize="2" maxGramSize="5"/>
</analyzer>
In: "babaloo"
Out:"ba", "bab", "baba", "babal"
Example:
Edge n-gram range of 2 to 5, from the back side:
<analyzer>
<tokenizer class="solr.EdgeNGramTokenizerFactory" minGramSize="2" maxGramSize="5" side="back"/>
</analyzer>
In: "babaloo"
Out: "oo", "loo", "aloo", "baloo"
ICU Tokenizer
This tokenizer processes multilingual text and tokenizes it appropriately based on its script attribute.
You can customize this tokenizer’s behavior by specifying per-script rule files. To add per-script rules, add a
rulefiles argument, which should contain a comma-separated list of code:rulefile pairs in the following
format: four-letter ISO 15924 script code, followed by a colon, then a resource path. For example, to specify
rules for Latin (script code "Latn") and Cyrillic (script code "Cyrl"), you would enter
Latn:my.Latin.rules.rbbi,Cyrl:my.Cyrillic.rules.rbbi .
The default configuration for solr.ICUTokenizerFactory provides UAX#29 word break rules tokenization
(like solr.StandardTokenizer ), but also includes custom tailorings for Hebrew (specializing handling of
double and single quotation marks), for syllable tokenization for Khmer, Lao, and Myanmar, and dictionary-
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based word segmentation for CJK characters.
Factory class: solr.ICUTokenizerFactory
Arguments:
rulefile : a comma-separated list of code:rulefile pairs in the following format: four-letter ISO 15924
script code, followed by a colon, then a resource path.
Example:
<analyzer>
<!-- no customization -->
<tokenizer class="solr.ICUTokenizerFactory"/>
</analyzer>
<analyzer>
<tokenizer class="solr.ICUTokenizerFactory"
rulefiles="Latn:my.Latin.rules.rbbi,Cyrl:my.Cyrillic.rules.rbbi"/>
</analyzer>
?
To use this tokenizer, you must add additional .jars to Solr’s classpath (as described in the
section Lib Directives in SolrConfig). See the solr/contrib/analysis-extras/README.txt
for information on which jars you need to add to your SOLR_HOME/lib .
Path Hierarchy Tokenizer
This tokenizer creates synonyms from file path hierarchies.
Factory class: solr.PathHierarchyTokenizerFactory
Arguments:
delimiter : (character, no default) You can specify the file path delimiter and replace it with a delimiter you
provide. This can be useful for working with backslash delimiters.
replace : (character, no default) Specifies the delimiter character Solr uses in the tokenized output.
Example:
<fieldType name="text_path" class="solr.TextField" positionIncrementGap="100">
<analyzer>
<tokenizer class="solr.PathHierarchyTokenizerFactory" delimiter="\" replace="/"/>
</analyzer>
</fieldType>
In: "c:\usr\local\apache"
Out: "c:", "c:/usr", "c:/usr/local", "c:/usr/local/apache"
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Regular Expression Pattern Tokenizer
This tokenizer uses a Java regular expression to break the input text stream into tokens. The expression
provided by the pattern argument can be interpreted either as a delimiter that separates tokens, or to
match patterns that should be extracted from the text as tokens.
See the Javadocs for java.util.regex.Pattern for more information on Java regular expression syntax.
Factory class: solr.PatternTokenizerFactory
Arguments:
pattern : (Required) The regular expression, as defined by in java.util.regex.Pattern .
group : (Optional, default -1) Specifies which regex group to extract as the token(s). The value -1 means the
regex should be treated as a delimiter that separates tokens. Non-negative group numbers (>= 0) indicate
that character sequences matching that regex group should be converted to tokens. Group zero refers to
the entire regex, groups greater than zero refer to parenthesized sub-expressions of the regex, counted
from left to right.
Example:
A comma separated list. Tokens are separated by a sequence of zero or more spaces, a comma, and zero or
more spaces.
<analyzer>
<tokenizer class="solr.PatternTokenizerFactory" pattern="\s*,\s*"/>
</analyzer>
In: "fee,fie, foe , fum, foo"
Out: "fee", "fie", "foe", "fum", "foo"
Example:
Extract simple, capitalized words. A sequence of at least one capital letter followed by zero or more letters of
either case is extracted as a token.
<analyzer>
<tokenizer class="solr.PatternTokenizerFactory" pattern="[A-Z][A-Za-z]*" group="0"/>
</analyzer>
In: "Hello. My name is Inigo Montoya. You killed my father. Prepare to die."
Out: "Hello", "My", "Inigo", "Montoya", "You", "Prepare"
Example:
Extract part numbers which are preceded by "SKU", "Part" or "Part Number", case sensitive, with an
optional semi-colon separator. Part numbers must be all numeric digits, with an optional hyphen. Regex
capture groups are numbered by counting left parenthesis from left to right. Group 3 is the subexpression
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"[0-9-]+", which matches one or more digits or hyphens.
<analyzer>
<tokenizer class="solr.PatternTokenizerFactory" pattern="(SKU|Part(\sNumber)?):?\s(\[0-9-\]+)"
group="3"/>
</analyzer>
In: "SKU: 1234, Part Number 5678, Part: 126-987"
Out: "1234", "5678", "126-987"
Simplified Regular Expression Pattern Tokenizer
This tokenizer is similar to the PatternTokenizerFactory described above, but uses Lucene RegExp pattern
matching to construct distinct tokens for the input stream. The syntax is more limited than
PatternTokenizerFactory , but the tokenization is quite a bit faster.
Factory class: solr.SimplePatternTokenizerFactory
Arguments:
pattern : (Required) The regular expression, as defined by in the RegExp javadocs, identifying the characters
to include in tokens. The matching is greedy such that the longest token matching at a given point is
created. Empty tokens are never created.
maxDeterminizedStates : (Optional, default 10000) the limit on total state count for the determined
automaton computed from the regexp.
Example:
To match tokens delimited by simple whitespace characters:
<analyzer>
<tokenizer class="solr.SimplePatternTokenizerFactory" pattern="[^ \t\r\n]+"/>
</analyzer>
Simplified Regular Expression Pattern Splitting Tokenizer
This tokenizer is similar to the SimplePatternTokenizerFactory described above, but uses Lucene RegExp
pattern matching to identify sequences of characters that should be used to split tokens. The syntax is more
limited than PatternTokenizerFactory , but the tokenization is quite a bit faster.
Factory class: solr.SimplePatternSplitTokenizerFactory
Arguments:
pattern : (Required) The regular expression, as defined by in the RegExp javadocs, identifying the characters
that should split tokens. The matching is greedy such that the longest token separator matching at a given
point is matched. Empty tokens are never created.
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maxDeterminizedStates : (Optional, default 10000) the limit on total state count for the determined
automaton computed from the regexp.
Example:
To match tokens delimited by simple whitespace characters:
<analyzer>
<tokenizer class="solr.SimplePatternSplitTokenizerFactory" pattern="[ \t\r\n]+"/>
</analyzer>
UAX29 URL Email Tokenizer
This tokenizer splits the text field into tokens, treating whitespace and punctuation as delimiters. Delimiter
characters are discarded, with the following exceptions:
? Periods (dots) that are not followed by whitespace are kept as part of the token.
? Words are split at hyphens, unless there is a number in the word, in which case the token is not split and
the numbers and hyphen(s) are preserved.
? Recognizes and preserves as single tokens the following:
? Internet domain names containing top-level domains validated against the white list in the IANA Root
Zone Database when the tokenizer was generated
? email addresses
? file:// , http(s):// , and ftp:// URLs
? IPv4 and IPv6 addresses
The UAX29 URL Email Tokenizer supports Unicode standard annex UAX#29 word boundaries with the
following token types: <ALPHANUM> , <NUM> , <URL> , <EMAIL> , <SOUTHEAST_ASIAN> , <IDEOGRAPHIC> , and
<HIRAGANA> .
Factory class: solr.UAX29URLEmailTokenizerFactory
Arguments:
maxTokenLength : (integer, default 255) Solr ignores tokens that exceed the number of characters specified
by maxTokenLength .
Example:
<analyzer>
<tokenizer class="solr.UAX29URLEmailTokenizerFactory"/>
</analyzer>
In: "Visit http://accarol.com/contact.htm?from=external&a=10 or e-mail bob.cratchet@accarol.com"
Out: "Visit", "http://accarol.com/contact.htm?from=external&a=10", "or", "e", "mail",
"bob.cratchet@accarol.com"
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White Space Tokenizer
Simple tokenizer that splits the text stream on whitespace and returns sequences of non-whitespace
characters as tokens. Note that any punctuation will be included in the tokens.
Factory class: solr.WhitespaceTokenizerFactory
Arguments: rule : Specifies how to define whitespace for the purpose of tokenization. Valid values:
? java : (Default) Uses Character.isWhitespace(int)
? unicode : Uses Unicode’s WHITESPACE property
Example:
<analyzer>
<tokenizer class="solr.WhitespaceTokenizerFactory" rule="java" />
</analyzer>
In: "To be, or what?"
Out: "To", "be,", "or", "what?"
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Filter Descriptions
Filters examine a stream of tokens and keep them, transform them or discard them, depending on the filter
type being used.
You configure each filter with a <filter> element in schema.xml as a child of <analyzer> , following the
<tokenizer> element. Filter definitions should follow a tokenizer or another filter definition because they
take a TokenStream as input. For example:
<fieldType name="text" class="solr.TextField">
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>...
</analyzer>
</fieldType>
The class attribute names a factory class that will instantiate a filter object as needed. Filter factory classes
must implement the org.apache.solr.analysis.TokenFilterFactory interface. Like tokenizers, filters are
also instances of TokenStream and thus are producers of tokens. Unlike tokenizers, filters also consume
tokens from a TokenStream. This allows you to mix and match filters, in any order you prefer, downstream
of a tokenizer.
Arguments may be passed to tokenizer factories to modify their behavior by setting attributes on the
<filter> element. For example:
<fieldType name="semicolonDelimited" class="solr.TextField">
<analyzer type="query">
<tokenizer class="solr.PatternTokenizerFactory" pattern="; " />
<filter class="solr.LengthFilterFactory" min="2" max="7"/>
</analyzer>
</fieldType>
The following sections describe the filter factories that are included in this release of Solr.
For user tips about Solr’s filters, see http://wiki.apache.org/solr/AnalyzersTokenizersTokenFilters.
ASCII Folding Filter
This filter converts alphabetic, numeric, and symbolic Unicode characters which are not in the Basic Latin
Unicode block (the first 127 ASCII characters) to their ASCII equivalents, if one exists. This filter converts
characters from the following Unicode blocks:
? C1 Controls and Latin-1 Supplement (PDF)
? Latin Extended-A (PDF)
? Latin Extended-B (PDF)
? Latin Extended Additional (PDF)
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? Latin Extended-C (PDF)
? Latin Extended-D (PDF)
? IPA Extensions (PDF)
? Phonetic Extensions (PDF)
? Phonetic Extensions Supplement (PDF)
? General Punctuation (PDF)
? Superscripts and Subscripts (PDF)
? Enclosed Alphanumerics (PDF)
? Dingbats (PDF)
? Supplemental Punctuation (PDF)
? Alphabetic Presentation Forms (PDF)
? Halfwidth and Fullwidth Forms (PDF)
Factory class: solr.ASCIIFoldingFilterFactory
Arguments:
preserveOriginal
(boolean, default false) If true, the original token is preserved: "thé" → "the", "thé"
Example:
<analyzer>
<tokenizer class="solr.WhitespaceTokenizer"/>
<filter class="solr.ASCIIFoldingFilterFactory" preserveOriginal="false" />
</analyzer>
In: "á" (Unicode character 00E1)
Out: "a" (ASCII character 97)
Beider-Morse Filter
Implements the Beider-Morse Phonetic Matching (BMPM) algorithm, which allows identification of similar
names, even if they are spelled differently or in different languages. More information about how this works
is available in the section on Phonetic Matching.
?
BeiderMorseFilter changed its behavior in Solr 5.0 due to an update to version 3.04 of the
BMPM algorithm. Older version of Solr implemented BMPM version 3.00 (see
http://stevemorse.org/phoneticinfo.htm). Any index built using this filter with earlier
versions of Solr will need to be rebuilt.
Factory class: solr.BeiderMorseFilterFactory
Arguments:
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nameType
Types of names. Valid values are GENERIC, ASHKENAZI, or SEPHARDIC. If not processing Ashkenazi or
Sephardic names, use GENERIC.
ruleType
Types of rules to apply. Valid values are APPROX or EXACT.
concat
Defines if multiple possible matches should be combined with a pipe ("|").
languageSet
The language set to use. The value "auto" will allow the Filter to identify the language, or a comma-
separated list can be supplied.
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.BeiderMorseFilterFactory" nameType="GENERIC" ruleType="APPROX" concat="
true" languageSet="auto">
</filter>
</analyzer>
Classic Filter
This filter takes the output of the Classic Tokenizer and strips periods from acronyms and "‘s" from
possessives.
Factory class: solr.ClassicFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.ClassicTokenizerFactory"/>
<filter class="solr.ClassicFilterFactory"/>
</analyzer>
In: "I.B.M. cat’s can’t"
Tokenizer to Filter: "I.B.M", "cat’s", "can’t"
Out: "IBM", "cat", "can’t"
Common Grams Filter
This filter creates word shingles by combining common tokens such as stop words with regular tokens. This
is useful for creating phrase queries containing common words, such as "the cat." Solr normally ignores
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stop words in queried phrases, so searching for "the cat" would return all matches for the word "cat."
Factory class: solr.CommonGramsFilterFactory
Arguments:
words
(a common word file in .txt format) Provide the name of a common word file, such as stopwords.txt .
format
(optional) If the stopwords list has been formatted for Snowball, you can specify format="snowball" so
Solr can read the stopwords file.
ignoreCase
(boolean) If true, the filter ignores the case of words when comparing them to the common word file. The
default is false.
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.CommonGramsFilterFactory" words="stopwords.txt" ignoreCase="true"/>
</analyzer>
In: "the Cat"
Tokenizer to Filter: "the", "Cat"
Out: "the_cat"
Collation Key Filter
Collation allows sorting of text in a language-sensitive way. It is usually used for sorting, but can also be
used with advanced searches. We’ve covered this in much more detail in the section on Unicode Collation.
Daitch-Mokotoff Soundex Filter
Implements the Daitch-Mokotoff Soundex algorithm, which allows identification of similar names, even if
they are spelled differently. More information about how this works is available in the section on Phonetic
Matching.
Factory class: solr.DaitchMokotoffSoundexFilterFactory
Arguments:
inject
(true/false) If true (the default), then new phonetic tokens are added to the stream. Otherwise, tokens
are replaced with the phonetic equivalent. Setting this to false will enable phonetic matching, but the
exact spelling of the target word may not match.
Example:
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<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.DaitchMokotoffSoundexFilterFactory" inject="true"/>
</analyzer>
Double Metaphone Filter
This filter creates tokens using the DoubleMetaphone encoding algorithm from commons-codec. For more
information, see the Phonetic Matching section.
Factory class: solr.DoubleMetaphoneFilterFactory
Arguments:
inject
(true/false) If true (the default), then new phonetic tokens are added to the stream. Otherwise, tokens
are replaced with the phonetic equivalent. Setting this to false will enable phonetic matching, but the
exact spelling of the target word may not match.
maxCodeLength
(integer) The maximum length of the code to be generated.
Example:
Default behavior for inject (true): keep the original token and add phonetic token(s) at the same position.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.DoubleMetaphoneFilterFactory"/>
</analyzer>
In: "four score and Kuczewski"
Tokenizer to Filter: "four"(1), "score"(2), "and"(3), "Kuczewski"(4)
Out: "four"(1), "FR"(1), "score"(2), "SKR"(2), "and"(3), "ANT"(3), "Kuczewski"(4), "KSSK"(4), "KXFS"(4)
The phonetic tokens have a position increment of 0, which indicates that they are at the same position as the
token they were derived from (immediately preceding). Note that "Kuczewski" has two encodings, which are
added at the same position.
Example:
Discard original token ( inject="false" ).
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.DoubleMetaphoneFilterFactory" inject="false"/>
</analyzer>
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In: "four score and Kuczewski"
Tokenizer to Filter: "four"(1), "score"(2), "and"(3), "Kuczewski"(4)
Out: "FR"(1), "SKR"(2), "ANT"(3), "KSSK"(4), "KXFS"(4)
Note that "Kuczewski" has two encodings, which are added at the same position.
Edge N-Gram Filter
This filter generates edge n-gram tokens of sizes within the given range.
Factory class: solr.EdgeNGramFilterFactory
Arguments:
minGramSize
(integer, default 1) The minimum gram size.
maxGramSize
(integer, default 1) The maximum gram size.
Example:
Default behavior.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.EdgeNGramFilterFactory"/>
</analyzer>
In: "four score and twenty"
Tokenizer to Filter: "four", "score", "and", "twenty"
Out: "f", "s", "a", "t"
Example:
A range of 1 to 4.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.EdgeNGramFilterFactory" minGramSize="1" maxGramSize="4"/>
</analyzer>
In: "four score"
Tokenizer to Filter: "four", "score"
Out: "f", "fo", "fou", "four", "s", "sc", "sco", "scor"
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Example:
A range of 4 to 6.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.EdgeNGramFilterFactory" minGramSize="4" maxGramSize="6"/>
</analyzer>
In: "four score and twenty"
Tokenizer to Filter: "four", "score", "and", "twenty"
Out: "four", "scor", "score", "twen", "twent", "twenty"
English Minimal Stem Filter
This filter stems plural English words to their singular form.
Factory class: solr.EnglishMinimalStemFilterFactory
Arguments: None
Example:
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.EnglishMinimalStemFilterFactory"/>
</analyzer>
In: "dogs cats"
Tokenizer to Filter: "dogs", "cats"
Out: "dog", "cat"
English Possessive Filter
This filter removes singular possessives (trailing ‘s) from words. Note that plural possessives, e.g. the s‘ in
"divers‘ snorkels", are not removed by this filter.
Factory class: solr.EnglishPossessiveFilterFactory
Arguments: None
Example:
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<analyzer>
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.EnglishPossessiveFilterFactory"/>
</analyzer>
In: "Man’s dog bites dogs‘ man"
Tokenizer to Filter: "Man’s", "dog", "bites", "dogs‘", "man"
Out: "Man", "dog", "bites", "dogs‘", "man"
Fingerprint Filter
This filter outputs a single token which is a concatenation of the sorted and de-duplicated set of input
tokens. This can be useful for clustering/linking use cases.
Factory class: solr.FingerprintFilterFactory
Arguments:
separator
The character used to separate tokens combined into the single output token. Defaults to " " (a space
character).
maxOutputTokenSize
The maximum length of the summarized output token. If exceeded, no output token is emitted. Defaults
to 1024.
Example:
<analyzer type="index">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.FingerprintFilterFactory" separator="_" />
</analyzer>
In: "the quick brown fox jumped over the lazy dog"
Tokenizer to Filter: "the", "quick", "brown", "fox", "jumped", "over", "the", "lazy", "dog"
Out: "brown_dog_fox_jumped_lazy_over_quick_the"
Flatten Graph Filter
This filter must be included on index-time analyzer specifications that include at least one graph-aware filter,
including Synonym Graph Filter and Word Delimiter Graph Filter.
Factory class: solr.FlattenGraphFilterFactory
Arguments: None
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See the examples below for Synonym Graph Filter and Word Delimiter Graph Filter.
Hunspell Stem Filter
The Hunspell Stem Filter provides support for several languages. You must provide the dictionary ( .dic )
and rules ( .aff ) files for each language you wish to use with the Hunspell Stem Filter. You can download
those language files here.
Be aware that your results will vary widely based on the quality of the provided dictionary and rules files. For
example, some languages have only a minimal word list with no morphological information. On the other
hand, for languages that have no stemmer but do have an extensive dictionary file, the Hunspell stemmer
may be a good choice.
Factory class: solr.HunspellStemFilterFactory
Arguments:
dictionary
(required) The path of a dictionary file.
affix
(required) The path of a rules file.
ignoreCase
(boolean) controls whether matching is case sensitive or not. The default is false.
strictAffixParsing
(boolean) controls whether the affix parsing is strict or not. If true, an error while reading an affix rule
causes a ParseException, otherwise is ignored. The default is true.
Example:
<analyzer type="index">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.HunspellStemFilterFactory"
dictionary="en_GB.dic"
affix="en_GB.aff"
ignoreCase="true"
strictAffixParsing="true" />
</analyzer>
In: "jump jumping jumped"
Tokenizer to Filter: "jump", "jumping", "jumped"
Out: "jump", "jump", "jump"
Hyphenated Words Filter
This filter reconstructs hyphenated words that have been tokenized as two tokens because of a line break or
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other intervening whitespace in the field test. If a token ends with a hyphen, it is joined with the following
token and the hyphen is discarded.
Note that for this filter to work properly, the upstream tokenizer must not remove trailing hyphen
characters. This filter is generally only useful at index time.
Factory class: solr.HyphenatedWordsFilterFactory
Arguments: None
Example:
<analyzer type="index">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.HyphenatedWordsFilterFactory"/>
</analyzer>
In: "A hyphen- ated word"
Tokenizer to Filter: "A", "hyphen-", "ated", "word"
Out: "A", "hyphenated", "word"
ICU Folding Filter
This filter is a custom Unicode normalization form that applies the foldings specified in Unicode Technical
Report 30 in addition to the NFKC_Casefold normalization form as described in ICU Normalizer 2 Filter. This
filter is a better substitute for the combined behavior of the ASCII Folding Filter, Lower Case Filter, and ICU
Normalizer 2 Filter.
To use this filter, see solr/contrib/analysis-extras/README.txt for instructions on which jars you need
to add to your solr_home/lib . For more information about adding jars, see the section Lib Directives in
Solrconfig.
Factory class: solr.ICUFoldingFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ICUFoldingFilterFactory"/>
</analyzer>
For detailed information on this normalization form, see http://www.unicode.org/reports/tr30/tr30-4.html.
ICU Normalizer 2 Filter
This filter factory normalizes text according to one of five Unicode Normalization Forms as described in
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Unicode Standard Annex #15:
? NFC: (name="nfc" mode="compose") Normalization Form C, canonical decomposition
? NFD: (name="nfc" mode="decompose") Normalization Form D, canonical decomposition, followed by
canonical composition
? NFKC: (name="nfkc" mode="compose") Normalization Form KC, compatibility decomposition
? NFKD: (name="nfkc" mode="decompose") Normalization Form KD, compatibility decomposition,
followed by canonical composition
? NFKC_Casefold: (name="nfkc_cf" mode="compose") Normalization Form KC, with additional Unicode
case folding. Using the ICU Normalizer 2 Filter is a better-performing substitution for the Lower Case
Filter and NFKC normalization.
Factory class: solr.ICUNormalizer2FilterFactory
Arguments:
name
(string) The name of the normalization form; nfc , nfd , nfkc , nfkd , nfkc_cf
mode
(string) The mode of Unicode character composition and decomposition; compose or decompose
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ICUNormalizer2FilterFactory" name="nfkc_cf" mode="compose"/>
</analyzer>
For detailed information about these Unicode Normalization Forms, see http://unicode.org/reports/tr15/.
To use this filter, see solr/contrib/analysis-extras/README.txt for instructions on which jars you need
to add to your solr_home/lib .
ICU Transform Filter
This filter applies ICU Tranforms to text. This filter supports only ICU System Transforms. Custom rule sets
are not supported.
Factory class: solr.ICUTransformFilterFactory
Arguments:
id
(string) The identifier for the ICU System Transform you wish to apply with this filter. For a full list of ICU
System Transforms, see http://demo.icu-project.org/icu-bin/translit?TEMPLATE_FILE=data/
translit_rule_main.html.
Example:
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<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ICUTransformFilterFactory" id="Traditional-Simplified"/>
</analyzer>
For detailed information about ICU Transforms, see http://userguide.icu-project.org/transforms/general.
To use this filter, see solr/contrib/analysis-extras/README.txt for instructions on which jars you need
to add to your solr_home/lib .
Keep Word Filter
This filter discards all tokens except those that are listed in the given word list. This is the inverse of the Stop
Words Filter. This filter can be useful for building specialized indices for a constrained set of terms.
Factory class: solr.KeepWordFilterFactory
Arguments:
words
(required) Path of a text file containing the list of keep words, one per line. Blank lines and lines that
begin with "#" are ignored. This may be an absolute path, or a simple filename in the Solr conf directory.
ignoreCase
(true/false) If true then comparisons are done case-insensitively. If this argument is true, then the words
file is assumed to contain only lowercase words. The default is false.
enablePositionIncrements
if luceneMatchVersion is 4.3 or earlier and enablePositionIncrements="false" , no position holes will
be left by this filter when it removes tokens. This argument is invalid if luceneMatchVersion is 5.0 or
later.
Example:
Where keepwords.txt contains:
happy funny silly
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.KeepWordFilterFactory" words="keepwords.txt"/>
</analyzer>
In: "Happy, sad or funny"
Tokenizer to Filter: "Happy", "sad", "or", "funny"
Out: "funny"
Example:
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Same keepwords.txt , case insensitive:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.KeepWordFilterFactory" words="keepwords.txt" ignoreCase="true"/>
</analyzer>
In: "Happy, sad or funny"
Tokenizer to Filter: "Happy", "sad", "or", "funny"
Out: "Happy", "funny"
Example:
Using LowerCaseFilterFactory before filtering for keep words, no ignoreCase flag.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.KeepWordFilterFactory" words="keepwords.txt"/>
</analyzer>
In: "Happy, sad or funny"
Tokenizer to Filter: "Happy", "sad", "or", "funny"
Filter to Filter: "happy", "sad", "or", "funny"
Out: "happy", "funny"
KStem Filter
KStem is an alternative to the Porter Stem Filter for developers looking for a less aggressive stemmer. KStem
was written by Bob Krovetz, ported to Lucene by Sergio Guzman-Lara (UMASS Amherst). This stemmer is
only appropriate for English language text.
Factory class: solr.KStemFilterFactory
Arguments: None
Example:
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory "/>
<filter class="solr.KStemFilterFactory"/>
</analyzer>
In: "jump jumping jumped"
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Tokenizer to Filter: "jump", "jumping", "jumped"
Out: "jump", "jump", "jump"
Length Filter
This filter passes tokens whose length falls within the min/max limit specified. All other tokens are
discarded.
Factory class: solr.LengthFilterFactory
Arguments:
min
(integer, required) Minimum token length. Tokens shorter than this are discarded.
max
(integer, required, must be >= min) Maximum token length. Tokens longer than this are discarded.
enablePositionIncrements
if luceneMatchVersion is 4.3 or earlier and enablePositionIncrements="false" , no position holes will
be left by this filter when it removes tokens. This argument is invalid if luceneMatchVersion is 5.0 or
later.
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LengthFilterFactory" min="3" max="7"/>
</analyzer>
In: "turn right at Albuquerque"
Tokenizer to Filter: "turn", "right", "at", "Albuquerque"
Out: "turn", "right"
Limit Token Count Filter
This filter limits the number of accepted tokens, typically useful for index analysis.
By default, this filter ignores any tokens in the wrapped TokenStream once the limit has been reached, which
can result in reset() being called prior to incrementToken() returning false . For most TokenStream
implementations this should be acceptable, and faster then consuming the full stream. If you are wrapping
a TokenStream which requires that the full stream of tokens be exhausted in order to function properly, use
the consumeAllTokens="true" option.
Factory class: solr.LimitTokenCountFilterFactory
Arguments:
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maxTokenCount
(integer, required) Maximum token count. After this limit has been reached, tokens are discarded.
consumeAllTokens
(boolean, defaults to false) Whether to consume (and discard) previous token filters‘ tokens after the
maximum token count has been reached. See description above.
Example:
<analyzer type="index">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.LimitTokenCountFilterFactory" maxTokenCount="10"
consumeAllTokens="false" />
</analyzer>
In: "1 2 3 4 5 6 7 8 9 10 11 12"
Tokenizer to Filter: "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"
Out: "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"
Limit Token Offset Filter
This filter limits tokens to those before a configured maximum start character offset. This can be useful to
limit highlighting, for example.
By default, this filter ignores any tokens in the wrapped TokenStream once the limit has been reached, which
can result in reset() being called prior to incrementToken() returning false . For most TokenStream
implementations this should be acceptable, and faster then consuming the full stream. If you are wrapping
a TokenStream which requires that the full stream of tokens be exhausted in order to function properly, use
the consumeAllTokens="true" option.
Factory class: solr.LimitTokenOffsetFilterFactory
Arguments:
maxStartOffset
(integer, required) Maximum token start character offset. After this limit has been reached, tokens are
discarded.
consumeAllTokens
(boolean, defaults to false) Whether to consume (and discard) previous token filters‘ tokens after the
maximum start offset has been reached. See description above.
Example:
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<analyzer>
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.LimitTokenOffsetFilterFactory" maxStartOffset="10"
consumeAllTokens="false" />
</analyzer>
In: "0 2 4 6 8 A C E"
Tokenizer to Filter: "0", "2", "4", "6", "8", "A", "C", "E"
Out: "0", "2", "4", "6", "8", "A"
Limit Token Position Filter
This filter limits tokens to those before a configured maximum token position.
By default, this filter ignores any tokens in the wrapped TokenStream once the limit has been reached, which
can result in reset() being called prior to incrementToken() returning false . For most TokenStream
implementations this should be acceptable, and faster then consuming the full stream. If you are wrapping
a TokenStream which requires that the full stream of tokens be exhausted in order to function properly, use
the consumeAllTokens="true" option.
Factory class: solr.LimitTokenPositionFilterFactory
Arguments:
maxTokenPosition
(integer, required) Maximum token position. After this limit has been reached, tokens are discarded.
consumeAllTokens
(boolean, defaults to false) Whether to consume (and discard) previous token filters‘ tokens after the
maximum start offset has been reached. See description above.
Example:
<analyzer>
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.LimitTokenPositionFilterFactory" maxTokenPosition="3"
consumeAllTokens="false" />
</analyzer>
In: "1 2 3 4 5"
Tokenizer to Filter: "1", "2", "3", "4", "5"
Out: "1", "2", "3"
Lower Case Filter
Converts any uppercase letters in a token to the equivalent lowercase token. All other characters are left
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unchanged.
Factory class: solr.LowerCaseFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
In: "Down With CamelCase"
Tokenizer to Filter: "Down", "With", "CamelCase"
Out: "down", "with", "camelcase"
Managed Stop Filter
This is specialized version of the Stop Words Filter Factory that uses a set of stop words that are managed
from a REST API.
Arguments:
managed
The name that should be used for this set of stop words in the managed REST API.
Example: With this configuration the set of words is named "english" and can be managed via
/solr/collection_name/schema/analysis/stopwords/english
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ManagedStopFilterFactory" managed="english"/>
</analyzer>
See Stop Filter for example input/output.
Managed Synonym Filter
This is specialized version of the Synonym Filter Factory that uses a mapping on synonyms that is managed
from a REST API.
Arguments:
managed
The name that should be used for this mapping on synonyms in the managed REST API.
Example: With this configuration the set of mappings is named "english" and can be managed via
/solr/collection_name/schema/analysis/synonyms/english
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<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ManagedSynonymFilterFactory" managed="english"/>
</analyzer>
See Synonym Filter for example input/output.
N-Gram Filter
Generates n-gram tokens of sizes in the given range. Note that tokens are ordered by position and then by
gram size.
Factory class: solr.NGramFilterFactory
Arguments:
minGramSize
(integer, default 1) The minimum gram size.
maxGramSize
(integer, default 2) The maximum gram size.
Example:
Default behavior.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.NGramFilterFactory"/>
</analyzer>
In: "four score"
Tokenizer to Filter: "four", "score"
Out: "f", "o", "u", "r", "fo", "ou", "ur", "s", "c", "o", "r", "e", "sc", "co", "or", "re"
Example:
A range of 1 to 4.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.NGramFilterFactory" minGramSize="1" maxGramSize="4"/>
</analyzer>
In: "four score"
Tokenizer to Filter: "four", "score"
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Out: "f", "fo", "fou", "four", "o", "ou", "our", "u", "ur", "r", "s", "sc", "sco", "scor", "c", "co", "cor", "core",
"o", "or", "ore", "r", "re", "e"
Example:
A range of 3 to 5.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.NGramFilterFactory" minGramSize="3" maxGramSize="5"/>
</analyzer>
In: "four score"
Tokenizer to Filter: "four", "score"
Out: "fou", "four", "our", "sco", "scor", "score", "cor", "core", "ore"
Numeric Payload Token Filter
This filter adds a numeric floating point payload value to tokens that match a given type. Refer to the
Javadoc for the org.apache.lucene.analysis.Token class for more information about token types and
payloads.
Factory class: solr.NumericPayloadTokenFilterFactory
Arguments:
payload
(required) A floating point value that will be added to all matching tokens.
typeMatch
(required) A token type name string. Tokens with a matching type name will have their payload set to the
above floating point value.
Example:
<analyzer>
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.NumericPayloadTokenFilterFactory" payload="0.75" typeMatch="word"/>
</analyzer>
In: "bing bang boom"
Tokenizer to Filter: "bing", "bang", "boom"
Out: "bing"[0.75], "bang"[0.75], "boom"[0.75]
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Pattern Replace Filter
This filter applies a regular expression to each token and, for those that match, substitutes the given
replacement string in place of the matched pattern. Tokens which do not match are passed though
unchanged.
Factory class: solr.PatternReplaceFilterFactory
Arguments:
pattern
(required) The regular expression to test against each token, as per java.util.regex.Pattern .
replacement
(required) A string to substitute in place of the matched pattern. This string may contain references to
capture groups in the regex pattern. See the Javadoc for java.util.regex.Matcher .
replace
("all" or "first", default "all") Indicates whether all occurrences of the pattern in the token should be
replaced, or only the first.
Example:
Simple string replace:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.PatternReplaceFilterFactory" pattern="cat" replacement="dog"/>
</analyzer>
In: "cat concatenate catycat"
Tokenizer to Filter: "cat", "concatenate", "catycat"
Out: "dog", "condogenate", "dogydog"
Example:
String replacement, first occurrence only:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.PatternReplaceFilterFactory" pattern="cat" replacement="dog" replace="
first"/>
</analyzer>
In: "cat concatenate catycat"
Tokenizer to Filter: "cat", "concatenate", "catycat"
Out: "dog", "condogenate", "dogycat"
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Example:
More complex pattern with capture group reference in the replacement. Tokens that start with non-numeric
characters and end with digits will have an underscore inserted before the numbers. Otherwise the token is
passed through.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.PatternReplaceFilterFactory" pattern="(\D+)(\d+)$" replacement="$1_$2"/>
</analyzer>
In: "cat foo1234 9987 blah1234foo"
Tokenizer to Filter: "cat", "foo1234", "9987", "blah1234foo"
Out: "cat", "foo_1234", "9987", "blah1234foo"
Phonetic Filter
This filter creates tokens using one of the phonetic encoding algorithms in the
org.apache.commons.codec.language package. For more information, see the section on Phonetic
Matching.
Factory class: solr.PhoneticFilterFactory
Arguments:
encoder
(required) The name of the encoder to use. The encoder name must be one of the following (case
insensitive): DoubleMetaphone , Metaphone , Soundex , RefinedSoundex , Caverphone (v2.0),
ColognePhonetic , or Nysiis .
inject
(true/false) If true (the default), then new phonetic tokens are added to the stream. Otherwise, tokens
are replaced with the phonetic equivalent. Setting this to false will enable phonetic matching, but the
exact spelling of the target word may not match.
maxCodeLength
(integer) The maximum length of the code to be generated by the Metaphone or Double Metaphone
encoders.
Example:
Default behavior for DoubleMetaphone encoding.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.PhoneticFilterFactory" encoder="DoubleMetaphone"/>
</analyzer>
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In: "four score and twenty"
Tokenizer to Filter: "four"(1), "score"(2), "and"(3), "twenty"(4)
Out: "four"(1), "FR"(1), "score"(2), "SKR"(2), "and"(3), "ANT"(3), "twenty"(4), "TNT"(4)
The phonetic tokens have a position increment of 0, which indicates that they are at the same position as the
token they were derived from (immediately preceding).
Example:
Discard original token.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.PhoneticFilterFactory" encoder="DoubleMetaphone" inject="false"/>
</analyzer>
In: "four score and twenty"
Tokenizer to Filter: "four"(1), "score"(2), "and"(3), "twenty"(4)
Out: "FR"(1), "SKR"(2), "ANT"(3), "TWNT"(4)
Example:
Default Soundex encoder.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.PhoneticFilterFactory" encoder="Soundex"/>
</analyzer>
In: "four score and twenty"
Tokenizer to Filter: "four"(1), "score"(2), "and"(3), "twenty"(4)
Out: "four"(1), "F600"(1), "score"(2), "S600"(2), "and"(3), "A530"(3), "twenty"(4), "T530"(4)
Porter Stem Filter
This filter applies the Porter Stemming Algorithm for English. The results are similar to using the Snowball
Porter Stemmer with the language="English" argument. But this stemmer is coded directly in Java and is
not based on Snowball. It does not accept a list of protected words and is only appropriate for English
language text. However, it has been benchmarked as four times faster than the English Snowball stemmer,
so can provide a performance enhancement.
Factory class: solr.PorterStemFilterFactory
Arguments: None
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Example:
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory "/>
<filter class="solr.PorterStemFilterFactory"/>
</analyzer>
In: "jump jumping jumped"
Tokenizer to Filter: "jump", "jumping", "jumped"
Out: "jump", "jump", "jump"
Remove Duplicates Token Filter
The filter removes duplicate tokens in the stream. Tokens are considered to be duplicates ONLY if they have
the same text and position values.
Because positions must be the same, this filter might not do what a user expects it to do based on its name.
It is a very specialized filter that is only useful in very specific circumstances. It has been so named for
brevity, even though it is potentially misleading.
Factory class: solr.RemoveDuplicatesTokenFilterFactory
Arguments: None
Example:
One example of where RemoveDuplicatesTokenFilterFactory is useful in situations where a synonym file is
being used in conjunction with a stemmer. In these situations, both the stemmer and the synonym filter can
cause completely identical terms with the same positions to end up in the stream, increasing index size with
no benefit.
Consider the following entry from a synonyms.txt file:
Television, Televisions, TV, TVs
When used in the following configuration:
<analyzer type="query">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.SynonymGraphFilterFactory" synonyms="synonyms.txt"/>
<filter class="solr.EnglishMinimalStemFilterFactory"/>
<filter class="solr.RemoveDuplicatesTokenFilterFactory"/>
</analyzer>
In: "Watch TV"
Tokenizer to Synonym Filter: "Watch"(1) "TV"(2)
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Synonym Filter to Stem Filter: "Watch"(1) "Television"(2) "Televisions"(2) "TV"(2) "TVs"(2)
Stem Filter to Remove Dups Filter: "Watch"(1) "Television"(2) "Television"(2) "TV"(2) "TV"(2)
Out: "Watch"(1) "Television"(2) "TV"(2)
Reversed Wildcard Filter
This filter reverses tokens to provide faster leading wildcard and prefix queries. Tokens without wildcards
are not reversed.
Factory class: solr.ReversedWildcardFilterFactory
Arguments:
withOriginal
(boolean) If true, the filter produces both original and reversed tokens at the same positions. If false,
produces only reversed tokens.
maxPosAsterisk
(integer, default = 2) The maximum position of the asterisk wildcard (‘*‘) that triggers the reversal of the
query term. Terms with asterisks at positions above this value are not reversed.
maxPosQuestion
(integer, default = 1) The maximum position of the question mark wildcard (‘?‘) that triggers the reversal
of query term. To reverse only pure suffix queries (queries with a single leading asterisk), set this to 0 and
maxPosAsterisk to 1.
maxFractionAsterisk
(float, default = 0.0) An additional parameter that triggers the reversal if asterisk (‘*‘) position is less than
this fraction of the query token length.
minTrailing
(integer, default = 2) The minimum number of trailing characters in a query token after the last wildcard
character. For good performance this should be set to a value larger than 1.
Example:
<analyzer type="index">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.ReversedWildcardFilterFactory" withOriginal="true"
maxPosAsterisk="2" maxPosQuestion="1" minTrailing="2" maxFractionAsterisk="0"/>
</analyzer>
In: "*foo *bar"
Tokenizer to Filter: "*foo", "*bar"
Out: "oof*", "rab*"
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Shingle Filter
This filter constructs shingles, which are token n-grams, from the token stream. It combines runs of tokens
into a single token.
Factory class: solr.ShingleFilterFactory
Arguments:
minShingleSize
(integer, must be >= 2, default 2) The minimum number of tokens per shingle.
maxShingleSize
(integer, must be >= minShingleSize , default 2) The maximum number of tokens per shingle.
outputUnigrams
(boolean, default true) If true, then each individual token is also included at its original position.
outputUnigramsIfNoShingles
(boolean, default false) If true, then individual tokens will be output if no shingles are possible.
tokenSeparator
(string, default is " ") The string to use when joining adjacent tokens to form a shingle.
Example:
Default behavior.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ShingleFilterFactory"/>
</analyzer>
In: "To be, or what?"
Tokenizer to Filter: "To"(1), "be"(2), "or"(3), "what"(4)
Out: "To"(1), "To be"(1), "be"(2), "be or"(2), "or"(3), "or what"(3), "what"(4)
Example:
A shingle size of four, do not include original token.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ShingleFilterFactory" maxShingleSize="4" outputUnigrams="false"/>
</analyzer>
In: "To be, or not to be."
Tokenizer to Filter: "To"(1), "be"(2), "or"(3), "not"(4), "to"(5), "be"(6)
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Out: "To be"(1), "To be or"(1), "To be or not"(1), "be or"(2), "be or not"(2), "be or not to"(2), "or not"(3), "or
not to"(3), "or not to be"(3), "not to"(4), "not to be"(4), "to be"(5)
Snowball Porter Stemmer Filter
This filter factory instantiates a language-specific stemmer generated by Snowball. Snowball is a software
package that generates pattern-based word stemmers. This type of stemmer is not as accurate as a table-
based stemmer, but is faster and less complex. Table-driven stemmers are labor intensive to create and
maintain and so are typically commercial products.
Solr contains Snowball stemmers for Armenian, Basque, Catalan, Danish, Dutch, English, Finnish, French,
German, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish and Turkish. For
more information on Snowball, visit http://snowball.tartarus.org/.
StopFilterFactory , CommonGramsFilterFactory , and CommonGramsQueryFilterFactory can optionally read
stopwords in Snowball format (specify format="snowball" in the configuration of those FilterFactories).
Factory class: solr.SnowballPorterFilterFactory
Arguments:
language
(default "English") The name of a language, used to select the appropriate Porter stemmer to use. Case is
significant. This string is used to select a package name in the org.tartarus.snowball.ext class
hierarchy.
protected
Path of a text file containing a list of protected words, one per line. Protected words will not be stemmed.
Blank lines and lines that begin with "#" are ignored. This may be an absolute path, or a simple file name
in the Solr conf directory.
Example:
Default behavior:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.SnowballPorterFilterFactory"/>
</analyzer>
In: "flip flipped flipping"
Tokenizer to Filter: "flip", "flipped", "flipping"
Out: "flip", "flip", "flip"
Example:
French stemmer, English words:
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<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.SnowballPorterFilterFactory" language="French"/>
</analyzer>
In: "flip flipped flipping"
Tokenizer to Filter: "flip", "flipped", "flipping"
Out: "flip", "flipped", "flipping"
Example:
Spanish stemmer, Spanish words:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.SnowballPorterFilterFactory" language="Spanish"/>
</analyzer>
In: "cante canta"
Tokenizer to Filter: "cante", "canta"
Out: "cant", "cant"
Standard Filter
This filter removes dots from acronyms and the substring "‘s" from the end of tokens. This filter depends on
the tokens being tagged with the appropriate term-type to recognize acronyms and words with
apostrophes.
Factory class: solr.StandardFilterFactory
Arguments: None
?
This filter is no longer operational in Solr when the luceneMatchVersion (in
solrconfig.xml ) is higher than "3.1".
Stop Filter
This filter discards, or stops analysis of, tokens that are on the given stop words list. A standard stop words
list is included in the Solr conf directory, named stopwords.txt , which is appropriate for typical English
language text.
Factory class: solr.StopFilterFactory
Arguments:
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words
(optional) The path to a file that contains a list of stop words, one per line. Blank lines and lines that begin
with "#" are ignored. This may be an absolute path, or path relative to the Solr conf directory.
format
(optional) If the stopwords list has been formatted for Snowball, you can specify format="snowball" so
Solr can read the stopwords file.
ignoreCase
(true/false, default false) Ignore case when testing for stop words. If true, the stop list should contain
lowercase words.
enablePositionIncrements
if luceneMatchVersion is 4.4 or earlier and enablePositionIncrements="false" , no position holes will
be left by this filter when it removes tokens. This argument is invalid if luceneMatchVersion is 5.0 or
later.
Example:
Case-sensitive matching, capitalized words not stopped. Token positions skip stopped words.
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.StopFilterFactory" words="stopwords.txt"/>
</analyzer>
In: "To be or what?"
Tokenizer to Filter: "To"(1), "be"(2), "or"(3), "what"(4)
Out: "To"(1), "what"(4)
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.StopFilterFactory" words="stopwords.txt" ignoreCase="true"/>
</analyzer>
In: "To be or what?"
Tokenizer to Filter: "To"(1), "be"(2), "or"(3), "what"(4)
Out: "what"(4)
Suggest Stop Filter
Like Stop Filter, this filter discards, or stops analysis of, tokens that are on the given stop words list.
Suggest Stop Filter differs from Stop Filter in that it will not remove the last token unless it is followed by a
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token separator. For example, a query “find the” would preserve the ‘the’ since it was not followed by a
space, punctuation etc., and mark it as a KEYWORD so that following filters will not change or remove it.
By contrast, a query like “find the popsicle” would remove “ the ” as a stopword, since it’s followed by a
space. When using one of the analyzing suggesters, you would normally use the ordinary
StopFilterFactory in your index analyzer and then SuggestStopFilter in your query analyzer.
Factory class: solr.SuggestStopFilterFactory
Arguments:
words
(optional; default: StopAnalyzer#ENGLISH_STOP_WORDS_SET ) The name of a stopwords file to parse.
format
(optional; default: wordset ) Defines how the words file will be parsed. If words is not specified, then
format must not be specified. The valid values for the format option are:
wordset
This is the default format, which supports one word per line (including any intra-word whitespace) and
allows whole line comments begining with the # character. Blank lines are ignored.
snowball
This format allows for multiple words specified on each line, and trailing comments may be specified
using the vertical line ( | ). Blank lines are ignored.
ignoreCase
(optional; default: false) If true, matching is case-insensitive.
Example:
<analyzer type="query">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.SuggestStopFilterFactory" ignoreCase="true"
words="stopwords.txt" format="wordset"/>
</analyzer>
In: "The The"
Tokenizer to Filter: "the"(1), "the"(2)
Out: "the"(2)
Synonym Filter
This filter does synonym mapping. Each token is looked up in the list of synonyms and if a match is found,
then the synonym is emitted in place of the token. The position value of the new tokens are set such they all
occur at the same position as the original token.
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Synonym Filter has been Deprecated
Synonym Filter has been deprecated in favor of Synonym Graph Filter, which is required for
multi-term synonym support.
Factory class: solr.SynonymFilterFactory
For arguments and examples, see the Synonym Graph Filter below.
Synonym Graph Filter
This filter maps single- or multi-token synonyms, producing a fully correct graph output. This filter is a
replacement for the Synonym Filter, which produces incorrect graphs for multi-token synonyms.
If you use this filter during indexing, you must follow it with a Flatten Graph Filter to squash tokens on top of
one another like the Synonym Filter, because the indexer can’t directly consume a graph. To get fully correct
positional queries when your synonym replacements are multiple tokens, you should instead apply
synonyms using this filter at query time.
Factory class: solr.SynonymGraphFilterFactory
Arguments:
synonyms
(required) The path of a file that contains a list of synonyms, one per line. In the (default) solr format -
see the format argument below for alternatives - blank lines and lines that begin with “#” are ignored.
This may be a comma-separated list of absolute paths, or paths relative to the Solr config directory.
There are two ways to specify synonym mappings:
? A comma-separated list of words. If the token matches any of the words, then all the words in the list
are substituted, which will include the original token.
? Two comma-separated lists of words with the symbol "?" between them. If the token matches any
word on the left, then the list on the right is substituted. The original token will not be included unless
it is also in the list on the right.
ignoreCase
(optional; default: false ) If true , synonyms will be matched case-insensitively.
expand
(optional; default: true ) If true , a synonym will be expanded to all equivalent synonyms. If false , all
equivalent synonyms will be reduced to the first in the list.
format
(optional; default: solr ) Controls how the synonyms will be parsed. The short names solr (for
SolrSynonymParser) and wordnet (for WordnetSynonymParser ) are supported, or you may alternatively
supply the name of your own SynonymMap.Builder subclass.
tokenizerFactory
(optional; default: WhitespaceTokenizerFactory ) The name of the tokenizer factory to use when parsing
the synonyms file. Arguments with the name prefix tokenizerFactory.* will be supplied as init params
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to the specified tokenizer factory.
Any arguments not consumed by the synonym filter factory, including those without the
tokenizerFactory.* prefix, will also be supplied as init params to the tokenizer factory.
If tokenizerFactory is specified, then analyzer may not be, and vice versa.
analyzer
(optional; default: WhitespaceTokenizerFactory ) The name of the analyzer class to use when parsing the
synonyms file. If analyzer is specified, then tokenizerFactory may not be, and vice versa.
For the following examples, assume a synonyms file named mysynonyms.txt :
couch,sofa,divan
teh => the
huge,ginormous,humungous => large
small => tiny,teeny,weeny
Example:
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.SynonymGraphFilterFactory" synonyms="mysynonyms.txt"/>
<filter class="solr.FlattenGraphFilterFactory"/> <!-- required on index analyzers after graph
filters -->
</analyzer>
<analyzer type="query">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.SynonymGraphFilterFactory" synonyms="mysynonyms.txt"/>
</analyzer>
In: "teh small couch"
Tokenizer to Filter: "teh"(1), "small"(2), "couch"(3)
Out: "the"(1), "tiny"(2), "teeny"(2), "weeny"(2), "couch"(3), "sofa"(3), "divan"(3)
Example:
In: "teh ginormous, humungous sofa"
Tokenizer to Filter: "teh"(1), "ginormous"(2), "humungous"(3), "sofa"(4)
Out: "the"(1), "large"(2), "large"(3), "couch"(4), "sofa"(4), "divan"(4)
Token Offset Payload Filter
This filter adds the numeric character offsets of the token as a payload value for that token.
Factory class: solr.TokenOffsetPayloadTokenFilterFactory
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Arguments: None
Example:
<analyzer>
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.TokenOffsetPayloadTokenFilterFactory"/>
</analyzer>
In: "bing bang boom"
Tokenizer to Filter: "bing", "bang", "boom"
Out: "bing"[0,4], "bang"[5,9], "boom"[10,14]
Trim Filter
This filter trims leading and/or trailing whitespace from tokens. Most tokenizers break tokens at whitespace,
so this filter is most often used for special situations.
Factory class: solr.TrimFilterFactory
Arguments:
updateOffsets
if luceneMatchVersion is 4.3 or earlier and updateOffsets="true" , trimmed tokens‘ start and end
offsets will be updated to those of the first and last characters (plus one) remaining in the token. This
argument is invalid if luceneMatchVersion is 5.0 or later.
Example:
The PatternTokenizerFactory configuration used here splits the input on simple commas, it does not remove
whitespace.
<analyzer>
<tokenizer class="solr.PatternTokenizerFactory" pattern=","/>
<filter class="solr.TrimFilterFactory"/>
</analyzer>
In: "one, two , three ,four "
Tokenizer to Filter: "one", " two ", " three ", "four "
Out: "one", "two", "three", "four"
Type As Payload Filter
This filter adds the token’s type, as an encoded byte sequence, as its payload.
Factory class: solr.TypeAsPayloadTokenFilterFactory
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Arguments: None
Example:
<analyzer>
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.TypeAsPayloadTokenFilterFactory"/>
</analyzer>
In: "Pay Bob’s I.O.U."
Tokenizer to Filter: "Pay", "Bob’s", "I.O.U."
Out: "Pay"[<ALPHANUM>], "Bob’s"[<APOSTROPHE>], "I.O.U."[<ACRONYM>]
Type Token Filter
This filter blacklists or whitelists a specified list of token types, assuming the tokens have type metadata
associated with them. For example, the UAX29 URL Email Tokenizer emits "<URL>" and "<EMAIL>" typed
tokens, as well as other types. This filter would allow you to pull out only e-mail addresses from text as
tokens, if you wish.
Factory class: solr.TypeTokenFilterFactory
Arguments:
types
Defines the location of a file of types to filter.
useWhitelist
If true, the file defined in types should be used as include list. If false, or undefined, the file defined in
types is used as a blacklist.
enablePositionIncrements
if luceneMatchVersion is 4.3 or earlier and enablePositionIncrements="false" , no position holes will
be left by this filter when it removes tokens. This argument is invalid if luceneMatchVersion is 5.0 or
later.
Example:
<analyzer>
<filter class="solr.TypeTokenFilterFactory" types="stoptypes.txt" useWhitelist="true"/>
</analyzer>
Word Delimiter Filter
This filter splits tokens at word delimiters.
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Word Delimiter Filter has been Deprecated
Word Delimiter Filter has been deprecated in favor of Word Delimiter Graph Filter, which is
required to produce a correct token graph so that e.g. phrase queries can work correctly.
Factory class: solr.WordDelimiterFilterFactory
For a full description, including arguments and examples, see the Word Delimiter Graph Filter below.
Word Delimiter Graph Filter
This filter splits tokens at word delimiters.
If you use this filter during indexing, you must follow it with a Flatten Graph Filter to squash tokens on top of
one another like the Word Delimiter Filter, because the indexer can’t directly consume a graph. To get fully
correct positional queries when tokens are split, you should instead use this filter at query time.
Note: although this filter produces correct token graphs, it cannot consume an input token graph correctly.
The rules for determining delimiters are determined as follows:
? A change in case within a word: "CamelCase" → "Camel", "Case". This can be disabled by setting
splitOnCaseChange="0" .
? A transition from alpha to numeric characters or vice versa: "Gonzo5000" → "Gonzo", "5000" "4500XL"
→ "4500", "XL". This can be disabled by setting splitOnNumerics="0" .
? Non-alphanumeric characters (discarded): "hot-spot" → "hot", "spot"
? A trailing "‘s" is removed: "O’Reilly’s" → "O", "Reilly"
? Any leading or trailing delimiters are discarded: "--hot-spot--" → "hot", "spot"
Factory class: solr.WordDelimiterGraphFilterFactory
Arguments:
generateWordParts
(integer, default 1) If non-zero, splits words at delimiters. For example:"CamelCase", "hot-spot" →
"Camel", "Case", "hot", "spot"
generateNumberParts
(integer, default 1) If non-zero, splits numeric strings at delimiters:"1947-32" →*"1947", "32"
splitOnCaseChange
(integer, default 1) If 0, words are not split on camel-case changes:"BugBlaster-XL" → "BugBlaster", "XL".
Example 1 below illustrates the default (non-zero) splitting behavior.
splitOnNumerics
(integer, default 1) If 0, don’t split words on transitions from alpha to numeric:"FemBot3000" → "Fem",
"Bot3000"
catenateWords
(integer, default 0) If non-zero, maximal runs of word parts will be joined: "hot-spot-sensor’s" →
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"hotspotsensor"
catenateNumbers
(integer, default 0) If non-zero, maximal runs of number parts will be joined: 1947-32" → "194732"
catenateAll
(0/1, default 0) If non-zero, runs of word and number parts will be joined: "Zap-Master-9000" →
"ZapMaster9000"
preserveOriginal
(integer, default 0) If non-zero, the original token is preserved: "Zap-Master-9000" → "Zap-Master-9000",
"Zap", "Master", "9000"
protected
(optional) The pathname of a file that contains a list of protected words that should be passed through
without splitting.
stemEnglishPossessive
(integer, default 1) If 1, strips the possessive ‘s from each subword.
types
(optional) The pathname of a file that contains character ? type mappings, which enable customization
of this filter’s splitting behavior. Recognized character types: LOWER , UPPER , ALPHA , DIGIT , ALPHANUM , and
SUBWORD_DELIM .
The default for any character without a customized mapping is computed from Unicode character
properties. Blank lines and comment lines starting with ‘#‘ are ignored. An example file:
# Don‘t split numbers at ‘$‘, ‘.‘ or ‘,‘
$ => DIGIT
. => DIGIT
\u002C => DIGIT
# Don‘t split on ZWJ: http://en.wikipedia.org/wiki/Zero-width_joiner
\u200D => ALPHANUM
Example:
Default behavior. The whitespace tokenizer is used here to preserve non-alphanumeric characters.
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<analyzer type="index">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.WordDelimiterGraphFilterFactory"/>
<filter class="solr.FlattenGraphFilterFactory"/> <!-- required on index analyzers after graph
filters -->
</analyzer>
<analyzer type="query">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.WordDelimiterGraphFilterFactory"/>
</analyzer>
In: "hot-spot RoboBlaster/9000 100XL"
Tokenizer to Filter: "hot-spot", "RoboBlaster/9000", "100XL"
Out: "hot", "spot", "Robo", "Blaster", "9000", "100", "XL"
Example:
Do not split on case changes, and do not generate number parts. Note that by not generating number parts,
tokens containing only numeric parts are ultimately discarded.
<analyzer type="query">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.WordDelimiterGraphFilterFactory" generateNumberParts="0" splitOnCaseChange
="0"/>
</analyzer>
In: "hot-spot RoboBlaster/9000 100-42"
Tokenizer to Filter: "hot-spot", "RoboBlaster/9000", "100-42"
Out: "hot", "spot", "RoboBlaster", "9000"
Example:
Concatenate word parts and number parts, but not word and number parts that occur in the same token.
<analyzer type="query">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.WordDelimiterGraphFilterFactory" catenateWords="1" catenateNumbers="1"/>
</analyzer>
In: "hot-spot 100+42 XL40"
Tokenizer to Filter: "hot-spot"(1), "100+42"(2), "XL40"(3)
Out: "hot"(1), "spot"(2), "hotspot"(2), "100"(3), "42"(4), "10042"(4), "XL"(5), "40"(6)
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Example:
Concatenate all. Word and/or number parts are joined together.
<analyzer type="query">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.WordDelimiterGraphFilterFactory" catenateAll="1"/>
</analyzer>
In: "XL-4000/ES"
Tokenizer to Filter: "XL-4000/ES"(1)
Out: "XL"(1), "4000"(2), "ES"(3), "XL4000ES"(3)
Example:
Using a protected words list that contains "AstroBlaster" and "XL-5000" (among others).
<analyzer type="query">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.WordDelimiterGraphFilterFactory" protected="protwords.txt"/>
</analyzer>
In: "FooBar AstroBlaster XL-5000 ==ES-34-"
Tokenizer to Filter: "FooBar", "AstroBlaster", "XL-5000", "==ES-34-"
Out: "FooBar", "FooBar", "AstroBlaster", "XL-5000", "ES", "34"
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CharFilterFactories
CharFilter is a component that pre-processes input characters.
CharFilters can be chained like Token Filters and placed in front of a Tokenizer. CharFilters can add, change,
or remove characters while preserving the original character offsets to support features like highlighting.
solr.MappingCharFilterFactory
This filter creates org.apache.lucene.analysis.MappingCharFilter , which can be used for changing one
string to another (for example, for normalizing é to e .).
This filter requires specifying a mapping argument, which is the path and name of a file containing the
mappings to perform.
Example:
<analyzer>
<charFilter class="solr.MappingCharFilterFactory" mapping="mapping-FoldToASCII.txt"/>
<tokenizer ...>
[...]
</analyzer>
Mapping file syntax:
? Comment lines beginning with a hash mark ( # ), as well as blank lines, are ignored.
? Each non-comment, non-blank line consists of a mapping of the form: "source" ? "target"
? Double-quoted source string, optional whitespace, an arrow ( ? ), optional whitespace, double-quoted
target string.
? Trailing comments on mapping lines are not allowed.
? The source string must contain at least one character, but the target string may be empty.
? The following character escape sequences are recognized within source and target strings:
Escape Sequence Resulting Character (ECMA-
48 alias)
Unicode Character Example Mapping Line
\\ \
U+005C
"\\" ? "/"
\" "
U+0022
"\"and\"" ? "‘and‘"
\b
backspace (BS) U+0008
"\b" ? " "
\t
tab (HT) U+0009
"\t" ? ","
\n
newline (LF) U+000A
"\n" ? "<br>"
\f
form feed (FF) U+000C
"\f" ? "\n"
\r
carriage return (CR) U+000D
"\r" ? "/carriage-
return/"
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Escape Sequence Resulting Character (ECMA-
48 alias)
Unicode Character Example Mapping Line
\uXXXX
Unicode char referenced by
the 4 hex digits
U+XXXX
"\uFEFF" ? ""
? A backslash followed by any other character is interpreted as if the character were present without
the backslash.
solr.HTMLStripCharFilterFactory
This filter creates org.apache.solr.analysis.HTMLStripCharFilter . This CharFilter strips HTML from the
input stream and passes the result to another CharFilter or a Tokenizer.
This filter:
? Removes HTML/XML tags while preserving other content.
? Removes attributes within tags and supports optional attribute quoting.
? Removes XML processing instructions, such as: <?foo bar?>
? Removes XML comments.
? Removes XML elements starting with <!>.
? Removes contents of <script> and <style> elements.
? Handles XML comments inside these elements (normal comment processing will not always work).
? Replaces numeric character entities references like A ; or  ; with the corresponding character.
? The terminating ‘;‘ is optional if the entity reference at the end of the input; otherwise the terminating ‘;‘
is mandatory, to avoid false matches on something like "Alpha&Omega Corp".
? Replaces all named character entity references with the corresponding character.
?   ; is replaced with a space instead of the 0xa0 character.
? Newlines are substituted for block-level elements.
? <CDATA> sections are recognized.
? Inline tags, such as <b> , <i> , or <span> will be removed.
? Uppercase character entities like quot , gt , lt and amp are recognized and handled as lowercase.
?
The input need not be an HTML document. The filter removes only constructs that look like
HTML. If the input doesn’t include anything that looks like HTML, the filter won’t remove
any input.
The table below presents examples of HTML stripping.
Input Output
my <a href="www.foo.bar">link</a>
my link
<br>hello<!--comment-→
hello
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Input Output
hello<script><!-- f(‘<!--internal-
→</script>‘); -→</script>
hello
if a<b then print a;
if a<b then print a;
hello <td height=22 nowrap align="left">
hello
a<b A Alpha&Omega Ω a<b A Alpha&Omega Ω
Example:
<analyzer>
<charFilter class="solr.HTMLStripCharFilterFactory"/>
<tokenizer ...>
[...]
</analyzer>
solr.ICUNormalizer2CharFilterFactory
This filter performs pre-tokenization Unicode normalization using ICU4J.
Arguments:
name
A Unicode Normalization Form, one of nfc , nfkc , nfkc_cf . Default is nfkc_cf .
mode
Either compose or decompose . Default is compose . Use decompose with name="nfc" or name="nfkc" to get
NFD or NFKD, respectively.
filter
A UnicodeSet pattern. Codepoints outside the set are always left unchanged. Default is [] (the null set, no
filtering - all codepoints are subject to normalization).
Example:
<analyzer>
<charFilter class="solr.ICUNormalizer2CharFilterFactory"/>
<tokenizer ...>
[...]
</analyzer>
solr.PatternReplaceCharFilterFactory
This filter uses regular expressions to replace or change character patterns.
Arguments:
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pattern
the regular expression pattern to apply to the incoming text.
replacement
the text to use to replace matching patterns.
You can configure this filter in schema.xml like this:
<analyzer>
<charFilter class="solr.PatternReplaceCharFilterFactory"
pattern="([nN][oO]\.)\s*(\d+)" replacement="$1$2"/>
<tokenizer ...>
[...]
</analyzer>
The table below presents examples of regex-based pattern replacement:
Input Pattern Replace
ment
Output Description
see-ing looking
(\w+)(ing) $1
see-ing look Removes "ing" from the end of
word.
see-ing looking
(\w+)ing $1
see-ing look Same as above. 2nd
parentheses can be omitted.
No.1 NO. no. 543
[nN][oO]\.\s*(\d+
)
#$1
#1 NO. #543 Replace some string literals
abc=1234=5678
(\w+)=(\d+)=(\d+) $3=$1=$2 5678=abc=1234
Change the order of the
groups.
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Language Analysis
This section contains information about tokenizers and filters related to character set conversion or for use
with specific languages.
For the European languages, tokenization is fairly straightforward. Tokens are delimited by white space
and/or a relatively small set of punctuation characters.
In other languages the tokenization rules are often not so simple. Some European languages may also
require special tokenization rules, such as rules for decompounding German words.
For information about language detection at index time, see Detecting Languages During Indexing.
KeywordMarkerFilterFactory
Protects words from being modified by stemmers. A customized protected word list may be specified with
the "protected" attribute in the schema. Any words in the protected word list will not be modified by any
stemmer in Solr.
A sample Solr protwords.txt with comments can be found in the sample_techproducts_configs config set
directory:
<fieldtype name="myfieldtype" class="solr.TextField">
<analyzer>
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.KeywordMarkerFilterFactory" protected="protwords.txt" />
<filter class="solr.PorterStemFilterFactory" />
</analyzer>
</fieldtype>
KeywordRepeatFilterFactory
Emits each token twice, one with the KEYWORD attribute and once without.
If placed before a stemmer, the result will be that you will get the unstemmed token preserved on the same
position as the stemmed one. Queries matching the original exact term will get a better score while still
maintaining the recall benefit of stemming. Another advantage of keeping the original token is that wildcard
truncation will work as expected.
To configure, add the KeywordRepeatFilterFactory early in the analysis chain. It is recommended to also
include RemoveDuplicatesTokenFilterFactory to avoid duplicates when tokens are not stemmed.
A sample fieldType configuration could look like this:
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<fieldtype name="english_stem_preserve_original" class="solr.TextField">
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.KeywordRepeatFilterFactory" />
<filter class="solr.PorterStemFilterFactory" />
<filter class="solr.RemoveDuplicatesTokenFilterFactory" />
</analyzer>
</fieldtype>
?
When adding the same token twice, it will also score twice (double), so you may have to re-
tune your ranking rules.
StemmerOverrideFilterFactory
Overrides stemming algorithms by applying a custom mapping, then protecting these terms from being
modified by stemmers.
A customized mapping of words to stems, in a tab-separated file, can be specified to the "dictionary"
attribute in the schema. Words in this mapping will be stemmed to the stems from the file, and will not be
further changed by any stemmer.
A sample stemdict.txt with comments can be found in the Source Repository.
<fieldtype name="myfieldtype" class="solr.TextField">
<analyzer>
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.StemmerOverrideFilterFactory" dictionary="stemdict.txt" />
<filter class="solr.PorterStemFilterFactory" />
</analyzer>
</fieldtype>
Dictionary Compound Word Token Filter
This filter splits, or decompounds, compound words into individual words using a dictionary of the
component words. Each input token is passed through unchanged. If it can also be decompounded into
subwords, each subword is also added to the stream at the same logical position.
Compound words are most commonly found in Germanic languages.
Factory class: solr.DictionaryCompoundWordTokenFilterFactory
Arguments:
dictionary
(required) The path of a file that contains a list of simple words, one per line. Blank lines and lines that
begin with "#" are ignored. This path may be an absolute path, or path relative to the Solr config
directory.
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minWordSize
(integer, default 5) Any token shorter than this is not decompounded.
minSubwordSize
(integer, default 2) Subwords shorter than this are not emitted as tokens.
maxSubwordSize
(integer, default 15) Subwords longer than this are not emitted as tokens.
onlyLongestMatch
(true/false) If true (the default), only the longest matching subwords will generate new tokens.
Example:
Assume that germanwords.txt contains at least the following words: dumm kopf donau dampf schiff
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.DictionaryCompoundWordTokenFilterFactory" dictionary="germanwords.txt"/>
</analyzer>
In: "Donaudampfschiff dummkopf"
Tokenizer to Filter: "Donaudampfschiff"(1), "dummkopf"(2),
Out: "Donaudampfschiff"(1), "Donau"(1), "dampf"(1), "schiff"(1), "dummkopf"(2), "dumm"(2), "kopf"(2)
Unicode Collation
Unicode Collation is a language-sensitive method of sorting text that can also be used for advanced search
purposes.
Unicode Collation in Solr is fast, because all the work is done at index time.
Rather than specifying an analyzer within <fieldtype … class="solr.TextField"> , the
solr.CollationField and solr.ICUCollationField field type classes provide this functionality.
solr.ICUCollationField , which is backed by the ICU4J library, provides more flexible configuration, has
more locales, is significantly faster, and requires less memory and less index space, since its keys are smaller
than those produced by the JDK implementation that backs solr.CollationField .
solr.ICUCollationField is included in the Solr analysis-extras contrib - see solr/contrib/analysis-
extras/README.txt for instructions on which jars you need to add to your SOLR_HOME/lib in order to use it.
solr.ICUCollationField and solr.CollationField fields can be created in two ways:
? Based upon a system collator associated with a Locale.
? Based upon a tailored RuleBasedCollator ruleset.
Arguments for solr.ICUCollationField , specified as attributes within the <fieldtype> element:
Using a System collator:
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locale
(required) RFC 3066 locale ID. See the ICU locale explorer for a list of supported locales.
strength
Valid values are primary , secondary , tertiary , quaternary , or identical . See Comparison Levels in ICU
Collation Concepts for more information.
decomposition
Valid values are no or canonical . See Normalization in ICU Collation Concepts for more information.
Using a Tailored ruleset:
custom
(required) Path to a UTF-8 text file containing rules supported by the ICU RuleBasedCollator
strength
Valid values are primary , secondary , tertiary , quaternary , or identical . See Comparison Levels in ICU
Collation Concepts for more information.
decomposition
Valid values are no or canonical . See Normalization in ICU Collation Concepts for more information.
Expert options:
alternate
Valid values are shifted or non-ignorable . Can be used to ignore punctuation/whitespace.
caseLevel
(true/false) If true, in combination with strength="primary" , accents are ignored but case is taken into
account. The default is false. See CaseLevel in ICU Collation Concepts for more information.
caseFirst
Valid values are lower or upper . Useful to control which is sorted first when case is not ignored.
numeric
(true/false) If true, digits are sorted according to numeric value, e.g. foobar-9 sorts before foobar-10. The
default is false.
variableTop
Single character or contraction. Controls what is variable for alternate .
Sorting Text for a Specific Language
In this example, text is sorted according to the default German rules provided by ICU4J.
Locales are typically defined as a combination of language and country, but you can specify just the
language if you want. For example, if you specify "de" as the language, you will get sorting that works well
for the German language. If you specify "de" as the language and "CH" as the country, you will get German
sorting specifically tailored for Switzerland.
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<!-- Define a field type for German collation -->
<fieldType name="collatedGERMAN" class="solr.ICUCollationField"
locale="de"
strength="primary" />
...
<!-- Define a field to store the German collated manufacturer names. -->
<field name="manuGERMAN" type="collatedGERMAN" indexed="false" stored="false" docValues="true"/>
...
<!-- Copy the text to this field. We could create French, English, Spanish versions too,
and sort differently for different users! -->
<copyField source="manu" dest="manuGERMAN"/>
In the example above, we defined the strength as "primary". The strength of the collation determines how
strict the sort order will be, but it also depends upon the language. For example, in English, "primary"
strength ignores differences in case and accents.
Another example:
<fieldType name="polishCaseInsensitive" class="solr.ICUCollationField"
locale="pl_PL"
strength="secondary" />
...
<field name="city" type="text_general" indexed="true" stored="true"/>
...
<field name="city_sort" type="polishCaseInsensitive" indexed="true" stored="false"/>
...
<copyField source="city" dest="city_sort"/>
The type will be used for the fields where the data contains Polish text. The "secondary" strength will ignore
case differences, but, unlike "primary" strength, a letter with diacritic(s) will be sorted differently from the
same base letter without diacritics.
An example using the "city_sort" field to sort:
q=*:*&fl=city&sort=city_sort+asc
Sorting Text for Multiple Languages
There are two approaches to supporting multiple languages: if there is a small list of languages you wish to
support, consider defining collated fields for each language and using copyField . However, adding a large
number of sort fields can increase disk and indexing costs. An alternative approach is to use the Unicode
default collator.
The Unicode default or ROOT locale has rules that are designed to work well for most languages. To use the
default locale, simply define the locale as the empty string. This Unicode default sort is still significantly
more advanced than the standard Solr sort.
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<fieldType name="collatedROOT" class="solr.ICUCollationField"
locale=""
strength="primary" />
Sorting Text with Custom Rules
You can define your own set of sorting rules. It’s easiest to take existing rules that are close to what you
want and customize them.
In the example below, we create a custom rule set for German called DIN 5007-2. This rule set treats umlauts
in German differently: it treats ? as equivalent to oe, ? as equivalent to ae, and ü as equivalent to ue. For
more information, see the ICU RuleBasedCollator javadocs.
This example shows how to create a custom rule set for solr.ICUCollationField and dump it to a file:
// get the default rules for Germany
// these are called DIN 5007-1 sorting
RuleBasedCollator baseCollator = (RuleBasedCollator) Collator.getInstance(new ULocale("de", "DE"
));
// define some tailorings, to make it DIN 5007-2 sorting.
// For example, this makes ? equivalent to oe
String DIN5007_2_tailorings =
"& ae , a\u0308 & AE , A\u0308"+
"& oe , o\u0308 & OE , O\u0308"+
"& ue , u\u0308 & UE , u\u0308";
// concatenate the default rules to the tailorings, and dump it to a String
RuleBasedCollator tailoredCollator = new RuleBasedCollator(baseCollator.getRules() +
DIN5007_2_tailorings);
String tailoredRules = tailoredCollator.getRules();
// write these to a file, be sure to use UTF-8 encoding!!!
FileOutputStream os = new FileOutputStream(new File("/solr_home/conf/customRules.dat"));
IOUtils.write(tailoredRules, os, "UTF-8");
This rule set can now be used for custom collation in Solr:
<fieldType name="collatedCUSTOM" class="solr.ICUCollationField"
custom="customRules.dat"
strength="primary" />
JDK Collation
As mentioned above, ICU Unicode Collation is better in several ways than JDK Collation, but if you cannot use
ICU4J for some reason, you can use solr.CollationField .
The principles of JDK Collation are the same as those of ICU Collation; you just specify language , country
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and variant arguments instead of the combined locale argument.
Arguments for solr.CollationField , specified as attributes within the <fieldtype> element:
Using a System collator (see Oracle’s list of locales supported in Java 8):
language
(required) ISO-639 language code
country
ISO-3166 country code
variant
Vendor or browser-specific code
strength
Valid values are primary , secondary , tertiary or identical . See Oracle Java 8 Collator javadocs for more
information.
decomposition
Valid values are no , canonical , or full . See Oracle Java 8 Collator javadocs for more information.
Using a Tailored ruleset:
custom
(required) Path to a UTF-8 text file containing rules supported by the JDK RuleBasedCollator
strength
Valid values are primary , secondary , tertiary or identical . See Oracle Java 8 Collator javadocs for more
information.
decomposition
Valid values are no , canonical , or full . See Oracle Java 8 Collator javadocs for more information.
A solr.CollationField example:
<fieldType name="collatedGERMAN" class="solr.CollationField"
language="de"
country="DE"
strength="primary" /> <!-- ignore Umlauts and letter case when sorting -->
...
<field name="manuGERMAN" type="collatedGERMAN" indexed="false" stored="false" docValues="true" />
...
<copyField source="manu" dest="manuGERMAN"/>
ASCII & Decimal Folding Filters
ASCII Folding
This filter converts alphabetic, numeric, and symbolic Unicode characters which are not in the first 127 ASCII
characters (the "Basic Latin" Unicode block) into their ASCII equivalents, if one exists. Only those characters
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with reasonable ASCII alternatives are converted.
This can increase recall by causing more matches. On the other hand, it can reduce precision because
language-specific character differences may be lost.
Factory class: solr.ASCIIFoldingFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ASCIIFoldingFilterFactory"/>
</analyzer>
In: "Bj?rn ?ngstr?m"
Tokenizer to Filter: "Bj?rn", "?ngstr?m"
Out: "Bjorn", "Angstrom"
Decimal Digit Folding
This filter converts any character in the Unicode "Decimal Number" general category ( Nd ) into their
equivalent Basic Latin digits (0-9).
This can increase recall by causing more matches. On the other hand, it can reduce precision because
language-specific character differences may be lost.
Factory class: solr.DecimalDigitFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.DecimalDigitFilterFactory"/>
</analyzer>
Language-Specific Factories
These factories are each designed to work with specific languages. The languages covered here are:
? Arabic
? Brazilian Portuguese
? Bulgarian
? Catalan
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? Traditional Chinese
? Simplified Chinese
? Czech
? Danish
? Dutch
? Finnish
? French
? Galician
? German
? Greek
? Hebrew, Lao, Myanmar, Khmer
? Hindi
? Indonesian
? Italian
? Irish
? Japanese
? Latvian
? Norwegian
? Persian
? Polish
? Portuguese
? Romanian
? Russian
? Scandinavian
? Serbian
? Spanish
? Swedish
? Thai
? Turkish
? Ukrainian
Arabic
Solr provides support for the Light-10 (PDF) stemming algorithm, and Lucene includes an example stopword
list.
This algorithm defines both character normalization and stemming, so these are split into two filters to
provide more flexibility.
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Factory classes: solr.ArabicStemFilterFactory , solr.ArabicNormalizationFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ArabicNormalizationFilterFactory"/>
<filter class="solr.ArabicStemFilterFactory"/>
</analyzer>
Brazilian Portuguese
This is a Java filter written specifically for stemming the Brazilian dialect of the Portuguese language. It uses
the Lucene class org.apache.lucene.analysis.br.BrazilianStemmer . Although that stemmer can be
configured to use a list of protected words (which should not be stemmed), this factory does not accept any
arguments to specify such a list.
Factory class: solr.BrazilianStemFilterFactory
Arguments: None
Example:
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.BrazilianStemFilterFactory"/>
</analyzer>
In: "praia praias"
Tokenizer to Filter: "praia", "praias"
Out: "pra", "pra"
Bulgarian
Solr includes a light stemmer for Bulgarian, following this algorithm (PDF), and Lucene includes an example
stopword list.
Factory class: solr.BulgarianStemFilterFactory
Arguments: None
Example:
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<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.BulgarianStemFilterFactory"/>
</analyzer>
Catalan
Solr can stem Catalan using the Snowball Porter Stemmer with an argument of language="Catalan" . Solr
includes a set of contractions for Catalan, which can be stripped using solr.ElisionFilterFactory .
Factory class: solr.SnowballPorterFilterFactory
Arguments:
language
(required) stemmer language, "Catalan" in this case
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.ElisionFilterFactory"
articles="lang/contractions_ca.txt"/>
<filter class="solr.SnowballPorterFilterFactory" language="Catalan" />
</analyzer>
In: "llengües llengua"
Tokenizer to Filter: "llengües"(1) "llengua"(2),
Out: "llengu"(1), "llengu"(2)
Traditional Chinese
The default configuration of the ICU Tokenizer is suitable for Traditional Chinese text. It follows the Word
Break rules from the Unicode Text Segmentation algorithm for non-Chinese text, and uses a dictionary to
segment Chinese words. To use this tokenizer, you must add additional .jars to Solr’s classpath (as described
in the section Lib Directives in SolrConfig). See the solr/contrib/analysis-extras/README.txt for
information on which jars you need to add to your SOLR_HOME/lib .
Standard Tokenizer can also be used to tokenize Traditional Chinese text. Following the Word Break rules
from the Unicode Text Segmentation algorithm, it produces one token per Chinese character. When
combined with CJK Bigram Filter, overlapping bigrams of Chinese characters are formed.
CJK Width Filter folds fullwidth ASCII variants into the equivalent Basic Latin forms.
Examples:
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<analyzer>
<tokenizer class="solr.ICUTokenizerFactory"/>
<filter class="solr.CJKWidthFilterFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.CJKBigramFilterFactory"/>
<filter class="solr.CJKWidthFilterFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
CJK Bigram Filter
Forms bigrams (overlapping 2-character sequences) of CJK characters that are generated from Standard
Tokenizer or ICU Tokenizer.
By default, all CJK characters produce bigrams, but finer grained control is available by specifying
orthographic type arguments han , hiragana , katakana , and hangul . When set to false , characters of the
corresponding type will be passed through as unigrams, and will not be included in any bigrams.
When a CJK character has no adjacent characters to form a bigram, it is output in unigram form. If you want
to always output both unigrams and bigrams, set the outputUnigrams argument to true .
In all cases, all non-CJK input is passed through unmodified.
Arguments:
han
(true/false) If false, Han (Chinese) characters will not form bigrams. Default is true.
hiragana
(true/false) If false, Hiragana (Japanese) characters will not form bigrams. Default is true.
katakana
(true/false) If false, Katakana (Japanese) characters will not form bigrams. Default is true.
hangul
(true/false) If false, Hangul (Korean) characters will not form bigrams. Default is true.
outputUnigrams
(true/false) If true, in addition to forming bigrams, all characters are also passed through as unigrams.
Default is false.
See the example under Traditional Chinese.
Simplified Chinese
For Simplified Chinese, Solr provides support for Chinese sentence and word segmentation with the HMM
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Chinese Tokenizer. This component includes a large dictionary and segments Chinese text into words with
the Hidden Markov Model. To use this tokenizer, you must add additional .jars to Solr’s classpath (as
described in the section Lib Directives in SolrConfig). See the solr/contrib/analysis-extras/README.txt
for information on which jars you need to add to your SOLR_HOME/lib .
The default configuration of the ICU Tokenizer is also suitable for Simplified Chinese text. It follows the Word
Break rules from the Unicode Text Segmentation algorithm for non-Chinese text, and uses a dictionary to
segment Chinese words. To use this tokenizer, you must add additional .jars to Solr’s classpath (as described
in the section Lib Directives in SolrConfig). See the solr/contrib/analysis-extras/README.txt for
information on which jars you need to add to your SOLR_HOME/lib .
Also useful for Chinese analysis:
CJK Width Filter folds fullwidth ASCII variants into the equivalent Basic Latin forms, and folds halfwidth
Katakana variants into their equivalent fullwidth forms.
Examples:
<analyzer>
<tokenizer class="solr.HMMChineseTokenizerFactory"/>
<filter class="solr.CJKWidthFilterFactory"/>
<filter class="solr.StopFilterFactory"
words="org/apache/lucene/analysis/cn/smart/stopwords.txt"/>
<filter class="solr.PorterStemFilterFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
<analyzer>
<tokenizer class="solr.ICUTokenizerFactory"/>
<filter class="solr.CJKWidthFilterFactory"/>
<filter class="solr.StopFilterFactory"
words="org/apache/lucene/analysis/cn/smart/stopwords.txt"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
HMM Chinese Tokenizer
For Simplified Chinese, Solr provides support for Chinese sentence and word segmentation with the
solr.HMMChineseTokenizerFactory in the analysis-extras contrib module. This component includes a
large dictionary and segments Chinese text into words with the Hidden Markov Model. To use this tokenizer,
see solr/contrib/analysis-extras/README.txt for instructions on which jars you need to add to your
solr_home/lib .
Factory class: solr.HMMChineseTokenizerFactory
Arguments: None
Examples:
To use the default setup with fallback to English Porter stemmer for English words, use:
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<analyzer class="org.apache.lucene.analysis.cn.smart.SmartChineseAnalyzer"/>
Or to configure your own analysis setup, use the solr.HMMChineseTokenizerFactory along with your
custom filter setup. See an example of this in the Simplified Chinese section.
Czech
Solr includes a light stemmer for Czech, following this algorithm, and Lucene includes an example stopword
list.
Factory class: solr.CzechStemFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.CzechStemFilterFactory"/>
<analyzer>
In: "preziden?tí, prezidenta, prezidentského"
Tokenizer to Filter: "preziden?tí", "prezidenta", "prezidentského"
Out: "preziden", "preziden", "preziden"
Danish
Solr can stem Danish using the Snowball Porter Stemmer with an argument of language="Danish" .
Also relevant are the Scandinavian normalization filters.
Factory class: solr.SnowballPorterFilterFactory
Arguments:
language
(required) stemmer language, "Danish" in this case
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.SnowballPorterFilterFactory" language="Danish" />
</analyzer>
In: "unders?g unders?gelse"
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Tokenizer to Filter: "unders?g"(1) "unders?gelse"(2),
Out: "unders?g"(1), "unders?g"(2)
Dutch
Solr can stem Dutch using the Snowball Porter Stemmer with an argument of language="Dutch" .
Factory class: solr.SnowballPorterFilterFactory
Arguments:
language
(required) stemmer language, "Dutch" in this case
Example:
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.SnowballPorterFilterFactory" language="Dutch"/>
</analyzer>
In: "kanaal kanalen"
Tokenizer to Filter: "kanaal", "kanalen"
Out: "kanal", "kanal"
Finnish
Solr includes support for stemming Finnish, and Lucene includes an example stopword list.
Factory class: solr.FinnishLightStemFilterFactory
Arguments: None
Example:
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.FinnishLightStemFilterFactory"/>
</analyzer>
In: "kala kalat"
Tokenizer to Filter: "kala", "kalat"
Out: "kala", "kala"
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French
Elision Filter
Removes article elisions from a token stream. This filter can be useful for languages such as French, Catalan,
Italian, and Irish.
Factory class: solr.ElisionFilterFactory
Arguments:
articles
The pathname of a file that contains a list of articles, one per line, to be stripped. Articles are words such
as "le", which are commonly abbreviated, such as in l’avion (the plane). This file should include the
abbreviated form, which precedes the apostrophe. In this case, simply "l". If no articles attribute is
specified, a default set of French articles is used.
ignoreCase
(boolean) If true, the filter ignores the case of words when comparing them to the common word file.
Defaults to false
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ElisionFilterFactory"
ignoreCase="true"
articles="lang/contractions_fr.txt"/>
</analyzer>
In: "L’histoire d’art"
Tokenizer to Filter: "L’histoire", "d’art"
Out: "histoire", "art"
French Light Stem Filter
Solr includes three stemmers for French: one in the solr.SnowballPorterFilterFactory , a lighter stemmer
called solr.FrenchLightStemFilterFactory , and an even less aggressive stemmer called
solr.FrenchMinimalStemFilterFactory . Lucene includes an example stopword list.
Factory classes: solr.FrenchLightStemFilterFactory , solr.FrenchMinimalStemFilterFactory
Arguments: None
Examples:
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<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.ElisionFilterFactory"
articles="lang/contractions_fr.txt"/>
<filter class="solr.FrenchLightStemFilterFactory"/>
</analyzer>
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.ElisionFilterFactory"
articles="lang/contractions_fr.txt"/>
<filter class="solr.FrenchMinimalStemFilterFactory"/>
</analyzer>
In: "le chat, les chats"
Tokenizer to Filter: "le", "chat", "les", "chats"
Out: "le", "chat", "le", "chat"
Galician
Solr includes a stemmer for Galician following this algorithm, and Lucene includes an example stopword list.
Factory class: solr.GalicianStemFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.GalicianStemFilterFactory"/>
</analyzer>
In: "felizmente Luzes"
Tokenizer to Filter: "felizmente", "luzes"
Out: "feliz", "luz"
German
Solr includes four stemmers for German: one in the solr.SnowballPorterFilterFactory
language="German" , a stemmer called solr.GermanStemFilterFactory , a lighter stemmer called
solr.GermanLightStemFilterFactory , and an even less aggressive stemmer called
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solr.GermanMinimalStemFilterFactory . Lucene includes an example stopword list.
Factory classes: solr.GermanStemFilterFactory , solr.LightGermanStemFilterFactory ,
solr.MinimalGermanStemFilterFactory
Arguments: None
Examples:
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory "/>
<filter class="solr.GermanStemFilterFactory"/>
</analyzer>
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.GermanLightStemFilterFactory"/>
</analyzer>
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory "/>
<filter class="solr.GermanMinimalStemFilterFactory"/>
</analyzer>
In: "haus h?user"
Tokenizer to Filter: "haus", "h?user"
Out: "haus", "haus"
Greek
This filter converts uppercase letters in the Greek character set to the equivalent lowercase character.
Factory class: solr.GreekLowerCaseFilterFactory
Arguments: None
?
Use of custom charsets is no longer supported as of Solr 3.1. If you need to index text in
these encodings, please use Java’s character set conversion facilities (InputStreamReader,
etc.) during I/O, so that Lucene can analyze this text as Unicode instead.
Example:
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.GreekLowerCaseFilterFactory"/>
</analyzer>
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Hindi
Solr includes support for stemming Hindi following this algorithm (PDF), support for common spelling
differences through the solr.HindiNormalizationFilterFactory , support for encoding differences
through the solr.IndicNormalizationFilterFactory following this algorithm, and Lucene includes an
example stopword list.
Factory classes: solr.IndicNormalizationFilterFactory , solr.HindiNormalizationFilterFactory ,
solr.HindiStemFilterFactory
Arguments: None
Example:
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.IndicNormalizationFilterFactory"/>
<filter class="solr.HindiNormalizationFilterFactory"/>
<filter class="solr.HindiStemFilterFactory"/>
</analyzer>
Indonesian
Solr includes support for stemming Indonesian (Bahasa Indonesia) following this algorithm (PDF), and
Lucene includes an example stopword list.
Factory class: solr.IndonesianStemFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.IndonesianStemFilterFactory" stemDerivational="true" />
</analyzer>
In: "sebagai sebagainya"
Tokenizer to Filter: "sebagai", "sebagainya"
Out: "bagai", "bagai"
Italian
Solr includes two stemmers for Italian: one in the solr.SnowballPorterFilterFactory
language="Italian" , and a lighter stemmer called solr.ItalianLightStemFilterFactory . Lucene includes
an example stopword list.
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Factory class: solr.ItalianStemFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.ElisionFilterFactory"
articles="lang/contractions_it.txt"/>
<filter class="solr.ItalianLightStemFilterFactory"/>
</analyzer>
In: "propaga propagare propagamento"
Tokenizer to Filter: "propaga", "propagare", "propagamento"
Out: "propag", "propag", "propag"
Irish
Solr can stem Irish using the Snowball Porter Stemmer with an argument of language="Irish" . Solr
includes solr.IrishLowerCaseFilterFactory , which can handle Irish-specific constructs. Solr also includes
a set of contractions for Irish which can be stripped using solr.ElisionFilterFactory .
Factory class: solr.SnowballPorterFilterFactory
Arguments:
language
(required) stemmer language, "Irish" in this case
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ElisionFilterFactory"
articles="lang/contractions_ga.txt"/>
<filter class="solr.IrishLowerCaseFilterFactory"/>
<filter class="solr.SnowballPorterFilterFactory" language="Irish" />
</analyzer>
In: "siopadóireacht síceapatacha b’fhearr m’athair"
Tokenizer to Filter: "siopadóireacht", "síceapatacha", "b’fhearr", "m’athair"
Out: "siopadóir", "síceapaite", "fearr", "athair"
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Japanese
Solr includes support for analyzing Japanese, via the Lucene Kuromoji morphological analyzer, which
includes several analysis components - more details on each below:
? JapaneseIterationMarkCharFilter normalizes Japanese horizontal iteration marks (odoriji) to their
expanded form.
? JapaneseTokenizer tokenizes Japanese using morphological analysis, and annotates each term with
part-of-speech, base form (a.k.a. lemma), reading and pronunciation.
? JapaneseBaseFormFilter replaces original terms with their base forms (a.k.a. lemmas).
? JapanesePartOfSpeechStopFilter removes terms that have one of the configured parts-of-speech.
? JapaneseKatakanaStemFilter normalizes common katakana spelling variations ending in a long sound
character (U+30FC) by removing the long sound character.
Also useful for Japanese analysis, from lucene-analyzers-common:
? CJKWidthFilter folds fullwidth ASCII variants into the equivalent Basic Latin forms, and folds halfwidth
Katakana variants into their equivalent fullwidth forms.
Japanese Iteration Mark CharFilter
Normalizes horizontal Japanese iteration marks (odoriji) to their expanded form. Vertical iteration marks are
not supported.
Factory class: JapaneseIterationMarkCharFilterFactory
Arguments:
normalizeKanji
set to false to not normalize kanji iteration marks (default is true )
normalizeKana
set to false to not normalize kana iteration marks (default is true )
Japanese Tokenizer
Tokenizer for Japanese that uses morphological analysis, and annotates each term with part-of-speech, base
form (a.k.a. lemma), reading and pronunciation.
JapaneseTokenizer has a search mode (the default) that does segmentation useful for search: a heuristic is
used to segment compound terms into their constituent parts while also keeping the original compound
terms as synonyms.
Factory class: solr.JapaneseTokenizerFactory
Arguments:
mode
Use search mode to get a noun-decompounding effect useful for search. search mode improves
segmentation for search at the expense of part-of-speech accuracy. Valid values for mode are:
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? normal : default segmentation
? search : segmentation useful for search (extra compound splitting)
? extended : search mode plus unigramming of unknown words (experimental)
For some applications it might be good to use search mode for indexing and normal mode for
queries to increase precision and prevent parts of compounds from being matched and highlighted.
userDictionary
filename for a user dictionary, which allows overriding the statistical model with your own entries for
segmentation, part-of-speech tags and readings without a need to specify weights. See
lang/userdict_ja.txt for a sample user dictionary file.
userDictionaryEncoding
user dictionary encoding (default is UTF-8)
discardPunctuation
set to false to keep punctuation, true to discard (the default)
Japanese Base Form Filter
Replaces original terms‘ text with the corresponding base form (lemma). ( JapaneseTokenizer annotates
each term with its base form.)
Factory class: JapaneseBaseFormFilterFactory
(no arguments)
Japanese Part Of Speech Stop Filter
Removes terms with one of the configured parts-of-speech. JapaneseTokenizer annotates terms with parts-
of-speech.
Factory class : JapanesePartOfSpeechStopFilterFactory
Arguments:
tags
filename for a list of parts-of-speech for which to remove terms; see conf/lang/stoptags_ja.txt in the
sample_techproducts_config config set for an example.
enablePositionIncrements
if luceneMatchVersion is 4.3 or earlier and enablePositionIncrements="false" , no position holes will
be left by this filter when it removes tokens. This argument is invalid if luceneMatchVersion is 5.0 or
later.
Japanese Katakana Stem Filter
Normalizes common katakana spelling variations ending in a long sound character (U+30FC) by removing
the long sound character.
solr.CJKWidthFilterFactory should be specified prior to this filter to normalize half-width katakana to full-
width.
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Factory class: JapaneseKatakanaStemFilterFactory
Arguments:
minimumLength
terms below this length will not be stemmed. Default is 4, value must be 2 or more.
CJK Width Filter
Folds fullwidth ASCII variants into the equivalent Basic Latin forms, and folds halfwidth Katakana variants
into their equivalent fullwidth forms.
Factory class: CJKWidthFilterFactory
(no arguments)
Example:
<fieldType name="text_ja" positionIncrementGap="100" autoGeneratePhraseQueries="false">
<analyzer>
<!-- Uncomment if you need to handle iteration marks: -->
<!-- <charFilter class="solr.JapaneseIterationMarkCharFilterFactory" /> -->
<tokenizer class="solr.JapaneseTokenizerFactory" mode="search" userDictionary=
"lang/userdict_ja.txt"/>
<filter class="solr.JapaneseBaseFormFilterFactory"/>
<filter class="solr.JapanesePartOfSpeechStopFilterFactory" tags="lang/stoptags_ja.txt"/>
<filter class="solr.CJKWidthFilterFactory"/>
<filter class="solr.StopFilterFactory" ignoreCase="true" words="lang/stopwords_ja.txt"/>
<filter class="solr.JapaneseKatakanaStemFilterFactory" minimumLength="4"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
</fieldType>
Hebrew, Lao, Myanmar, Khmer
Lucene provides support, in addition to UAX#29 word break rules, for Hebrew’s use of the double and single
quote characters, and for segmenting Lao, Myanmar, and Khmer into syllables with the
solr.ICUTokenizerFactory in the analysis-extras contrib module. To use this tokenizer, see
solr/contrib/analysis-extras/README.txt for instructions on which jars you need to add to your
solr_home/lib .
See the ICUTokenizer for more information.
Latvian
Solr includes support for stemming Latvian, and Lucene includes an example stopword list.
Factory class: solr.LatvianStemFilterFactory
Arguments: None
Example:
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<fieldType name="text_lvstem" class="solr.TextField" positionIncrementGap="100">
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.LatvianStemFilterFactory"/>
</analyzer>
</fieldType>
In: "tirgiem tirgus"
Tokenizer to Filter: "tirgiem", "tirgus"
Out: "tirg", "tirg"
Norwegian
Solr includes two classes for stemming Norwegian, NorwegianLightStemFilterFactory and
NorwegianMinimalStemFilterFactory . Lucene includes an example stopword list.
Another option is to use the Snowball Porter Stemmer with an argument of language="Norwegian".
Also relevant are the Scandinavian normalization filters.
Norwegian Light Stemmer
The NorwegianLightStemFilterFactory requires a "two-pass" sort for the -dom and -het endings. This
means that in the first pass the word "kristendom" is stemmed to "kristen", and then all the general rules
apply so it will be further stemmed to "krist". The effect of this is that "kristen," "kristendom,"
"kristendommen," and "kristendommens" will all be stemmed to "krist."
The second pass is to pick up -dom and -het endings. Consider this example:
One pass Two passes
Before After Before After
forlegen forleg forlegen forleg
forlegenhet forlegen forlegenhet forleg
forlegenheten forlegen forlegenheten forleg
forlegenhetens forlegen forlegenhetens forleg
firkantet firkant firkantet firkant
firkantethet firkantet firkantethet firkant
firkantetheten firkantet firkantetheten firkant
Factory class: solr.NorwegianLightStemFilterFactory
Arguments:
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variant
Choose the Norwegian language variant to use. Valid values are:
? nb: Bokm?l (default)
? nn: Nynorsk
? no: both
Example:
<fieldType name="text_no" class="solr.TextField" positionIncrementGap="100">
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.StopFilterFactory" ignoreCase="true" words="lang/stopwords_no.txt"
format="snowball"/>
<filter class="solr.NorwegianLightStemFilterFactory"/>
</analyzer>
</fieldType>
In: "Forelskelsen"
Tokenizer to Filter: "forelskelsen"
Out: "forelske"
Norwegian Minimal Stemmer
The NorwegianMinimalStemFilterFactory stems plural forms of Norwegian nouns only.
Factory class: solr.NorwegianMinimalStemFilterFactory
Arguments:
variant
Choose the Norwegian language variant to use. Valid values are:
? nb: Bokm?l (default)
? nn: Nynorsk
? no: both
Example:
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<fieldType name="text_no" class="solr.TextField" positionIncrementGap="100">
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.StopFilterFactory" ignoreCase="true" words="lang/stopwords_no.txt"
format="snowball"/>
<filter class="solr.NorwegianMinimalStemFilterFactory"/>
</analyzer>
</fieldType>
In: "Bilens"
Tokenizer to Filter: "bilens"
Out: "bil"
Persian
Persian Filter Factories
Solr includes support for normalizing Persian, and Lucene includes an example stopword list.
Factory class: solr.PersianNormalizationFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ArabicNormalizationFilterFactory"/>
<filter class="solr.PersianNormalizationFilterFactory">
</analyzer>
Polish
Solr provides support for Polish stemming with the solr.StempelPolishStemFilterFactory , and
solr.MorphologikFilterFactory for lemmatization, in the contrib/analysis-extras module. The
solr.StempelPolishStemFilterFactory component includes an algorithmic stemmer with tables for Polish.
To use either of these filters, see solr/contrib/analysis-extras/README.txt for instructions on which jars
you need to add to your solr_home/lib .
Factory class: solr.StempelPolishStemFilterFactory and solr.MorfologikFilterFactory
Arguments: None
Example:
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<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.StempelPolishStemFilterFactory"/>
</analyzer>
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.MorfologikFilterFactory" dictionary="
morfologik/stemming/polish/polish.dict"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
In: ""studenta studenci"
Tokenizer to Filter: "studenta", "studenci"
Out: "student", "student"
More information about the Stempel stemmer is available in the Lucene javadocs.
Note the lower case filter is applied after the Morfologik stemmer; this is because the Polish dictionary
contains proper names and then proper term case may be important to resolve disambiguities (or even
lookup the correct lemma at all).
The Morfologik dictionary parameter value is a constant specifying which dictionary to choose. The
dictionary resource must be named path/to/language.dict and have an associated .info metadata file.
See the Morfologik project for details. If the dictionary attribute is not provided, the Polish dictionary is
loaded and used by default.
Portuguese
Solr includes four stemmers for Portuguese: one in the solr.SnowballPorterFilterFactory , an alternative
stemmer called solr.PortugueseStemFilterFactory , a lighter stemmer called
solr.PortugueseLightStemFilterFactory , and an even less aggressive stemmer called
solr.PortugueseMinimalStemFilterFactory . Lucene includes an example stopword list.
Factory classes: solr.PortugueseStemFilterFactory , solr.PortugueseLightStemFilterFactory ,
solr.PortugueseMinimalStemFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.PortugueseStemFilterFactory"/>
</analyzer>
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<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.PortugueseLightStemFilterFactory"/>
</analyzer>
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.PortugueseMinimalStemFilterFactory"/>
</analyzer>
In: "praia praias"
Tokenizer to Filter: "praia", "praias"
Out: "pra", "pra"
Romanian
Solr can stem Romanian using the Snowball Porter Stemmer with an argument of language="Romanian" .
Factory class: solr.SnowballPorterFilterFactory
Arguments:
language
(required) stemmer language, "Romanian" in this case
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.SnowballPorterFilterFactory" language="Romanian" />
</analyzer>
Russian
Russian Stem Filter
Solr includes two stemmers for Russian: one in the solr.SnowballPorterFilterFactory
language="Russian" , and a lighter stemmer called solr.RussianLightStemFilterFactory . Lucene includes
an example stopword list.
Factory class: solr.RussianLightStemFilterFactory
Arguments: None
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Example:
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.RussianLightStemFilterFactory"/>
</analyzer>
Scandinavian
Scandinavian is a language group spanning three languages Norwegian, Swedish and Danish which are very
similar.
Swedish ?, ?, ? are in fact the same letters as Norwegian and Danish ?, ?, ? and thus interchangeable when
used between these languages. They are however folded differently when people type them on a keyboard
lacking these characters.
In that situation almost all Swedish people use a, a, o instead of ?, ?, ?. Norwegians and Danes on the other
hand usually type aa, ae and oe instead of ?, ? and ?. Some do however use a, a, o, oo, ao and sometimes
permutations of everything above.
There are two filters for helping with normalization between Scandinavian languages: one is
solr.ScandinavianNormalizationFilterFactory trying to preserve the special characters (????) and
another solr.ScandinavianFoldingFilterFactory which folds these to the more broad ?/?→o etc.
See also each language section for other relevant filters.
Scandinavian Normalization Filter
This filter normalize use of the interchangeable Scandinavian characters ???????? and folded variants
(aa, ao, ae, oe and oo) by transforming them to ??????.
It’s a semantically less destructive solution than ScandinavianFoldingFilter , most useful when a person
with a Norwegian or Danish keyboard queries a Swedish index and vice versa. This filter does not perform
the common Swedish folds of ? and ? to a nor ? to o.
Factory class: solr.ScandinavianNormalizationFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.ScandinavianNormalizationFilterFactory"/>
</analyzer>
In: "bl?b?rsyltet?j bl?b?rsyltet?j blaabaarsyltetoej blabarsyltetoj"
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Tokenizer to Filter: "bl?b?rsyltet?j", "bl?b?rsyltet?j", "blaabaersyltetoej", "blabarsyltetoj"
Out: "bl?b?rsyltet?j", "bl?b?rsyltet?j", "bl?b?rsyltet?j", "blabarsyltetoj"
Scandinavian Folding Filter
This filter folds Scandinavian characters ??????→a and ????→o. It also discriminate against use of
double vowels aa, ae, ao, oe and oo, leaving just the first one.
It’s a semantically more destructive solution than ScandinavianNormalizationFilter , but can in addition
help with matching raksmorgas as r?ksm?rg?s.
Factory class: solr.ScandinavianFoldingFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.ScandinavianFoldingFilterFactory"/>
</analyzer>
In: "bl?b?rsyltet?j bl?b?rsyltet?j blaabaarsyltetoej blabarsyltetoj"
Tokenizer to Filter: "bl?b?rsyltet?j", "bl?b?rsyltet?j", "blaabaersyltetoej", "blabarsyltetoj"
Out: "blabarsyltetoj", "blabarsyltetoj", "blabarsyltetoj", "blabarsyltetoj"
Serbian
Serbian Normalization Filter
Solr includes a filter that normalizes Serbian Cyrillic and Latin characters. Note that this filter only works with
lowercased input.
See the Solr wiki for tips & advice on using this filter: https://wiki.apache.org/solr/SerbianLanguageSupport
Factory class: solr.SerbianNormalizationFilterFactory
Arguments:
haircut
Select the extend of normalization. Valid values are:
? bald : (Default behavior) Cyrillic characters are first converted to Latin; then, Latin characters have
their diacritics removed, with the exception of LATIN SMALL LETTER D WITH STROKE (U+0111) which is
converted to “ dj ”
? regular : Only Cyrillic to Latin normalization will be applied, preserving the Latin diatrics
Example:
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<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.SerbianNormalizationFilterFactory" haircut="bald"/>
</analyzer>
Spanish
Solr includes two stemmers for Spanish: one in the solr.SnowballPorterFilterFactory
language="Spanish" , and a lighter stemmer called solr.SpanishLightStemFilterFactory . Lucene includes
an example stopword list.
Factory class: solr.SpanishStemFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.SpanishLightStemFilterFactory"/>
</analyzer>
In: "torear toreara torearlo"
Tokenizer to Filter: "torear", "toreara", "torearlo"
Out: "tor", "tor", "tor"
Swedish
Swedish Stem Filter
Solr includes two stemmers for Swedish: one in the solr.SnowballPorterFilterFactory
language="Swedish" , and a lighter stemmer called solr.SwedishLightStemFilterFactory . Lucene includes
an example stopword list.
Also relevant are the Scandinavian normalization filters.
Factory class: solr.SwedishStemFilterFactory
Arguments: None
Example:
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<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.SwedishLightStemFilterFactory"/>
</analyzer>
In: "kloke klokhet klokheten"
Tokenizer to Filter: "kloke", "klokhet", "klokheten"
Out: "klok", "klok", "klok"
Thai
This filter converts sequences of Thai characters into individual Thai words. Unlike European languages, Thai
does not use whitespace to delimit words.
Factory class: solr.ThaiTokenizerFactory
Arguments: None
Example:
<analyzer type="index">
<tokenizer class="solr.ThaiTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
Turkish
Solr includes support for stemming Turkish with the solr.SnowballPorterFilterFactory ; support for case-
insensitive search with the solr.TurkishLowerCaseFilterFactory ; support for stripping apostrophes and
following suffixes with solr.ApostropheFilterFactory (see Role of Apostrophes in Turkish Information
Retrieval); support for a form of stemming that truncating tokens at a configurable maximum length
through the solr.TruncateTokenFilterFactory (see Information Retrieval on Turkish Texts); and Lucene
includes an example stopword list.
Factory class: solr.TurkishLowerCaseFilterFactory
Arguments: None
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ApostropheFilterFactory"/>
<filter class="solr.TurkishLowerCaseFilterFactory"/>
<filter class="solr.SnowballPorterFilterFactory" language="Turkish"/>
</analyzer>
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Another example, illustrating diacritics-insensitive search:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ApostropheFilterFactory"/>
<filter class="solr.TurkishLowerCaseFilterFactory"/>
<filter class="solr.ASCIIFoldingFilterFactory" preserveOriginal="true"/>
<filter class="solr.KeywordRepeatFilterFactory"/>
<filter class="solr.TruncateTokenFilterFactory" prefixLength="5"/>
<filter class="solr.RemoveDuplicatesTokenFilterFactory"/>
</analyzer>
[[LanguageAnalysis-BacktoTop#main]] ===
Ukrainian
Solr provides support for Ukrainian lemmatization with the solr.MorphologikFilterFactory , in the
contrib/analysis-extras module. To use this filter, see solr/contrib/analysis-extras/README.txt for
instructions on which jars you need to add to your solr_home/lib .
Lucene also includes an example Ukrainian stopword list, in the lucene-analyzers-morfologik jar.
Factory class: solr.MorfologikFilterFactory
Arguments:
dictionary
(required) lemmatizer dictionary - the lucene-analyzers-morfologik jar contains a Ukrainian dictionary
at org/apache/lucene/analysis/uk/ukrainian.dict .
Example:
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.StopFilterFactory" words="org/apache/lucene/analysis/uk/stopwords.txt"/>
<filter class="solr.MorfologikFilterFactory" dictionary=
"org/apache/lucene/analysis/uk/ukrainian.dict"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
Note the lower case filter is applied after the Morfologik stemmer; this is because the Ukrainian dictionary
contains proper names and then proper term case may be important to resolve disambiguities (or even
lookup the correct lemma at all).
The Morfologik dictionary param value is a constant specifying which dictionary to choose. The dictionary
resource must be named path/to/language.dict and have an associated .info metadata file. See the
Morfologik project for details. If the dictionary attribute is not provided, the Polish dictionary is loaded and
used by default.
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Phonetic Matching
Phonetic matching algorithms may be used to encode tokens so that two different spellings that are
pronounced similarly will match.
For overviews of and comparisons between algorithms, see http://en.wikipedia.org/wiki/Phonetic_algorithm
and http://ntz-develop.blogspot.com/2011/03/phonetic-algorithms.html
Beider-Morse Phonetic Matching (BMPM)
For examples of how to use this encoding in your analyzer, see Beider Morse Filter in the Filter Descriptions
section.
Beider-Morse Phonetic Matching (BMPM) is a "soundalike" tool that lets you search using a new phonetic
matching system. BMPM helps you search for personal names (or just surnames) in a Solr/Lucene index,
and is far superior to the existing phonetic codecs, such as regular soundex, metaphone, caverphone, etc.
In general, phonetic matching lets you search a name list for names that are phonetically equivalent to the
desired name. BMPM is similar to a soundex search in that an exact spelling is not required. Unlike soundex,
it does not generate a large quantity of false hits.
From the spelling of the name, BMPM attempts to determine the language. It then applies phonetic rules for
that particular language to transliterate the name into a phonetic alphabet. If it is not possible to determine
the language with a fair degree of certainty, it uses generic phonetic instead. Finally, it applies language-
independent rules regarding such things as voiced and unvoiced consonants and vowels to further insure
the reliability of the matches.
For example, assume that the matches found when searching for Stephen in a database are "Stefan",
"Steph", "Stephen", "Steve", "Steven", "Stove", and "Stuffin". "Stefan", "Stephen", and "Steven" are
probably relevant, and are names that you want to see. "Stuffin", however, is probably not relevant. Also
rejected were "Steph", "Steve", and "Stove". Of those, "Stove" is probably not one that we would have
wanted. But "Steph" and "Steve" are possibly ones that you might be interested in.
For Solr, BMPM searching is available for the following languages:
? English
? French
? German
? Greek
? Hebrew written in Hebrew letters
? Hungarian
? Italian
? Polish
? Romanian
? Russian written in Cyrillic letters
? Russian transliterated into English letters
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? Spanish
? Turkish
The name matching is also applicable to non-Jewish surnames from the countries in which those languages
are spoken.
For more information, see here: http://stevemorse.org/phoneticinfo.htm and
http://stevemorse.org/phonetics/bmpm.htm.
Daitch-Mokotoff Soundex
To use this encoding in your analyzer, see Daitch-Mokotoff Soundex Filter in the Filter Descriptions section.
The Daitch-Mokotoff Soundex algorithm is a refinement of the Russel and American Soundex algorithms,
yielding greater accuracy in matching especially Slavic and Yiddish surnames with similar pronunciation but
differences in spelling.
The main differences compared to the other soundex variants are:
? coded names are 6 digits long
? initial character of the name is coded
? rules to encoded multi-character n-grams
? multiple possible encodings for the same name (branching)
Note: the implementation used by Solr (commons-codec’s DaitchMokotoffSoundex ) has additional
branching rules compared to the original description of the algorithm.
For more information, see http://en.wikipedia.org/wiki/Daitch%E2%80%93Mokotoff_Soundex and
http://www.avotaynu.com/soundex.htm
Double Metaphone
To use this encoding in your analyzer, see Double Metaphone Filter in the Filter Descriptions section.
Alternatively, you may specify encoding="DoubleMetaphone" with the Phonetic Filter, but note that the
Phonetic Filter version will not provide the second ("alternate") encoding that is generated by the Double
Metaphone Filter for some tokens.
Encodes tokens using the double metaphone algorithm by Lawrence Philips. See the original article at
http://www.drdobbs.com/the-double-metaphone-search-algorithm/184401251?pgno=2
Metaphone
To use this encoding in your analyzer, specify encoding="Metaphone" with the Phonetic Filter.
Encodes tokens using the Metaphone algorithm by Lawrence Philips, described in "Hanging on the
Metaphone" in Computer Language, Dec. 1990.
Another reference for more information is Double Metaphone Search Algorithm, by Lawrence Philips.
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Soundex
To use this encoding in your analyzer, specify encoding="Soundex" with the Phonetic Filter.
Encodes tokens using the Soundex algorithm, which is used to relate similar names, but can also be used as
a general purpose scheme to find words with similar phonemes.
See also http://en.wikipedia.org/wiki/Soundex.
Refined Soundex
To use this encoding in your analyzer, specify encoding="RefinedSoundex" with the Phonetic Filter.
Encodes tokens using an improved version of the Soundex algorithm.
See http://en.wikipedia.org/wiki/Soundex.
Caverphone
To use this encoding in your analyzer, specify encoding="Caverphone" with the Phonetic Filter.
Caverphone is an algorithm created by the Caversham Project at the University of Otago. The algorithm is
optimised for accents present in the southern part of the city of Dunedin, New Zealand.
See http://en.wikipedia.org/wiki/Caverphone and the Caverphone 2.0 specification at
http://caversham.otago.ac.nz/files/working/ctp150804.pdf
K?lner Phonetik a.k.a. Cologne Phonetic
To use this encoding in your analyzer, specify encoding="ColognePhonetic" with the Phonetic Filter.
The K?lner Phonetik, an algorithm published by Hans Joachim Postel in 1969, is optimized for the German
language.
See http://de.wikipedia.org/wiki/K%C3%B6lner_Phonetik
NYSIIS
To use this encoding in your analyzer, specify encoding="Nysiis" with the Phonetic Filter.
NYSIIS is an encoding used to relate similar names, but can also be used as a general purpose scheme to
find words with similar phonemes.
See http://en.wikipedia.org/wiki/NYSIIS and http://www.dropby.com/NYSIIS.html
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Running Your Analyzer
Once you’ve defined a field type in your Schema, and specified the analysis steps that you want applied to it,
you should test it out to make sure that it behaves the way you expect it to.
Luckily, there is a very handy page in the Solr admin interface that lets you do just that. You can invoke the
analyzer for any text field, provide sample input, and display the resulting token stream.
For example, let’s look at some of the "Text" field types available in the bin/solr -e techproducts
example configuration, and use the Analysis Screen
( http://localhost:8983/solr/#/techproducts/analysis ) to compare how the tokens produced at index
time for the sentence "Running an Analyzer" match up with a slightly different query text of "run my
analyzer"
We can begin with “ text_ws ” - one of the most simplified Text field types available:
By looking at the start and end positions for each term, we can see that the only thing this field type does is
tokenize text on whitespace. Notice in this image that the term "Running" has a start position of 0 and an
end position of 7, while "an" has a start position of 8 and an end position of 10, and "Analyzer" starts at 11
and ends at 19. If the whitespace between the terms was also included, the count would be 21; since it is 19,
we know that whitespace has been removed from this query.
Note also that the indexed terms and the query terms are still very different. "Running" doesn’t match
"run", "Analyzer" doesn’t match "analyzer" (to a computer), and obviously "an" and "my" are totally
different words. If our objective is to allow queries like "run my analyzer" to match indexed text like
"Running an Analyzer" then we will evidently need to pick a different field type with index & query time text
analysis that does more processing of the inputs.
In particular we will want:
? Case insensitivity, so "Analyzer" and "analyzer" match.
? Stemming, so words like "Run" and "Running" are considered equivalent terms.
? Stop Word Pruning, so small words like "an" and "my" don’t affect the query.
For our next attempt, let’s try the “ text_general ” field type:
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With the verbose output enabled, we can see how each stage of our new analyzers modify the tokens they
receive before passing them on to the next stage. As we scroll down to the final output, we can see that we
do start to get a match on "analyzer" from each input string, thanks to the "LCF" stage?—?which if you hover
over with your mouse, you’ll see is the “ LowerCaseFilter ”:
The “ text_general ” field type is designed to be generally useful for any language, and it has definitely
gotten us closer to our objective than “ text_ws ” from our first example by solving the problem of case
sensitivity. It’s still not quite what we are looking for because we don’t see stemming or stopword rules
being applied. So now let us try the “ text_en ” field type:
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Now we can see the "SF" ( StopFilter ) stage of the analyzers solving the problem of removing Stop Words
("an"), and as we scroll down, we also see the "PSF" ( PorterStemFilter ) stage apply stemming rules
suitable for our English language input, such that the terms produced by our "index analyzer" and the terms
produced by our "query analyzer" match the way we expect.
At this point, we can continue to experiment with additional inputs, verifying that our analyzers produce
matching tokens when we expect them to match, and disparate tokens when we do not expect them to
match, as we iterate and tweak our field type configuration.
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Indexing and Basic Data Operations
This section describes how Solr adds data to its index. It covers the following topics:
? Introduction to Solr Indexing: An overview of Solr’s indexing process.
? Post Tool: Information about using post.jar to quickly upload some content to your system.
? Uploading Data with Index Handlers: Information about using Solr’s Index Handlers to upload
XML/XSLT, JSON and CSV data.
? Transforming and Indexing Custom JSON : Index any JSON of your choice
? Uploading Data with Solr Cell using Apache Tika: Information about using the Solr Cell framework to
upload data for indexing.
? Uploading Structured Data Store Data with the Data Import Handler: Information about uploading
and indexing data from a structured data store.
? Updating Parts of Documents: Information about how to use atomic updates and optimistic
concurrency with Solr.
? Detecting Languages During Indexing: Information about using language identification during the
indexing process.
? De-Duplication: Information about configuring Solr to mark duplicate documents as they are indexed.
? Content Streams: Information about streaming content to Solr Request Handlers.
? UIMA Integration: Information about integrating Solr with Apache’s Unstructured Information
Management Architecture (UIMA). UIMA lets you define custom pipelines of Analysis Engines that
incrementally add metadata to your documents as annotations.
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Indexing Using Client APIs
Using client APIs, such as SolrJ, from your applications is an important option for updating Solr indexes. See
the Client APIs section for more information.
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Introduction to Solr Indexing
This section describes the process of indexing: adding content to a Solr index and, if necessary, modifying
that content or deleting it.
By adding content to an index, we make it searchable by Solr.
A Solr index can accept data from many different sources, including XML files, comma-separated value (CSV)
files, data extracted from tables in a database, and files in common file formats such as Microsoft Word or
PDF.
Here are the three most common ways of loading data into a Solr index:
? Using the Solr Cell framework built on Apache Tika for ingesting binary files or structured files such as
Office, Word, PDF, and other proprietary formats.
? Uploading XML files by sending HTTP requests to the Solr server from any environment where such
requests can be generated.
? Writing a custom Java application to ingest data through Solr’s Java Client API (which is described in
more detail in Client APIs). Using the Java API may be the best choice if you’re working with an
application, such as a Content Management System (CMS), that offers a Java API.
Regardless of the method used to ingest data, there is a common basic data structure for data being fed
into a Solr index: a document containing multiple fields, each with a name and containing content, which may
be empty. One of the fields is usually designated as a unique ID field (analogous to a primary key in a
database), although the use of a unique ID field is not strictly required by Solr.
If the field name is defined in the Schema that is associated with the index, then the analysis steps
associated with that field will be applied to its content when the content is tokenized. Fields that are not
explicitly defined in the Schema will either be ignored or mapped to a dynamic field definition (see
Documents, Fields, and Schema Design), if one matching the field name exists.
For more information on indexing in Solr, see the Solr Wiki.
The Solr Example Directory
When starting Solr with the "-e" option, the example/ directory will be used as base directory for the
example Solr instances that are created. This directory also includes an example/exampledocs/ subdirectory
containing sample documents in a variety of formats that you can use to experiment with indexing into the
various examples.
The curl Utility for Transferring Files
Many of the instructions and examples in this section make use of the curl utility for transferring content
through a URL. curl posts and retrieves data over HTTP, FTP, and many other protocols. Most Linux
distributions include a copy of curl . You’ll find curl downloads for Linux, Windows, and many other
operating systems at http://curl.haxx.se/download.html. Documentation for curl is available here:
http://curl.haxx.se/docs/manpage.html.
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?
Using curl or other command line tools for posting data is just fine for examples or tests,
but it’s not the recommended method for achieving the best performance for updates in
production environments. You will achieve better performance with Solr Cell or the other
methods described in this section.
Instead of curl , you can use utilities such as GNU wget (http://www.gnu.org/software/
wget/) or manage GETs and POSTS with Perl, although the command line options will
differ.
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Post Tool
Solr includes a simple command line tool for POSTing various types of content to a Solr server.
The tool is bin/post . The bin/post tool is a Unix shell script; for Windows (non-Cygwin) usage, see the
Windows section below.
To run it, open a window and enter:
bin/post -c gettingstarted example/films/films.json
This will contact the server at localhost:8983 . Specifying the collection/core name is mandatory. The
-help (or simply -h ) option will output information on its usage (i.e., bin/post -help) .
Using the bin/post Tool
Specifying either the collection/core name or the full update url is mandatory when using bin/post .
The basic usage of bin/post is:
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$ bin/post -h
Usage: post -c <collection> [OPTIONS] <files|directories|urls|-d ["...",...]>
or post -help
collection name defaults to DEFAULT_SOLR_COLLECTION if not specified
OPTIONS
=======
Solr options:
-url <base Solr update URL> (overrides collection, host, and port)
-host <host> (default: localhost)
-p or -port <port> (default: 8983)
-commit yes|no (default: yes)
-u or -user <user:pass> (sets BasicAuth credentials)
Web crawl options:
-recursive <depth> (default: 1)
-delay <seconds> (default: 10)
Directory crawl options:
-delay <seconds> (default: 0)
stdin/args options:
-type <content/type> (default: application/xml)
Other options:
-filetypes <type>[,<type>,...] (default:
xml,json,csv,pdf,doc,docx,ppt,pptx,xls,xlsx,odt,odp,ods,ott,otp,ots,rtf,htm,html,txt,log)
-params "<key>=<value>[&<key>=<value>...]" (values must be URL-encoded; these pass through to
Solr update request)
-out yes|no (default: no; yes outputs Solr response to console)
...
Examples
There are several ways to use bin/post . This section presents several examples.
Indexing XML
Add all documents with file extension .xml to collection or core named gettingstarted .
bin/post -c gettingstarted *.xml
Add all documents with file extension .xml to the gettingstarted collection/core on Solr running on port
8984 .
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bin/post -c gettingstarted -p 8984 *.xml
Send XML arguments to delete a document from gettingstarted .
bin/post -c gettingstarted -d ‘<delete><id>42</id></delete>‘
Indexing CSV
Index all CSV files into gettingstarted :
bin/post -c gettingstarted *.csv
Index a tab-separated file into gettingstarted :
bin/post -c signals -params "separator=%09" -type text/csv data.tsv
The content type ( -type ) parameter is required to treat the file as the proper type, otherwise it will be
ignored and a WARNING logged as it does not know what type of content a .tsv file is. The CSV handler
supports the separator parameter, and is passed through using the -params setting.
Indexing JSON
Index all JSON files into gettingstarted .
bin/post -c gettingstarted *.json
Indexing Rich Documents (PDF, Word, HTML, etc)
Index a PDF file into gettingstarted .
bin/post -c gettingstarted a.pdf
Automatically detect content types in a folder, and recursively scan it for documents for indexing into
gettingstarted .
bin/post -c gettingstarted afolder/
Automatically detect content types in a folder, but limit it to PPT and HTML files and index into
gettingstarted .
bin/post -c gettingstarted -filetypes ppt,html afolder/
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Indexing to a Password Protected Solr (Basic Auth)
Index a pdf as the user solr with password SolrRocks :
bin/post -u solr:SolrRocks -c gettingstarted a.pdf
Windows Support
bin/post exists currently only as a Unix shell script, however it delegates its work to a cross-platform
capable Java program. The SimplePostTool can be run directly in supported environments, including
Windows.
SimplePostTool
The bin/post script currently delegates to a standalone Java program called SimplePostTool .
This tool, bundled into a executable JAR, can be run directly using java -jar
example/exampledocs/post.jar . See the help output and take it from there to post files, recurse a website
or file system folder, or send direct commands to a Solr server.
$ java -jar example/exampledocs/post.jar -h
SimplePostTool version 5.0.0
Usage: java [SystemProperties] -jar post.jar [-h|-] [<file|folder|url|arg>
[<file|folder|url|arg>...]]
.
.
.
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Uploading Data with Index Handlers
Index Handlers are Request Handlers designed to add, delete and update documents to the index. In
addition to having plugins for importing rich documents using Tika or from structured data sources using
the Data Import Handler, Solr natively supports indexing structured documents in XML, CSV and JSON.
The recommended way to configure and use request handlers is with path based names that map to paths
in the request url. However, request handlers can also be specified with the qt (query type) parameter if the
requestDispatcher is appropriately configured. It is possible to access the same handler using more than
one name, which can be useful if you wish to specify different sets of default options.
A single unified update request handler supports XML, CSV, JSON, and javabin update requests, delegating
to the appropriate ContentStreamLoader based on the Content-Type of the ContentStream.
UpdateRequestHandler Configuration
The default configuration file has the update request handler configured by default.
<requestHandler name="/update" class="solr.UpdateRequestHandler" />
XML Formatted Index Updates
Index update commands can be sent as XML message to the update handler using Content-type:
application/xml or Content-type: text/xml .
Adding Documents
The XML schema recognized by the update handler for adding documents is very straightforward:
? The <add> element introduces one more documents to be added.
? The <doc> element introduces the fields making up a document.
? The <field> element presents the content for a specific field.
For example:
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<add>
<doc>
<field name="authors">Patrick Eagar</field>
<field name="subject">Sports</field>
<field name="dd">796.35</field>
<field name="numpages">128</field>
<field name="desc"></field>
<field name="price">12.40</field>
<field name="title">Summer of the all-rounder: Test and championship cricket in England
1982</field>
<field name="isbn">0002166313</field>
<field name="yearpub">1982</field>
<field name="publisher">Collins</field>
</doc>
<doc>
...
</doc>
</add>
The add command supports some optional attributes which may be specified.
Optional Parameter Parameter Description
commitWithin=number Add the document within the specified number of milliseconds
overwrite=boolean Default is true. Indicates if the unique key constraints should be checked
to overwrite previous versions of the same document (see below)
If the document schema defines a unique key, then by default an /update operation to add a document will
overwrite (ie: replace) any document in the index with the same unique key. If no unique key has been
defined, indexing performance is somewhat faster, as no check has to be made for an existing documents to
replace.
If you have a unique key field, but you feel confident that you can safely bypass the uniqueness check (eg:
you build your indexes in batch, and your indexing code guarantees it never adds the same document more
than once) you can specify the overwrite="false" option when adding your documents.
XML Update Commands
Commit and Optimize Operations
The <commit> operation writes all documents loaded since the last commit to one or more segment files on
the disk. Before a commit has been issued, newly indexed content is not visible to searches. The commit
operation opens a new searcher, and triggers any event listeners that have been configured.
Commits may be issued explicitly with a <commit/> message, and can also be triggered from <autocommit>
parameters in solrconfig.xml .
The <optimize> operation requests Solr to merge internal data structures in order to improve search
performance. For a large index, optimization will take some time to complete, but by merging many small
segment files into a larger one, search performance will improve. If you are using Solr’s replication
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mechanism to distribute searches across many systems, be aware that after an optimize, a complete index
will need to be transferred. In contrast, post-commit transfers are usually much smaller.
The <commit> and <optimize> elements accept these optional attributes:
Optional Attribute Description
waitSearcher Default is true. Blocks until a new searcher is opened and registered as the
main query searcher, making the changes visible.
expungeDeletes (commit only) Default is false. Merges segments that have more than 10%
deleted docs, expunging them in the process.
maxSegments (optimize only) Default is 1. Merges the segments down to no more than
this number of segments.
Here are examples of <commit> and <optimize> using optional attributes:
<commit waitSearcher="false"/>
<commit waitSearcher="false" expungeDeletes="true"/>
<optimize waitSearcher="false"/>
Delete Operations
Documents can be deleted from the index in two ways. "Delete by ID" deletes the document with the
specified ID, and can be used only if a UniqueID field has been defined in the schema. "Delete by Query"
deletes all documents matching a specified query, although commitWithin is ignored for a Delete by Query.
A single delete message can contain multiple delete operations.
<delete>
<id>0002166313</id>
<id>0031745983</id>
<query>subject:sport</query>
<query>publisher:penguin</query>
</delete>
?
When using the Join query parser in a Delete By Query, you should use the score
parameter with a value of " none`" to avoid a `ClassCastException . See the section on
the Join Query Parser for more details on the score parameter.
Rollback Operations
The rollback command rolls back all add and deletes made to the index since the last commit. It neither calls
any event listeners nor creates a new searcher. Its syntax is simple: <rollback/> .
Using curl to Perform Updates
You can use the curl utility to perform any of the above commands, using its --data-binary option to
append the XML message to the curl command, and generating a HTTP POST request. For example:
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curl http://localhost:8983/solr/my_collection/update -H "Content-Type: text/xml" --data-binary ‘
<add>
<doc>
<field name="authors">Patrick Eagar</field>
<field name="subject">Sports</field>
<field name="dd">796.35</field>
<field name="isbn">0002166313</field>
<field name="yearpub">1982</field>
<field name="publisher">Collins</field>
</doc>
</add>‘
For posting XML messages contained in a file, you can use the alternative form:
curl http://localhost:8983/solr/my_collection/update -H "Content-Type: text/xml" --data-binary
@myfile.xml
Short requests can also be sent using a HTTP GET command, URL-encoding the request, as in the following.
Note the escaping of "<" and ">":
curl http://localhost:8983/solr/my_collection/update?stream.body=%3Ccommit/%3E
Responses from Solr take the form shown here:
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">127</int>
</lst>
</response>
The status field will be non-zero in case of failure.
Using XSLT to Transform XML Index Updates
The UpdateRequestHandler allows you to index any arbitrary XML using the <tr> parameter to apply an XSL
transformation. You must have an XSLT stylesheet in the conf/xslt directory of your config set that can
transform the incoming data to the expected <add><doc/></add> format, and use the tr parameter to
specify the name of that stylesheet.
Here is an example XSLT stylesheet:
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<xsl:stylesheet version=‘1.0‘ xmlns:xsl=‘http://www.w3.org/1999/XSL/Transform‘>
<xsl:output media-type="text/xml" method="xml" indent="yes"/>
<xsl:template match=‘/‘>
<add>
<xsl:apply-templates select="response/result/doc"/>
</add>
</xsl:template>
<!-- Ignore score (makes no sense to index) -->
<xsl:template match="doc/*[@name=‘score‘]" priority="100"></xsl:template>
<xsl:template match="doc">
<xsl:variable name="pos" select="position()"/>
<doc>
<xsl:apply-templates>
<xsl:with-param name="pos"><xsl:value-of select="$pos"/></xsl:with-param>
</xsl:apply-templates>
</doc>
</xsl:template>
<!-- Flatten arrays to duplicate field lines -->
<xsl:template match="doc/arr" priority="100">
<xsl:variable name="fn" select="@name"/>
<xsl:for-each select="*">
<xsl:element name="field">
<xsl:attribute name="name"><xsl:value-of select="$fn"/></xsl:attribute>
<xsl:value-of select="."/>
</xsl:element>
</xsl:for-each>
</xsl:template>
<xsl:template match="doc/*">
<xsl:variable name="fn" select="@name"/>
<xsl:element name="field">
<xsl:attribute name="name"><xsl:value-of select="$fn"/></xsl:attribute>
<xsl:value-of select="."/>
</xsl:element>
</xsl:template>
<xsl:template match="*"/>
</xsl:stylesheet>
This stylesheet transforms Solr’s XML search result format into Solr’s Update XML syntax. One example
usage would be to copy a Solr 1.3 index (which does not have CSV response writer) into a format which can
be indexed into another Solr file (provided that all fields are stored):
http://localhost:8983/solr/my_collection/select?q=*:*&wt=xslt&tr=updateXml.xsl&rows=1000
You can also use the stylesheet in XsltUpdateRequestHandler to transform an index when updating:
curl "http://localhost:8983/solr/my_collection/update?commit=true&tr=updateXml.xsl" -H "Content-
Type: text/xml" --data-binary @myexporteddata.xml
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JSON Formatted Index Updates
Solr can accept JSON that conforms to a defined structure, or can accept arbitrary JSON-formatted
documents. If sending arbitrarily formatted JSON, there are some additional parameters that need to be
sent with the update request, described below in the section Transforming and Indexing Custom JSON.
Solr-Style JSON
JSON formatted update requests may be sent to Solr’s /update handler using Content-Type:
application/json or Content-Type: text/json .
JSON formatted updates can take 3 basic forms, described in depth below:
? A single document to add, expressed as a top level JSON Object. To differentiate this from a set of
commands, the json.command=false request parameter is required.
? A list of documents to add, expressed as a top level JSON Array containing a JSON Object per document.
? A sequence of update commands, expressed as a top level JSON Object (aka: Map).
Adding a Single JSON Document
The simplest way to add Documents via JSON is to send each document individually as a JSON Object, using
the /update/json/docs path:
curl -X POST -H ‘Content-Type: application/json‘
‘http://localhost:8983/solr/my_collection/update/json/docs‘ --data-binary ‘
{
"id": "1",
"title": "Doc 1"
}‘
Adding Multiple JSON Documents
Adding multiple documents at one time via JSON can be done via a JSON Array of JSON Objects, where each
object represents a document:
curl -X POST -H ‘Content-Type: application/json‘
‘http://localhost:8983/solr/my_collection/update‘ --data-binary ‘
[
{
"id": "1",
"title": "Doc 1"
},
{
"id": "2",
"title": "Doc 2"
}
]‘
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A sample JSON file is provided at example/exampledocs/books.json and contains an array of objects that
you can add to the Solr techproducts example:
curl ‘http://localhost:8983/solr/techproducts/update?commit=true‘ --data-binary
@example/exampledocs/books.json -H ‘Content-type:application/json‘
Sending JSON Update Commands
In general, the JSON update syntax supports all of the update commands that the XML update handler
supports, through a straightforward mapping. Multiple commands, adding and deleting documents, may be
contained in one message:
curl -X POST -H ‘Content-Type: application/json‘
‘http://localhost:8983/solr/my_collection/update‘ --data-binary ‘
{
"add": {
"doc": {
"id": "DOC1",
"my_field": 2.3,
"my_multivalued_field": [ "aaa", "bbb" ] ①
}
},
"add": {
"commitWithin": 5000, ②
"overwrite": false, ③
"doc": {
"f1": "v1", ④
"f1": "v2"
}
},
"commit": {},
"optimize": { "waitSearcher":false },
"delete": { "id":"ID" }, ⑤
"delete": { "query":"QUERY" } ⑥
}‘
①
Can use an array for a multi-valued field
②
Commit this document within 5 seconds
③
Don’t check for existing documents with the same uniqueKey
④
Can use repeated keys for a multi-valued field
⑤
Delete by ID (uniqueKey field)
⑥
Delete by Query
As with other update handlers, parameters such as commit , commitWithin , optimize , and overwrite may be
specified in the URL instead of in the body of the message.
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The JSON update format allows for a simple delete-by-id. The value of a delete can be an array which
contains a list of zero or more specific document id’s (not a range) to be deleted. For example, a single
document:
{ "delete":"myid" }
Or a list of document IDs:
{ "delete":["id1","id2"] }
The value of a "delete" can be an array which contains a list of zero or more id’s to be deleted. It is not a
range (start and end).
You can also specify _version_ with each "delete":
{
"delete":"id":50,
"_version_":12345
}
You can specify the version of deletes in the body of the update request as well.
JSON Update Convenience Paths
In addition to the /update handler, there are a few additional JSON specific request handler paths available
by default in Solr, that implicitly override the behavior of some request parameters:
Path Default Parameters
/update/json stream.contentType=application/json
/update/json/docs stream.contentType=application/json
json.command=false
The /update/json path may be useful for clients sending in JSON formatted update commands from
applications where setting the Content-Type proves difficult, while the /update/json/docs path can be
particularly convenient for clients that always want to send in documents – either individually or as a list –
without needing to worry about the full JSON command syntax.
Custom JSON Documents
Solr can support custom JSON. This is covered in the section Transforming and Indexing Custom JSON.
CSV Formatted Index Updates
CSV formatted update requests may be sent to Solr’s /update handler using Content-Type:
application/csv or Content-Type: text/csv .
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A sample CSV file is provided at example/exampledocs/books.csv that you can use to add some documents
to the Solr techproducts example:
curl ‘http://localhost:8983/solr/my_collection/update?commit=true‘ --data-binary
@example/exampledocs/books.csv -H ‘Content-type:application/csv‘
CSV Update Parameters
The CSV handler allows the specification of many parameters in the URL in the form: f.parameter
.optional_fieldname=value .
The table below describes the parameters for the update handler.
Parameter Usage Global (g) or Per
Field (f)
Example
separator Character used as field separator; default
is ","
g,(f: see split) separator=%09
trim If true, remove leading and trailing
whitespace from values. Default=false.
g,f f.isbn.trim=true
trim=false
header Set to true if first line of input contains
field names. These will be used if the
fieldnames parameter is absent.
g
fieldnames Comma separated list of field names to
use when adding documents.
g fieldnames=isbn,pri
ce,title
literal.<field_name> A literal value for a specified field name. g literal.color=red
skip Comma separated list of field names to
skip.
g skip=uninteresting,s
hoesize
skipLines Number of lines to discard in the input
stream before the CSV data starts,
including the header, if present.
Default=0.
g skipLines=5
encapsulator The character optionally used to surround
values to preserve characters such as the
CSV separator or whitespace. This
standard CSV format handles the
encapsulator itself appearing in an
encapsulated value by doubling the
encapsulator.
g,(f: see split) encapsulator="
escape The character used for escaping CSV
separators or other reserved characters.
If an escape is specified, the encapsulator
is not used unless also explicitly specified
since most formats use either
encapsulation or escaping, not both
g escape=\
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Parameter Usage Global (g) or Per
Field (f)
Example
keepEmpty Keep and index zero length (empty) fields.
Default=false.
g,f f.price.keepEmpty=t
rue
map Map one value to another. Format is
value:replacement (which can be empty.)
g,f map=left:right
f.subject.map=histor
y:bunk
split If true, split a field into multiple values by
a separate parser.
f
overwrite If true (the default), check for and
overwrite duplicate documents, based on
the uniqueKey field declared in the Solr
schema. If you know the documents you
are indexing do not contain any
duplicates then you may see a
considerable speed up setting this to
false.
g
commit Issues a commit after the data has been
ingested.
g
commitWithin Add the document within the specified
number of milliseconds.
g commitWithin=1000
0
rowid Map the rowid (line number) to a field
specified by the value of the parameter,
for instance if your CSV doesn’t have a
unique key and you want to use the row
id as such.
g rowid=id
rowidOffset Add the given offset (as an int) to the
rowid before adding it to the document.
Default is 0
g rowidOffset=10
Indexing Tab-Delimited files
The same feature used to index CSV documents can also be easily used to index tab-delimited files (TSV files)
and even handle backslash escaping rather than CSV encapsulation.
For example, one can dump a MySQL table to a tab delimited file with:
SELECT * INTO OUTFILE ‘/tmp/result.txt‘ FROM mytable;
This file could then be imported into Solr by setting the separator to tab (%09) and the escape to backslash
(%5c).
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curl ‘http://localhost:8983/solr/my_collection/update/csv?commit=true&separator=%09&escape=%5c‘
--data-binary @/tmp/result.txt
CSV Update Convenience Paths
In addition to the /update handler, there is an additional CSV specific request handler path available by
default in Solr, that implicitly override the behavior of some request parameters:
Path Default Parameters
/update/csv stream.contentType=application/csv
The /update/csv path may be useful for clients sending in CSV formatted update commands from
applications where setting the Content-Type proves difficult.
Nested Child Documents
Solr indexes nested documents in blocks as a way to model documents containing other documents, such as
a blog post parent document and comments as child documents?—?or products as parent documents and
sizes, colors, or other variations as child documents. At query time, the Block Join Query Parsers can search
these relationships. In terms of performance, indexing the relationships between documents may be more
efficient than attempting to do joins only at query time, since the relationships are already stored in the
index and do not need to be computed.
Nested documents may be indexed via either the XML or JSON data syntax (or using SolrJ) - but regardless of
syntax, you must include a field that identifies the parent document as a parent; it can be any field that suits
this purpose, and it will be used as input for the block join query parsers.
To support nested documents, the schema must include an indexed/non-stored field _root_ . The value of
that field is populated automatically and is the same for all documents in the block, regardless of the
inheritance depth.
XML Examples
For example, here are two documents and their child documents:
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<add>
<doc>
<field name="id">1</field>
<field name="title">Solr adds block join support</field>
<field name="content_type">parentDocument</field>
<doc>
<field name="id">2</field>
<field name="comments">SolrCloud supports it too!</field>
</doc>
</doc>
<doc>
<field name="id">3</field>
<field name="title">New Lucene and Solr release is out</field>
<field name="content_type">parentDocument</field>
<doc>
<field name="id">4</field>
<field name="comments">Lots of new features</field>
</doc>
</doc>
</add>
In this example, we have indexed the parent documents with the field content_type , which has the value
"parentDocument". We could have also used a boolean field, such as isParent , with a value of "true", or any
other similar approach.
JSON Examples
This example is equivalent to the XML example above, note the special _childDocuments_ key need to
indicate the nested documents in JSON.
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[
{
"id": "1",
"title": "Solr adds block join support",
"content_type": "parentDocument",
"_childDocuments_": [
{
"id": "2",
"comments": "SolrCloud supports it too!"
}
]
},
{
"id": "3",
"title": "New Lucene and Solr release is out",
"content_type": "parentDocument",
"_childDocuments_": [
{
"id": "4",
"comments": "Lots of new features"
}
]
}
]
?
Note
One limitation of indexing nested documents is that the whole block of parent-children
documents must be updated together whenever any changes are required. In other words,
even if a single child document or the parent document is changed, the whole block of
parent-child documents must be indexed together.
Transforming and Indexing Custom JSON
If you have JSON documents that you would like to index without transforming them into Solr’s structure,
you can add them to Solr by including some parameters with the update request. These parameters provide
information on how to split a single JSON file into multiple Solr documents and how to map fields to Solr’s
schema. One or more valid JSON documents can be sent to the /update/json/docs path with the
configuration params.
Mapping Parameters
These parameters allow you to define how a JSON file should be read for multiple Solr documents.
? split: Defines the path at which to split the input JSON into multiple Solr documents and is required if
you have multiple documents in a single JSON file. If the entire JSON makes a single solr document, the
path must be “ / ”. It is possible to pass multiple split paths by separating them with a pipe (|) example :
split=/|/foo|/foo/bar . If one path is a child of another, they automatically become a child document
f: This is a multivalued mapping parameter. The format of the parameter is` target-field-name:json-
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path`. The json-path is required. The target-field-name is the Solr document field name, and is
optional. If not specified, it is automatically derived from the input JSON.The default target field name is
the fully qualified name of the field. Wildcards can be used here, see the Wildcards below for more
information.
? mapUniqueKeyOnly (boolean): This parameter is particularly convenient when the fields in the input
JSON are not available in the schema and schemaless mode is not enabled. This will index all the fields
into the default search field (using the df parameter, below) and only the uniqueKey field is mapped to
the corresponding field in the schema. If the input JSON does not have a value for the uniqueKey field
then a UUID is generated for the same.
? df: If the mapUniqueKeyOnly flag is used, the update handler needs a field where the data should be
indexed to. This is the same field that other handlers use as a default search field.
? srcField: This is the name of the field to which the JSON source will be stored into. This can only be used
if split=/ (i.e., you want your JSON input file to be indexed as a single Solr document). Note that atomic
updates will cause the field to be out-of-sync with the document.
? echo: This is for debugging purpose only. Set it to true if you want the docs to be returned as a response.
Nothing will be indexed.
For example, if we have a JSON file that includes two documents, we could define an update request like this:
curl ‘http://localhost:8983/solr/my_collection/update/json/docs‘\
‘?split=/exams‘\
‘&f=first:/first‘\
‘&f=last:/last‘\
‘&f=grade:/grade‘\
‘&f=subject:/exams/subject‘\
‘&f=test:/exams/test‘\
‘&f=marks:/exams/marks‘\
-H ‘Content-type:application/json‘ -d ‘
{
"first": "John",
"last": "Doe",
"grade": 8,
"exams": [
{
"subject": "Maths",
"test" : "term1",
"marks" : 90},
{
"subject": "Biology",
"test" : "term1",
"marks" : 86}
]
}‘
You can store and reuse the params by using Request Parameters.
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curl http://localhost:8983/solr/my_collection/config/params -H ‘Content-type:application/json‘
-d ‘{
"set": {
"my_params": {
"split": "/exams",
"f": ["first:/first","last:/last","grade:/grade","subject:/exams/subject","test:/exams/test"]
}}}‘
and use it as follows:
curl ‘http://localhost:8983/solr/my_collection/update/json/docs?useParams=my_params‘ -H ‘Content-
type:application/json‘ -d ‘{
"first": "John",
"last": "Doe",
"grade": 8,
"exams": [
{
"subject": "Maths",
"test" : "term1",
"marks" : 90},
{
"subject": "Biology",
"test" : "term1",
"marks" : 86}
]
}‘
With this request, we have defined that "exams" contains multiple documents. In addition, we have mapped
several fields from the input document to Solr fields.
When the update request is complete, the following two documents will be added to the index:
{
"first":"John",
"last":"Doe",
"marks":90,
"test":"term1",
"subject":"Maths",
"grade":8
}
{
"first":"John",
"last":"Doe",
"marks":86,
"test":"term1",
"subject":"Biology",
"grade":8
}
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In the prior example, all of the fields we wanted to use in Solr had the same names as they did in the input
JSON. When that is the case, we can simplify the request as follows:
curl ‘http://localhost:8983/solr/my_collection/update/json/docs‘\
‘?split=/exams‘\
‘&f=/first‘\
‘&f=/last‘\
‘&f=/grade‘\
‘&f=/exams/subject‘\
‘&f=/exams/test‘\
‘&f=/exams/marks‘\
-H ‘Content-type:application/json‘ -d ‘
{
"first": "John",
"last": "Doe",
"grade": 8,
"exams": [
{
"subject": "Maths",
"test" : "term1",
"marks" : 90},
{
"subject": "Biology",
"test" : "term1",
"marks" : 86}
]
}‘
In this example, we simply named the field paths (such as /exams/test ). Solr will automatically attempt to
add the content of the field from the JSON input to the index in a field with the same name.
?
If you are working in Schemaless Mode, fields that don’t exist will be created on the fly with
Solr’s best guess for the field type. Documents WILL get rejected if the fields do not exist in
the schema before indexing. So, if you are NOT using schemaless mode, pre-create those
fields.
Wildcards
Instead of specifying all the field names explicitly, it is possible to specify wildcards to map fields
automatically. There are two restrictions: wildcards can only be used at the end of the json-path , and the
split path cannot use wildcards. A single asterisk * maps only to direct children, and a double asterisk \*\*
maps recursively to all descendants. The following are example wildcard path mappings:
? f=$FQN:/** : maps all fields to the fully qualified name ( $FQN ) of the JSON field. The fully qualified name is
obtained by concatenating all the keys in the hierarchy with a period ( . ) as a delimiter. This is the default
behavior if no f path mappings are specified.
? f=/docs/* : maps all the fields under docs and in the name as given in json
? f=/docs/** : maps all the fields under docs and its children in the name as given in json
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? f=searchField:/docs/* : maps all fields under /docs to a single field called ‘searchField’
? f=searchField:/docs/** : maps all fields under /docs and its children to searchField
With wildcards we can further simplify our previous example as follows:
curl ‘http://localhost:8983/solr/my_collection/update/json/docs‘\
‘?split=/exams‘\
‘&f=/**‘\
-H ‘Content-type:application/json‘ -d ‘
{
"first": "John",
"last": "Doe",
"grade": 8,
"exams": [
{
"subject": "Maths",
"test" : "term1",
"marks" : 90},
{
"subject": "Biology",
"test" : "term1",
"marks" : 86}
]
}‘
Because we want the fields to be indexed with the field names as they are found in the JSON input, the
double wildcard in f=/** will map all fields and their descendants to the same fields in Solr.
It is also possible to send all the values to a single field and do a full text search on that. This is a good option
to blindly index and query JSON documents without worrying about fields and schema.
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curl ‘http://localhost:8983/solr/my_collection/update/json/docs‘\
‘?split=/‘\
‘&f=txt:/**‘\
-H ‘Content-type:application/json‘ -d ‘
{
"first": "John",
"last": "Doe",
"grade": 8,
"exams": [
{
"subject": "Maths",
"test" : "term1",
"marks" : 90},
{
"subject": "Biology",
"test" : "term1",
"marks" : 86}
]
}‘
In the above example, we’ve said all of the fields should be added to a field in Solr named ‘txt‘. This will add
multiple fields to a single field, so whatever field you choose should be multi-valued.
The default behavior is to use the fully qualified name (FQN) of the node. So, if we don’t define any field
mappings, like this:
curl ‘http://localhost:8983/solr/my_collection/update/json/docs?split=/exams‘\
-H ‘Content-type:application/json‘ -d ‘
{
"first": "John",
"last": "Doe",
"grade": 8,
"exams": [
{
"subject": "Maths",
"test" : "term1",
"marks" : 90},
{
"subject": "Biology",
"test" : "term1",
"marks" : 86}
]
}‘
The indexed documents would be added to the index with fields that look like this:
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{
"first":"John",
"last":"Doe",
"grade":8,
"exams.subject":"Maths",
"exams.test":"term1",
"exams.marks":90},
{
"first":"John",
"last":"Doe",
"grade":8,
"exams.subject":"Biology",
"exams.test":"term1",
"exams.marks":86}
Multiple documents in a Single Payload
This functionality supports documents in the JSON Lines format ( .jsonl ), which specifies one document per
line.
For example:
curl ‘http://localhost:8983/solr/my_collection/update/json/docs‘ -H ‘Content-
type:application/json‘ -d ‘
{ "first":"Steve", "last":"Jobs", "grade":1, "subject": "Social Science", "test" : "term1",
"marks" : 90}
{ "first":"Steve", "last":"Woz", "grade":1, "subject": "Political Science", "test" : "term1",
"marks" : 86}‘
Or even an array of documents, as in this example:
curl ‘http://localhost:8983/solr/my_collection/update/json/docs‘ -H ‘Content-
type:application/json‘ -d ‘[
{ "first":"Steve", "last":"Jobs", "grade":1, "subject": "Computer Science", "test" : "term1",
"marks" : 90},
{ "first":"Steve", "last":"Woz", "grade":1, "subject": "Calculus", "test" : "term1", "marks" :
86}]‘
Indexing Nested Documents
The following is an example of indexing nested documents:
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curl ‘http://localhost:8983/solr/my_collection/update/json/docs?split=/|/orgs‘\
-H ‘Content-type:application/json‘ -d ‘{
"name": "Joe Smith",
"phone": 876876687,
"orgs": [
{
"name": "Microsoft",
"city": "Seattle",
"zip": 98052
},
{
"name": "Apple",
"city": "Cupertino",
"zip": 95014
}
]
}‘
With this example, the documents indexed would be, as follows:
{
"name":"Joe Smith",
"phone":876876687,
"_childDocuments_":[
{
"name":"Microsoft",
"city":"Seattle",
"zip":98052},
{
"name":"Apple",
"city":"Cupertino",
"zip":95014}]}
Tips for Custom JSON Indexing
1. Schemaless mode: This handles field creation automatically. The field guessing may not be exactly as you
expect, but it works. The best thing to do is to setup a local server in schemaless mode, index a few
sample docs and create those fields in your real setup with proper field types before indexing
2. Pre-created Schema : Post your docs to the /update/`json /docs` endpoint with echo=true . This gives
you the list of field names you need to create. Create the fields before you actually index
3. No schema, only full-text search : All you need to do is to do full-text search on your JSON. Set the
configuration as given in the Setting JSON Defaults section.
Setting JSON Defaults
It is possible to send any json to the /update/json/docs endpoint and the default configuration of the
component is as follows:
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<initParams path="/update/json/docs">
<lst name="defaults">
<!-- this ensures that the entire json doc will be stored verbatim into one field -->
<str name="srcField">_src_</str>
<!-- This means a the uniqueKeyField will be extracted from the fields and
all fields go into the ‘df‘ field. In this config df is already configured to be ‘text‘
-->
<str name="mapUniqueKeyOnly">true</str>
<!-- The default search field where all the values are indexed to -->
<str name="df">text</str>
</lst>
</initParams>
So, if no params are passed, the entire json file would get indexed to the _src_ field and all the values in the
input JSON would go to a field named text . If there is a value for the uniqueKey it is stored and if no value
could be obtained from the input JSON, a UUID is created and used as the uniqueKey field value.
Alternately, use the Request Parameters feature to set these params
curl http://localhost:8983/solr/my_collection/config/params -H ‘Content-type:application/json‘
-d ‘{
"set": {
"full_txt": {
"srcField": "_src_",
"mapUniqueKeyOnly" : true,
"df": "text"
}}}‘
Send the parameter useParams=full_txt with each request.
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Uploading Data with Solr Cell using Apache Tika
Solr uses code from the Apache Tika project to provide a framework for incorporating many different file-
format parsers such as Apache PDFBox and Apache POI into Solr itself. Working with this framework, Solr’s
ExtractingRequestHandler can use Tika to support uploading binary files, including files in popular formats
such as Word and PDF, for data extraction and indexing.
When this framework was under development, it was called the Solr Content Extraction Library or CEL; from
that abbreviation came this framework’s name: Solr Cell.
If you want to supply your own ContentHandler for Solr to use, you can extend the
ExtractingRequestHandler and override the createFactory() method. This factory is responsible for
constructing the SolrContentHandler that interacts with Tika, and allows literals to override Tika-parsed
values. Set the parameter literalsOverride , which normally defaults to *true, to *false" to append Tika-
parsed values to literal values.
For more information on Solr’s Extracting Request Handler, see https://wiki.apache.org/solr/
ExtractingRequestHandler.
Key Concepts
When using the Solr Cell framework, it is helpful to keep the following in mind:
? Tika will automatically attempt to determine the input document type (Word, PDF, HTML) and extract the
content appropriately. If you like, you can explicitly specify a MIME type for Tika with the stream.type
parameter.
? Tika works by producing an XHTML stream that it feeds to a SAX ContentHandler. SAX is a common
interface implemented for many different XML parsers. For more information, see
http://www.saxproject.org/quickstart.html.
? Solr then responds to Tika’s SAX events and creates the fields to index.
? Tika produces metadata such as Title, Subject, and Author according to specifications such as the
DublinCore. See http://tika.apache.org/1.7/formats.html for the file types supported.
? Tika adds all the extracted text to the content field.
? You can map Tika’s metadata fields to Solr fields.
? You can pass in literals for field values. Literals will override Tika-parsed values, including fields in the
Tika metadata object, the Tika content field, and any "captured content" fields.
? You can apply an XPath expression to the Tika XHTML to restrict the content that is produced.
?
While Apache Tika is quite powerful, it is not perfect and fails on some files. PDF files are
particularly problematic, mostly due to the PDF format itself. In case of a failure processing
any file, the ExtractingRequestHandler does not have a secondary mechanism to try to
extract some text from the file; it will throw an exception and fail.
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Trying out Tika with the Solr techproducts Example
You can try out the Tika framework using the techproducts example included in Solr.
Start the example:
bin/solr -e techproducts
You can now use curl to send a sample PDF file via HTTP POST:
curl ‘http://localhost:8983/solr/techproducts/update/extract?literal.id=doc1&commit=true‘ -F
"myfile=@example/exampledocs/solr-word.pdf"
The URL above calls the Extracting Request Handler, uploads the file solr-word.pdf and assigns it the
unique ID doc1 . Here’s a closer look at the components of this command:
? The literal.id=doc1 parameter provides the necessary unique ID for the document being indexed.
? The commit=true parameter causes Solr to perform a commit after indexing the document, making it
immediately searchable. For optimum performance when loading many documents, don’t call the
commit command until you are done.
? The -F flag instructs curl to POST data using the Content-Type multipart/form-data and supports the
uploading of binary files. The @ symbol instructs curl to upload the attached file.
? The argument myfile=@tutorial.html needs a valid path, which can be absolute or relative.
You can also use bin/post to send a PDF file into Solr (without the params, the literal.id parameter
would be set to the absolute path to the file):
bin/post -c techproducts example/exampledocs/solr-word.pdf -params "literal.id=a"
Now you should be able to execute a query and find that document. You can make a request like
http://localhost:8983/solr/techproducts/select?q=pdf .
You may notice that although the content of the sample document has been indexed and stored, there are
not a lot of metadata fields associated with this document. This is because unknown fields are ignored
according to the default parameters configured for the /update/extract handler in solrconfig.xml , and
this behavior can be easily changed or overridden. For example, to store and see all metadata and content,
execute the following:
bin/post -c techproducts example/exampledocs/solr-word.pdf -params
"literal.id=doc1&uprefix=attr_"
In this command, the uprefix=attr_ parameter causes all generated fields that aren’t defined in the
schema to be prefixed with attr_ , which is a dynamic field that is stored and indexed.
This command allows you to query the document using an attribute, as in:
http://localhost:8983/solr/techproducts/select?q=attr_meta:microsoft .
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Input Parameters
The table below describes the parameters accepted by the Extracting Request Handler.
Parameter Description
capture Captures XHTML elements with the specified name for a supplementary
addition to the Solr document. This parameter can be useful for copying
chunks of the XHTML into a separate field. For instance, it could be used to
grab paragraphs ( <p> ) and index them into a separate field. Note that
content is still also captured into the overall "content" field.
captureAttr Indexes attributes of the Tika XHTML elements into separate fields, named
after the element. If set to true, for example, when extracting from HTML,
Tika can return the href attributes in <a> tags as fields named "a". See the
examples below.
commitWithin Add the document within the specified number of milliseconds.
date.formats Defines the date format patterns to identify in the documents.
defaultField If the uprefix parameter (see below) is not specified and a field cannot be
determined, the default field will be used.
extractOnly Default is false. If true, returns the extracted content from Tika without
indexing the document. This literally includes the extracted XHTML as a
string in the response. When viewing manually, it may be useful to use a
response format other than XML to aid in viewing the embedded XHTML
tags.For an example, see http://wiki.apache.org/solr/
TikaExtractOnlyExampleOutput.
extractFormat Default is "xml", but the other option is "text". Controls the serialization
format of the extract content. The xml format is actually XHTML, the same
format that results from passing the -x command to the Tika command
line application, while the text format is like that produced by Tika’s -t
command. This parameter is valid only if extractOnly is set to true.
fmap.<source_field> Maps (moves) one field name to another. The source_field must be a
field in incoming documents, and the value is the Solr field to map to.
Example: fmap.content=text causes the data in the content field
generated by Tika to be moved to the Solr’s text field.
ignoreTikaException If true, exceptions found during processing will be skipped. Any metadata
available, however, will be indexed.
literal.<fieldname> Populates a field with the name supplied with the specified value for each
document. The data can be multivalued if the field is multivalued.
literalsOverride If true (the default), literal field values will override other values with the
same field name. If false, literal values defined with literal.<fieldname>
will be appended to data already in the fields extracted from Tika. If
setting literalsOverride to "false", the field must be multivalued.
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Parameter Description
lowernames Values are "true" or "false". If true, all field names will be mapped to
lowercase with underscores, if needed. For example, "Content-Type"
would be mapped to "content_type."
multipartUploadLimitInKB Useful if uploading very large documents, this defines the KB size of
documents to allow.
passwordsFile Defines a file path and name for a file of file name to password mappings.
resource.name Specifies the optional name of the file. Tika can use it as a hint for
detecting a file’s MIME type.
resource.password Defines a password to use for a password-protected PDF or OOXML file
tika.config Defines a file path and name to a customized Tika configuration file. This is
only required if you have customized your Tika implementation.
uprefix Prefixes all fields that are not defined in the schema with the given prefix.
This is very useful when combined with dynamic field definitions. Example:
uprefix=ignored_ would effectively ignore all unknown fields generated
by Tika given the example schema contains <dynamicField
name="ignored_*" type="ignored"/>
xpath When extracting, only return Tika XHTML content that satisfies the given
XPath expression. See http://tika.apache.org/1.7/index.html for details on
the format of Tika XHTML. See also http://wiki.apache.org/solr/
TikaExtractOnlyExampleOutput.
Order of Operations
Here is the order in which the Solr Cell framework, using the Extracting Request Handler and Tika, processes
its input.
1. Tika generates fields or passes them in as literals specified by literal.<fieldname>=<value> . If
literalsOverride=false , literals will be appended as multi-value to the Tika-generated field.
2. If lowernames=true , Tika maps fields to lowercase.
3. Tika applies the mapping rules specified by fmap.source=target parameters.
4. If uprefix is specified, any unknown field names are prefixed with that value, else if defaultField is
specified, any unknown fields are copied to the default field.
Configuring the Solr ExtractingRequestHandler
If you are not working with the supplied sample_techproducts_configs `or data_driven_schema_configs`
config set, you must configure your own solrconfig.xml to know about the Jar’s containing the
ExtractingRequestHandler and its dependencies:
<lib dir="${solr.install.dir:../../..}/contrib/extraction/lib" regex=".*\.jar" />
<lib dir="${solr.install.dir:../../..}/dist/" regex="solr-cell-\d.*\.jar" />
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You can then configure the ExtractingRequestHandler in solrconfig.xml .
<requestHandler name="/update/extract" class=
"org.apache.solr.handler.extraction.ExtractingRequestHandler">
<lst name="defaults">
<str name="fmap.Last-Modified">last_modified</str>
<str name="uprefix">ignored_</str>
</lst>
<!--Optional. Specify a path to a tika configuration file. See the Tika docs for details.-->
<str name="tika.config">/my/path/to/tika.config</str>
<!-- Optional. Specify one or more date formats to parse. See DateUtil.DEFAULT_DATE_FORMATS
for default date formats -->
<lst name="date.formats">
<str>yyyy-MM-dd</str>
</lst>
<!-- Optional. Specify an external file containing parser-specific properties.
This file is located in the same directory as solrconfig.xml by default.-->
<str name="parseContext.config">parseContext.xml</str>
</requestHandler>
In the defaults section, we are mapping Tika’s Last-Modified Metadata attribute to a field named
last_modified . We are also telling it to ignore undeclared fields. These are all overridden parameters.
The tika.config entry points to a file containing a Tika configuration. The date.formats allows you to
specify various java.text.SimpleDateFormats date formats for working with transforming extracted input
to a Date. Solr comes configured with the following date formats (see the DateUtil in Solr):
? yyyy-MM-dd’T’HH:mm:ss’Z‘
? yyyy-MM-dd’T’HH:mm:ss
? yyyy-MM-dd
? yyyy-MM-dd hh:mm:ss
? yyyy-MM-dd HH:mm:ss
? EEE MMM d hh:mm:ss z yyyy
? EEE, dd MMM yyyy HH:mm:ss zzz
? EEEE, dd-MMM-yy HH:mm:ss zzz
? EEE MMM d HH:mm:ss yyyy
You may also need to adjust the multipartUploadLimitInKB attribute as follows if you are submitting very
large documents.
<requestDispatcher handleSelect="true" >
<requestParsers enableRemoteStreaming="false" multipartUploadLimitInKB="20480" />
...
Parser specific properties
Parsers used by Tika may have specific properties to govern how data is extracted. For instance, when using
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the Tika library from a Java program, the PDFParserConfig class has a method setSortByPosition(boolean)
that can extract vertically oriented text. To access that method via configuration with the
ExtractingRequestHandler, one can add the parseContext.config property to the solrconfig.xml file (see
above) and then set properties in Tika’s PDFParserConfig as below. Consult the Tika Java API documentation
for configuration parameters that can be set for any particular parsers that require this level of control.
<entries>
<entry class="org.apache.tika.parser.pdf.PDFParserConfig" impl=
"org.apache.tika.parser.pdf.PDFParserConfig">
<property name="extractInlineImages" value="true"/>
<property name="sortByPosition" value="true"/>
</entry>
<entry>...</entry>
</entries>
Multi-Core Configuration
For a multi-core configuration, you can specify sharedLib=‘lib‘ in the <solr/> section of solr.xml and
place the necessary jar files there.
For more information about Solr cores, see The Well-Configured Solr Instance.
Indexing Encrypted Documents with the
ExtractingUpdateRequestHandler
The ExtractingRequestHandler will decrypt encrypted files and index their content if you supply a password
in either resource.password on the request, or in a passwordsFile file.
In the case of passwordsFile , the file supplied must be formatted so there is one line per rule. Each rule
contains a file name regular expression, followed by "=", then the password in clear-text. Because the
passwords are in clear-text, the file should have strict access restrictions.
# This is a comment
myFileName = myPassword
.*\.docx$ = myWordPassword
.*\.pdf$ = myPdfPassword
Examples
Metadata
As mentioned before, Tika produces metadata about the document. Metadata describes different aspects of
a document, such as the author’s name, the number of pages, the file size, and so on. The metadata
produced depends on the type of document submitted. For instance, PDFs have different metadata than
Word documents do.
In addition to Tika’s metadata, Solr adds the following metadata (defined in
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ExtractingMetadataConstants ):
Solr Metadata Description
stream_name The name of the Content Stream as uploaded to Solr. Depending on how
the file is uploaded, this may or may not be set
stream_source_info Any source info about the stream. (See the section on Content Streams
later in this section.)
stream_size The size of the stream in bytes.
stream_content_type The content type of the stream, if available.
?
We recommend that you try using the extractOnly option to discover which values Solr is
setting for these metadata elements.
Examples of Uploads Using the Extracting Request Handler
Capture and Mapping
The command below captures <div> tags separately, and then maps all the instances of that field to a
dynamic field named foo_t .
bin/post -c techproducts example/exampledocs/sample.html -params
"literal.id=doc2&captureAttr=true&defaultField=_text_&fmap.div=foo_t&capture=div"
Capture & Mapping
The command below captures <div> tags separately and maps the field to a dynamic field named foo_t .
bin/post -c techproducts example/exampledocs/sample.html -params
"literal.id=doc3&captureAttr=true&defaultField=_text_&capture=div&fmap.div=foo_t"
Using Literals to Define Your Own Metadata
To add in your own metadata, pass in the literal parameter along with the file:
bin/post -c techproducts -params
"literal.id=doc4&captureAttr=true&defaultField=text&capture=div&fmap.div=foo_t&literal.blah_s=Bah
" example/exampledocs/sample.html
XPath
The example below passes in an XPath expression to restrict the XHTML returned by Tika:
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bin/post -c techproducts -params
"literal.id=doc5&captureAttr=true&defaultField=text&capture=div&fmap.div=foo_t&xpath=/xhtml:html/
xhtml:body/xhtml:div//node()" example/exampledocs/sample.html
Extracting Data without Indexing It
Solr allows you to extract data without indexing. You might want to do this if you’re using Solr solely as an
extraction server or if you’re interested in testing Solr extraction.
The example below sets the extractOnly=true parameter to extract data without indexing it.
curl "http://localhost:8983/solr/techproducts/update/extract?&extractOnly=true" --data-binary
@example/exampledocs/sample.html -H ‘Content-type:text/html‘
The output includes XML generated by Tika (and further escaped by Solr’s XML) using a different output
format to make it more readable ( -out yes instructs the tool to echo Solr’s output to the console):
bin/post -c techproducts -params "extractOnly=true&wt=ruby&indent=true" -out yes
example/exampledocs/sample.html
Sending Documents to Solr with a POST
The example below streams the file as the body of the POST, which does not, then, provide information to
Solr about the name of the file.
curl
"http://localhost:8983/solr/techproducts/update/extract?literal.id=doc6&defaultField=text&commit=
true" --data-binary @example/exampledocs/sample.html -H ‘Content-type:text/html‘
Sending Documents to Solr with Solr Cell and SolrJ
SolrJ is a Java client that you can use to add documents to the index, update the index, or query the index.
You’ll find more information on SolrJ in Client APIs.
Here’s an example of using Solr Cell and SolrJ to add documents to a Solr index.
First, let’s use SolrJ to create a new SolrClient, then we’ll construct a request containing a ContentStream
(essentially a wrapper around a file) and sent it to Solr:
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public class SolrCellRequestDemo {
public static void main (String[] args) throws IOException, SolrServerException {
SolrClient client = new HttpSolrClient.Builder("http://localhost:8983/solr/my_collection")
.build();
ContentStreamUpdateRequest req = new ContentStreamUpdateRequest("/update/extract");
req.addFile(new File("my-file.pdf"));
req.setParam(ExtractingParams.EXTRACT_ONLY, "true");
NamedList<Object> result = client.request(req);
System.out.println("Result: " + result);
}
This operation streams the file my-file.pdf into the Solr index for my_collection .
The sample code above calls the extract command, but you can easily substitute other commands that are
supported by Solr Cell. The key class to use is the ContentStreamUpdateRequest , which makes sure the
ContentStreams are set properly. SolrJ takes care of the rest.
Note that the ContentStreamUpdateRequest is not just specific to Solr Cell. You can send CSV to the CSV
Update handler and to any other Request Handler that works with Content Streams for updates.
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Uploading Structured Data Store Data with the Data
Import Handler
Many search applications store the content to be indexed in a structured data store, such as a relational
database. The Data Import Handler (DIH) provides a mechanism for importing content from a data store
and indexing it.
In addition to relational databases, DIH can index content from HTTP based data sources such as RSS and
ATOM feeds, e-mail repositories, and structured XML where an XPath processor is used to generate fields.
DIH Concepts and Terminology
Descriptions of the Data Import Handler use several familiar terms, such as entity and processor, in specific
ways, as explained in the table below.
Datasource
As its name suggests, a datasource defines the location of the data of interest. For a database, it’s a DSN.
For an HTTP datasource, it’s the base URL.
Entity
Conceptually, an entity is processed to generate a set of documents, containing multiple fields, which
(after optionally being transformed in various ways) are sent to Solr for indexing. For a RDBMS data
source, an entity is a view or table, which would be processed by one or more SQL statements to
generate a set of rows (documents) with one or more columns (fields).
Processor
An entity processor does the work of extracting content from a data source, transforming it, and adding it
to the index. Custom entity processors can be written to extend or replace the ones supplied.
Transformer
Each set of fields fetched by the entity may optionally be transformed. This process can modify the fields,
create new fields, or generate multiple rows/documents form a single row. There are several built-in
transformers in the DIH, which perform functions such as modifying dates and stripping HTML. It is
possible to write custom transformers using the publicly available interface.
Solr’s DIH Examples
The example/example-DIH directory contains several collections to demonstrate many of the features of the
data import handler. These are available with the dih example from the Solr Control Script:
bin/solr -e dih
This launches a standalone Solr instance with several collections that correspond to detailed examples. The
available examples are atom , db , mail , solr , and tika .
All examples in this section assume you are running the DIH example server.
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Configuring DIH
Configuring solrconfig.xml for DIH
The Data Import Handler has to be registered in solrconfig.xml . For example:
<requestHandler name="/dataimport" class="org.apache.solr.handler.dataimport.DataImportHandler">
<lst name="defaults">
<str name="config">/path/to/my/DIHconfigfile.xml</str>
</lst>
</requestHandler>
The only required parameter is the config parameter, which specifies the location of the DIH configuration
file that contains specifications for the data source, how to fetch data, what data to fetch, and how to
process it to generate the Solr documents to be posted to the index.
You can have multiple DIH configuration files. Each file would require a separate definition in the
solrconfig.xml file, specifying a path to the file.
Configuring the DIH Configuration File
An annotated configuration file, based on the db collection in the dih example server, is shown below (this
file is located in example/example-DIH/solr/db/conf/db-data-config.xml ).
This example shows how to extract fields from four tables defining a simple product database. More
information about the parameters and options shown here will be described in the sections following.
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<dataConfig> ①
<dataSource driver="org.hsqldb.jdbcDriver" url="jdbc:hsqldb:./example-DIH/hsqldb/ex" user="sa"
password="secret"/> ②
<document> ③
<entity name="item" query="select * from item"
deltaQuery="select id from item where last_modified >
‘${dataimporter.last_index_time}‘"> ④
<field column="NAME" name="name" />
<entity name="feature"
query="select DESCRIPTION from FEATURE where ITEM_ID=‘${item.ID}‘"
deltaQuery="select ITEM_ID from FEATURE where last_modified >
‘${dataimporter.last_index_time}‘"
parentDeltaQuery="select ID from item where ID=${feature.ITEM_ID}"> ⑤
<field name="features" column="DESCRIPTION" />
</entity>
<entity name="item_category"
query="select CATEGORY_ID from item_category where ITEM_ID=‘${item.ID}‘"
deltaQuery="select ITEM_ID, CATEGORY_ID from item_category where last_modified >
‘${dataimporter.last_index_time}‘"
parentDeltaQuery="select ID from item where ID=${item_category.ITEM_ID}">
<entity name="category"
query="select DESCRIPTION from category where ID =
‘${item_category.CATEGORY_ID}‘"
deltaQuery="select ID from category where last_modified >
‘${dataimporter.last_index_time}‘"
parentDeltaQuery="select ITEM_ID, CATEGORY_ID from item_category where
CATEGORY_ID=${category.ID}">
<field column="description" name="cat" />
</entity>
</entity>
</entity>
</document>
</dataConfig>
①
The first element is the dataSource , in this case an HSQLDB database. The path to the JDBC driver and
the JDBC URL and login credentials are all specified here. Other permissible attributes include whether
or not to autocommit to Solr, the batchsize used in the JDBC connection, and a readOnly flag. The
password attribute is optional if there is no password set for the DB.
②
Alternately the password can be encrypted as follows. This is the value obtained as a result of the
command openssl enc -aes-128-cbc -a -salt -in pwd.txt
password="U2FsdGVkX18QMjY0yfCqlfBMvAB4d3XkwY96L7gfO2o=" . When the password is encrypted, you
must provide an extra attribute encryptKeyFile="/location/of/encryptionkey" . This file should a
text file with a single line containing the encrypt/decrypt password.
③
A document element follows, containing multiple entity elements. Note that entity elements can be
nested, and this allows the entity relationships in the sample database to be mirrored here, so that we
can generate a denormalized Solr record which may include multiple features for one item, for instance.
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④
The possible attributes for the entity element are described in later sections. Entity elements may
contain one or more field elements, which map the data source field names to Solr fields, and
optionally specify per-field transformations. This entity is the root entity.
⑤
This entity is nested and reflects the one-to-many relationship between an item and its multiple
features. Note the use of variables; ${item.ID} is the value of the column ‘ID‘ for the current item ( item
referring to the entity name).
Datasources can still be specified in solrconfig.xml . These must be specified in the defaults section of the
handler in solrconfig.xml . However, these are not parsed until the main configuration is loaded.
The entire configuration itself can be passed as a request parameter using the dataConfig parameter rather
than using a file. When configuration errors are encountered, the error message is returned in XML format.
A reload-config command is also supported, which is useful for validating a new configuration file, or if you
want to specify a file, load it, and not have it reloaded again on import. If there is an xml mistake in the
configuration a user-friendly message is returned in xml format. You can then fix the problem and do a
reload-config .
?
You can also view the DIH configuration in the Solr Admin UI from the Dataimport Screen.
It includes an interface to import content.
DIH Request Parameters
Request parameters can be substituted in configuration with placeholder
${dataimporter.request.paramname} , as in this example:
<dataSource driver="org.hsqldb.jdbcDriver"
url="${dataimporter.request.jdbcurl}"
user="${dataimporter.request.jdbcuser}"
password="${dataimporter.request.jdbcpassword}" />
These parameters can then be passed to the full-import command or defined in the <defaults> section in
solrconfig.xml . This example shows the parameters with the full-import command:
http://localhost:8983/solr/dih/dataimport?command=full-import&jdbcurl=jdbc:hsqldb:./example-
DIH/hsqldb/ex&jdbcuser=sa&jdbcpassword=secret
DataImportHandler Commands
DIH commands are sent to Solr via an HTTP request. The following operations are supported.
abort
Aborts an ongoing operation. For example:
http://localhost:8983/solr/dih/dataimport?command=abort .
delta-import
For incremental imports and change detection. Only the SqlEntityProcessor supports delta imports.
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For example: http://localhost:8983/solr/dih/dataimport?command=delta-import .
This command supports the same clean , commit , optimize and debug parameters as full-import
command described below.
full-import
A Full Import operation can be started with a URL such as
http://localhost:8983/solr/dih/dataimport?command=full-import . The command returns
immediately.
The operation will be started in a new thread and the status attribute in the response should be shown as
busy. The operation may take some time depending on the size of dataset. Queries to Solr are not
blocked during full-imports.
When a full-import command is executed, it stores the start time of the operation in a file located at
conf/dataimport.properties . This stored timestamp is used when a delta-import operation is
executed.
Commands available to full-import are:
clean
Default is true. Tells whether to clean up the index before the indexing is started.
commit
Default is true. Tells whether to commit after the operation.
debug
Default is false. Runs the command in debug mode and is used by the interactive development mode.
Note that in debug mode, documents are never committed automatically. If you want to run debug
mode and commit the results too, add commit=true as a request parameter.
entity
The name of an entity directly under the <document> tag in the configuration file. Use this to execute
one or more entities selectively.
Multiple "entity" parameters can be passed on to run multiple entities at once. If nothing is passed, all
entities are executed.
optimize
Default is true. Tells Solr whether to optimize after the operation.
synchronous
Blocks request until import is completed. Default is false.
reload-config
If the configuration file has been changed and you wish to reload it without restarting Solr, run the
command http://localhost:8983/solr/dih/dataimport?command=reload-config
status
This command returns statistics on the number of documents created, deleted, queries run, rows
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fetched, status, and so on. For example:
http://localhost:8983/solr/dih/dataimport?command=status .
show-config
This command responds with configuration:
http://localhost:8983/solr/dih/dataimport?command=show-config .
Property Writer
The propertyWriter element defines the date format and locale for use with delta queries. It is an optional
configuration. Add the element to the DIH configuration file, directly under the dataConfig element.
<propertyWriter dateFormat="yyyy-MM-dd HH:mm:ss" type="SimplePropertiesWriter"
directory="data" filename="my_dih.properties" locale="en-US" />
The parameters available are:
dateFormat
A java.text.SimpleDateFormat to use when converting the date to text. The default is yyyy-MM-dd
HH:mm:ss .
type
The implementation class. Use SimplePropertiesWriter for non-SolrCloud installations. If using
SolrCloud, use ZKPropertiesWriter .
If this is not specified, it will default to the appropriate class depending on if SolrCloud mode is enabled.
directory
Used with the SimplePropertiesWriter only. The directory for the properties file. If not specified, the
default is conf .
filename
Used with the SimplePropertiesWriter only. The name of the properties file.
If not specified, the default is the requestHandler name (as defined in solrconfig.xml , appended by
".properties" (such as, dataimport.properties ).
locale
The locale. If not defined, the ROOT locale is used. It must be specified as language-country (BCP 47
language tag). For example, en-US .
Data Sources
A data source specifies the origin of data and its type. Somewhat confusingly, some data sources are
configured within the associated entity processor. Data sources can also be specified in solrconfig.xml ,
which is useful when you have multiple environments (for example, development, QA, and production)
differing only in their data sources.
You can create a custom data source by writing a class that extends
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org.apache.solr.handler.dataimport.DataSource .
The mandatory attributes for a data source definition are its name and type. The name identifies the data
source to an Entity element.
The types of data sources available are described below.
ContentStreamDataSource
This takes the POST data as the data source. This can be used with any EntityProcessor that uses a
DataSource<Reader> .
FieldReaderDataSource
This can be used where a database field contains XML which you wish to process using the
XPathEntityProcessor. You would set up a configuration with both JDBC and FieldReader data sources, and
two entities, as follows:
<dataSource name="a1" driver="org.hsqldb.jdbcDriver" ... />
<dataSource name="a2" type="FieldReaderDataSource" />
<document>
<!-- processor for database -->
<entity name ="e1" dataSource="a1" processor="SqlEntityProcessor" pk="docid"
query="select * from t1 ...">
<!-- nested XpathEntity; the field in the parent which is to be used for
XPath is set in the "datafield" attribute in place of the "url" attribute -->
<entity name="e2" dataSource="a2" processor="XPathEntityProcessor"
dataField="e1.fieldToUseForXPath">
<!-- XPath configuration follows -->
...
</entity>
</entity>
</document>
The FieldReaderDataSource can take an encoding parameter, which will default to "UTF-8" if not specified.
It must be specified as language-country. For example, en-US .
FileDataSource
This can be used like a URLDataSource, but is used to fetch content from files on disk. The only difference
from URLDataSource , when accessing disk files, is how a pathname is specified.
This data source accepts these optional attributes.
basePath
The base path relative to which the value is evaluated if it is not absolute.
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encoding
Defines the character encoding to use. If not defined, UTF-8 is used.
JdbcDataSource
This is the default datasource. It’s used with the SqlEntityProcessor. See the example in the
FieldReaderDataSource section for details on configuration. JdbcDatasource supports at least the following
attributes:
driver, url, user, password, encryptKeyFile
Usual JDBC connection properties.
batchSize
Passed to Statement#setFetchSize , default value 500.
For MySQL driver, which doesn’t honor fetchSize and pulls whole resultSet, which often lead to
OutOfMemoryError.
In this case, set batchSize=-1 that pass setFetchSize(Integer.MIN_VALUE), and switch result set to pull
row by row
All of them substitute properties via ${placeholders} .
URLDataSource
This data source is often used with XPathEntityProcessor to fetch content from an underlying file:// or
http:// location. Here’s an example:
<dataSource name="a"
type="URLDataSource"
baseUrl="http://host:port/"
encoding="UTF-8"
connectionTimeout="5000"
readTimeout="10000"/>
The URLDataSource type accepts these optional parameters:
baseURL
Specifies a new baseURL for pathnames. You can use this to specify host/port changes between
Dev/QA/Prod environments. Using this attribute isolates the changes to be made to the solrconfig.xml
connectionTimeout
Specifies the length of time in milliseconds after which the connection should time out. The default value
is 5000ms.
encoding
By default the encoding in the response header is used. You can use this property to override the default
encoding.
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readTimeout
Specifies the length of time in milliseconds after which a read operation should time out. The default
value is 10000ms.
Entity Processors
Entity processors extract data, transform it, and add it to a Solr index. Examples of entities include views or
tables in a data store.
Each processor has its own set of attributes, described in its own section below. In addition, there are
several attributes common to all entities which may be specified:
dataSource
The name of a data source. If there are multiple data sources defined, use this attribute with the name of
the data source for this entity.
name
Required. The unique name used to identify an entity.
pk
The primary key for the entity. It is optional, and required only when using delta-imports. It has no
relation to the uniqueKey defined in schema.xml but they can both be the same.
This attribute is mandatory if you do delta-imports and then refer to the column name in
${dataimporter.delta.<column-name>} which is used as the primary key.
processor
Default is SqlEntityProcessor. Required only if the datasource is not RDBMS.
onError
Defines what to do if an error is encountered.
Permissible values are:
abort
Stops the import.
skip
Skips the current document.
continue
Ignores the error and processing continues.
preImportDeleteQuery
Before a full-import command, use this query this to cleanup the index instead of using *:* . This is
honored only on an entity that is an immediate sub-child of <document> .
postImportDeleteQuery
Similar to preImportDeleteQuery , but it executes after the import has completed.
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rootEntity
By default the entities immediately under <document> are root entities. If this attribute is set to false, the
entity directly falling under that entity will be treated as the root entity (and so on). For every row
returned by the root entity, a document is created in Solr.
transformer
Optional. One or more transformers to be applied on this entity.
cacheImpl
Optional. A class (which must implement DIHCache ) to use for caching this entity when doing lookups
from an entity which wraps it. Provided implementation is SortedMapBackedCache .
cacheKey
The name of a property of this entity to use as a cache key if cacheImpl is specified.
cacheLookup
An entity + property name that will be used to lookup cached instances of this entity if cacheImpl is
specified.
where
An alternative way to specify cacheKey and cacheLookup concatenated with ‘=‘.
For example, where="CODE=People.COUNTRY_CODE" is equivalent to cacheKey="CODE"
cacheLookup="People.COUNTRY_CODE"
child="true"
Enables indexing document blocks aka Nested Child Documents for searching with Block Join Query
Parsers. It can be only specified on the <entity> element under another root entity. It switches from
default behavior (merging field values) to nesting documents as children documents.
Note: parent <entity> should add a field which is used as a parent filter in query time.
join="zipper"
Enables merge join, aka "zipper" algorithm, for joining parent and child entities without cache. It should
be specified at child (nested) <entity> . It implies that parent and child queries return results ordered by
keys, otherwise it throws an exception. Keys should be specified either with where attribute or with
cacheKey and cacheLookup .
Entity Caching
Caching of entities in DIH is provided to avoid repeated lookups for same entities again and again. The
default SortedMapBackedCache is a HashMap where a key is a field in the row and the value is a bunch of rows
for that same key.
In the example below, each manufacturer entity is cached using the id property as a cache key. Cache
lookups will be performed for each product entity based on the product’s manu property. When the cache
has no data for a particular key, the query is run and the cache is populated
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<entity name="product" query="select description,sku, manu from product" >
<entity name="manufacturer" query="select id, name from manufacturer"
cacheKey="id" cacheLookup="product.manu" cacheImpl="SortedMapBackedCache"/>
</entity>
The SQL Entity Processor
The SqlEntityProcessor is the default processor. The associated JdbcDataSource should be a JDBC URL.
The entity attributes specific to this processor are shown in the table below. These are in addition to the
attributes common to all entity processors described above.
query
Required. The SQL query used to select rows.
deltaQuery
SQL query used if the operation is delta-import. This query selects the primary keys of the rows which will
be parts of the delta-update. The pks will be available to the deltaImportQuery through the variable
${dataimporter.delta.<column-name>} .
parentDeltaQuery
SQL query used if the operation is delta-import .
deletedPkQuery
SQL query used if the operation is delta-import .
deltaImportQuery
SQL query used if the operation is delta-import . If this is not present, DIH tries to construct the import
query by (after identifying the delta) modifying the ‘query‘ (this is error prone).
There is a namespace ${dataimporter.delta.<column-name>} which can be used in this query. For
example, select * from tbl where id=${dataimporter.delta.id} .
The XPathEntityProcessor
This processor is used when indexing XML formatted data. The data source is typically URLDataSource or
FileDataSource. XPath can also be used with the FileListEntityProcessor described below, to generate a
document from each file.
The entity attributes unique to this processor are shown below. These are in addition to the attributes
common to all entity processors described above.
Processor
Required. Must be set to XpathEntityProcessor .
url
Required. The HTTP URL or file location.
stream
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Optional: Set to true for a large file or download.
forEach
Required unless you define useSolrAddSchema . The XPath expression which demarcates each record. This
will be used to set up the processing loop.
xsl
Optional: Its value (a URL or filesystem path) is the name of a resource used as a preprocessor for
applying the XSL transformation.
useSolrAddSchema
Set this to true if the content is in the form of the standard Solr update XML schema.
Each <field> element in the entity can have the following attributes as well as the default ones.
xpath
Required. The XPath expression which will extract the content from the record for this field. Only a subset
of XPath syntax is supported.
commonField
Optional. If true, then when this field is encountered in a record it will be copied to future records when
creating a Solr document.
flatten
Optional. If set to true, then any children text nodes are collected to form the value of a field.
?
The default value is false, meaning that if there are any sub-elements of the node
pointed to by the XPath expression, they will be quietly omitted.
Here is an example from the atom collection in the dih example (data-config file found at example/example-
DIH/solr/atom/conf/atom-data-config.xml ):
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<dataConfig>
<dataSource type="URLDataSource"/>
<document>
<entity name="stackoverflow"
url="https://stackoverflow.com/feeds/tag/solr"
processor="XPathEntityProcessor"
forEach="/feed|/feed/entry"
transformer="HTMLStripTransformer,RegexTransformer">
<!-- Pick this value up from the feed level and apply to all documents -->
<field column="lastchecked_dt" xpath="/feed/updated" commonField="true"/>
<!-- Keep only the final numeric part of the URL -->
<field column="id" xpath="/feed/entry/id" regex=".*/" replaceWith=""/>
<field column="title" xpath="/feed/entry/title"/>
<field column="author" xpath="/feed/entry/author/name"/>
<field column="category" xpath="/feed/entry/category/@term"/>
<field column="link" xpath="/feed/entry/link[@rel=‘alternate‘]/@href"/>
<!-- Use transformers to convert HTML into plain text.
There is also an UpdateRequestProcess to trim remaining spaces.
-->
<field column="summary" xpath="/feed/entry/summary" stripHTML="true" regex="( |\n)+"
replaceWith=" "/>
<!-- Ignore namespaces when matching XPath -->
<field column="rank" xpath="/feed/entry/rank"/>
<field column="published_dt" xpath="/feed/entry/published"/>
<field column="updated_dt" xpath="/feed/entry/updated"/>
</entity>
</document>
</dataConfig>
The MailEntityProcessor
The MailEntityProcessor uses the Java Mail API to index email messages using the IMAP protocol.
The MailEntityProcessor works by connecting to a specified mailbox using a username and password,
fetching the email headers for each message, and then fetching the full email contents to construct a
document (one document for each mail message).
The entity attributes unique to the MailEntityProcessor are shown below. These are in addition to the
attributes common to all entity processors described above.
processor
Required. Must be set to MailEntityProcessor .
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user
Required. Username for authenticating to the IMAP server; this is typically the email address of the
mailbox owner.
password
Required. Password for authenticating to the IMAP server.
host
Required. The IMAP server to connect to.
protocol
Required. The IMAP protocol to use, valid values are: imap, imaps, gimap, and gimaps.
fetchMailsSince
Optional. Date/time used to set a filter to import messages that occur after the specified date; expected
format is: yyyy-MM-dd HH:mm:ss .
folders
Required. Comma-delimited list of folder names to pull messages from, such as "inbox".
recurse
Optional. Default is true. Flag to indicate if the processor should recurse all child folders when looking for
messages to import.
include
Optional. Comma-delimited list of folder patterns to include when processing folders (can be a literal
value or regular expression).
exclude
Optional. Comma-delimited list of folder patterns to exclude when processing folders (can be a literal
value or regular expression). Excluded folder patterns take precedence over include folder patterns.
processAttachement or processAttachments
Optional. Default is true. Use Tika to process message attachments.
includeContent
Optional. Default is true. Include the message body when constructing Solr documents for indexing.
Here is an example from the mail collection of the dih example (data-config file found at example/example-
DIH/mail/conf/mail-data-config.xml ):
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<dataConfig>
<document>
<entity processor="MailEntityProcessor"
user="email@gmail.com"
password="password"
host="imap.gmail.com"
protocol="imaps"
fetchMailsSince="2014-06-30 00:00:00"
batchSize="20"
folders="inbox"
processAttachement="false"
name="mail_entity"/>
</document>
</dataConfig>
Importing New Emails Only
After running a full import, the MailEntityProcessor keeps track of the timestamp of the previous import so
that subsequent imports can use the fetchMailsSince filter to only pull new messages from the mail server.
This occurs automatically using the DataImportHandler dataimport.properties file (stored in conf ).
For instance, if you set fetchMailsSince="2014-08-22 00:00:00" in your mail-data-config.xml , then all
mail messages that occur after this date will be imported on the first run of the importer. Subsequent
imports will use the date of the previous import as the fetchMailsSince filter, so that only new emails since
the last import are indexed each time.
GMail Extensions
When connecting to a GMail account, you can improve the efficiency of the MailEntityProcessor by setting
the protocol to gimap or gimaps.
This allows the processor to send the fetchMailsSince filter to the GMail server to have the date filter
applied on the server, which means the processor only receives new messages from the server. However,
GMail only supports date granularity, so the server-side filter may return previously seen messages if run
more than once a day.
The TikaEntityProcessor
The TikaEntityProcessor uses Apache Tika to process incoming documents. This is similar to Uploading Data
with Solr Cell using Apache Tika, but using DataImportHandler options instead.
The parameters for this processor are described in the table below. These are in addition to the attributes
common to all entity processors described above.
dataSource
This parameter defines the data source and an optional name which can be referred to in later parts of
the configuration if needed. This is the same dataSource explained in the description of general entity
processor attributes above.
The available data source types for this processor are:
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? BinURLDataSource: used for HTTP resources, but can also be used for files.
? BinContentStreamDataSource: used for uploading content as a stream.
? BinFileDataSource: used for content on the local filesystem.
url
Required. The path to the source file(s), as a file path or a traditional internet URL.
htmlMapper
Optional. Allows control of how Tika parses HTML. If this parameter is defined, it must be either default
or identity; if it is absent, "default" is assumed.
The "default" mapper strips much of the HTML from documents while the "identity" mapper passes all
HTML as-is with no modifications.
format
The output format. The options are text, xml, html or none. The default is "text" if not defined. The
format "none" can be used if metadata only should be indexed and not the body of the documents.
parser
Optional. The default parser is org.apache.tika.parser.AutoDetectParser . If a custom or other parser
should be used, it should be entered as a fully-qualified name of the class and path.
fields
The list of fields from the input documents and how they should be mapped to Solr fields. If the attribute
meta is defined as "true", the field will be obtained from the metadata of the document and not parsed
from the body of the main text.
extractEmbedded
Instructs the TikaEntityProcessor to extract embedded documents or attachments when true. If false,
embedded documents and attachments will be ignored.
onError
By default, the TikaEntityProcessor will stop processing documents if it finds one that generates an error.
If you define onError to "skip", the TikaEntityProcessor will instead skip documents that fail processing
and log a message that the document was skipped.
Here is an example from the tika collection of the dih example (data-config file found in example/example-
DIH/tika/conf/tika-data-config.xml ):
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<dataConfig>
<dataSource type="BinFileDataSource"/>
<document>
<entity name="file" processor="FileListEntityProcessor" dataSource="null"
baseDir="${solr.install.dir}/example/exampledocs" fileName=".*pdf"
rootEntity="false">
<field column="file" name="id"/>
<entity name="pdf" processor="TikaEntityProcessor"
url="${file.fileAbsolutePath}" format="text">
<field column="Author" name="author" meta="true"/>
<!-- in the original PDF, the Author meta-field name is upper-cased,
but in Solr schema it is lower-cased
-->
<field column="title" name="title" meta="true"/>
<field column="dc:format" name="format" meta="true"/>
<field column="text" name="text"/>
</entity>
</entity>
</document>
</dataConfig>
The FileListEntityProcessor
This processor is basically a wrapper, and is designed to generate a set of files satisfying conditions specified
in the attributes which can then be passed to another processor, such as the XPathEntityProcessor.
The entity information for this processor would be nested within the FileListEntity entry. It generates five
implicit fields: fileAbsolutePath , fileDir , fileSize , fileLastModified , and file , which can be used in the
nested processor. This processor does not use a data source.
The attributes specific to this processor are described in the table below:
fileName
Required. A regular expression pattern to identify files to be included.
basedir
Required. The base directory (absolute path).
recursive
Whether to search directories recursively. Default is ‘false‘.
excludes
A regular expression pattern to identify files which will be excluded.
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newerThan
A date in the format yyyy-MM-ddHH:mm:ss or a date math expression ( NOW - 2YEARS ).
olderThan
A date, using the same formats as newerThan.
rootEntity
This should be set to false. This ensures that each row (filepath) emitted by this processor is considered
to be a document.
dataSource
Must be set to null.
The example below shows the combination of the FileListEntityProcessor with another processor which will
generate a set of fields from each file found.
<dataConfig>
<dataSource type="FileDataSource"/>
<document>
<!-- this outer processor generates a list of files satisfying the conditions
specified in the attributes -->
<entity name="f" processor="FileListEntityProcessor"
fileName=".*xml"
newerThan="‘NOW-30DAYS‘"
recursive="true"
rootEntity="false"
dataSource="null"
baseDir="/my/document/directory">
<!-- this processor extracts content using XPath from each file found -->
<entity name="nested" processor="XPathEntityProcessor"
forEach="/rootelement" url="${f.fileAbsolutePath}" >
<field column="name" xpath="/rootelement/name"/>
<field column="number" xpath="/rootelement/number"/>
</entity>
</entity>
</document>
</dataConfig>
LineEntityProcessor
This EntityProcessor reads all content from the data source on a line by line basis and returns a field called
rawLine for each line read. The content is not parsed in any way; however, you may add transformers to
manipulate the data within the rawLine field, or to create other additional fields.
The lines read can be filtered by two regular expressions specified with the acceptLineRegex and
omitLineRegex attributes.
The LineEntityProcessor has the following attributes:
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url
A required attribute that specifies the location of the input file in a way that is compatible with the
configured data source. If this value is relative and you are using FileDataSource or URLDataSource, it
assumed to be relative to baseLoc.
acceptLineRegex
An optional attribute that if present discards any line which does not match the regular expression.
omitLineRegex
An optional attribute that is applied after any acceptLineRegex and that discards any line which matches
this regular expression.
For example:
<entity name="jc"
processor="LineEntityProcessor"
acceptLineRegex="^.*\.xml$"
omitLineRegex="/obsolete"
url="file:///Volumes/ts/files.lis"
rootEntity="false"
dataSource="myURIreader1"
transformer="RegexTransformer,DateFormatTransformer">
</entity>
While there are use cases where you might need to create a Solr document for each line read from a file, it is
expected that in most cases that the lines read by this processor will consist of a pathname, which in turn
will be consumed by another entity processor, such as the XPathEntityProcessor.
PlainTextEntityProcessor
This EntityProcessor reads all content from the data source into an single implicit field called plainText . The
content is not parsed in any way, however you may add transformers to manipulate the data within the
plainText as needed, or to create other additional fields.
For example:
<entity processor="PlainTextEntityProcessor" name="x" url="http://abc.com/a.txt" dataSource=
"data-source-name">
<!-- copies the text to a field called ‘text‘ in Solr-->
<field column="plainText" name="text"/>
</entity>
Ensure that the dataSource is of type DataSource<Reader> ( FileDataSource , URLDataSource ).
SolrEntityProcessor
This EntityProcessor imports data from different Solr instances and cores. The data is retrieved based on a
specified filter query. This EntityProcessor is useful in cases you want to copy your Solr index and want to
modify the data in the target index.
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The SolrEntityProcessor can only copy fields that are stored in the source index.
The SolrEntityProcessor supports the following parameters:
url
Required. The URL of the source Solr instance and/or core.
query
Required. The main query to execute on the source index.
fq
Any filter queries to execute on the source index. If more than one filter query is defined, they must be
separated by a comma.
rows
The number of rows to return for each iteration. The default is 50 rows.
fl
A comma-separated list of fields to fetch from the source index. Note, these fields must be stored in the
source Solr instance.
qt
The search handler to use, if not the default.
wt
The response format to use, either javabin or xml.
timeout
The query timeout in seconds. The default is 5 minutes (300 seconds).
cursorMark="true"
Use this to enable cursor for efficient result set scrolling
sort="id asc"
This should be used to specify a sort parameter referencing the uniqueKey field of the source Solr
instance. See Pagination of Results for details.
Here is a simple example of a SolrEntityProcessor:
<dataConfig>
<document>
<entity name="sep" processor="SolrEntityProcessor"
url="http://127.0.0.1:8983/solr/db "
query="*:*"
fl="*,orig_version_l:_version_,ignored_price_c:price_c"/>
</document>
</dataConfig>
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Transformers
Transformers manipulate the fields in a document returned by an entity. A transformer can create new fields
or modify existing ones. You must tell the entity which transformers your import operation will be using, by
adding an attribute containing a comma separated list to the <entity> element.
<entity name="abcde" transformer="org.apache.solr....,my.own.transformer,..." />
Specific transformation rules are then added to the attributes of a <field> element, as shown in the
examples below. The transformers are applied in the order in which they are specified in the transformer
attribute.
The DataImportHandler contains several built-in transformers. You can also write your own custom
transformers, as described in the DIHCustomTransformer section of the Solr Wiki. The ScriptTransformer
(described below) offers an alternative method for writing your own transformers.
ClobTransformer
You can use the ClobTransformer to create a string out of a CLOB in a database. A CLOB is a character large
object: a collection of character data typically stored in a separate location that is referenced in the
database.
The ClobTransformer accepts these attributes:
clob
Boolean value to signal if ClobTransformer should process this field or not. If this attribute is omitted,
then the corresponding field is not transformed.
sourceColName
The source column to be used as input. If this is absent source and target are same
Here’s an example of invoking the ClobTransformer.
<entity name="example" transformer="ClobTransformer" ...>
<field column="hugeTextField" clob="true" />
...
</entity>
The DateFormatTransformer
This transformer converts dates from one format to another. This would be useful, for example, in a
situation where you wanted to convert a field with a fully specified date/time into a less precise date format,
for use in faceting.
DateFormatTransformer applies only on the fields with an attribute dateTimeFormat . Other fields are not
modified.
This transformer recognizes the following attributes:
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dateTimeFormat
The format used for parsing this field. This must comply with the syntax of the Java SimpleDateFormat
class.
sourceColName
The column on which the dateFormat is to be applied. If this is absent source and target are same.
locale
The locale to use for date transformations. If not defined, the ROOT locale is used. It must be specified as
language-country (BCP 47 language tag). For example, en-US .
Here is example code that returns the date rounded up to the month "2007-JUL":
<entity name="en" pk="id" transformer="DateFormatTransformer" ... >
...
<field column="date" sourceColName="fulldate" dateTimeFormat="yyyy-MMM"/>
</entity>
The HTMLStripTransformer
You can use this transformer to strip HTML out of a field.
There is one attribute for this transformer, stripHTML , which is a boolean value (true or false) to signal if the
HTMLStripTransformer should process the field or not.
For example:
<entity name="e" transformer="HTMLStripTransformer" ... >
<field column="htmlText" stripHTML="true" />
...
</entity>
The LogTransformer
You can use this transformer to log data to the console or log files. For example:
<entity ...
transformer="LogTransformer"
logTemplate="The name is ${e.name}" logLevel="debug">
....
</entity>
Unlike other transformers, the LogTransformer does not apply to any field, so the attributes are applied on
the entity itself.
The NumberFormatTransformer
Use this transformer to parse a number from a string, converting it into the specified format, and optionally
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using a different locale.
NumberFormatTransformer will be applied only to fields with an attribute formatStyle .
This transformer recognizes the following attributes:
formatStyle
The format used for parsing this field. The value of the attribute must be one of number , percent ,
integer , or currency . This uses the semantics of the Java NumberFormat class.
sourceColName
The column on which the NumberFormat is to be applied. This is attribute is absent. The source column
and the target column are the same.
locale
The locale to be used for parsing the strings. The locale. If not defined, the ROOT locale is used. It must
be specified as language-country (BCP 47 language tag). For example, en-US .
For example:
<entity name="en" pk="id" transformer="NumberFormatTransformer" ...>
...
<!-- treat this field as UK pounds -->
<field name="price_uk" column="price" formatStyle="currency" locale="en-UK"/>
</entity>
The RegexTransformer
The regex transformer helps in extracting or manipulating values from fields (from the source) using
Regular Expressions. The actual class name is org.apache.solr.handler.dataimport.RegexTransformer .
But as it belongs to the default package the package-name can be omitted.
The table below describes the attributes recognized by the regex transformer.
regex
The regular expression that is used to match against the column or sourceColName’s value(s). If
replaceWith is absent, each regex group is taken as a value and a list of values is returned.
sourceColName
The column on which the regex is to be applied. If not present, then the source and target are identical.
splitBy
Used to split a string. It returns a list of values. Note, this is a regular expression so it may need to be
escaped (e.g., via back-slashes).
groupNames
A comma separated list of field column names, used where the regex contains groups and each group is
to be saved to a different field. If some groups are not to be named leave a space between commas.
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replaceWith
Used along with regex . It is equivalent to the method new
String(<sourceColVal>).replaceAll(<regex>, <replaceWith>) .
Here is an example of configuring the regex transformer:
<entity name="foo" transformer="RegexTransformer"
query="select full_name, emailids from foo"> ①
<field column="full_name"/> ②
<field column="firstName" regex="Mr(\w*)\b.*" sourceColName="full_name"/>
<field column="lastName" regex="Mr.*?\b(\w*)" sourceColName="full_name"/>
<!-- another way of doing the same -->
<field column="fullName" regex="Mr(\w*)\b(.*)" groupNames="firstName,lastName"/>
<field column="mailId" splitBy="," sourceColName="emailids"/> ③
</entity>
①
In this example, regex and sourceColName are custom attributes used by the transformer.
②
The transformer reads the field full_name from the result set and transforms it to two new target fields,
firstName and lastName . Even though the query returned only one column, full_name , in the result set,
the Solr document gets two extra fields firstName and lastName which are "derived" fields. These new
fields are only created if the regexp matches.
③
The emailids field in the table can be a comma-separated value. It ends up producing one or more
email IDs, and we expect the mailId to be a multivalued field in Solr.
Note that this transformer can be used to either split a string into tokens based on a splitBy pattern, or to
perform a string substitution as per replaceWith , or it can assign groups within a pattern to a list of
groupNames . It decides what it is to do based upon the above attributes splitBy , replaceWith and
groupNames which are looked for in order. This first one found is acted upon and other unrelated attributes
are ignored.
The ScriptTransformer
The script transformer allows arbitrary transformer functions to be written in any scripting language
supported by Java, such as Javascript, JRuby, Jython, Groovy, or BeanShell. Javascript is integrated into Java 8;
you’ll need to integrate other languages yourself.
Each function you write must accept a row variable (which corresponds to a Java Map<String,Object> , thus
permitting get,put,remove operations). Thus you can modify the value of an existing field or add new fields.
The return value of the function is the returned object.
The script is inserted into the DIH configuration file at the top level and is called once for each row.
Here is a simple example.
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<dataconfig>
<!-- simple script to generate a new row, converting a temperature from Fahrenheit to
Centigrade -->
<script><![CDATA[
function f2c(row) {
var tempf, tempc;
tempf = row.get(‘temp_f‘);
if (tempf != null) {
tempc = (tempf - 32.0)*5.0/9.0;
row.put(‘temp_c‘, temp_c);
}
return row;
}
]]>
</script>
<document>
<!-- the function is specified as an entity attribute -->
<entity name="e1" pk="id" transformer="script:f2c" query="select * from X">
....
</entity>
</document>
</dataConfig>
The TemplateTransformer
You can use the template transformer to construct or modify a field value, perhaps using the value of other
fields. You can insert extra text into the template.
<entity name="en" pk="id" transformer="TemplateTransformer" ...>
...
<!-- generate a full address from fields containing the component parts -->
<field column="full_address" template="${en.street},${en.city},${en.zip}" />
</entity>
Special Commands for DIH
You can pass special commands to the DIH by adding any of the variables listed below to any row returned
by any component:
$skipDoc
Skip the current document; that is, do not add it to Solr. The value can be the string true or false .
$skipRow
Skip the current row. The document will be added with rows from other entities. The value can be the
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string true or false .
$deleteDocById
Delete a document from Solr with this ID. The value has to be the uniqueKey value of the document.
$deleteDocByQuery
Delete documents from Solr using this query. The value must be a Solr Query.
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Updating Parts of Documents
Once you have indexed the content you need in your Solr index, you will want to start thinking about your
strategy for dealing with changes to those documents. Solr supports three approaches to updating
documents that have only partially changed.
The first is atomic updates. This approach allows changing only one or more fields of a document without
having to re-index the entire document.
The second approach is known as in-place updates. This approach is similar to atomic updates (is a subset of
atomic updates in some sense), but can be used only for updating single valued non-indexed and non-
stored docValue-based numeric fields.
The third approach is known as optimistic concurrency or optimistic locking. It is a feature of many NoSQL
databases, and allows conditional updating a document based on its version. This approach includes
semantics and rules for how to deal with version matches or mis-matches.
Atomic Updates (and in-place updates) and Optimistic Concurrency may be used as independent strategies
for managing changes to documents, or they may be combined: you can use optimistic concurrency to
conditionally apply an atomic update.
Atomic Updates
Solr supports several modifiers that atomically update values of a document. This allows updating only
specific fields, which can help speed indexing processes in an environment where speed of index additions is
critical to the application.
To use atomic updates, add a modifier to the field that needs to be updated. The content can be updated,
added to, or incrementally increased if a number.
Modifier Usage
set Set or replace the field value(s) with the specified value(s), or remove the
values if ‘null‘ or empty list is specified as the new value.
May be specified as a single value, or as a list for multiValued fields
add Adds the specified values to a multiValued field.
May be specified as a single value, or as a list.
remove Removes (all occurrences of) the specified values from a multiValued field.
May be specified as a single value, or as a list.
removeregex Removes all occurrences of the specified regex from a multiValued field.
May be specified as a single value, or as a list.
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Modifier Usage
inc Increments a numeric value by a specific amount.
Must be specified as a single numeric value.
Field Storage
The core functionality of atomically updating a document requires that all fields in your schema must be
configured as stored ( stored="true" ) or docValues ( docValues="true" ) except for fields which are
<copyField/> destinations, which must be configured as stored="false" . Atomic updates are applied to
the document represented by the existing stored field values. All data in copyField destinations fields must
originate from ONLY copyField sources.
If <copyField/> destinations are configured as stored, then Solr will attempt to index both the current value
of the field as well as an additional copy from any source fields. If such fields contain some information that
comes from the indexing program and some information that comes from copyField, then the information
which originally came from the indexing program will be lost when an atomic update is made.
There are other kinds of derived fields that must also be set so they aren’t stored. Some spatial field types
use derived fields. Examples of this are solr.BBoxField and solr.LatLonType. These types create additional
fields which are normally specified by a dynamic field definition. That dynamic field definition must be not
stored, or indexing will fail.
Example
If the following document exists in our collection:
{"id":"mydoc",
"price":10,
"popularity":42,
"categories":["kids"],
"promo_ids":["a123x"],
"tags":["free_to_try","buy_now","clearance","on_sale"]
}
And we apply the following update command:
{"id":"mydoc",
"price":{"set":99},
"popularity":{"inc":20},
"categories":{"add":["toys","games"]},
"promo_ids":{"remove":"a123x"},
"tags":{"remove":["free_to_try","on_sale"]}
}
The resulting document in our collection will be:
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{"id":"mydoc",
"price":99,
"popularity":62,
"categories":["kids","toys","games"],
"tags":["buy_now","clearance"]
}
In-Place Updates
In-place updates are very similar to atomic updates; in some sense, this is a subset of atomic updates. In
regular atomic updates, the entire document is re-indexed internally during the application of the update.
However, in this approach, only the fields to be updated are affected and the rest of the documents are not
re-indexed internally. Hence, the efficiency of updating in-place is unaffected by the size of the documents
that are updated (i.e., number of fields, size of fields, etc.). Apart from these internal differences, there is no
functional difference between atomic updates and in-place updates.
An atomic update operation is performed using this approach only when the fields to be updated meet
these three conditions:
? are non-indexed ( indexed="false" ), non-stored ( stored="false" ), single valued (
multiValued="false" ) numeric docValues ( docValues="true" ) fields;
? the _version_ field is also a non-indexed, non-stored single valued docValues field; and,
? copy targets of updated fields, if any, are also non-indexed, non-stored single valued numeric docValues
fields.
To use in-place updates, add a modifier to the field that needs to be updated. The content can be updated or
incrementally increased.
Modifier Usage
set Set or replace the field value(s) with the specified value(s).
May be specified as a single value.
inc Increments a numeric value by a specific amount.
Must be specified as a single numeric value.
Example
If the price and popularity fields are defined in the schema as:
<field name="price" type="float" indexed="false" stored="false" docValues="true"/>
<field name="popularity" type="float" indexed="false" stored="false" docValues="true"/>
If the following document exists in our collection:
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{
"id":"mydoc",
"price":10,
"popularity":42,
"categories":["kids"],
"promo_ids":["a123x"],
"tags":["free_to_try","buy_now","clearance","on_sale"]
}
And we apply the following update command:
{
"id":"mydoc",
"price":{"set":99},
"popularity":{"inc":20}
}
The resulting document in our collection will be:
{
"id":"mydoc",
"price":99,
"popularity":62,
"categories":["kids"],
"promo_ids":["a123x"],
"tags":["free_to_try","buy_now","clearance","on_sale"]
}
Optimistic Concurrency
Optimistic Concurrency is a feature of Solr that can be used by client applications which update/replace
documents to ensure that the document they are replacing/updating has not been concurrently modified by
another client application. This feature works by requiring a _version_ field on all documents in the index,
and comparing that to a _version_ specified as part of the update command. By default, Solr’s Schema
includes a _version_ field, and this field is automatically added to each new document.
In general, using optimistic concurrency involves the following work flow:
1. A client reads a document. In Solr, one might retrieve the document with the /get handler to be sure to
have the latest version.
2. A client changes the document locally.
3. The client resubmits the changed document to Solr, for example, perhaps with the /update handler.
4. If there is a version conflict (HTTP error code 409), the client starts the process over.
When the client resubmits a changed document to Solr, the _version_ can be included with the update to
invoke optimistic concurrency control. Specific semantics are used to define when the document should be
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updated or when to report a conflict.
? If the content in the _version_ field is greater than ‘1‘ (i.e., ‘12345‘), then the _version_ in the document
must match the _version_ in the index.
? If the content in the _version_ field is equal to ‘1‘, then the document must simply exist. In this case, no
version matching occurs, but if the document does not exist, the updates will be rejected.
? If the content in the _version_ field is less than ‘0‘ (i.e., ‘-1‘), then the document must not exist. In this
case, no version matching occurs, but if the document exists, the updates will be rejected.
? If the content in the _version_ field is equal to ‘0‘, then it doesn’t matter if the versions match or if the
document exists or not. If it exists, it will be overwritten; if it does not exist, it will be added.
If the document being updated does not include the _version_ field, and atomic updates are not being
used, the document will be treated by normal Solr rules, which is usually to discard the previous version.
When using Optimistic Concurrency, clients can include an optional versions=true request parameter to
indicate that the new versions of the documents being added should be included in the response. This allows
clients to immediately know what the _version_ is of every documented added with out needing to make a
redundant /get request.
For example…
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$ curl -X POST -H ‘Content-Type: application/json‘
‘http://localhost:8983/solr/techproducts/update?versions=true‘ --data-binary ‘
[ { "id" : "aaa" },
{ "id" : "bbb" } ]‘
{"responseHeader":{"status":0,"QTime":6},
"adds":["aaa",1498562471222312960,
"bbb",1498562471225458688]}
$ curl -X POST -H ‘Content-Type: application/json‘
‘http://localhost:8983/solr/techproducts/update?_version_=999999&versions=true‘ --data-binary ‘
[{ "id" : "aaa",
"foo_s" : "update attempt with wrong existing version" }]‘
{"responseHeader":{"status":409,"QTime":3},
"error":{"msg":"version conflict for aaa expected=999999 actual=1498562471222312960",
"code":409}}
$ curl -X POST -H ‘Content-Type: application/json‘
‘http://localhost:8983/solr/techproducts/update?_version_=1498562471222312960&versions=true&commi
t=true‘ --data-binary ‘
[{ "id" : "aaa",
"foo_s" : "update attempt with correct existing version" }]‘
{"responseHeader":{"status":0,"QTime":5},
"adds":["aaa",1498562624496861184]}
$ curl ‘http://localhost:8983/solr/techproducts/query?q=*:*&fl=id,_version_‘
{
"responseHeader":{
"status":0,
"QTime":5,
"params":{
"fl":"id,_version_",
"q":"*:*"}},
"response":{"numFound":2,"start":0,"docs":[
{
"id":"bbb",
"_version_":1498562471225458688},
{
"id":"aaa",
"_version_":1498562624496861184}]
}}
For more information, please also see Yonik Seeley’s presentation on NoSQL features in Solr 4 from Apache
Lucene EuroCon 2012.
Document Centric Versioning Constraints
Optimistic Concurrency is extremely powerful, and works very efficiently because it uses an internally
assigned, globally unique values for the _version_ field. However, In some situations users may want to
configure their own document specific version field, where the version values are assigned on a per-
document basis by an external system, and have Solr reject updates that attempt to replace a document
with an "older" version. In situations like this the DocBasedVersionConstraintsProcessorFactory can be
useful.
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The basic usage of DocBasedVersionConstraintsProcessorFactory is to configure it in solrconfig.xml as
part of the UpdateRequestProcessorChain and specify the name of your custom versionField in your
schema that should be checked when validating updates:
<processor class="solr.DocBasedVersionConstraintsProcessorFactory">
<str name="versionField">my_version_l</str>
</processor>
Once configured, this update processor will reject (HTTP error code 409) any attempt to update an existing
document where the value of the my_version_l field in the "new" document is not greater then the value of
that field in the existing document.
?
versionField vs _version_
The _version_ field used by Solr for its normal optimistic concurrency also has important
semantics in how updates are distributed to replicas in SolrCloud, and MUST be assigned
internally by Solr. Users can not re-purpose that field and specify it as the versionField for
use in the DocBasedVersionConstraintsProcessorFactory configuration.
DocBasedVersionConstraintsProcessorFactory supports two additional configuration params which are
optional:
? ignoreOldUpdates - A boolean option which defaults to false . If set to true then instead of rejecting
updates where the versionField is too low, the update will be silently ignored (and return a status 200
to the client).
? deleteVersionParam - A String parameter that can be specified to indicate that this processor should
also inspect Delete By Id commands. The value of this configuration option should be the name of a
request parameter that the processor will now consider mandatory for all attempts to Delete By Id, and
must be be used by clients to specify a value for the versionField which is greater then the existing
value of the document to be deleted. When using this request param, any Delete By Id command with a
high enough document version number to succeed will be internally converted into an Add Document
command that replaces the existing document with a new one which is empty except for the Unique Key
and versionField to keeping a record of the deleted version so future Add Document commands will
fail if their "new" version is not high enough.
Please consult the DocBasedVersionConstraintsProcessorFactory javadocs and test solrconfig.xml file for
additional information and example usages.
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Detecting Languages During Indexing
Solr can identify languages and map text to language-specific fields during indexing using the langid
UpdateRequestProcessor.
Solr supports two implementations of this feature:
? Tika’s language detection feature: http://tika.apache.org/0.10/detection.html
? LangDetect language detection: https://github.com/shuyo/language-detection
You can see a comparison between the two implementations here: http://blog.mikemccandless.com/2011/
10/accuracy-and-performance-of-googles.html. In general, the LangDetect implementation supports more
languages with higher performance.
For specific information on each of these language identification implementations, including a list of
supported languages for each, see the relevant project websites.
For more information about language analysis in Solr, see Language Analysis.
Configuring Language Detection
You can configure the langid UpdateRequestProcessor in solrconfig.xml . Both implementations take the
same parameters, which are described in the following section. At a minimum, you must specify the fields
for language identification and a field for the resulting language code.
Configuring Tika Language Detection
Here is an example of a minimal Tika langid configuration in solrconfig.xml :
<processor class="org.apache.solr.update.processor.TikaLanguageIdentifierUpdateProcessorFactory">
<lst name="defaults">
<str name="langid.fl">title,subject,text,keywords</str>
<str name="langid.langField">language_s</str>
</lst>
</processor>
Configuring LangDetect Language Detection
Here is an example of a minimal LangDetect langid configuration in solrconfig.xml :
<processor class=
"org.apache.solr.update.processor.LangDetectLanguageIdentifierUpdateProcessorFactory">
<lst name="defaults">
<str name="langid.fl">title,subject,text,keywords</str>
<str name="langid.langField">language_s</str>
</lst>
</processor>
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langid Parameters
As previously mentioned, both implementations of the langid UpdateRequestProcessor take the same
parameters.
Parameter Type Default Required Description
langid Boolean true no Enables and disables language detection.
langid.fl string none yes A comma- or space-delimited list of fields
to be processed by langid .
langid.langField string none yes Specifies the field for the returned
language code.
langid.langsField multivalu
ed string
none no Specifies the field for a list of returned
language codes. If you use
langid.map.individual , each detected
language will be added to this field.
langid.overwrite Boolean false no Specifies whether the content of the
langField and langsField fields will be
overwritten if they already contain values.
langid.lcmap string none false A space-separated list specifying colon
delimited language code mappings to
apply to the detected languages. For
example, you might use this to map
Chinese, Japanese, and Korean to a
common cjk code, and map both
American and British English to a single
en code by using langid.lcmap=ja:cjk
zh:cjk ko:cjk en_GB:en en_US:en . This
affects both the values put into the
langField and langsField fields, as well
as the field suffixes when using
langid.map , unless overridden by
langid.map.lcmap
langid.threshold float 0.5 no Specifies a threshold value between 0 and
1 that the language identification score
must reach before langid accepts it. With
longer text fields, a high threshold such at
0.8 will give good results. For shorter text
fields, you may need to lower the
threshold for language identification,
though you will be risking somewhat
lower quality results. We recommend
experimenting with your data to tune
your results.
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Parameter Type Default Required Description
langid.whitelist string none no Specifies a list of allowed language
identification codes. Use this in
combination with langid.map to ensure
that you only index documents into fields
that are in your schema.
langid.map Boolean false no Enables field name mapping. If true, Solr
will map field names for all fields listed in
langid.fl .
langid.map.fl string none no A comma-separated list of fields for
langid.map that is different than the
fields specified in langid.fl .
langid.map.keepOrig Boolean false no If true, Solr will copy the field during the
field name mapping process, leaving the
original field in place.
langid.map.individual Boolean false no If true, Solr will detect and map languages
for each field individually.
langid.map.individual.fl string none no A comma-separated list of fields for use
with langid.map.individual that is
different than the fields specified in
langid.fl .
langid.fallbackFields string none no If no language is detected that meets the
langid.threshold score, or if the
detected language is not on the
langid.whitelist , this field specifies
language codes to be used as fallback
values. If no appropriate fallback
languages are found, Solr will use the
language code specified in
langid.fallback .
langid.fallback string none no Specifies a language code to use if no
language is detected or specified in
langid.fallbackFields .
langid.map.lcmap string determin
ed by
langid.l
cmap
no A space-separated list specifying colon
delimited language code mappings to use
when mapping field names. For example,
you might use this to make Chinese,
Japanese, and Korean language fields use
a common *_cjk suffix, and map both
American and British English fields to a
single *_en by using
langid.map.lcmap=ja:cjk zh:cjk
ko:cjk en_GB:en en_US:en .
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Parameter Type Default Required Description
langid.map.pattern Java
regular
expressio
n
none no By default, fields are mapped as
<field>_<language>. To change this
pattern, you can specify a Java regular
expression in this parameter.
langid.map.replace Java
replace
none no By default, fields are mapped as
<field>_<language>. To change this
pattern, you can specify a Java replace in
this parameter.
langid.enforceSchema Boolean true no If false, the langid processor does not
validate field names against your schema.
This may be useful if you plan to rename
or delete fields later in the UpdateChain.
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De-Duplication
If duplicate, or near-duplicate documents are a concern in your index, de-duplication may be worth
implementing.
Preventing duplicate or near duplicate documents from entering an index or tagging documents with a
signature/fingerprint for duplicate field collapsing can be efficiently achieved with a low collision or fuzzy
hash algorithm. Solr natively supports de-duplication techniques of this type via the Signature class and
allows for the easy addition of new hash/signature implementations. A Signature can be implemented
several ways:
Method Description
MD5Signature 128-bit hash used for exact duplicate detection.
Lookup3Signature 64-bit hash used for exact duplicate detection. This is much faster than
MD5 and smaller to index.
TextProfileSignature Fuzzy hashing implementation from Apache Nutch for near duplicate
detection. It’s tunable but works best on longer text.
Other, more sophisticated algorithms for fuzzy/near hashing can be added later.
?
Adding in the de-duplication process will change the allowDups setting so that it applies to
an update term (with signatureField in this case) rather than the unique field Term.
Of course the signatureField could be the unique field, but generally you want the unique
field to be unique. When a document is added, a signature will automatically be generated
and attached to the document in the specified signatureField .
Configuration Options
There are two places in Solr to configure de-duplication: in solrconfig.xml and in schema.xml .
In solrconfig.xml
The SignatureUpdateProcessorFactory has to be registered in solrconfig.xml as part of an Update
Request Processor Chain, as in this example:
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<updateRequestProcessorChain name="dedupe">
<processor class="solr.processor.SignatureUpdateProcessorFactory">
<bool name="enabled">true</bool>
<str name="signatureField">id</str>
<bool name="overwriteDupes">false</bool>
<str name="fields">name,features,cat</str>
<str name="signatureClass">solr.processor.Lookup3Signature</str>
</processor>
<processor class="solr.LogUpdateProcessorFactory" />
<processor class="solr.RunUpdateProcessorFactory" />
</updateRequestProcessorChain>
The SignatureUpdateProcessorFactory takes several properties:
Parameter Default Description
signatureClass
org.apache.solr.update.pro
cessor.Lookup3Signature
A Signature implementation for generating a
signature hash. The full classpath of the
implementation must be specified. The available
options are described above, the associated
classpaths to use are:
? org.apache.solr.update.processor.Lookup3Si
gnature
? org.apache.solr.update.processor.MD5Signat
ure
? org.apache.solr.update.process.TextProfile
Signature
fields all fields The fields to use to generate the signature hash in a
comma separated list. By default, all fields on the
document will be used.
signatureField signatureField The name of the field used to hold the
fingerprint/signature. The field should be defined in
schema.xml.
enabled true Enable/disable de-duplication processing.
overwriteDupes true If true, when a document exists that already matches
this signature, it will be overwritten.
In schema.xml
If you are using a separate field for storing the signature, you must have it indexed:
<field name="signatureField" type="string" stored="true" indexed="true" multiValued="false" />
Be sure to change your update handlers to use the defined chain, as below:
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<requestHandler name="/update" class="solr.UpdateRequestHandler" >
<lst name="defaults">
<str name="update.chain">dedupe</str>
</lst>
...
</requestHandler>
This example assumes you have other sections of your request handler defined.
?
The update processor can also be specified per request with a parameter of
update.chain=dedupe .
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Content Streams
Content streams are bulk data passed with a request to Solr.
When Solr RequestHandlers are accessed using path based URLs, the SolrQueryRequest object containing
the parameters of the request may also contain a list of ContentStreams containing bulk data for the
request. (The name SolrQueryRequest is a bit misleading: it is involved in all requests, regardless of whether
it is a query request or an update request.)
Stream Sources
Currently request handlers can get content streams in a variety of ways:
? For multipart file uploads, each file is passed as a stream.
? For POST requests where the content-type is not application/x-www-form-urlencoded , the raw POST
body is passed as a stream. The full POST body is parsed as parameters and included in the Solr
parameters.
? The contents of parameter stream.body is passed as a stream.
? If remote streaming is enabled and URL content is called for during request handling, the contents of
each stream.url and stream.file parameters are fetched and passed as a stream.
By default, curl sends a contentType="application/x-www-form-urlencoded" header. If you need to test a
SolrContentHeader content stream, you will need to set the content type with curl’s -H flag.
RemoteStreaming
Remote streaming lets you send the contents of a URL as a stream to a given SolrRequestHandler. You could
use remote streaming to send a remote or local file to an update plugin.
For convenience, remote streaming is enabled in most of the example solrconfig.xml files included with
Solr, however it is not recommended in a production situation without additional security between you and
untrusted remote clients.
<!-- *** WARNING ***
The settings below authorize Solr to fetch remote files, You
should make sure your system has some authentication before
using enableRemoteStreaming="true"
-->
<requestParsers enableRemoteStreaming="true" />
The default behavior, when enableRemoteStreaming is not specified in solrconfig.xml is to not allow
remote streaming (i.e., enableRemoteStreaming="false" ).
?
If you enableRemoteStreaming="true" is used, be aware that this allows anyone to send a
request to any URL or local file. If DumpRequestHandler is enabled, it will allow anyone to
view any file on your system.
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Debugging Requests
The implicit "dump" RequestHandler (see Implicit RequestHandlers) simply outputs the contents of the
SolrQueryRequest using the specified writer type wt . This is a useful tool to help understand what streams
are available to the RequestHandlers.
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UIMA Integration
You can integrate the Apache Unstructured Information Management Architecture (UIMA) with Solr. UIMA
lets you define custom pipelines of Analysis Engines that incrementally add metadata to your documents as
annotations.
Configuring UIMA
The SolrUIMA UpdateRequestProcessor is a custom update request processor that takes documents being
indexed, sends them to a UIMA pipeline, and then returns the documents enriched with the specified
metadata. To configure UIMA for Solr, follow these steps:
1. Copy solr-uima-VERSION.jar (under /solr-VERSION/dist/ ) and its libraries (under contrib/uima/lib )
to a Solr libraries directory, or set <lib/> tags in solrconfig.xml appropriately to point to those jar files:
<lib dir="../../contrib/uima/lib" />
<lib dir="../../dist/" regex="solr-uima-\d.*\.jar" />
2. Modify schema.xml , adding your desired metadata fields specifying proper values for type, indexed,
stored, and multiValued options. For example:
<field name="language" type="string" indexed="true" stored="true" required="false"/>
<field name="concept" type="string" indexed="true" stored="true" multiValued="true" required=
"false"/>
<field name="sentence" type="text" indexed="true" stored="true" multiValued="true" required=
"false" />
3. Add the following snippet to solrconfig.xml :
<updateRequestProcessorChain name="uima">
<processor class="org.apache.solr.uima.processor.UIMAUpdateRequestProcessorFactory">
<lst name="uimaConfig">
<lst name="runtimeParameters">
<str name="keyword_apikey">VALID_ALCHEMYAPI_KEY</str>
<str name="concept_apikey">VALID_ALCHEMYAPI_KEY</str>
<str name="lang_apikey">VALID_ALCHEMYAPI_KEY</str>
<str name="cat_apikey">VALID_ALCHEMYAPI_KEY</str>
<str name="entities_apikey">VALID_ALCHEMYAPI_KEY</str>
<str name="oc_licenseID">VALID_OPENCALAIS_KEY</str>
</lst>
<str name="analysisEngine">/org/apache/uima/desc/OverridingParamsExtServicesAE.xml</str>
<!-- Set to true if you want to continue indexing even if text processing fails.
Default is false. That is, Solr throws RuntimeException and
never indexed documents entirely in your session. -->
<bool name="ignoreErrors">true</bool>
<!-- This is optional. It is used for logging when text processing fails.
If logField is not specified, uniqueKey will be used as logField.
<str name="logField">id</str>
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-->
<lst name="analyzeFields">
<bool name="merge">false</bool>
<arr name="fields">
<str>text</str>
</arr>
</lst>
<lst name="fieldMappings">
<lst name="type">
<str name="name">org.apache.uima.alchemy.ts.concept.ConceptFS</str>
<lst name="mapping">
<str name="feature">text</str>
<str name="field">concept</str>
</lst>
</lst>
<lst name="type">
<str name="name">org.apache.uima.alchemy.ts.language.LanguageFS</str>
<lst name="mapping">
<str name="feature">language</str>
<str name="field">language</str>
</lst>
</lst>
<lst name="type">
<str name="name">org.apache.uima.SentenceAnnotation</str>
<lst name="mapping">
<str name="feature">coveredText</str>
<str name="field">sentence</str>
</lst>
</lst>
</lst>
</lst>
</processor>
<processor class="solr.LogUpdateProcessorFactory" />
<processor class="solr.RunUpdateProcessorFactory" />
</updateRequestProcessorChain>
?
? VALID_ALCHEMYAPI_KEY is your AlchemyAPI Access Key. You need to register an
AlchemyAPI Access key to use AlchemyAPI services: http://www.alchemyapi.com/
api/register.html.
? VALID_OPENCALAIS_KEY is your Calais Service Key. You need to register a Calais
Service key to use the Calais services: http://www.opencalais.com/apikey.
? analysisEngine must contain an AE descriptor inside the specified path in the
classpath.
? analyzeFields must contain the input fields that need to be analyzed by UIMA. If
merge=true then their content will be merged and analyzed only once.
? Field mapping describes which features of which types should go in a field.
4. In your solrconfig.xml replace the existing default UpdateRequestHandler or create a new
UpdateRequestHandler:
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<requestHandler name="/update" class="solr.XmlUpdateRequestHandler">
<lst name="defaults">
<str name="update.chain">uima</str>
</lst>
</requestHandler>
Once you are done with the configuration your documents will be automatically enriched with the specified
fields when you index them.
For more information about Solr UIMA integration, see https://wiki.apache.org/solr/SolrUIMA.
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Searching
This section describes how Solr works with search requests. It covers the following topics:
? Overview of Searching in Solr: An introduction to searching with Solr.
? Velocity Search UI: A simple search UI using the VelocityResponseWriter.
? Relevance: Conceptual information about understanding relevance in search results.
? Query Syntax and Parsing: A brief conceptual overview of query syntax and parsing. It also contains the
following sub-sections:
? Common Query Parameters: No matter the query parser, there are several parameters that are
common to all of them.
? The Standard Query Parser: Detailed information about the standard Lucene query parser.
? The DisMax Query Parser: Detailed information about Solr’s DisMax query parser.
? The Extended DisMax Query Parser: Detailed information about Solr’s Extended DisMax (eDisMax)
Query Parser.
? Function Queries: Detailed information about parameters for generating relevancy scores using
values from one or more numeric fields.
? Local Parameters in Queries: How to add local arguments to queries.
? Other Parsers: More parsers designed for use in specific situations.
? Faceting: Detailed information about categorizing search results based on indexed terms.
? Highlighting: Detailed information about Solr’s highlighting capabilities, including multiple underlying
highlighter implementations.
? Spell Checking: Detailed information about Solr’s spelling checker.
? Query Re-Ranking: Detailed information about re-ranking top scoring documents from simple queries
using more complex scores.
? Learning To Rank: How to use LTR to run machine learned ranking models in Solr.
? Transforming Result Documents: Detailed information about using DocTransformers to add computed
information to individual documents
? Suggester: Detailed information about Solr’s powerful autosuggest component.
? MoreLikeThis: Detailed information about Solr’s similar results query component.
? Pagination of Results: Detailed information about fetching paginated results for display in a UI, or for
fetching all documents matching a query.
? Result Grouping: Detailed information about grouping results based on common field values.
? Result Clustering: Detailed information about grouping search results based on cluster analysis applied
to text fields. A bit like "unsupervised" faceting.
? Spatial Search: How to use Solr’s spatial search capabilities.
? The Terms Component: Detailed information about accessing indexed terms and the documents that
include them.
? The Term Vector Component: How to get term information about specific documents.
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? The Stats Component: How to return information from numeric fields within a document set.
? The Query Elevation Component: How to force documents to the top of the results for certain queries.
? Response Writers: Detailed information about configuring and using Solr’s response writers.
? Near Real Time Searching: How to include documents in search results nearly immediately after they are
indexed.
? RealTime Get: How to get the latest version of a document without opening a searcher.
? Exporting Result Sets: Functionality to export large result sets out of Solr.
? Streaming Expressions: A stream processing language for Solr, with a suite of functions to perform many
types of queries and parallel execution tasks.
? Parallel SQL Interface: An interface for sending SQL statements to Solr, and using advanced parallel
query processing and relational algebra for complex data analysis.
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Overview of Searching in Solr
Solr offers a rich, flexible set of features for search. To understand the extent of this flexibility, it’s helpful to
begin with an overview of the steps and components involved in a Solr search.
When a user runs a search in Solr, the search query is processed by a request handler. A request handler is
a Solr plug-in that defines the logic to be used when Solr processes a request. Solr supports a variety of
request handlers. Some are designed for processing search queries, while others manage tasks such as
index replication.
Search applications select a particular request handler by default. In addition, applications can be configured
to allow users to override the default selection in preference of a different request handler.
To process a search query, a request handler calls a query parser, which interprets the terms and
parameters of a query. Different query parsers support different syntax. Solr’s default query parser is known
as the Standard Query Parser,or more commonly just the "lucene" query parser. Solr also includes the
DisMaxquery parser, and the Extended DisMax (eDisMax) query parser. The standard query parser’s syntax
allows for greater precision in searches, but the DisMax query parser is much more tolerant of errors. The
DisMax query parser is designed to provide an experience similar to that of popular search engines such as
Google, which rarely display syntax errors to users. The Extended DisMax query parser is an improved
version of DisMax that handles the full Lucene query syntax while still tolerating syntax errors. It also
includes several additional features.
In addition, there are common query parameters that are accepted by all query parsers.
Input to a query parser can include:
? search strings---that is, terms to search for in the index
? parameters for fine-tuning the query by increasing the importance of particular strings or fields, by
applying Boolean logic among the search terms, or by excluding content from the search results
? parameters for controlling the presentation of the query response, such as specifying the order in which
results are to be presented or limiting the response to particular fields of the search application’s
schema.
Search parameters may also specify a filter query. As part of a search response, a filter query runs a query
against the entire index and caches the results. Because Solr allocates a separate cache for filter queries, the
strategic use of filter queries can improve search performance. (Despite their similar names, query filters are
not related to analysis filters. Filter queries perform queries at search time against data already in the index,
while analysis filters, such as Tokenizers, parse content for indexing, following specified rules).
A search query can request that certain terms be highlighted in the search response; that is, the selected
terms will be displayed in colored boxes so that they "jump out" on the screen of search results.
Highlighting can make it easier to find relevant passages in long documents returned in a search. Solr
supports multi-term highlighting. Solr includes a rich set of search parameters for controlling how terms are
highlighted.
Search responses can also be configured to include snippets (document excerpts) featuring highlighted
text. Popular search engines such as Google and Yahoo! return snippets in their search results: 3-4 lines of
text offering a description of a search result.
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To help users zero in on the content they’re looking for, Solr supports two special ways of grouping search
results to aid further exploration: faceting and clustering.
Faceting is the arrangement of search results into categories (which are based on indexed terms). Within
each category, Solr reports on the number of hits for relevant term, which is called a facet constraint.
Faceting makes it easy for users to explore search results on sites such as movie sites and product review
sites, where there are many categories and many items within a category.
The screen shot below shows an example of faceting from the CNET Web site (CBS Interactive Inc.) , which
was the first site to use Solr.
Faceting makes use of fields defined when the search applications were indexed. In the example above,
these fields include categories of information that are useful for describing digital cameras: manufacturer,
resolution, and zoom range.
Clustering groups search results by similarities discovered when a search is executed, rather than when
content is indexed. The results of clustering often lack the neat hierarchical organization found in faceted
search results, but clustering can be useful nonetheless. It can reveal unexpected commonalities among
search results, and it can help users rule out content that isn’t pertinent to what they’re really searching for.
Solr also supports a feature called MoreLikeThis, which enables users to submit new queries that focus on
particular terms returned in an earlier query. MoreLikeThis queries can make use of faceting or clustering to
provide additional aid to users.
A Solr component called a response writer manages the final presentation of the query response. Solr
includes a variety of response writers, including an XML Response Writer and a JSON Response Writer.
The diagram below summarizes some key elements of the search process.
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Velocity Search UI
Solr includes a sample search UI based on the VelocityResponseWriter (also known as Solritas) that
demonstrates several useful features, such as searching, faceting, highlighting, autocomplete, and
geospatial searching.
When using the sample_techproducts_configs config set, you can access the Velocity sample Search UI:
http://localhost:8983/solr/techproducts/browse
The Velocity Search UI
For more information about the Velocity Response Writer, see the Response Writer page.
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Relevance
Relevance is the degree to which a query response satisfies a user who is searching for information.
The relevance of a query response depends on the context in which the query was performed. A single
search application may be used in different contexts by users with different needs and expectations. For
example, a search engine of climate data might be used by a university researcher studying long-term
climate trends, a farmer interested in calculating the likely date of the last frost of spring, a civil engineer
interested in rainfall patterns and the frequency of floods, and a college student planning a vacation to a
region and wondering what to pack. Because the motivations of these users vary, the relevance of any
particular response to a query will vary as well.
How comprehensive should query responses be? Like relevance in general, the answer to this question
depends on the context of a search. The cost of not finding a particular document in response to a query is
high in some contexts, such as a legal e-discovery search in response to a subpoena, and quite low in others,
such as a search for a cake recipe on a Web site with dozens or hundreds of cake recipes. When configuring
Solr, you should weigh comprehensiveness against other factors such as timeliness and ease-of-use.
The e-discovery and recipe examples demonstrate the importance of two concepts related to relevance:
? Precision is the percentage of documents in the returned results that are relevant.
? Recall is the percentage of relevant results returned out of all relevant results in the system. Obtaining
perfect recall is trivial: simply return every document in the collection for every query.
Returning to the examples above, it’s important for an e-discovery search application to have 100% recall
returning all the documents that are relevant to a subpoena. It’s far less important that a recipe application
offer this degree of precision, however. In some cases, returning too many results in casual contexts could
overwhelm users. In some contexts, returning fewer results that have a higher likelihood of relevance may
be the best approach.
Using the concepts of precision and recall, it’s possible to quantify relevance across users and queries for a
collection of documents. A perfect system would have 100% precision and 100% recall for every user and
every query. In other words, it would retrieve all the relevant documents and nothing else. In practical
terms, when talking about precision and recall in real systems, it is common to focus on precision and recall
at a certain number of results, the most common (and useful) being ten results.
Through faceting, query filters, and other search components, a Solr application can be configured with the
flexibility to help users fine-tune their searches in order to return the most relevant results for users. That is,
Solr can be configured to balance precision and recall to meet the needs of a particular user community.
The configuration of a Solr application should take into account:
? the needs of the application’s various users (which can include ease of use and speed of response, in
addition to strictly informational needs)
? the categories that are meaningful to these users in their various contexts (e.g., dates, product
categories, or regions)
? any inherent relevance of documents (e.g., it might make sense to ensure that an official product
description or FAQ is always returned near the top of the search results)
? whether or not the age of documents matters significantly (in some contexts, the most recent
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documents might always be the most important)
Keeping all these factors in mind, it’s often helpful in the planning stages of a Solr deployment to sketch out
the types of responses you think the search application should return for sample queries. Once the
application is up and running, you can employ a series of testing methodologies, such as focus groups, in-
house testing, TREC tests and A/B testing to fine tune the configuration of the application to best meet the
needs of its users.
For more information about relevance, see Grant Ingersoll’s tech article Debugging Search Application
Relevance Issues which is available on SearchHub.org.
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Query Syntax and Parsing
Solr supports several query parsers, offering search application designers great flexibility in controlling how
queries are parsed.
This section explains how to specify the query parser to be used. It also describes the syntax and features
supported by the main query parsers included with Solr and describes some other parsers that may be
useful for particular situations. There are some query parameters common to all Solr parsers; these are
discussed in the section Common Query Parameters.
The parsers discussed in this Guide are:
? The Standard Query Parser
? The DisMax Query Parser
? The Extended DisMax Query Parser
? Other Parsers
The query parser plugins are all subclasses of QParserPlugin. If you have custom parsing needs, you may
want to extend that class to create your own query parser.
Common Query Parameters
Several query parsers share supported query parameters.
The table below summarizes Solr’s common query parameters, which are supported by the Search
RequestHandlers
Parameter Description
defType Selects the query parser to be used to process the query.
sort Sorts the response to a query in either ascending or descending order
based on the response’s score or another specified characteristic.
start Specifies an offset (by default, 0) into the responses at which Solr should
begin displaying content.
rows Controls how many rows of responses are displayed at a time (default
value: 10)
fq Applies a filter query to the search results.
fl Limits the information included in a query response to a specified list of
fields. The fields need to either be stored="true" or docValues="true"
debug Request additional debugging information in the response. Specifying the
debug=timing parameter returns just the timing information; specifying
the debug=results parameter returns "explain" information for each of
the documents returned; specifying the debug=query parameter returns
all of the debug information.
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Parameter Description
explainOther Allows clients to specify a Lucene query to identify a set of documents. If
non-blank, the explain info of each document which matches this query,
relative to the main query (specified by the q parameter) will be returned
along with the rest of the debugging information.
timeAllowed Defines the time allowed for the query to be processed. If the time elapses
before the query response is complete, partial information may be
returned.
segmentTerminateEarly Indicates that, if possible, Solr should stop collecting documents from each
individual (sorted) segment once it can determine that any subsequent
documents in that segment will not be candidates for the rows being
returned. The default is false.
omitHeader Excludes the header from the returned results, if set to true. The header
contains information about the request, such as the time the request took
to complete. The default is false.
wt Specifies the Response Writer to be used to format the query response.
logParamsList By default, Solr logs all parameters. Set this parameter to restrict which
parameters are logged. Valid entries are the parameters to be logged,
separated by commas (i.e., logParamsList=param1,param2 ). An empty list
will log no parameters, so if logging all parameters is desired, do not
define this additional parameter at all.
echoParams The response header can include parameters sent with the query request.
This parameter controls what is contained in that section of the response
header. Valid values are none , all , and explicit . The default value is
explicit.
The following sections describe these parameters in detail.
The defType Parameter
The defType parameter selects the query parser that Solr should use to process the main query parameter
( q ) in the request. For example:
defType=dismax
If no defType param is specified, then by default, the The Standard Query Parser is used. (eg:
defType=lucene )
The sort Parameter
The sort parameter arranges search results in either ascending ( asc ) or descending ( desc ) order. The
parameter can be used with either numerical or alphabetical content. The directions can be entered in either
all lowercase or all uppercase letters (i.e., both asc or ASC ).
Solr can sort query responses according to document scores or the value of any field with a single value that
is either indexed or uses DocValues (that is, any field whose attributes in the Schema include
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multiValued="false" and either docValues="true" or indexed="true" – if the field does not have
DocValues enabled, the indexed terms are used to build them on the fly at runtime), provided that:
? the field is non-tokenized (that is, the field has no analyzer and its contents have been parsed into
tokens, which would make the sorting inconsistent), or
? the field uses an analyzer (such as the KeywordTokenizer) that produces only a single term.
If you want to be able to sort on a field whose contents you want to tokenize to facilitate searching, use a
copyField directive in the the Schema to clone the field. Then search on the field and sort on its clone.
The table explains how Solr responds to various settings of the sort parameter.
Example Result
If the sort parameter is omitted, sorting is performed as though the
parameter were set to score desc .
score desc Sorts in descending order from the highest score to the lowest score.
price asc Sorts in ascending order of the price field
inStock desc, price asc Sorts by the contents of the inStock field in descending order, then within
those results sorts in ascending order by the contents of the price field.
Regarding the sort parameter’s arguments:
? A sort ordering must include a field name (or score as a pseudo field), followed by whitespace (escaped
as + or %20 in URL strings), followed by a sort direction ( asc or desc ).
? Multiple sort orderings can be separated by a comma, using this syntax: sort=<field
name><direction>,<field name><direction>],…
? When more than one sort criteria is provided, the second entry will only be used if the first entry
results in a tie. If there is a third entry, it will only be used if the first AND second entries are tied. This
pattern continues with further entries.
The start Parameter
When specified, the start parameter specifies an offset into a query’s result set and instructs Solr to begin
displaying results from this offset.
The default value is "0". In other words, by default, Solr returns results without an offset, beginning where
the results themselves begin.
Setting the start parameter to some other number, such as 3, causes Solr to skip over the preceding
records and start at the document identified by the offset.
You can use the start parameter this way for paging. For example, if the rows parameter is set to 10, you
could display three successive pages of results by setting start to 0, then re-issuing the same query and
setting start to 10, then issuing the query again and setting start to 20.
The rows Parameter
You can use the rows parameter to paginate results from a query. The parameter specifies the maximum
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number of documents from the complete result set that Solr should return to the client at one time.
The default value is 10. That is, by default, Solr returns 10 documents at a time in response to a query.
The fq (Filter Query) Parameter
The fq parameter defines a query that can be used to restrict the superset of documents that can be
returned, without influencing score. It can be very useful for speeding up complex queries, since the queries
specified with fq are cached independently of the main query. When a later query uses the same filter,
there’s a cache hit, and filter results are returned quickly from the cache.
When using the fq parameter, keep in mind the following:
? The fq parameter can be specified multiple times in a query. Documents will only be included in the
result if they are in the intersection of the document sets resulting from each instance of the parameter.
In the example below, only documents which have a popularity greater then 10 and have a section of 0
will match.
fq=popularity:[10 TO *]&fq=section:0
? Filter queries can involve complicated Boolean queries. The above example could also be written as a
single fq with two mandatory clauses like so:
fq=+popularity:[10 TO *] +section:0
? The document sets from each filter query are cached independently. Thus, concerning the previous
examples: use a single fq containing two mandatory clauses if those clauses appear together often, and
use two separate fq parameters if they are relatively independent. (To learn about tuning cache sizes
and making sure a filter cache actually exists, see The Well-Configured Solr Instance.)
? It is also possible to use filter(condition) syntax inside the fq to cache clauses individually and - among
other things - to achieve union of cached filter queries.
? As with all parameters: special characters in an URL need to be properly escaped and encoded as hex
values. Online tools are available to help you with URL-encoding. For example: http://meyerweb.com/
eric/tools/dencoder/.
The fl (Field List) Parameter
The fl parameter limits the information included in a query response to a specified list of fields. The fields
need to either be stored="true" or docValues="true"` .`
The field list can be specified as a space-separated or comma-separated list of field names. The string
"score" can be used to indicate that the score of each document for the particular query should be returned
as a field. The wildcard character * selects all the fields in the document which are either stored="true" or
docValues="true" and useDocValuesAsStored="true" (which is the default when docValues are enabled).
You can also add pseudo-fields, functions and transformers to the field list request.
This table shows some basic examples of how to use fl :
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Field List Result
id name price Return only the id, name, and price fields.
id,name,price Return only the id, name, and price fields.
id name, price Return only the id, name, and price fields.
id score Return the id field and the score.
* Return all the stored fields in each document, as well as any docValues
fields that have useDocValuesAsStored="true" . This is the default value of
the fl parameter.
* score Return all the fields in each document, along with each field’s score.
*,dv_field_name Return all the stored fields in each document, and any docValues fields
that have `useDocValuesAsStored="true`" and the docValues from
dv_field_name even if it has `useDocValuesAsStored="false`"
Function Values
Functions can be computed for each document in the result and returned as a pseudo-field:
fl=id,title,product(price,popularity)
Document Transformers
Document Transformers can be used to modify the information returned about each documents in the
results of a query:
fl=id,title,[explain]
Field Name Aliases
You can change the key used to in the response for a field, function, or transformer by prefixing it with a
`"displayName:`". For example:
fl=id,sales_price:price,secret_sauce:prod(price,popularity),why_score:[explain style=nl]
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{
"response": {
"numFound": 2,
"start": 0,
"docs": [{
"id": "6H500F0",
"secret_sauce": 2100.0,
"sales_price": 350.0,
"why_score": {
"match": true,
"value": 1.052226,
"description": "weight(features:cache in 2) [DefaultSimilarity], result of:",
"details": [{
"..."
}]}}]}}
The debug Parameter
The debug parameter can be specified multiple times and supports the following arguments:
? debug=query : return debug information about the query only.
? debug=timing : return debug information about how long the query took to process.
? debug=results : return debug information about the score results (also known as "explain").
? By default, score explanations are returned as large string values, using newlines and tab indenting
for structure & readability, but an additional debug.explain.structured=true parameter may be
specified to return this information as nested data structures native to the response format
requested by wt .
? debug=all : return all available debug information about the request request. (alternatively usage:
debug=true )
For backwards compatibility with older versions of Solr, debugQuery=true may instead be specified as an
alternative way to indicate debug=all
The default behavior is not to include debugging information.
The explainOther Parameter
The explainOther parameter specifies a Lucene query in order to identify a set of documents. If this
parameter is included and is set to a non-blank value, the query will return debugging information, along
with the "explain info" of each document that matches the Lucene query, relative to the main query (which
is specified by the q parameter). For example:
q=supervillians&debugQuery=on&explainOther=id:juggernaut
The query above allows you to examine the scoring explain info of the top matching documents, compare it
to the explain info for documents matching id:juggernaut , and determine why the rankings are not as you
expect.
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The default value of this parameter is blank, which causes no extra "explain info" to be returned.
The timeAllowed Parameter
This parameter specifies the amount of time, in milliseconds, allowed for a search to complete. If this time
expires before the search is complete, any partial results will be returned, but values such as numFound , facet
counts, and result stats may not be accurate for the entire result set.
This value is only checked at the time of:
1. Query Expansion, and
2. Document collection
As this check is periodically performed, the actual time for which a request can be processed before it is
aborted would be marginally greater than or equal to the value of timeAllowed . If the request consumes
more time in other stages, e.g., custom components, etc., this parameter is not expected to abort the
request.
The segmentTerminateEarly Parameter
This parameter may be set to either true or false.
If set to true, and if the mergePolicyFactory for this collection is a SortingMergePolicyFactory which uses a
sort option which is compatible with the sort parameter specified for this query, then Solr will attempt to
use an EarlyTerminatingSortingCollector .
If early termination is used, a segmentTerminatedEarly header will be included in the responseHeader .
Similar to using the timeAllowed `Parameter , when early segment termination happens values such as
`numFound, Facet counts, and result Stats may not be accurate for the entire result set.
The default value of this parameter is false.
The omitHeader Parameter
This parameter may be set to either true or false.
If set to true, this parameter excludes the header from the returned results. The header contains
information about the request, such as the time it took to complete. The default value for this parameter is
false.
The wt Parameter
The wt parameter selects the Response Writer that Solr should use to format the query’s response. For
detailed descriptions of Response Writers, see Response Writers.
The cache=false Parameter
Solr caches the results of all queries and filter queries by default. To disable result caching, set the
cache=false parameter.
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You can also use the cost option to control the order in which non-cached filter queries are evaluated. This
allows you to order less expensive non-cached filters before expensive non-cached filters.
For very high cost filters, if cache=false and cost>=100 and the query implements the PostFilter interface,
a Collector will be requested from that query and used to filter documents after they have matched the main
query and all other filter queries. There can be multiple post filters; they are also ordered by cost.
For example:
// normal function range query used as a filter, all matching documents
// generated up front and cached
fq={!frange l=10 u=100}mul(popularity,price)
// function range query run in parallel with the main query like a traditional
// lucene filter
fq={!frange l=10 u=100 cache=false}mul(popularity,price)
// function range query checked after each document that already matches the query
// and all other filters. Good for really expensive function queries.
fq={!frange l=10 u=100 cache=false cost=100}mul(popularity,price)
The logParamsList Parameter
By default, Solr logs all parameters of requests. Set this parameter to restrict which parameters of a request
are logged. This may help control logging to only those parameters considered important to your
organization.
For example, you could define this like:
logParamsList=q,fq
And only the ‘q‘ and ‘fq‘ parameters will be logged.
If no parameters should be logged, you can send logParamsList as empty (i.e., logParamsList= ).
?
This parameter does not only apply to query requests, but to any kind of request to Solr.
The echoParams Parameter
The echoParams parameter controls what information about request parameters is included in the response
header.
The table explains how Solr responds to various settings of the echoParams parameter:
Value Meaning
explicit This is the default value. Only parameters included in the actual request,
plus the _ parameter (which is a 64-bit numeric timestamp) will be added
to the params section of the response header.
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Value Meaning
all Include all request parameters that contributed to the query. This will
include everything defined in the request handler definition found in
solrconfig.xml as well as parameters included with the request, plus the
_ parameter. If a parameter is included in the request handler definition
AND the request, it will appear multiple times in the response header.
none Entirely removes the "params" section of the response header. No
information about the request parameters will be available in the
response.
Here is an example of a JSON response where the echoParams parameter was not included, so the default of
explicit is active. The request URL that created this response included three parameters - q , wt , and
indent :
{
"responseHeader": {
"status": 0,
"QTime": 0,
"params": {
"q": "solr",
"indent": "true",
"wt": "json",
"_": "1458227751857"
}
},
"response": {
"numFound": 0,
"start": 0,
"docs": []
}
}
This is what happens if a similar request is sent that adds echoParams=all to the three parameters used in
the previous example:
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{
"responseHeader": {
"status": 0,
"QTime": 0,
"params": {
"q": "solr",
"df": "text",
"preferLocalShards": "false",
"indent": "true",
"echoParams": "all",
"rows": "10",
"wt": "json",
"_": "1458228887287"
}
},
"response": {
"numFound": 0,
"start": 0,
"docs": []
}
}
The Standard Query Parser
Solr’s default Query Parser is also known as the “ lucene ” parser.
The key advantage of the standard query parser is that it supports a robust and fairly intuitive syntax
allowing you to create a variety of structured queries. The largest disadvantage is that it’s very intolerant of
syntax errors, as compared with something like the DisMax query parser which is designed to throw as few
errors as possible.
Standard Query Parser Parameters
In addition to the Common Query Parameters, Faceting Parameters, Highlighting Parameters, and
MoreLikeThis Parameters, the standard query parser supports the parameters described in the table below.
Parameter Description
q Defines a query using standard query syntax. This parameter is
mandatory.
q.op Specifies the default operator for query expressions, overriding the default
operator specified in the Schema. Possible values are "AND" or "OR".
df Specifies a default field, overriding the definition of a default field in the
Schema.
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Parameter Description
sow Split on whitespace: if set to false , whitespace-separated term sequences
will be provided to text analysis in one shot, enabling proper function of
analysis filters that operate over term sequences, e.g. multi-word
synonyms and shingles. Defaults to true : text analysis is invoked
separately for each individual whitespace-separated term.
Default parameter values are specified in solrconfig.xml , or overridden by query-time values in the
request.
The Standard Query Parser’s Response
By default, the response from the standard query parser contains one <result> block, which is unnamed. If
the debug parameter is used, then an additional <lst> block will be returned, using the name "debug". This
will contain useful debugging info, including the original query string, the parsed query string, and explain
info for each document in the <result> block. If the explainOther parameter is also used, then additional
explain info will be provided for all the documents matching that query.
Sample Responses
This section presents examples of responses from the standard query parser.
The URL below submits a simple query and requests the XML Response Writer to use indentation to make
the XML response more readable.
http://localhost:8983/solr/techproducts/select?q=id:SP2514N
Results:
<response>
<responseHeader><status>0</status><QTime>1</QTime></responseHeader>
<result numFound="1" start="0">
<doc>
<arr name="cat"><str>electronics</str><str>hard drive</str></arr>
<arr name="features"><str>7200RPM, 8MB cache, IDE Ultra ATA-133</str>
<str>NoiseGuard, SilentSeek technology, Fluid Dynamic Bearing (FDB) motor</str></arr>
<str name="id">SP2514N</str>
<bool name="inStock">true</bool>
<str name="manu">Samsung Electronics Co. Ltd.</str>
<str name="name">Samsung SpinPoint P120 SP2514N - hard drive - 250 GB - ATA-133</str>
<int name="popularity">6</int>
<float name="price">92.0</float>
<str name="sku">SP2514N</str>
</doc>
</result>
</response>
Here’s an example of a query with a limited field list.
http://localhost:8983/solr/techproducts/select?q=id:SP2514N&fl=id+name
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Results:
<response>
<responseHeader><status>0</status><QTime>2</QTime></responseHeader>
<result numFound="1" start="0">
<doc>
<str name="id">SP2514N</str>
<str name="name">Samsung SpinPoint P120 SP2514N - hard drive - 250 GB - ATA-133</str>
</doc>
</result>
</response>
Specifying Terms for the Standard Query Parser
A query to the standard query parser is broken up into terms and operators. There are two types of terms:
single terms and phrases.
? A single term is a single word such as "test" or "hello"
? A phrase is a group of words surrounded by double quotes such as "hello dolly"
Multiple terms can be combined together with Boolean operators to form more complex queries (as
described below).
?
It is important that the analyzer used for queries parses terms and phrases in a way that is
consistent with the way the analyzer used for indexing parses terms and phrases;
otherwise, searches may produce unexpected results.
Term Modifiers
Solr supports a variety of term modifiers that add flexibility or precision, as needed, to searches. These
modifiers include wildcard characters, characters for making a search "fuzzy" or more general, and so on.
The sections below describe these modifiers in detail.
Wildcard Searches
Solr’s standard query parser supports single and multiple character wildcard searches within single terms.
Wildcard characters can be applied to single terms, but not to search phrases.
Wildcard Search Type Special Character Example
Single character (matches a
single character)
? The search string te?t would match both test and
text.
Multiple characters (matches
zero or more sequential
characters)
* The wildcard search: tes* would match test, testing,
and tester. You can also use wildcard characters in
the middle of a term. For example: te*t would
match test and text. *est would match pest and test.
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Fuzzy Searches
Solr’s standard query parser supports fuzzy searches based on the Damerau-Levenshtein Distance or Edit
Distance algorithm. Fuzzy searches discover terms that are similar to a specified term without necessarily
being an exact match. To perform a fuzzy search, use the tilde ~ symbol at the end of a single-word term. For
example, to search for a term similar in spelling to "roam," use the fuzzy search:
roam~
This search will match terms like roams, foam, & foams. It will also match the word "roam" itself.
An optional distance parameter specifies the maximum number of edits allowed, between 0 and 2,
defaulting to 2. For example:
roam~1
This will match terms like roams & foam - but not foams since it has an edit distance of "2".
?
In many cases, stemming (reducing terms to a common stem) can produce similar effects
to fuzzy searches and wildcard searches.
Proximity Searches
A proximity search looks for terms that are within a specific distance from one another.
To perform a proximity search, add the tilde character ~ and a numeric value to the end of a search phrase.
For example, to search for a "apache" and "jakarta" within 10 words of each other in a document, use the
search:
"jakarta apache"~10
The distance referred to here is the number of term movements needed to match the specified phrase. In
the example above, if "apache" and "jakarta" were 10 spaces apart in a field, but "apache" appeared before
"jakarta", more than 10 term movements would be required to move the terms together and position
"apache" to the right of "jakarta" with a space in between.
Range Searches
A range search specifies a range of values for a field (a range with an upper bound and a lower bound). The
query matches documents whose values for the specified field or fields fall within the range. Range queries
can be inclusive or exclusive of the upper and lower bounds. Sorting is done lexicographically, except on
numeric fields. For example, the range query below matches all documents whose popularity field has a
value between 52 and 10,000, inclusive.
popularity:[52 TO 10000]
Range queries are not limited to date fields or even numerical fields. You could also use range queries with
non-date fields:
title:{Aida TO Carmen}
This will find all documents whose titles are between Aida and Carmen, but not including Aida and Carmen.
The brackets around a query determine its inclusiveness.
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? Square brackets [ & ] denote an inclusive range query that matches values including the upper and
lower bound.
? Curly brackets { & } denote an exclusive range query that matches values between the upper and lower
bounds, but excluding the upper and lower bounds themselves.
? You can mix these types so one end of the range is inclusive and the other is exclusive. Here’s an
example: count:{1 TO 10]
Boosting a Term with ^
Lucene/Solr provides the relevance level of matching documents based on the terms found. To boost a term
use the caret symbol ^ with a boost factor (a number) at the end of the term you are searching. The higher
the boost factor, the more relevant the term will be.
Boosting allows you to control the relevance of a document by boosting its term. For example, if you are
searching for
"jakarta apache" and you want the term "jakarta" to be more relevant, you can boost it by adding the ^
symbol along with the boost factor immediately after the term. For example, you could type:
jakarta^4 apache
This will make documents with the term jakarta appear more relevant. You can also boost Phrase Terms as
in the example:
"jakarta apache"^4 "Apache Lucene"
By default, the boost factor is 1. Although the boost factor must be positive, it can be less than 1 (for
example, it could be 0.2).
Constant Score with ^=
Constant score queries are created with <query_clause>^=<score> , which sets the entire clause to the
specified score for any documents matching that clause. This is desirable when you only care about matches
for a particular clause and don’t want other relevancy factors such as term frequency (the number of times
the term appears in the field) or inverse document frequency (a measure across the whole index for how
rare a term is in a field).
Example:
(description:blue OR color:blue)^=1.0 text:shoes
Specifying Fields in a Query to the Standard Query Parser
Data indexed in Solr is organized in fields, which are defined in the Solr Schema. Searches can take
advantage of fields to add precision to queries. For example, you can search for a term only in a specific
field, such as a title field.
The Schema defines one field as a default field. If you do not specify a field in a query, Solr searches only the
default field. Alternatively, you can specify a different field or a combination of fields in a query.
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To specify a field, type the field name followed by a colon ":" and then the term you are searching for within
the field.
For example, suppose an index contains two fields, title and text,and that text is the default field. If you want
to find a document called "The Right Way" which contains the text "don’t go this way," you could include
either of the following terms in your search query:
title:"The Right Way" AND text:go
title:"Do it right" AND go
Since text is the default field, the field indicator is not required; hence the second query above omits it.
The field is only valid for the term that it directly precedes, so the query title:Do it right will find only
"Do" in the title field. It will find "it" and "right" in the default field (in this case the text field).
Boolean Operators Supported by the Standard Query Parser
Boolean operators allow you to apply Boolean logic to queries, requiring the presence or absence of specific
terms or conditions in fields in order to match documents. The table below summarizes the Boolean
operators supported by the standard query parser.
Boolean Operator Alternative Symbol Description
AND
&&
Requires both terms on either side of the
Boolean operator to be present for a
match.
NOT
!
Requires that the following term not be
present.
OR
||
Requires that either term (or both terms)
be present for a match.
+
Requires that the following term be
present.
-
Prohibits the following term (that is,
matches on fields or documents that do
not include that term). The - operator is
functionally similar to the Boolean
operator ! . Because it’s used by popular
search engines such as Google, it may be
more familiar to some user communities.
Boolean operators allow terms to be combined through logic operators. Lucene supports AND, “+”, OR, NOT
and “-” as Boolean operators.
?
When specifying Boolean operators with keywords such as AND or NOT, the keywords
must appear in all uppercase.
?
The standard query parser supports all the Boolean operators listed in the table above. The
DisMax query parser supports only + and - .
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The OR operator is the default conjunction operator. This means that if there is no Boolean operator
between two terms, the OR operator is used. The OR operator links two terms and finds a matching
document if either of the terms exist in a document. This is equivalent to a union using sets. The symbol ||
can be used in place of the word OR.
To search for documents that contain either "jakarta apache" or just "jakarta," use the query:
"jakarta apache" jakarta
or
"jakarta apache" OR jakarta
The Boolean Operator +
The + symbol (also known as the "required" operator) requires that the term after the + symbol exist
somewhere in a field in at least one document in order for the query to return a match.
For example, to search for documents that must contain "jakarta" and that may or may not contain
"lucene," use the following query:
+jakarta lucene
?
This operator is supported by both the standard query parser and the DisMax query parser.
The Boolean Operator AND ( && )
The AND operator matches documents where both terms exist anywhere in the text of a single document.
This is equivalent to an intersection using sets. The symbol && can be used in place of the word AND.
To search for documents that contain "jakarta apache" and "Apache Lucene," use either of the following
queries:
"jakarta apache" AND "Apache Lucene"
"jakarta apache" && "Apache Lucene"
The Boolean Operator NOT ( ! )
The NOT operator excludes documents that contain the term after NOT. This is equivalent to a difference
using sets. The symbol ! can be used in place of the word NOT.
The following queries search for documents that contain the phrase "jakarta apache" but do not contain the
phrase "Apache Lucene":
"jakarta apache" NOT "Apache Lucene"
"jakarta apache" ! "Apache Lucene"
The Boolean Operator -
The - symbol or "prohibit" operator excludes documents that contain the term after the - symbol.
For example, to search for documents that contain "jakarta apache" but not "Apache Lucene," use the
following query:
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"jakarta apache" -"Apache Lucene"
Escaping Special Characters
Solr gives the following characters special meaning when they appear in a query:
+ - && || ! ( ) { } [ ] ^ " ~ * ? : /
To make Solr interpret any of these characters literally, rather as a special character, precede the character
with a backslash character \ . For example, to search for (1+1):2 without having Solr interpret the plus sign
and parentheses as special characters for formulating a sub-query with two terms, escape the characters by
preceding each one with a backslash:
\(1\+1\)\:2
Grouping Terms to Form Sub-Queries
Lucene/Solr supports using parentheses to group clauses to form sub-queries. This can be very useful if you
want to control the Boolean logic for a query.
The query below searches for either "jakarta" or "apache" and "website":
(jakarta OR apache) AND website
This adds precision to the query, requiring that the term "website" exist, along with either term "jakarta"
and "apache."
Grouping Clauses within a Field
To apply two or more Boolean operators to a single field in a search, group the Boolean clauses within
parentheses. For example, the query below searches for a title field that contains both the word "return"
and the phrase "pink panther":
title:(+return +"pink panther")
Comments
C-Style comments are supported in query strings.
Example:
"jakarta apache" /* this is a comment in the middle of a normal query string */ OR jakarta
Comments may be nested.
Differences between Lucene Query Parser and the Solr Standard Query Parser
Solr’s standard query parser differs from the Lucene Query Parser in the following ways:
? A * may be used for either or both endpoints to specify an open-ended range query
? field:[* TO 100] finds all field values less than or equal to 100
? field:[100 TO *] finds all field values greater than or equal to 100
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? field:[* TO *] matches all documents with the field
? Pure negative queries (all clauses prohibited) are allowed (only as a top-level clause)
? -inStock:false finds all field values where inStock is not false
? -field:[* TO *] finds all documents without a value for field
? A hook into FunctionQuery syntax. You’ll need to use quotes to encapsulate the function if it includes
parentheses, as shown in the second example below:
? val:myfield
? val:"recip(rord(myfield),1,2,3)"
? Support for using any type of query parser as a nested clause.
? inStock:true OR {!dismax qf=‘name manu‘ v=‘ipod‘}
? Support for a special filter(…) syntax to indicate that some query clauses should be cached in the filter
cache (as a constant score boolean query). This allows sub-queries to be cached and re-used in other
queries. For example inStock:true will be cached and re-used in all three of the queries below:
? q=features:songs OR filter(inStock:true)
? q=+manu:Apple +filter(inStock:true)
? q=+manu:Apple & fq=inStock:true
This can even be used to cache individual clauses of complex filter queries. In the first query below, 3
items will be added to the filter cache (the top level fq and both filter(…) clauses) and in the second
query, there will be 2 cache hits, and one new cache insertion (for the new top level fq ):
? q=features:songs & fq=+filter(inStock:true) +filter(price:[* TO 100])
? q=manu:Apple & fq=-filter(inStock:true) -filter(price:[* TO 100])
? Range queries ("[a TO z]"), prefix queries ("a*"), and wildcard queries ("a*b") are constant-scoring (all
matching documents get an equal score). The scoring factors TF, IDF, index boost, and "coord" are not
used. There is no limitation on the number of terms that match (as there was in past versions of Lucene).
? Constant score queries are created with <query_clause>^=<score> , which sets the entire clause to the
specified score for any documents matching that clause:
? q=(description:blue color:blue)^=1.0 title:blue^=5.0
Specifying Dates and Times
Queries against fields using the TrieDateField type (typically range queries) should use the appropriate
date syntax:
? timestamp:[* TO NOW]
? createdate:[1976-03-06T23:59:59.999Z TO *]
? createdate:[1995-12-31T23:59:59.999Z TO 2007-03-06T00:00:00Z]
? pubdate:[NOW-1YEAR/DAY TO NOW/DAY+1DAY]
? createdate:[1976-03-06T23:59:59.999Z TO 1976-03-06T23:59:59.999Z+1YEAR]
? createdate:[1976-03-06T23:59:59.999Z/YEAR TO 1976-03-06T23:59:59.999Z]
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Related Topics
? Local Parameters in Queries
? Other Parsers
The DisMax Query Parser
The DisMax query parser is designed to process simple phrases (without complex syntax) entered by users
and to search for individual terms across several fields using different weighting (boosts) based on the
significance of each field. Additional options enable users to influence the score based on rules specific to
each use case (independent of user input).
In general, the DisMax query parser’s interface is more like that of Google than the interface of the
‘standard‘ Solr request handler. This similarity makes DisMax the appropriate query parser for many
consumer applications. It accepts a simple syntax, and it rarely produces error messages.
The DisMax query parser supports an extremely simplified subset of the Lucene QueryParser syntax. As in
Lucene, quotes can be used to group phrases, and +/- can be used to denote mandatory and optional
clauses. All other Lucene query parser special characters (except AND and OR) are escaped to simplify the
user experience. The DisMax query parser takes responsibility for building a good query from the user’s
input using Boolean clauses containing DisMax queries across fields and boosts specified by the user. It also
lets the Solr administrator provide additional boosting queries, boosting functions, and filtering queries to
artificially affect the outcome of all searches. These options can all be specified as default parameters for the
handler in the solrconfig.xml file or overridden in the Solr query URL.
Interested in the technical concept behind the DisMax name? DisMax stands for Maximum Disjunction.
Here’s a definition of a Maximum Disjunction or "DisMax" query:
A query that generates the union of documents produced by its subqueries, and that scores each document
with the maximum score for that document as produced by any subquery, plus a tie breaking increment for
any additional matching subqueries.
Whether or not you remember this explanation, do remember that the DisMax Query Parser was primarily
designed to be easy to use and to accept almost any input without returning an error.
DisMax Parameters
In addition to the common request parameter, highlighting parameters, and simple facet parameters, the
DisMax query parser supports the parameters described below. Like the standard query parser, the DisMax
query parser allows default parameter values to be specified in solrconfig.xml , or overridden by query-
time values in the request.
Parameter Description
q Defines the raw input strings for the query.
q.alt Calls the standard query parser and defines query input strings, when the
q parameter is not used.
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Parameter Description
qf Query Fields: specifies the fields in the index on which to perform the
query. If absent, defaults to df .
mm Minimum "Should" Match: specifies a minimum number of clauses that
must match in a query. If no ‘mm‘ parameter is specified in the query, or
as a default in solrconfig.xml , the effective value of the q.op parameter
(either in the query, as a default in solrconfig.xml , or from the
defaultOperator option in the Schema) is used to influence the behavior.
If q.op is effectively AND’ed, then mm=100%; if q.op is OR’ed, then mm=1.
Users who want to force the legacy behavior should set a default value for
the ‘mm‘ parameter in their solrconfig.xml file. Users should add this as
a configured default for their request handlers. This parameter tolerates
miscellaneous white spaces in expressions (e.g., " 3 < -25% 10 < -3\n",
" \n-25%\n ", " \n3\n " ).
pf Phrase Fields: boosts the score of documents in cases where all of the
terms in the q parameter appear in close proximity.
ps Phrase Slop: specifies the number of positions two terms can be apart in
order to match the specified phrase.
qs Query Phrase Slop: specifies the number of positions two terms can be
apart in order to match the specified phrase. Used specifically with the qf
parameter.
tie Tie Breaker: specifies a float value (which should be something much less
than 1) to use as tiebreaker in DisMax queries. Default: 0.0
bq Boost Query: specifies a factor by which a term or phrase should be
"boosted" in importance when considering a match.
bf Boost Functions: specifies functions to be applied to boosts. (See for
details about function queries.)
The sections below explain these parameters in detail.
The q Parameter
The q parameter defines the main "query" constituting the essence of the search. The parameter supports
raw input strings provided by users with no special escaping. The + and - characters are treated as
"mandatory" and "prohibited" modifiers for terms. Text wrapped in balanced quote characters (for
example, "San Jose") is treated as a phrase. Any query containing an odd number of quote characters is
evaluated as if there were no quote characters at all.
?
The q parameter does not support wildcard characters such as *.
The q.alt Parameter
If specified, the q.alt parameter defines a query (which by default will be parsed using standard query
parsing syntax) when the main q parameter is not specified or is blank. The q.alt parameter comes in
handy when you need something like a query to match all documents (don’t forget &rows=0 for that one!) in
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order to get collection-wide faceting counts.
The qf (Query Fields) Parameter
The qf parameter introduces a list of fields, each of which is assigned a boost factor to increase or decrease
that particular field’s importance in the query. For example, the query below:
qf="fieldOne^2.3 fieldTwo fieldThree^0.4"
assigns fieldOne a boost of 2.3, leaves fieldTwo with the default boost (because no boost factor is
specified), and fieldThree a boost of 0.4. These boost factors make matches in fieldOne much more
significant than matches in fieldTwo , which in turn are much more significant than matches in fieldThree .
The mm (Minimum Should Match) Parameter
When processing queries, Lucene/Solr recognizes three types of clauses: mandatory, prohibited, and
"optional" (also known as "should" clauses). By default, all words or phrases specified in the q parameter
are treated as "optional" clauses unless they are preceded by a "+" or a "-". When dealing with these
"optional" clauses, the mm parameter makes it possible to say that a certain minimum number of those
clauses must match. The DisMax query parser offers great flexibility in how the minimum number can be
specified.
The table below explains the various ways that mm values can be specified.
Syntax Example Description
Positive integer 3 Defines the minimum number of clauses that must match,
regardless of how many clauses there are in total.
Negative integer -2 Sets the minimum number of matching clauses to the total
number of optional clauses, minus this value.
Percentage 75% Sets the minimum number of matching clauses to this
percentage of the total number of optional clauses. The
number computed from the percentage is rounded down and
used as the minimum.
Negative percentage -25% Indicates that this percent of the total number of optional
clauses can be missing. The number computed from the
percentage is rounded down, before being subtracted from the
total to determine the minimum number.
An expression beginning with a
positive integer followed by a >
or < sign and another value
3<90% Defines a conditional expression indicating that if the number
of optional clauses is equal to (or less than) the integer, they
are all required, but if it’s greater than the integer, the
specification applies. In this example: if there are 1 to 3 clauses
they are all required, but for 4 or more clauses only 90% are
required.
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Syntax Example Description
Multiple conditional
expressions involving > or <
signs
2<-25%
9<-3
Defines multiple conditions, each one being valid only for
numbers greater than the one before it. In the example at left,
if there are 1 or 2 clauses, then both are required. If there are 3-
9 clauses all but 25% are required. If there are more then 9
clauses, all but three are required.
When specifying mm values, keep in mind the following:
? When dealing with percentages, negative values can be used to get different behavior in edge cases. 75%
and -25% mean the same thing when dealing with 4 clauses, but when dealing with 5 clauses 75% means
3 are required, but -25% means 4 are required.
? If the calculations based on the parameter arguments determine that no optional clauses are needed,
the usual rules about Boolean queries still apply at search time. (That is, a Boolean query containing no
required clauses must still match at least one optional clause).
? No matter what number the calculation arrives at, Solr will never use a value greater than the number of
optional clauses, or a value less than 1. In other words, no matter how low or how high the calculated
result, the minimum number of required matches will never be less than 1 or greater than the number of
clauses.
? When searching across multiple fields that are configured with different query analyzers, the number of
optional clauses may differ between the fields. In such a case, the value specified by mm applies to the
maximum number of optional clauses. For example, if a query clause is treated as stopword for one of
the fields, the number of optional clauses for that field will be smaller than for the other fields. A query
with such a stopword clause would not return a match in that field if mm is set to 100% because the
removed clause does not count as matched.
The default value of mm is 100% (meaning that all clauses must match).
The pf (Phrase Fields) Parameter
Once the list of matching documents has been identified using the fq and qf parameters, the pf parameter
can be used to "boost" the score of documents in cases where all of the terms in the q parameter appear in
close proximity.
The format is the same as that used by the qf parameter: a list of fields and "boosts" to associate with each
of them when making phrase queries out of the entire q parameter.
The ps (Phrase Slop) Parameter
The ps parameter specifies the amount of "phrase slop" to apply to queries specified with the pf parameter.
Phrase slop is the number of positions one token needs to be moved in relation to another token in order to
match a phrase specified in a query.
The qs (Query Phrase Slop) Parameter
The qs parameter specifies the amount of slop permitted on phrase queries explicitly included in the user’s
query string with the qf parameter. As explained above, slop refers to the number of positions one token
needs to be moved in relation to another token in order to match a phrase specified in a query.
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The tie (Tie Breaker) Parameter
The tie parameter specifies a float value (which should be something much less than 1) to use as tiebreaker
in DisMax queries.
When a term from the user’s input is tested against multiple fields, more than one field may match. If so,
each field will generate a different score based on how common that word is in that field (for each
document relative to all other documents). The tie parameter lets you control how much the final score of
the query will be influenced by the scores of the lower scoring fields compared to the highest scoring field.
A value of "0.0" - the default - makes the query a pure "disjunction max query": that is, only the maximum
scoring subquery contributes to the final score. A value of "1.0" makes the query a pure "disjunction sum
query" where it doesn’t matter what the maximum scoring sub query is, because the final score will be the
sum of the subquery scores. Typically a low value, such as 0.1, is useful.
The bq (Boost Query) Parameter
The bq parameter specifies an additional, optional, query clause that will be added to the user’s main query
to influence the score. For example, if you wanted to add a relevancy boost for recent documents:
q=cheese
bq=date:[NOW/DAY-1YEAR TO NOW/DAY]
You can specify multiple bq parameters. If you want your query to be parsed as separate clauses with
separate boosts, use multiple bq parameters.
The bf (Boost Functions) Parameter
The bf parameter specifies functions (with optional boosts) that will be used to construct FunctionQueries
which will be added to the user’s main query as optional clauses that will influence the score. Any function
supported natively by Solr can be used, along with a boost value. For example:
recip(rord(myfield),1,2,3)^1.5
Specifying functions with the bf parameter is essentially just shorthand for using the bq param combined
with the {!func} parser.
For example, if you want to show the most recent documents first, you could use either of the following:
bf=recip(rord(creationDate),1,1000,1000)
...or...
bq={!func}recip(rord(creationDate),1,1000,1000)
Examples of Queries Submitted to the DisMax Query Parser
All of the sample URLs in this section assume you are running Solr’s "techproducts" example:
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bin/solr -e techproducts
Normal results for the word "video" using the StandardRequestHandler with the default search field:
http://localhost:8983/solr/techproducts/select?q=video&fl=name+score
The "dismax" handler is configured to search across the text, features, name, sku, id, manu, and cat fields all
with varying boosts designed to ensure that "better" matches appear first, specifically: documents which
match on the name and cat fields get higher scores.
http://localhost:8983/solr/techproducts/select?defType=dismax&q=video
Note that this instance is also configured with a default field list, which can be overridden in the URL.
http://localhost:8983/solr/techproducts/select?defType=dismax&q=video&fl=*,score
You can also override which fields are searched on and how much boost each field gets.
http://localhost:8983/solr/techproducts/select?defType=dismax&q=video&qf=features^20.0+text^
0.3
You can boost results that have a field that matches a specific value.
http://localhost:8983/solr/techproducts/select?defType=dismax&q=video&bq=cat:electronics^5.0
Another instance of the handler is registered using the qt "instock" and has slightly different configuration
options, notably: a filter for (you guessed it) inStock:true) .
http://localhost:8983/solr/techproducts/select?defType=dismax&q=video&fl=name,score,inStock
http://localhost:8983/solr/techproducts/select?defType=dismax&q=video&qt=instock&fl=name,sco
re,inStock
One of the other really cool features in this handler is robust support for specifying the
"BooleanQuery.minimumNumberShouldMatch" you want to be used based on how many terms are in your
user’s query. These allows flexibility for typos and partial matches. For the dismax handler, one and two
word queries require that all of the optional clauses match, but for three to five word queries one missing
word is allowed.
http://localhost:8983/solr/techproducts/select?defType=dismax&q=belkin+ipod
http://localhost:8983/solr/techproducts/select?defType=dismax&q=belkin+ipod+gibberish
http://localhost:8983/solr/techproducts/select?defType=dismax&q=belkin+ipod+apple
Just like the StandardRequestHandler, it supports the debugQuery option to viewing the parsed query, and
the score explanations for each document.
http://localhost:8983/solr/techproducts/select?defType=dismax&q=belkin+ipod+gibberish&debugQ
uery=true
http://localhost:8983/solr/techproducts/select?defType=dismax&q=video+card&debugQuery=true
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The Extended DisMax Query Parser
The Extended DisMax (eDisMax) query parser is an improved version of the DisMax query parser.
In addition to supporting all the DisMax query parser parameters, Extended Dismax:
? supports the full Lucene query parser syntax.
? supports queries such as AND, OR, NOT, -, and +.
? treats "and" and "or" as "AND" and "OR" in Lucene syntax mode.
? respects the ‘magic field‘ names _val_ and _query_ . These are not a real fields in the Schema, but if
used it helps do special things (like a function query in the case of _val_ or a nested query in the case of
_query_ ). If _val_ is used in a term or phrase query, the value is parsed as a function.
? includes improved smart partial escaping in the case of syntax errors; fielded queries, +/-, and phrase
queries are still supported in this mode.
? improves proximity boosting by using word shingles; you do not need the query to match all words in
the document before proximity boosting is applied.
? includes advanced stopword handling: stopwords are not required in the mandatory part of the query
but are still used in the proximity boosting part. If a query consists of all stopwords, such as "to be or not
to be", then all words are required.
? includes improved boost function: in Extended DisMax, the boost function is a multiplier rather than an
addend, improving your boost results; the additive boost functions of DisMax ( bf and bq ) are also
supported.
? supports pure negative nested queries: queries such as +foo (-foo) will match all documents.
? lets you specify which fields the end user is allowed to query, and to disallow direct fielded searches.
Extended DisMax Parameters
In addition to all the DisMax parameters, Extended DisMax includes these query parameters:
The sow Parameter
Split on whitespace: if set to false , whitespace-separated term sequences will be provided to text analysis in
one shot, enabling proper function of analysis filters that operate over term sequences, e.g. multi-word
synonyms and shingles. Defaults to true : text analysis is invoked separately for each individual whitespace-
separated term.
The mm.autoRelax Parameter
If true, the number of clauses required (minimum should match) will automatically be relaxed if a clause is
removed (by e.g. stopwords filter) from some but not all qf fields. Use this parameter as a workaround if
you experience that queries return zero hits due to uneven stopword removal between the qf fields.
Note that relaxing mm may cause undesired side effects, hurting the precision of the search, depending on
the nature of your index content.
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The boost Parameter
A multivalued list of strings parsed as queries with scores multiplied by the score from the main query for all
matching documents. This parameter is shorthand for wrapping the query produced by eDisMax using the
BoostQParserPlugin
The lowercaseOperators Parameter
A Boolean parameter indicating if lowercase "and" and "or" should be treated the same as operators "AND"
and "OR".
The ps Parameter
Default amount of slop on phrase queries built with pf , pf2 and/or pf3 fields (affects boosting).
The pf2 Parameter
A multivalued list of fields with optional weights, based on pairs of word shingles.
The ps2 Parameter
This is similar to ps but overrides the slop factor used for pf2 . If not specified, ps is used.
The pf3 Parameter
A multivalued list of fields with optional weights, based on triplets of word shingles. Similar to pf , except that
instead of building a phrase per field out of all the words in the input, it builds a set of phrases for each field
out of each triplet of word shingles.
The ps3 Parameter
This is similar to ps but overrides the slop factor used for pf3 . If not specified, ps is used.
The stopwords Parameter
A Boolean parameter indicating if the StopFilterFactory configured in the query analyzer should be
respected when parsing the query: if it is false, then the StopFilterFactory in the query analyzer is
ignored.
The uf Parameter
Specifies which schema fields the end user is allowed to explicitly query. This parameter supports wildcards.
The default is to allow all fields, equivalent to uf=* . To allow only title field, use uf=title . To allow title and
all fields ending with ‘_s‘, use uf=title,*_s . To allow all fields except title, use uf=*,-title . To disallow all
fielded searches, use uf=-* .
Field aliasing using per-field qf overrides
Per-field overrides of the qf parameter may be specified to provide 1-to-many aliasing from field names
specified in the query string, to field names used in the underlying query. By default, no aliasing is used and
field names specified in the query string are treated as literal field names in the index.
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Examples of Queries Submitted to the Extended DisMax Query Parser
All of the sample URLs in this section assume you are running Solr’s “ techproducts ” example:
bin/solr -e techproducts
Boost the result of the query term "hello" based on the document’s popularity:
http://localhost:8983/solr/techproducts/select?defType=edismax&q=hello&pf=text&qf=text&boost=popu
larity
Search for iPods OR video:
http://localhost:8983/solr/techproducts/select?defType=edismax&q=ipod+OR+video
Search across multiple fields, specifying (via boosts) how important each field is relative each other:
http://localhost:8983/solr/techproducts/select?q=video&defType=edismax&qf=features^20.0+text^0.3
You can boost results that have a field that matches a specific value:
http://localhost:8983/solr/techproducts/select?q=video&defType=edismax&qf=features^20.0+text^0.3&
bq=cat:electronics^5.0
Using the "mm" param, 1 and 2 word queries require that all of the optional clauses match, but for queries
with three or more clauses one missing clause is allowed:
http://localhost:8983/solr/techproducts/select?q=belkin+ipod&defType=edismax&mm=2
http://localhost:8983/solr/techproducts/select?q=belkin+ipod+gibberish&defType=edismax&mm=2
http://localhost:8983/solr/techproducts/select?q=belkin+ipod+apple&defType=edismax&mm=2
In the example below, we see a per-field override of the qf parameter being used to alias "name" in the
query string to either the “ last_name ” and “ first_name ” fields:
defType=edismax
q=sysadmin name:Mike
qf=title text last_name first_name
f.name.qf=last_name first_name
Using negative boost
Negative query boosts have been supported at the "Query" object level for a long time (resulting in negative
scores for matching documents). Now the QueryParsers have been updated to handle this too.
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Using ‘slop‘
Dismax and Edismax can run queries against all query fields, and also run a query in the form of a phrase
against the phrase fields. (This will work only for boosting documents, not actually for matching.) However,
that phrase query can have a ‘slop,‘ which is the distance between the terms of the query while still
considering it a phrase match. For example:
q=foo bar
qf=field1^5 field2^10
pf=field1^50 field2^20
defType=dismax
With these parameters, the Dismax Query Parser generates a query that looks something like this:
(+(field1:foo^5 OR field2:foo^10) AND (field1:bar^5 OR field2:bar^10))
But it also generates another query that will only be used for boosting results:
field1:"foo bar"^50 OR field2:"foo bar"^20
Thus, any document that has the terms "foo" and "bar" will match; however if some of those documents
have both of the terms as a phrase, it will score much higher because it’s more relevant.
If you add the parameter ps (phrase slop), the second query will instead be:
ps=10 field1:"foo bar"~10^50 OR field2:"foo bar"~10^20
This means that if the terms "foo" and "bar" appear in the document with less than 10 terms between each
other, the phrase will match. For example the doc that says:
*Foo* term1 term2 term3 *bar*
will match the phrase query.
How does one use phrase slop? Usually it is configured in the request handler (in solrconfig ).
With query slop ( qs ) the concept is similar, but it applies to explicit phrase queries from the user. For
example, if you want to search for a name, you could enter:
q="Hans Anderson"
A document that contains "Hans Anderson" will match, but a document that contains the middle name
"Christian" or where the name is written with the last name first ("Anderson, Hans") won’t. For those cases
one could configure the query field qs , so that even if the user searches for an explicit phrase query, a slop is
applied.
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Finally, in addition to the phrase fields ( pf ) parameter, edismax also supports the pf2 and pf3 parameters,
for fields over which to create bigram and trigram phrase queries. The phrase slop for these parameters‘
queries can be specified using the ps2 and ps3 parameters, respectively. If you use pf2 / pf3 but ps2 / ps3 ,
then the phrase slop for these parameters‘ queries will be taken from the ps parameter, if any.
Using the ‘magic fields‘ _val_ and _query_
The Solr Query Parser’s use of _val_ and _query_ differs from the Lucene Query Parser in the following
ways:
? If the magic field name _val_ is used in a term or phrase query, the value is parsed as a function.
? It provides a hook into FunctionQuery syntax. Quotes are necessary to encapsulate the function when it
includes parentheses. For example:
_val_:myfield
_val_:"recip(rord(myfield),1,2,3)"
? The Solr Query Parser offers nested query support for any type of query parser (via QParserPlugin).
Quotes are often necessary to encapsulate the nested query if it contains reserved characters. For
example:
_query_:"{!dismax qf=myfield}how now brown cow"
Although not technically a syntax difference, note that if you use the Solr TrieDateField type, any queries
on those fields (typically range queries) should use either the Complete ISO 8601 Date syntax that field
supports, or the DateMath Syntax to get relative dates. For example:
timestamp:[* TO NOW]
createdate:[1976-03-06T23:59:59.999Z TO *]
createdate:[1995-12-31T23:59:59.999Z TO 2007-03-06T00:00:00Z]
pubdate:[NOW-1YEAR/DAY TO NOW/DAY+1DAY]
createdate:[1976-03-06T23:59:59.999Z TO 1976-03-06T23:59:59.999Z+1YEAR]
createdate:[1976-03-06T23:59:59.999Z/YEAR TO 1976-03-06T23:59:59.999Z]
?
TO must be uppercase, or Solr will report a ‘Range Group‘ error.
Function Queries
Function queries enable you to generate a relevancy score using the actual value of one or more numeric
fields.
Function queries are supported by the DisMax, Extended DisMax, and standard query parsers.
Function queries use functions. The functions can be a constant (numeric or string literal), a field, another
function or a parameter substitution argument. You can use these functions to modify the ranking of results
for users. These could be used to change the ranking of results based on a user’s location, or some other
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calculation.
Using Function Query
Functions must be expressed as function calls (for example, sum(a,b) instead of simply a+b ).
There are several ways of using function queries in a Solr query:
? Via an explicit QParser that expects function arguments, such func or frange . For example:
q={!func}div(popularity,price)&fq={!frange l=1000}customer_ratings
? In a Sort expression. For example:
sort=div(popularity,price) desc, score desc
? Add the results of functions as pseudo-fields to documents in query results. For instance, for:
&fl=sum(x, y),id,a,b,c,score
the output would be:
...
<str name="id">foo</str>
<float name="sum(x,y)">40</float>
<float name="score">0.343</float>
...
? Use in a parameter that is explicitly for specifying functions, such as the EDisMax query parser’s boost
param, or DisMax query parser’s bf (boost function) parameter. (Note that the bf parameter actually
takes a list of function queries separated by white space and each with an optional boost. Make sure you
eliminate any internal white space in single function queries when using bf ). For example:
q=dismax&bf="ord(popularity)^0.5 recip(rord(price),1,1000,1000)^0.3"
? Introduce a function query inline in the lucene QParser with the _val_ keyword. For example:
q=_val_:mynumericfield _val_:"recip(rord(myfield),1,2,3)"
Only functions with fast random access are recommended.
Available Functions
The table below summarizes the functions available for function queries.
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Function Description Syntax Examples
abs Returns the absolute value of the
specified value or function.
abs(x) abs(-5)
childfield(field) Returns the value of the given field for
one of the matched child docs when
searching by {!parent}. It can be used only
in sort parameter.
? sort=childfield(name) asc implies
$q as a second argument and therefor
it assumes q={!parent ..}.. ;
? sort=childfield(field,$bjq) asc
refers to a separate parameter
bjq={!parent ..}.. ;
? sort=childfield(field,{!parent
of=…}…) desc allows to inline block
join parent query
"constant" Specifies a floating point constant.
1.5
def def is short for default. Returns the value
of field "field", or if the field does not
exist, returns the default value specified.
and yields the first value where
exists()==true .)
def(rating,5): This def() function
returns the rating, or if no rating specified
in the doc, returns 5 def(myfield, 1.0):
equivalent to
if(exists(myfield),myfield,1.0)
div Divides one value or function by another.
div(x,y) divides x by y.
div(1,y)
div(sum(x,100),max(y,1))
dist Return the distance between two vectors
(points) in an n-dimensional space. Takes
in the power, plus two or more
ValueSource instances and calculates the
distances between the two vectors. Each
ValueSource must be a number. There
must be an even number of ValueSource
instances passed in and the method
assumes that the first half represent the
first vector and the second half represent
the second vector.
dist(2, x, y, 0, 0): calculates the
Euclidean distance between (0,0) and (x,y)
for each document.
dist(1, x, y, 0, 0) : calculates the
Manhattan (taxicab) distance between
(0,0) and (x,y) for each document.
dist(2, x,y,z,0,0,0): Euclidean
distance between (0,0,0) and (x,y,z) for
each document.
dist(1,x,y,z,e,f,g) : Manhattan
distance between (x,y,z) and (e,f,g) where
each letter is a field name.
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Function Description Syntax Examples
docfreq(field,val) Returns the number of documents that
contain the term in the field. This is a
constant (the same value for all
documents in the index).
You can quote the term if it’s more
complex, or do parameter substitution for
the term value.
docfreq(text,‘solr‘)
…&defType=func
&q=docfreq(text,$myterm)&myterm=sol
r
field Returns the numeric docValues or
indexed value of the field with the
specified name. In its simplest (single
argument) form, this function can only be
used on single valued fields, and can be
called using the name of the field as a
string, or for most conventional field
names simply use the field name by itself
with out using the field(…) syntax.
When using docValues, an optional 2nd
argument can be specified to select the
“min"` or " `max ” value of multivalued
fields.
0 is returned for documents without a
value in the field.
These 3 examples are all equivalent:
? myFloatFieldName
? field(myFloatFieldName)
? field("myFloatFieldName")
The last form is convinient when your
field name is atypical:
? field("my complex float
fieldName")
For multivalued docValues fields:
? field(myMultiValuedFloatField,mi
n)
? field(myMultiValuedFloatField,ma
x)
hsin The Haversine distance calculates the
distance between two points on a sphere
when traveling along the sphere. The
values must be in radians. hsin also take
a Boolean argument to specify whether
the function should convert its output to
radians.
hsin(2, true, x, y, 0, 0)
idf Inverse document frequency; a measure
of whether the term is common or rare
across all documents. Obtained by
dividing the total number of documents
by the number of documents containing
the term, and then taking the logarithm of
that quotient. See also tf .
idf(fieldName,‘solr‘) : measures the
inverse of the frequency of the occurrence
of the term ‘solr‘ in fieldName .
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Function Description Syntax Examples
if Enables conditional function queries. In
if(test,value1,value2) :
? test is or refers to a logical value or
expression that returns a logical value
(TRUE or FALSE).
? value1 is the value that is returned by
the function if test yields TRUE.
? value2 is the value that is returned by
the function if test yields FALSE.
An expression can be any function which
outputs boolean values, or even functions
returning numeric values, in which case
value 0 will be interpreted as false, or
strings, in which case empty string is
interpreted as false.
if(termfreq (cat,‘electronics‘),
popularity,42) : This function checks
each document for the to see if it contains
the term “ electronics ” in the cat field. If
it does, then the value of the popularity
field is returned, otherwise the value of 42
is returned.
linear Implements m*x+c where m and c are
constants and x is an arbitrary function.
This is equivalent to
sum(product(m,x),c) , but slightly more
efficient as it is implemented as a single
function.
linear(x,m,c)
linear(x,2,4) returns 2*x+4
log Returns the log base 10 of the specified
function.
log(x)
log(sum(x,100))
map Maps any values of an input function x
that fall within min and max inclusive to
the specified target. The arguments min
and max must be constants. The
arguments target and default can be
constants or functions. If the value of x
does not fall between min and max, then
either the value of x is returned, or a
default value is returned if specified as a
5th argument.
map(x,min,max,target) map(x,0,0,1) -
changes any values of 0 to 1. This can be
useful in handling default 0 values.
map(x,min,max,target,default)
map(x,0,100,1,-1) - changes any values
between 0 and 100 to 1 , and all other
values to` -1`.
map(x,0,100, sum(x,599),
docfreq(text,solr)) - changes any
values between 0 and 100 to x+599, and
all other values to frequency of the term
‘solr‘ in the field text.
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Function Description Syntax Examples
max Returns the maximum numeric value of
multiple nested functions or constants,
which are specified as arguments:
max(x,y,…) . The max function can also be
useful for "bottoming out" another
function or field at some specified
constant.
(Use the field(myfield,max) syntax for
selecting the maximum value of a single
multivalued field)
max(myfield,myotherfield,0)
maxdoc Returns the number of documents in the
index, including those that are marked as
deleted but have not yet been purged.
This is a constant (the same value for all
documents in the index).
maxdoc()
min Returns the minimum numeric value of
multiple nested functions of constants,
which are specified as arguments:
min(x,y,…) . The min function can also be
useful for providing an "upper bound" on
a function using a constant.
(Use the field(myfield,min) syntax for
selecting the minimum value of a single
multivalued field)
min(myfield,myotherfield,0)
ms Returns milliseconds of difference
between its arguments. Dates are relative
to the Unix or POSIX time epoch,
midnight, January 1, 1970 UTC.
Arguments may be the name of an
indexed TrieDateField , or date math
based on a constant date or NOW .
? ms() : Equivalent to ms(NOW) , number
of milliseconds since the epoch.
? ms(a): Returns the number of
milliseconds since the epoch that the
argument represents.
? ms(a,b) : Returns the number of
milliseconds that b occurs before a
(that is, a - b)
ms(NOW/DAY)
ms(2000-01-01T00:00:00Z)
ms(mydatefield)
ms(NOW,mydatefield)
ms(mydatefield, 2000-01-
01T00:00:00Z)
ms(datefield1, datefield2)
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Function Description Syntax Examples
norm(field) Returns the "norm" stored in the index
for the specified field. This is the product
of the index time boost and the length
normalization factor, according to the
Similarity for the field.
norm(fieldName)
numdocs Returns the number of documents in the
index, not including those that are
marked as deleted but have not yet been
purged. This is a constant (the same value
for all documents in the index).
numdocs()
ord Returns the ordinal of the indexed field
value within the indexed list of terms for
that field in Lucene index order
(lexicographically ordered by unicode
value), starting at 1. In other words, for a
given field, all values are ordered
lexicographically; this function then
returns the offset of a particular value in
that ordering. The field must have a
maximum of one value per document
(not multi-valued). 0 is returned for
documents without a value in the field.
?
ord() depends on the
position in an index and
can change when other
documents are inserted
or deleted.
See also rord below.
ord(myIndexedField)
Example: If there were only three values
("apple","banana","pear") for a particular
field X, then ord(X) would be 1 for
documents containing "apple", 2 for
documents containing "banana", etc.
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Function Description Syntax Examples
payload Returns the float value computed from
the decoded payloads of the term
specified. The return value is computed
using the min , max , or average of the
decoded payloads. A special first
function can be used instead of the
others, to short-circuit term enumeration
and return only the decoded payload of
the first term. The field specified must
have float or integer payload encoding
capability (via
DelimitedPayloadTokenFilter or
NumericPayloadTokenFilter ). If no
payload is found for the term, the default
value is returned.
? payload(field_name,term) : default
value is 0.0, average function is used.
? payload(field_name,term,default_
value) : default value can be a
constant, field name, or another float
returning function. average function
used.
? payload(field_name,term,default_
value,function) : function values can
be min , max , average , or first .
payload(payloaded_field_dpf,term,0.0
,first)
pow Raises the specified base to the specified
power. pow(x,y) raises x to the power of
y.
pow(x,y)
pow(x,log(y))
pow(x,0.5): the same as sqrt
product Returns the product of multiple values or
functions, which are specified in a
comma-separated list. mul(…) may also be
used as an alias for this function.
product(x,y,…) product(x,2)
product(x,y)mul(x,y)
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Function Description Syntax Examples
query Returns the score for the given subquery,
or the default value for documents not
matching the query. Any type of subquery
is supported through either parameter
de-referencing $otherparam or direct
specification of the query string in the
Local Parameters through the v key.
query(subquery, default)
q=product (popularity, query({!dismax
v=‘solr rocks‘}) : returns the product of
the popularity and the score of the
DisMax query.
q=product (popularity,
query($qq))&qq={!dismax}solr rocks :
equivalent to the previous query, using
parameter de-referencing.
q=product (popularity,
query($qq,0.1)) &qq={!dismax} solr
rocks : specifies a default score of 0.1 for
documents that don’t match the DisMax
query.
recip Performs a reciprocal function with
recip(x,m,a,b) implementing a/(m*x+b)
where m,a,b are constants, and x is any
arbitrarily complex function.
When a and b are equal, and x>=0, this
function has a maximum value of 1 that
drops as x increases. Increasing the value
of a and b together results in a movement
of the entire function to a flatter part of
the curve. These properties can make this
an ideal function for boosting more
recent documents when x is
rord(datefield) .
recip(myfield,m,a,b)
recip(rord (creationDate),
1,1000,1000)
rord Returns the reverse ordering of that
returned by ord .
rord(myDateField)
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Function Description Syntax Examples
scale Scales values of the function x such that
they fall between the specified minTarget
and maxTarget inclusive. The current
implementation traverses all of the
function values to obtain the min and
max, so it can pick the correct scale.
The current implementation cannot
distinguish when documents have been
deleted or documents that have no value.
It uses 0.0 values for these cases. This
means that if values are normally all
greater than 0.0, one can still end up with
0.0 as the min value to map from. In these
cases, an appropriate map() function
could be used as a workaround to change
0.0 to a value in the real range, as shown
here: scale(map(x,0,0,5),1,2)
scale(x, minTarget, maxTarget)
scale(x,1,2) : scales the values of x such
that all values will be between 1 and 2
inclusive.
sqedist The Square Euclidean distance calculates
the 2-norm (Euclidean distance) but does
not take the square root, thus saving a
fairly expensive operation. It is often the
case that applications that care about
Euclidean distance do not need the actual
distance, but instead can use the square
of the distance. There must be an even
number of ValueSource instances passed
in and the method assumes that the first
half represent the first vector and the
second half represent the second vector.
sqedist(x_td, y_td, 0, 0)
sqrt Returns the square root of the specified
value or function.
sqrt(x)sqrt(100)sqrt(sum(x,100))
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Function Description Syntax Examples
strdist Calculate the distance between two
strings. Uses the Lucene spell checker
StringDistance interface and supports
all of the implementations available in
that package, plus allows applications to
plug in their own via Solr’s resource
loading capabilities. strdist takes
(string1, string2, distance measure).
Possible values for distance measure are:
? jw: Jaro-Winkler
? edit: Levenstein or Edit distance
? ngram: The NGramDistance, if
specified, can optionally pass in the
ngram size too. Default is 2.
? FQN: Fully Qualified class Name for an
implementation of the StringDistance
interface. Must have a no-arg
constructor.
strdist("SOLR",id,edit)
sub Returns x-y from sub(x,y) . sub(myfield,myfield2)
sub(100, sqrt(myfield))
sum Returns the sum of multiple values or
functions, which are specified in a
comma-separated list. add(…) may be
used as an alias for this function
sum(x,y,…) sum(x,1)
sum(x,y)
sum(sqrt(x),log(y),z,0.5)
add(x,y)
sumtotaltermfreq Returns the sum of totaltermfreq values
for all terms in the field in the entire index
(i.e., the number of indexed tokens for
that field). (Aliases sumtotaltermfreq to
sttf .)
If doc1:(fieldX:A B C) and doc2:(fieldX:A A
A A):
docFreq(fieldX:A) = 2 (A appears in 2
docs)
freq(doc1, fieldX:A) = 4 (A appears 4
times in doc 2)
totalTermFreq(fieldX:A) = 5 (A appears
5 times across all docs)
sumTotalTermFreq(fieldX) = 7 in fieldX ,
there are 5 As, 1 B, 1 C
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Function Description Syntax Examples
termfreq Returns the number of times the term
appears in the field for that document.
termfreq(text,‘memory‘)
tf Term frequency; returns the term
frequency factor for the given term, using
the Similarity for the field. The tf-idf
value increases proportionally to the
number of times a word appears in the
document, but is offset by the frequency
of the word in the document, which helps
to control for the fact that some words
are generally more common than others.
See also idf .
tf(text,‘solr‘)
top Causes the function query argument to
derive its values from the top-level
IndexReader containing all parts of an
index. For example, the ordinal of a value
in a single segment will be different from
the ordinal of that same value in the
complete index.
The ord() and rord() functions implicitly
use top() , and hence ord(foo) is
equivalent to top(ord(foo)) .
totaltermfreq Returns the number of times the term
appears in the field in the entire index.
(Aliases totaltermfreq to ttf .)
ttf(text,‘memory‘)
The following functions are boolean – they return true or false. They are mostly useful as the first argument
of the if function, and some of these can be combined. If used somewhere else, it will yield a ‘1‘ or ‘0‘.
Function Description Syntax Examples
and Returns a value of true if and only
if all of its operands evaluate to
true.
and(not (exists (popularity)),
exists (price)): returns true for
any document which has a value in
the price field, but does not have
a value in the popularity field
or A logical disjunction. or(value1,value2): TRUE if
either value1 or value2 is true.
xor Logical exclusive disjunction, or
one or the other but not both.
xor(field1,field2) returns TRUE
if either field1 or field2 is true;
FALSE if both are true.
not The logically negated value of the
wrapped function.
not(exists(author)) : TRUE only
when exists(author) is false.
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Function Description Syntax Examples
exists Returns TRUE if any member of
the field exists.
exists(author) returns TRUE for
any document has a value in the
"author" field.
exists(query(price:5.00))
returns TRUE if "price" matches
"5.00".
gt, gte, lt, lte, eq 5 comparison functions: Greater
Than, Greater Than or Equal, Less
Than, Less Than or Equal, Equal
if(lt(ms(mydatefield),3155692
59747),0.8,1) translates to this
pseudocode: if mydatefield <
315569259747 then 0.8 else 1
Example Function Queries
To give you a better understanding of how function queries can be used in Solr, suppose an index stores the
dimensions in meters x,y,z of some hypothetical boxes with arbitrary names stored in field boxname .
Suppose we want to search for box matching name findbox but ranked according to volumes of boxes. The
query parameters would be:
q=boxname:findbox val:"product(x,y,z)"
This query will rank the results based on volumes. In order to get the computed volume, you will need to
request the score , which will contain the resultant volume:
&fl=*, score
Suppose that you also have a field storing the weight of the box as weight . To sort by the density of the box
and return the value of the density in score, you would submit the following query:
http://localhost:8983/solr/collection_name/select?q=boxname:findbox
_val_:"div(weight,product(x,y,z))"&fl=boxname x y z weight score
Sort By Function
You can sort your query results by the output of a function. For example, to sort results by distance, you
could enter:
http://localhost:8983/solr/collection_name/select?q=*:*&sort=dist(2, point1, point2) desc
Sort by function also supports pseudo-fields: fields can be generated dynamically and return results as
though it was normal field in the index. For example,
&fl=id,sum(x, y),score
would return:
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<str name="id">foo</str>
<float name="sum(x,y)">40</float>
<float name="score">0.343</float>
Local Parameters in Queries
Local parameters are arguments in a Solr request that are specific to a query parameter.
Local parameters provide a way to add meta-data to certain argument types such as query strings. (In Solr
documentation, local parameters are sometimes referred to as LocalParams.)
Local parameters are specified as prefixes to arguments. Take the following query argument, for example:
q=solr rocks
We can prefix this query string with local parameters to provide more information to the Standard Query
Parser. For example, we can change the default operator type to "AND" and the default field to "title":
q={!q.op=AND df=title}solr rocks
These local parameters would change the query to require a match on both "solr" and "rocks" while
searching the "title" field by default.
Basic Syntax of Local Parameters
To specify a local parameter, insert the following before the argument to be modified:
? Begin with {!
? Insert any number of key=value pairs separated by white space
? End with } and immediately follow with the query argument
You may specify only one local parameters prefix per argument. Values in the key-value pairs may be quoted
via single or double quotes, and backslash escaping works within quoted strings.
Query Type Short Form
If a local parameter value appears without a name, it is given the implicit name of "type". This allows short-
form representation for the type of query parser to use when parsing a query string. Thus
q={!dismax qf=myfield}solr rocks
is equivalent to:
q={!type=dismax qf=myfield}solr rocks
If no "type" is specified (either explicitly or implicitly) then the lucene parser is used by default. Thus
fq={!df=summary}solr rocks
is equivilent to:
fq={!type=lucene df=summary}solr rocks
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Specifying the Parameter Value with the v Key
A special key of v within local parameters is an alternate way to specify the value of that parameter.
q={!dismax qf=myfield}solr rocks
is equivalent to
q={!type=dismax qf=myfield v=‘solr rocks‘ }
Parameter Dereferencing
Parameter dereferencing, or indirection, lets you use the value of another argument rather than specifying it
directly. This can be used to simplify queries, decouple user input from query parameters, or decouple front-
end GUI parameters from defaults set in solrconfig.xml .
q={!dismax qf=myfield}solr rocks
is equivalent to:
q={!type=dismax qf=myfield v=$qq}&qq=solr rocks
Other Parsers
In addition to the main query parsers discussed earlier, there are several other query parsers that can be
used instead of or in conjunction with the main parsers for specific purposes.
This section details the other parsers, and gives examples for how they might be used.
Many of these parsers are expressed the same way as Local Parameters in Queries.
Block Join Query Parsers
There are two query parsers that support block joins. These parsers allow indexing and searching for
relational content that has beenindexed as nested documents.
The example usage of the query parsers below assumes these two documents and each of their child
documents have been indexed:
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<add>
<doc>
<field name="id">1</field>
<field name="title">Solr has block join support</field>
<field name="content_type">parentDocument</field>
<doc>
<field name="id">2</field>
<field name="comments">SolrCloud supports it too!</field>
</doc>
</doc>
<doc>
<field name="id">3</field>
<field name="title">New Lucene and Solr release</field>
<field name="content_type">parentDocument</field>
<doc>
<field name="id">4</field>
<field name="comments">Lots of new features</field>
</doc>
</doc>
</add>
Block Join Children Query Parser
This parser takes a query that matches some parent documents and returns their children.
The syntax for this parser is: q={!child of=<allParents>}<someParents> .
The parameter allParents is a filter that matches only parent documents; here you would define the field
and value that you used to identify all parent documents.
The parameter someParents identifies a query that will match some of the parent documents. The output is
the children.
Using the example documents above, we can construct a query such as q={!child
of="content_type:parentDocument"}title:lucene . We only get one document in response:
<result name="response" numFound="1" start="0">
<doc>
<str name="id">4</str>
<str name="comments">Lots of new features</str>
</doc>
</result>
Note that the query for someParents should match only parent documents passed by allParents or you
may get an exception:
Parent query must not match any docs besides parent filter. Combine them as must (+) and must-not
(-) clauses to find a problem doc.
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In older version the error is:
Parent query yields document which is not matched by parents filter.
You can search for q=+(someParents) -(allParents) to find a cause.
Block Join Parent Query Parser
This parser takes a query that matches child documents and returns their parents.
The syntax for this parser is similar: q={!parent which=<allParents>}<someChildren> .
The parameter allParents is a filter that matches only parent documents; here you would define the field
and value that you used to identify all parent documents.
The parameter someChildren is a query that matches some or all of the child documents.
Note that the query for someChildren should match only child documents or you may get an exception:
Child query must not match same docs with parent filter. Combine them as must clauses (+) to find
a problem doc.
In older version it’s:
child query must only match non-parent docs.
You can search for q=+(parentFilter) +(someChildren) to find a cause .
Again using the example documents above, we can construct a query such as q={!parent
which="content_type:parentDocument"}comments:SolrCloud . We get this document in response:
<result name="response" numFound="1" start="0">
<doc>
<str name="id">1</str>
<arr name="title"><str>Solr has block join support</str></arr>
<arr name="content_type"><str>parentDocument</str></arr>
</doc>
</result>
?
Using which
A common mistake is to try to filter parents with a which filter, as in this bad example:
q={!parent which="title:join"}comments:SolrCloud
Instead, you should use a sibling mandatory clause as a filter:
q= +title:join +{!parent which="
content_type:parentDocument"}comments:SolrCloud
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Scoring
You can optionally use the score local parameter to return scores of the subordinate query. The values to
use for this parameter define the type of aggregation, which are avg (average), max (maximum), min
(minimum), total (sum) . Implicit default is none which returns 0.0 .
Boost Query Parser
BoostQParser extends the QParserPlugin and creates a boosted query from the input value. The main value
is the query to be boosted. Parameter b is the function query to use as the boost. The query to be boosted
may be of any type.
Examples:
Creates a query "foo" which is boosted (scores are multiplied) by the function query log(popularity) :
{!boost b=log(popularity)}foo
Creates a query "foo" which is boosted by the date boosting function referenced in
ReciprocalFloatFunction :
{!boost b=recip(ms(NOW,mydatefield),3.16e-11,1,1)}foo
Collapsing Query Parser
The CollapsingQParser is really a post filter that provides more performant field collapsing than Solr’s
standard approach when the number of distinct groups in the result set is high.
This parser collapses the result set to a single document per group before it forwards the result set to the
rest of the search components. So all downstream components (faceting, highlighting, etc.) will work with
the collapsed result set.
Details about using the CollapsingQParser can be found in the section Collapse and Expand Results.
Complex Phrase Query Parser
The ComplexPhraseQParser provides support for wildcards, ORs, etc., inside phrase queries using Lucene’s
ComplexPhraseQueryParser .
Under the covers, this query parser makes use of the Span group of queries, e.g., spanNear, spanOr, etc.,
and is subject to the same limitations as that family or parsers.
Parameter Description
inOrder
Set to true to force phrase queries to match terms in the order specified.
Default: true
df
The default search field.
Examples:
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{!complexphrase inOrder=true}name:"Jo* Smith"
{!complexphrase inOrder=false}name:"(john jon jonathan~) peters*"
A mix of ordered and unordered complex phrase queries:
+_query_:"{!complexphrase inOrder=true}manu:\"a* c*\"" +_query_:"{!complexphrase inOrder=false
df=name}\"bla* pla*\""
Limitations
Performance is sensitive to the number of unique terms that are associated with a pattern. For instance,
searching for "a*" will form a large OR clause (technically a SpanOr with many terms) for all of the terms in
your index for the indicated field that start with the single letter ‘a‘. It may be prudent to restrict wildcards
to at least two or preferably three letters as a prefix. Allowing very short prefixes may result in to many low-
quality documents being returned.
Notice that it also supports leading wildcards "*a" as well with consequent performance implications.
Applying ReversedWildcardFilterFactory in index-time analysis is usually a good idea.
MaxBooleanClauses
You may need to increase MaxBooleanClauses in solrconfig.xml as a result of the term expansion above:
<maxBooleanClauses>4096</maxBooleanClauses>
This property is described in more detail in the section Query Sizing and Warming.
Stopwords
It is recommended not to use stopword elimination with this query parser.
Lets say we add the terms the, up, and to to stopwords.txt for your collection, and index a document
containing the text "Stores up to 15,000 songs, 25,00 photos, or 150 yours of video" in a field named "features".
While the query below does not use this parser:
q=features:"Stores up to 15,000"
the document is returned. The next query that does use the Complex Phrase Query Parser, as in this query:
q=features:"sto* up to 15*"&defType=complexphrase
does not return that document because SpanNearQuery has no good way to handle stopwords in a way
analogous to PhraseQuery. If you must remove stopwords for your use case, use a custom filter factory or
perhaps a customized synonyms filter that reduces given stopwords to some impossible token.
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Escaping
Special care has to be given when escaping: clauses between double quotes (usually whole query) is parsed
twice, these parts have to be escaped as twice. eg "foo\\: bar\\^" .
Field Query Parser
The FieldQParser extends the QParserPlugin and creates a field query from the input value, applying text
analysis and constructing a phrase query if appropriate. The parameter f is the field to be queried.
Example:
{!field f=myfield}Foo Bar
This example creates a phrase query with "foo" followed by "bar" (assuming the analyzer for myfield is a
text field with an analyzer that splits on whitespace and lowercase terms). This is generally equivalent to the
Lucene query parser expression myfield:"Foo Bar" .
Function Query Parser
The FunctionQParser extends the QParserPlugin and creates a function query from the input value. This is
only one way to use function queries in Solr; for another, more integrated, approach, see the section on
Function Queries.
Example:
{!func}log(foo)
Function Range Query Parser
The FunctionRangeQParser extends the QParserPlugin and creates a range query over a function. This is
also referred to as frange , as seen in the examples below.
Other parameters:
Parameter Description
l The lower bound, optional
u The upper bound, optional
incl Include the lower bound: true/false, optional, default=true
incu Include the upper bound: true/false, optional, default=true
Examples:
{!frange l=1000 u=50000}myfield
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fq={!frange l=0 u=2.2} sum(user_ranking,editor_ranking)
Both of these examples restrict the results by a range of values found in a declared field or a function query.
In the second example, we’re doing a sum calculation, and then defining only values between 0 and 2.2
should be returned to the user.
For more information about range queries over functions, see Yonik Seeley’s introductory blog post Ranges
over Functions in Solr 1.4.
Graph Query Parser
The graph query parser does a breadth first, cyclic aware, graph traversal of all documents that are
"reachable" from a starting set of root documents identified by a wrapped query.
The graph is built according to linkages between documents based on the terms found in “ from ” and “ to ”
fields that you specify as part of the query
Parameters
Parameter Description
to The field name of matching documents to inspect to identify outgoing
edges for graph traversal. Defaults to edge_ids .
from The field name to of candidate documents to inspect to identify incoming
graph edges. Defaults to node_id .
traversalFilter An optional query that can be supplied to limit the scope of documents
that are traversed.
maxDepth Integer specifying how deep the breadth first search of the graph should
go begining with the initial query. Defaults to -1 (unlimited)
returnRoot Boolean to indicate if the documents that matched the original query (to
define the starting points for graph) should be included in the final results.
Defaults to true
returnOnlyLeaf Boolean that indicates if the results of the query should be filtered so that
only documents with no outgoing edges are returned. Defaults to false
useAutn Boolean that indicates if an Automatons should be compiled for each
iteration of the breadth first search, which may be faster for some graphs.
Defaults to false.
Limitations
The graph parser only works in single node Solr installations, or with SolrCloud collections that use exactly 1
shard.
Examples
To understand how the graph parser works, consider the following Directed Cyclic Graph, containing 8
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nodes (A to H) and 9 edges (1 to 9):
One way to model this graph as Solr documents, would be to create one document per node, with
mutivalued fields identifying the incoming and outgoing edges for each node:
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curl -H ‘Content-Type: application/json‘ ‘http://localhost:8983/solr/my_graph/update?commit=true‘
--data-binary ‘[
{"id":"A","foo": 7, "out_edge":["1","9"], "in_edge":["4","2"] },
{"id":"B","foo": 12, "out_edge":["3","6"], "in_edge":["1"] },
{"id":"C","foo": 10, "out_edge":["5","2"], "in_edge":["9"] },
{"id":"D","foo": 20, "out_edge":["4","7"], "in_edge":["3","5"] },
{"id":"E","foo": 17, "out_edge":[], "in_edge":["6"] },
{"id":"F","foo": 11, "out_edge":[], "in_edge":["7"] },
{"id":"G","foo": 7, "out_edge":["8"], "in_edge":[] },
{"id":"H","foo": 10, "out_edge":[], "in_edge":["8"] }
]‘
With the model shown above, the following query demonstrates a simple traversal of all nodes reachable
from node A:
http://localhost:8983/solr/my_graph/query?fl=id&q={!graph+from=in_edge+to=out_edge}id:A
"response":{"numFound":6,"start":0,"docs":[
{ "id":"A" },
{ "id":"B" },
{ "id":"C" },
{ "id":"D" },
{ "id":"E" },
{ "id":"F" } ]
}
We can also use the traversalFilter to limit the graph traversal to only nodes with maximum value of 15
in the foo field. In this case that means D, E, and F are excluded – F has a value of foo=11 , but it is
unreachable because the traversal skipped D:
http://localhost:8983/solr/my_graph/query?fl=id&q={!graph+from=in_edge+to=out_edge+traversalFilte
r=‘foo:[*+TO+15]‘}id:A
...
"response":{"numFound":3,"start":0,"docs":[
{ "id":"A" },
{ "id":"B" },
{ "id":"C" } ]
}
The examples shown so far have all used a query for a single document ( "id:A" ) as the root node for the
graph traversal, but any query can be used to identify multiple documents to use as root nodes. The next
example demonstrates using the maxDepth param to find all nodes that are at most one edge away from an
root node with a value in the foo field less then or equal to 10:
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http://localhost:8983/solr/my_graph/query?fl=id&q={!graph+from=in_edge+to=out_edge+maxDepth=1}foo
:[*+TO+10]
...
"response":{"numFound":6,"start":0,"docs":[
{ "id":"A" },
{ "id":"B" },
{ "id":"C" },
{ "id":"D" },
{ "id":"G" },
{ "id":"H" } ]
}
Simplified Models
The Document & Field modeling used in the above examples enumerated all of the outgoing and income
edges for each node explicitly, to help demonstrate exactly how the "from" and "to" params work, and to
give you an idea of what is possible. With multiple sets of fields like these for identifying incoming and
outgoing edges, it’s possible to model many independent Directed Graphs that contain some or all of the
documents in your collection.
But in many cases it can also be possible to drastically simplify the model used.
For example, the same graph shown in the diagram above can be modelled by Solr Documents that
represent each node and know only the ids of the nodes they link to, with out knowing anything about the
incoming links:
curl -H ‘Content-Type: application/json‘
‘http://localhost:8983/solr/alt_graph/update?commit=true‘ --data-binary ‘[
{"id":"A","foo": 7, "out_edge":["B","C"] },
{"id":"B","foo": 12, "out_edge":["E","D"] },
{"id":"C","foo": 10, "out_edge":["A","D"] },
{"id":"D","foo": 20, "out_edge":["A","F"] },
{"id":"E","foo": 17, "out_edge":[] },
{"id":"F","foo": 11, "out_edge":[] },
{"id":"G","foo": 7, "out_edge":["H"] },
{"id":"H","foo": 10, "out_edge":[] }
]‘
With this alternative document model, all of the same queries demonstrated above can still be executed,
simply by changing the “ from ” param to replace the “ in_edge ” field with the “ id ” field:
http://localhost:8983/solr/alt_graph/query?fl=id&q={!graph+from=id+to=out_edge+maxDepth=1}foo:[*+
TO+10]
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...
"response":{"numFound":6,"start":0,"docs":[
{ "id":"A" },
{ "id":"B" },
{ "id":"C" },
{ "id":"D" },
{ "id":"G" },
{ "id":"H" } ]
}
Join Query Parser
JoinQParser extends the QParserPlugin . It allows normalizing relationships between documents with a join
operation. This is different from the concept of a join in a relational database because no information is
being truly joined. An appropriate SQL analogy would be an "inner query".
Examples:
Find all products containing the word "ipod", join them against manufacturer docs and return the list of
manufacturers:
{!join from=manu_id_s to=id}ipod
Find all manufacturer docs named "belkin", join them against product docs, and filter the list to only
products with a price less than $12:
q = {!join from=id to=manu_id_s}compName_s:Belkin
fq = price:[* TO 12]
The join operation is done on a term basis, so the "from" and "to" fields must use compatible field types. For
example: joining between a StrField and a TrieIntField will not work, likewise joining between a
StrField and a TextField that uses LowerCaseFilterFactory will only work for values that are already
lower cased in the string field.
Scoring
You can optionally use the score parameter to return scores of the subordinate query. The values to use for
this parameter define the type of aggregation, which are avg (average), max (maximum), min (minimum)
total , or none .
?
Score parameter and single value numerics
Specifying score local parameter switches the join algorithm. This might have performance
implication on large indices, but it’s more important that this algorithm won’t work for
single value numeric field starting from 7.0. Users are encouraged to change field types to
string and rebuild indexes during migration.
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Joining Across Collections
You can also specify a fromIndex parameter to join with a field from another core or collection. If running in
SolrCloud mode, then the collection specified in the fromIndex parameter must have a single shard and a
replica on all Solr nodes where the collection you’re joining to has a replica.
Let’s consider an example where you want to use a Solr join query to filter movies by directors that have
won an Oscar. Specifically, imagine we have two collections with the following fields:
movies: id, title, director_id, …
movie_directors: id, name, has_oscar, …
To filter movies by directors that have won an Oscar using a Solr join on the movie_directors collection, you
can send the following filter query to the movies collection:
fq={!join from=id fromIndex=movie_directors to=director_id}has_oscar:true
Notice that the query criteria of the filter ( has_oscar:true ) is based on a field in the collection specified
using fromIndex . Keep in mind that you cannot return fields from the fromIndex collection using join
queries, you can only use the fields for filtering results in the "to" collection (movies).
Next, let’s understand how these collections need to be deployed in your cluster. Imagine the movies
collection is deployed to a four node SolrCloud cluster and has two shards with a replication factor of two.
Specifically, the movies collection has replicas on the following four nodes:
node 1: movies_shard1_replica1
node 2: movies_shard1_replica2
node 3: movies_shard2_replica1
node 4: movies_shard2_replica2
To use the movie_directors collection in Solr join queries with the movies collection, it needs to have a
replica on each of the four nodes. In other words, movie_directors must have one shard and replication
factor of four:
node 1: movie_directors_shard1_replica1
node 2: movie_directors_shard1_replica2
node 3: movie_directors_shard1_replica3
node 4: movie_directors_shard1_replica4
At query time, the JoinQParser will access the local replica of the movie_directors collection to perform the
join. If a local replica is not available or active, then the query will fail. At this point, it should be clear that
since you’re limited to a single shard and the data must be replicated across all nodes where it is needed,
this approach works better with smaller data sets where there is a one-to-many relationship between the
from collection and the to collection. Moreover, if you add a replica to the to collection, then you also need to
add a replica for the from collection.
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For more information about join queries, see the Solr Wiki page on Joins. Erick Erickson has also written a
blog post about join performance titled Solr and Joins.
Lucene Query Parser
The LuceneQParser extends the QParserPlugin by parsing Solr’s variant on the Lucene QueryParser syntax.
This is effectively the same query parser that is used in Lucene. It uses the operators q.op , the default
operator ("OR" or "AND") and df , the default field name.
Example:
{!lucene q.op=AND df=text}myfield:foo +bar -baz
For more information about the syntax for the Lucene Query Parser, see the Classic QueryParser javadocs.
Learning To Rank Query Parser
The LTRQParserPlugin is a special purpose parser for reranking the top results of a simple query using a
more complex ranking query which is based on a machine learnt model.
Example:
{!ltr model=myModel reRankDocs=100}
Details about using the LTRQParserPlugin can be found in the Learning To Rank section.
Max Score Query Parser
The MaxScoreQParser extends the LuceneQParser but returns the Max score from the clauses. It does this
by wrapping all SHOULD clauses in a DisjunctionMaxQuery with tie=1.0. Any MUST or PROHIBITED clauses are
passed through as-is. Non-boolean queries, e.g., NumericRange falls-through to the LuceneQParser parser
behavior.
Example:
{!maxscore tie=0.01}C OR (D AND E)
More Like This Query Parser
MLTQParser enables retrieving documents that are similar to a given document. It uses Lucene’s existing
MoreLikeThis logic and also works in SolrCloud mode. The document identifier used here is the unique id
value and not the Lucene internal document id. The list of returned documents excludes the queried
document.
This query parser takes the following parameters:
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Parameter Description
qf Specifies the fields to use for similarity.
mintf Specifies the Minimum Term Frequency, the frequency below which terms
will be ignored in the source document.
mindf Specifies the Minimum Document Frequency, the frequency at which
words will be ignored when they do not occur in at least this many
documents.
maxdf Specifies the Maximum Document Frequency, the frequency at which
words will be ignored when they occur in more than this many documents.
minwl Sets the minimum word length below which words will be ignored.
maxwl Sets the maximum word length above which words will be ignored.
maxqt Sets the maximum number of query terms that will be included in any
generated query.
maxntp Sets the maximum number of tokens to parse in each example document
field that is not stored with TermVector support.
boost Specifies if the query will be boosted by the interesting term relevance. It
can be either "true" or "false".
Examples:
Find documents like the document with id=1 and using the name field for similarity.
{!mlt qf=name}1
Adding more constraints to what qualifies as similar using mintf and mindf.
{!mlt qf=name mintf=2 mindf=3}1
Nested Query Parser
The NestedParser extends the QParserPlugin and creates a nested query, with the ability for that query to
redefine its type via local parameters. This is useful in specifying defaults in configuration and letting clients
indirectly reference them.
Example:
{!query defType=func v=$q1}
If the q1 parameter is price, then the query would be a function query on the price field. If the q1 parameter
is \{!lucene}inStock:true}} then a term query is created from the Lucene syntax string that matches
documents with inStock=true . These parameters would be defined in solrconfig.xml , in the defaults
section:
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<lst name="defaults">
<str name="q1">{!lucene}inStock:true</str>
</lst>
For more information about the possibilities of nested queries, see Yonik Seeley’s blog post Nested Queries
in Solr.
Old Lucene Query Parser
OldLuceneQParser extends the QParserPlugin by parsing Solr’s variant of Lucene’s QueryParser syntax,
including the deprecated sort specification after the query.
Example:
{!lucenePlusSort} myfield:foo +bar -baz;price asc
Payload Query Parsers
These query parsers utilize payloads encoded on terms during indexing.
The main query, for both of these parsers, is parsed straightforwardly from the field type’s query analysis
into a SpanQuery . The generated SpanQuery will be either a SpanTermQuery or an ordered, zero slop
SpanNearQuery , depending on how many tokens are emitted. Payloads can be encoded on terms using
either the DelimitedPayloadTokenFilter or the NumericPayloadTokenFilter . The payload using parsers
are:
? PayloadScoreQParser
? PayloadCheckQParser
Payload Score Parser
PayloadScoreQParser incorporates each matching term’s numeric (integer or float) payloads into the
scores.
This parser accepts the following parameters:
Parameter Description
f
Field to use (required)
func
Payload function: min, max, average (required)
includeSpanScore
If true, multiples computed payload factor by the score of the original
query. If false, the computed payload factor is the score. (default: false)
Example:
{!payload_score f=my_field_dpf v=some_term func=max}
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Payload Check Parser
PayloadCheckQParser only matches when the matching terms also have the specified payloads.
This parser accepts the following parameters:
Parameter Description
f
Field to use (required)
payloads A space-separated list of payloads that must match the query terms
(required)
Each specified payload will be encoded using the encoder determined
from the field type and encoded accordingly for matching.
DelimitedPayloadTokenFilter ‘identity‘ encoded payloads also work
here, as well as float and integer encoded ones.
{!payload_check f=words_dps payloads="VERB NOUN"}searching stuff
Prefix Query Parser
PrefixQParser extends the QParserPlugin by creating a prefix query from the input value. Currently no
analysis or value transformation is done to create this prefix query.
The parameter is f , the field. The string after the prefix declaration is treated as a wildcard query.
Example:
{!prefix f=myfield}foo
This would be generally equivalent to the Lucene query parser expression myfield:foo* .
Raw Query Parser
RawQParser extends the QParserPlugin by creating a term query from the input value without any text
analysis or transformation. This is useful in debugging, or when raw terms are returned from the terms
component (this is not the default).
The only parameter is f , which defines the field to search.
Example:
{!raw f=myfield}Foo Bar
This example constructs the query: TermQuery(Term("myfield","Foo Bar")) .
For easy filter construction to drill down in faceting, the TermQParserPlugin is recommended.
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For full analysis on all fields, including text fields, you may want to use the FieldQParserPlugin.
Re-Ranking Query Parser
The ReRankQParserPlugin is a special purpose parser for Re-Ranking the top results of a simple query using
a more complex ranking query.
Details about using the ReRankQParserPlugin can be found in the Query Re-Ranking section.
Simple Query Parser
The Simple query parser in Solr is based on Lucene’s SimpleQueryParser. This query parser is designed to
allow users to enter queries however they want, and it will do its best to interpret the query and return
results.
This parser takes the following parameters:
q.operators
Comma-separated list of names of parsing operators to enable. By default, all operations are enabled,
and this parameter can be used to effectively disable specific operators as needed, by excluding them
from the list. Passing an empty string with this parameter disables all operators.
Name Operator Description Example
query
AND +
Specifies AND
token1+token
2
OR |
Specifies OR
token1|token
2
NOT -
Specifies NOT
-token3
PREFIX *
Specifies a prefix query
term*
PHRASE "
Creates a phrase
"term1
term2"
PRECEDENCE ( )
Specifies precedence; tokens inside the parenthesis
will be analyzed first. Otherwise, normal order is left
to right.
token1 +
(token2 |
token3)
ESCAPE \
Put it in front of operators to match them literally
C\\
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Name Operator Description Example
query
WHITESPACE
space or [\r\t\n] Delimits tokens on whitespace. If not enabled,
whitespace splitting will not be performed prior to
analysis – usually most desirable.
Not splitting whitespace is a unique feature of this
parser that enables multi-word synonyms to work.
However, it probably actually won’t unless synonyms
are configured to normalize instead of expand to all
that match a given synonym. Such a configuration
requires normalizing synonyms at both index time
and query time. Solr’s analysis screen can help here.
term1 term2
FUZZY ~
~N
At the end of terms, specifies a fuzzy query.
"N" is optional and may be either "1" or "2" (the
default)
term~1
NEAR ~N
At the end of phrases, specifies a NEAR query
"term1
term2"~5
q.op
Defines the default operator to use if none is defined by the user. Allowed values are AND and OR . OR is
used if none is specified.
qf
A list of query fields and boosts to use when building the query.
df
Defines the default field if none is defined in the Schema, or overrides the default field if it is already
defined.
Any errors in syntax are ignored and the query parser will interpret queries as best it can. However, this can
lead to odd results in some cases.
Spatial Query Parsers
There are two spatial QParsers in Solr: geofilt and bbox . But there are other ways to query spatially: using
the frange parser with a distance function, using the standard (lucene) query parser with the range syntax
to pick the corners of a rectangle, or with RPT and BBoxField you can use the standard query parser but use
a special syntax within quotes that allows you to pick the spatial predicate.
All these options are documented further in the section Spatial Search.
Surround Query Parser
The SurroundQParser enables the Surround query syntax, which provides proximity search functionality.
There are two positional operators: w creates an ordered span query and n creates an unordered one. Both
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operators take a numeric value to indicate distance between two terms. The default is 1, and the maximum
is 99.
Note that the query string is not analyzed in any way.
Example:
{!surround} 3w(foo, bar)
This example finds documents where the terms "foo" and "bar" are no more than 3 terms away from each
other (i.e., no more than 2 terms between them).
This query parser will also accept boolean operators ( AND , OR , and NOT , in either upper- or lowercase),
wildcards, quoting for phrase searches, and boosting. The w and n operators can also be expressed in upper-
or lowercase.
The non-unary operators (everything but NOT ) support both infix (a AND b AND c) and prefix AND(a, b, c)
notation.
Switch Query Parser
SwitchQParser is a QParserPlugin that acts like a "switch" or "case" statement.
The primary input string is trimmed and then prefixed with case. for use as a key to lookup a "switch case"
in the parser’s local params. If a matching local param is found the resulting param value will then be
parsed as a subquery, and returned as the parse result.
The case local param can be optionally be specified as a switch case to match missing (or blank) input
strings. The default local param can optionally be specified as a default case to use if the input string does
not match any other switch case local params. If default is not specified, then any input which does not
match a switch case local param will result in a syntax error.
In the examples below, the result of each query is "XXX":
{!switch case.foo=XXX case.bar=zzz case.yak=qqq}foo
The extra whitespace between } and bar is trimmed automatically.
{!switch case.foo=qqq case.bar=XXX case.yak=zzz} bar
The result will fallback to the default.
{!switch case.foo=qqq case.bar=zzz default=XXX}asdf
No input uses the value for case instead.
{!switch case=XXX case.bar=zzz case.yak=qqq}
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A practical usage of this QParserPlugin , is in specifying appends fq params in the configuration of a
SearchHandler, to provide a fixed set of filter options for clients using custom parameter names.
Using the example configuration below, clients can optionally specify the custom parameters in_stock and
shipping to override the default filtering behavior, but are limited to the specific set of legal values
(shipping=any|free, in_stock=yes|no|all).
<requestHandler name="/select" class="solr.SearchHandler">
<lst name="defaults">
<str name="in_stock">yes</str>
<str name="shipping">any</str>
</lst>
<lst name="appends">
<str name="fq">{!switch case.all=‘*:*‘
case.yes=‘inStock:true‘
case.no=‘inStock:false‘
v=$in_stock}</str>
<str name="fq">{!switch case.any=‘*:*‘
case.free=‘shipping_cost:0.0‘
v=$shipping}</str>
</lst>
</requestHandler>
Term Query Parser
TermQParser extends the QParserPlugin by creating a single term query from the input value equivalent to
readableToIndexed() . This is useful for generating filter queries from the external human readable terms
returned by the faceting or terms components. The only parameter is f , for the field.
Example:
{!term f=weight}1.5
For text fields, no analysis is done since raw terms are already returned from the faceting and terms
components. To apply analysis to text fields as well, see the Field Query Parser, above.
If no analysis or transformation is desired for any type of field, see the Raw Query Parser, above.
Terms Query Parser
TermsQParser , functions similarly to the Term Query Parser but takes in multiple values separated by
commas and returns documents matching any of the specified values.
This can be useful for generating filter queries from the external human readable terms returned by the
faceting or terms components, and may be more efficient in some cases than using the Standard Query
Parser to generate an boolean query since the default implementation “ method ” avoids scoring.
This query parser takes the following parameters:
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Parameter Description
f The field on which to search. Required.
separator Separator to use when parsing the input. If set to " " (a single blank
space), will trim additional white space from the input terms. Defaults to
“,”.
method The internal query-building implementation: termsFilter , booleanQuery ,
automaton , or docValuesTermsFilter . Defaults to “ termsFilter ”.
Examples:
{!terms f=tags}software,apache,solr,lucene
{!terms f=categoryId method=booleanQuery separator=" "}8 6 7 5309
XML Query Parser
The XmlQParserPlugin extends the QParserPlugin and supports the creation of queries from XML. Example:
Parameter Value
defType
xmlparser
q
<BooleanQuery fieldName="description">
<Clause occurs="must">
<TermQuery>shirt</TermQuery>
</Clause>
<Clause occurs="mustnot">
<TermQuery>plain</TermQuery>
</Clause>
<Clause occurs="should">
<TermQuery>cotton</TermQuery>
</Clause>
<Clause occurs="must">
<BooleanQuery fieldName="size">
<Clause occurs="should">
<TermsQuery>S M L</TermsQuery>
</Clause>
</BooleanQuery>
</Clause>
</BooleanQuery>
The XmlQParser implementation uses the SolrCoreParser class which extends Lucene’s CoreParser class.
XML elements are mapped to QueryBuilder classes as follows:
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XML element QueryBuilder class
<BooleanQuery> BooleanQueryBuilder
<BoostingTermQuery> BoostingTermBuilder
<ConstantScoreQuery> ConstantScoreQueryBuilder
<DisjunctionMaxQuery> DisjunctionMaxQueryBuilder
<MatchAllDocsQuery> MatchAllDocsQueryBuilder
<RangeQuery> RangeQueryBuilder
<SpanFirst> SpanFirstBuilder
<SpanNear> SpanNearBuilder
<SpanNot> SpanNotBuilder
<SpanOr> SpanOrBuilder
<SpanOrTerms> SpanOrTermsBuilder
<SpanTerm> SpanTermBuilder
<TermQuery> TermQueryBuilder
<TermsQuery> TermsQueryBuilder
<UserQuery> UserInputQueryBuilder
<LegacyNumericRangeQuery> LegacyNumericRangeQuery(Builder) is deprecated
Customizing XML Query Parser
You can configure your own custom query builders for additional XML elements. The custom builders need
to extend the SolrQueryBuilder or the SolrSpanQueryBuilder class. Example solrconfig.xml snippet:
<queryParser name="xmlparser" class="XmlQParserPlugin">
<str name="MyCustomQuery">com.mycompany.solr.search.MyCustomQueryBuilder</str>
</queryParser>
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Faceting
Faceting is the arrangement of search results into categories based on indexed terms.
Searchers are presented with the indexed terms, along with numerical counts of how many matching
documents were found were each term. Faceting makes it easy for users to explore search results,
narrowing in on exactly the results they are looking for.
General Parameters
There are two general parameters for controlling faceting.
The facet Parameter
If set to true, this parameter enables facet counts in the query response. If set to false, a blank or missing
value, this parameter disables faceting. None of the other parameters listed below will have any effect
unless this parameter is set to true. The default value is blank (false).
The facet.query Parameter
This parameter allows you to specify an arbitrary query in the Lucene default syntax to generate a facet
count.
By default, Solr’s faceting feature automatically determines the unique terms for a field and returns a count
for each of those terms. Using facet.query , you can override this default behavior and select exactly which
terms or expressions you would like to see counted. In a typical implementation of faceting, you will specify
a number of facet.query parameters. This parameter can be particularly useful for numeric-range-based
facets or prefix-based facets.
You can set the facet.query parameter multiple times to indicate that multiple queries should be used as
separate facet constraints.
To use facet queries in a syntax other than the default syntax, prefix the facet query with the name of the
query notation. For example, to use the hypothetical myfunc query parser, you could set the facet.query
parameter like so:
facet.query={!myfunc}name~fred
Field-Value Faceting Parameters
Several parameters can be used to trigger faceting based on the indexed terms in a field.
When using these parameters, it is important to remember that "term" is a very specific concept in Lucene:
it relates to the literal field/value pairs that are indexed after any analysis occurs. For text fields that include
stemming, lowercasing, or word splitting, the resulting terms may not be what you expect.
If you want Solr to perform both analysis (for searching) and faceting on the full literal strings, use the
copyField directive in your Schema to create two versions of the field: one Text and one String. Make sure
both are indexed="true" . (For more information about the copyField directive, see Documents, Fields, and
Schema Design.)
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The table below summarizes Solr’s field value faceting parameters.
Parameter Description
facet.field Identifies a field to be treated as a facet.
facet.prefix Limits the terms used for faceting to those that begin with the specified
prefix.
facet.contains Limits the terms used for faceting to those that contain the specified
substring.
facet.contains.ignoreCase If facet.contains is used, ignore case when searching for the specified
substring.
facet.sort Controls how faceted results are sorted.
facet.limit Controls how many constraints should be returned for each facet.
facet.offset Specifies an offset into the facet results at which to begin displaying facets.
facet.mincount Specifies the minimum counts required for a facet field to be included in
the response.
facet.missing Controls whether Solr should compute a count of all matching results
which have no value for the field, in addition to the term-based constraints
of a facet field.
facet.method Selects the algorithm or method Solr should use when faceting a field.
facet.exists Caps facet counts by one. Available only for facet.method=enum as
performance optimization.
facet.excludeTerms Removes specific terms from facet counts. This allows you to exclude
certain terms from faceting, while maintaining the terms in the index for
general queries.
facet.enum.cache.minDf (Advanced) Specifies the minimum document frequency (the number of
documents matching a term) for which the filterCache should be used
when determining the constraint count for that term.
facet.overrequest.count (Advanced) A number of documents, beyond the effective facet.limit to
request from each shard in a distributed search
facet.overrequest.ratio (Advanced) A multiplier of the effective facet.limit to request from each
shard in a distributed search
facet.threads (Advanced) Controls parallel execution of field faceting
These parameters are described in the sections below.
The facet.field Parameter
The facet.field parameter identifies a field that should be treated as a facet. It iterates over each Term in
the field and generate a facet count using that Term as the constraint. This parameter can be specified
multiple times in a query to select multiple facet fields.
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If you do not set this parameter to at least one field in the schema, none of the other
parameters described in this section will have any effect.
The facet.prefix Parameter
The facet.prefix parameter limits the terms on which to facet to those starting with the given string prefix.
This does not limit the query in any way, only the facets that would be returned in response to the query.
This parameter can be specified on a per-field basis with the syntax of f.<fieldname>.facet.prefix .
The facet.contains Parameter
The facet.contains parameter limits the terms on which to facet to those containing the given substring.
This does not limit the query in any way, only the facets that would be returned in response to the query.
This parameter can be specified on a per-field basis with the syntax of f.<fieldname>.facet.contains .
The facet.contains.ignoreCase Parameter
If facet.contains is used, the facet.contains.ignoreCase parameter causes case to be ignored when
matching the given substring against candidate facet terms.
This parameter can be specified on a per-field basis with the syntax of
f.<fieldname>.facet.contains.ignoreCase .
The facet.sort Parameter
This parameter determines the ordering of the facet field constraints.
There are two options for this parameter.
count
Sort the constraints by count (highest count first).
index
Return the constraints sorted in their index order (lexicographic by indexed term). For terms in the ASCII
range, this will be alphabetically sorted.
The default is count if facet.limit is greater than 0, otherwise, the default is index .
This parameter can be specified on a per-field basis with the syntax of f.<fieldname>.facet.sort .
The facet.limit Parameter
This parameter specifies the maximum number of constraint counts (essentially, the number of facets for a
field that are returned) that should be returned for the facet fields. A negative value means that Solr will
return unlimited number of constraint counts.
The default value is 100.
This parameter can be specified on a per-field basis to apply a distinct limit to each field with the syntax of
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f.<fieldname>.facet.limit .
The facet.offset Parameter
The facet.offset parameter indicates an offset into the list of constraints to allow paging.
The default value is 0.
This parameter can be specified on a per-field basis with the syntax of f.<fieldname>.facet.offset .
The facet.mincount Parameter
The facet.mincount parameter specifies the minimum counts required for a facet field to be included in the
response. If a field’s counts are below the minimum, the field’s facet is not returned.
The default value is 0.
This parameter can be specified on a per-field basis with the syntax of f.<fieldname>.facet.mincount .
The facet.missing Parameter
If set to true, this parameter indicates that, in addition to the Term-based constraints of a facet field, a count
of all results that match the query but which have no facet value for the field should be computed and
returned in the response.
The default value is false.
This parameter can be specified on a per-field basis with the syntax of f.<fieldname>.facet.missing .
The facet.method Parameter
The facet.method parameter selects the type of algorithm or method Solr should use when faceting a field.
The following methods are available.
enum
Enumerates all terms in a field, calculating the set intersection of documents that match the term with
documents that match the query.
This method is recommended for faceting multi-valued fields that have only a few distinct values. The
average number of values per document does not matter.
For example, faceting on a field with U.S. States such as Alabama, Alaska, … Wyoming would lead to
fifty cached filters which would be used over and over again. The filterCache should be large enough to
hold all the cached filters.
fc
Calculates facet counts by iterating over documents that match the query and summing the terms that
appear in each document.
This is currently implemented using an UnInvertedField cache if the field either is multi-valued or is
tokenized (according to FieldType.isTokened() ). Each document is looked up in the cache to see what
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terms/values it contains, and a tally is incremented for each value.
This method is excellent for situations where the number of indexed values for the field is high, but the
number of values per document is low. For multi-valued fields, a hybrid approach is used that uses term
filters from the filterCache for terms that match many documents. The letters fc stand for field cache.
fcs
Per-segment field faceting for single-valued string fields. Enable with facet.method=fcs and control the
number of threads used with the threads local parameter. This parameter allows faceting to be faster in
the presence of rapid index changes.
The default value is fc (except for fields using the BoolField field type and when facet.exists=true is
requested) since it tends to use less memory and is faster when a field has many unique terms in the index.
This parameter can be specified on a per-field basis with the syntax of f.<fieldname>.facet.method .
The facet.enum.cache.minDf Parameter
This parameter indicates the minimum document frequency (the number of documents matching a term)
for which the filterCache should be used when determining the constraint count for that term. This is only
used with the facet.method=enum method of faceting.
A value greater than zero decreases the filterCache’s memory usage, but increases the time required for the
query to be processed. If you are faceting on a field with a very large number of terms, and you wish to
decrease memory usage, try setting this parameter to a value between 25 and 50, and run a few tests. Then,
optimize the parameter setting as necessary.
The default value is 0, causing the filterCache to be used for all terms in the field.
This parameter can be specified on a per-field basis with the syntax of
f.<fieldname>.facet.enum.cache.minDf .
The facet.exists Parameter
To cap facet counts by 1, specify facet.exists=true . It can be used with facet.method=enum or when it’s
omitted. It can be used only on non-trie fields (such as strings). It may speed up facet counting on large
indices and/or high-cardinality facet values..
This parameter can be specified on a per-field basis with the syntax of f.<fieldname>.facet.exists or via
local parameter` facet.field={!facet.method=enum facet.exists=true}size`.
The facet.excludeTerms Parameter
If you want to remove terms from facet counts but keep them in the index, the facet.excludeTerms
parameter allows you to do that.
Over-Request Parameters
In some situations, the accuracy in selecting the "top" constraints returned for a facet in a distributed Solr
query can be improved by "Over Requesting" the number of desired constraints (ie: facet.limit ) from
each of the individual Shards. In these situations, each shard is by default asked for the top “10 + (1.5 *
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facet.limit)” constraints.
In some situations, depending on how your docs are partitioned across your shards, and what facet.limit
value you used, you may find it advantageous to increase or decrease the amount of over-requesting Solr
does. This can be achieved by setting the facet.overrequest.count (defaults to 10) and
facet.overrequest.ratio (defaults to 1.5) parameters.
The facet.threads Parameter
This param will cause loading the underlying fields used in faceting to be executed in parallel with the
number of threads specified. Specify as facet.threads=N where N is the maximum number of threads used.
Omitting this parameter or specifying the thread count as 0 will not spawn any threads, and only the main
request thread will be used. Specifying a negative number of threads will create up to Integer.MAX_VALUE
threads.
Range Faceting
You can use Range Faceting on any date field or any numeric field that supports range queries. This is
particularly useful for stitching together a series of range queries (as facet by query) for things like prices.
Parameter Description
facet.range Specifies the field to facet by range.
facet.range.start Specifies the start of the facet range.
facet.range.end Specifies the end of the facet range.
facet.range.gap Specifies the span of the range as a value to be added to the lower bound.
facet.range.hardend A boolean parameter that specifies how Solr handles a range gap that
cannot be evenly divided between the range start and end values. If true,
the last range constraint will have the facet.range.end value an upper
bound. If false, the last range will have the smallest possible upper bound
greater then facet.range.end such that the range is the exact width of
the specified range gap. The default value for this parameter is false.
facet.range.include Specifies inclusion and exclusion preferences for the upper and lower
bounds of the range. See the facet.range.include topic for more
detailed information.
facet.range.other Specifies counts for Solr to compute in addition to the counts for each
facet range constraint.
facet.range.method Specifies the algorithm or method to use for calculating facets.
The facet.range Parameter
The facet.range parameter defines the field for which Solr should create range facets. For example:
facet.range=price&facet.range=age
facet.range=lastModified_dt
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The facet.range.start Parameter
The facet.range.start parameter specifies the lower bound of the ranges. You can specify this parameter
on a per field basis with the syntax of f.<fieldname>.facet.range.start . For example:
f.price.facet.range.start=0.0&f.age.facet.range.start=10
f.lastModified_dt.facet.range.start=NOW/DAY-30DAYS
The facet.range.end Parameter
The facet.range.end specifies the upper bound of the ranges. You can specify this parameter on a per field
basis with the syntax of f.<fieldname>.facet.range.end . For example:
f.price.facet.range.end=1000.0&f.age.facet.range.start=99
f.lastModified_dt.facet.range.end=NOW/DAY+30DAYS
The facet.range.gap Parameter
The span of each range expressed as a value to be added to the lower bound. For date fields, this should be
expressed using the DateMathParser syntax (such as, facet.range.gap=%2B1DAY … ‘+1DAY‘ ). You can
specify this parameter on a per-field basis with the syntax of f.<fieldname>.facet.range.gap . For
example:
f.price.facet.range.gap=100&f.age.facet.range.gap=10
f.lastModified_dt.facet.range.gap=+1DAY
The facet.range.hardend Parameter
The facet.range.hardend parameter is a Boolean parameter that specifies how Solr should handle cases
where the facet.range.gap does not divide evenly between facet.range.start and facet.range.end .
If true, the last range constraint will have the facet.range.end value as an upper bound. If false, the last
range will have the smallest possible upper bound greater then facet.range.end such that the range is the
exact width of the specified range gap. The default value for this parameter is false.
This parameter can be specified on a per field basis with the syntax f.<fieldname>.facet.range.hardend .
The facet.range.include Parameter
By default, the ranges used to compute range faceting between facet.range.start and facet.range.end
are inclusive of their lower bounds and exclusive of the upper bounds. The "before" range defined with the
facet.range.other parameter is exclusive and the "after" range is inclusive. This default, equivalent to
"lower" below, will not result in double counting at the boundaries. You can use the facet.range.include
parameter to modify this behavior using the following options:
Option Description
lower All gap-based ranges include their lower bound.
upper All gap-based ranges include their upper bound.
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Option Description
edge The first and last gap ranges include their edge bounds (lower for the first
one, upper for the last one) even if the corresponding upper/lower option
is not specified.
outer The "before" and "after" ranges will be inclusive of their bounds, even if
the first or last ranges already include those boundaries.
all Includes all options: lower, upper, edge, outer.
You can specify this parameter on a per field basis with the syntax of f.<fieldname>.facet.range.include ,
and you can specify it multiple times to indicate multiple choices.
?
To ensure you avoid double-counting, do not choose both lower and upper , do not choose
outer , and do not choose all .
The facet.range.other Parameter
The facet.range.other parameter specifies that in addition to the counts for each range constraint
between facet.range.start and facet.range.end , counts should also be computed for these options:
Option Description
before All records with field values lower then lower bound of the first range.
after All records with field values greater then the upper bound of the last
range.
between All records with field values between the start and end bounds of all
ranges.
none Do not compute any counts.
all Compute counts for before, between, and after.
This parameter can be specified on a per field basis with the syntax of f.<fieldname>.facet.range.other .
In addition to the all option, this parameter can be specified multiple times to indicate multiple choices, but
none will override all other options.
The facet.range.method Parameter
The facet.range.method parameter selects the type of algorithm or method Solr should use for range
faceting. Both methods produce the same results, but performance may vary.
filter
This method generates the ranges based on other facet.range parameters, and for each of them
executes a filter that later intersects with the main query resultset to get the count. It will make use of the
filterCache, so it will benefit of a cache large enough to contain all ranges.
dv
This method iterates the documents that match the main query, and for each of them finds the correct
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range for the value. This method will make use of docValues (if enabled for the field) or fieldCache. The dv
method is not supported for field type DateRangeField or when using group.facets.
Default value for this parameter is "filter".
The facet.mincount Parameter in Range Faceting
The facet.mincount parameter, the same one as used in field faceting is also applied to range faceting.
When used, no ranges with a count below the minimum will be included in the response.
?
Date Ranges & Time Zones
Range faceting on date fields is a common situation where the TZ parameter can be useful
to ensure that the "facet counts per day" or "facet counts per month" are based on a
meaningful definition of when a given day/month "starts" relative to a particular
TimeZone.
For more information, see the examples in the Working with Dates section.
Pivot (Decision Tree) Faceting
Pivoting is a summarization tool that lets you automatically sort, count, total or average data stored in a
table. The results are typically displayed in a second table showing the summarized data. Pivot faceting lets
you create a summary table of the results from a faceting documents by multiple fields.
Another way to look at it is that the query produces a Decision Tree, in that Solr tells you "for facet A, the
constraints/counts are X/N, Y/M, etc. If you were to constrain A by X, then the constraint counts for B would
be S/P, T/Q, etc.". In other words, it tells you in advance what the "next" set of facet results would be for a
field if you apply a constraint from the current facet results.
facet.pivot
The facet.pivot parameter defines the fields to use for the pivot. Multiple facet.pivot values will create
multiple "facet_pivot" sections in the response. Separate each list of fields with a comma.
facet.pivot.mincount
The facet.pivot.mincount parameter defines the minimum number of documents that need to match in
order for the facet to be included in results. The default is 1.
Using the “bin/solr -e techproducts” example, A query URL like this one will return the data below, with the
pivot faceting results found in the section "facet_pivot":
http://localhost:8983/solr/techproducts/select?q=*:*&facet.pivot=cat,popularity,inStock
&facet.pivot=popularity,cat&facet=true&facet.field=cat&facet.limit=5
&rows=0&wt=json&indent=true&facet.pivot.mincount=2
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{ "facet_counts":{
"facet_queries":{},
"facet_fields":{
"cat":[
"electronics",14,
"currency",4,
"memory",3,
"connector",2,
"graphics card",2]},
"facet_dates":{},
"facet_ranges":{},
"facet_pivot":{
"cat,popularity,inStock":[{
"field":"cat",
"value":"electronics",
"count":14,
"pivot":[{
"field":"popularity",
"value":6,
"count":5,
"pivot":[{
"field":"inStock",
"value":true,
"count":5}]}]
}]}}}
Combining Stats Component With Pivots
In addition to some of the general local parameters supported by other types of faceting, a stats local
parameters can be used with facet.pivot to refer to stats.field instances (by tag) that you would like to
have computed for each Pivot Constraint.
In the example below, two different (overlapping) sets of statistics are computed for each of the facet.pivot
result hierarchies:
stats=true
stats.field={!tag=piv1,piv2 min=true max=true}price
stats.field={!tag=piv2 mean=true}popularity
facet=true
facet.pivot={!stats=piv1}cat,inStock
facet.pivot={!stats=piv2}manu,inStock
Results:
{"facet_pivot":{
"cat,inStock":[{
"field":"cat",
"value":"electronics",
"count":12,
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"pivot":[{
"field":"inStock",
"value":true,
"count":8,
"stats":{
"stats_fields":{
"price":{
"min":74.98999786376953,
"max":399.0}}}},
{
"field":"inStock",
"value":false,
"count":4,
"stats":{
"stats_fields":{
"price":{
"min":11.5,
"max":649.989990234375}}}}],
"stats":{
"stats_fields":{
"price":{
"min":11.5,
"max":649.989990234375}}}},
{
"field":"cat",
"value":"currency",
"count":4,
"pivot":[{
"field":"inStock",
"value":true,
"count":4,
"stats":{
"stats_fields":{
"price":{
"..."
"manu,inStock":[{
"field":"manu",
"value":"inc",
"count":8,
"pivot":[{
"field":"inStock",
"value":true,
"count":7,
"stats":{
"stats_fields":{
"price":{
"min":74.98999786376953,
"max":2199.0},
"popularity":{
"mean":5.857142857142857}}}},
{
"field":"inStock",
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"value":false,
"count":1,
"stats":{
"stats_fields":{
"price":{
"min":479.95001220703125,
"max":479.95001220703125},
"popularity":{
"mean":7.0}}}}],
"..."}]}}}}]}]}}
Combining Facet Queries And Facet Ranges With Pivot Facets
A query local parameter can be used with facet.pivot to refer to facet.query instances (by tag) that
should be computed for each pivot constraint. Similarly, a range local parameter can be used with
facet.pivot to refer to facet.range instances.
In the example below, two query facets are computed for h of the facet.pivot result hierarchies:
facet=true
facet.query={!tag=q1}manufacturedate_dt:[2006-01-01T00:00:00Z TO NOW]
facet.query={!tag=q1}price:[0 TO 100]
facet.pivot={!query=q1}cat,inStock
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{"facet_counts": {
"facet_queries": {
"{!tag=q1}manufacturedate_dt:[2006-01-01T00:00:00Z TO NOW]": 9,
"{!tag=q1}price:[0 TO 100]": 7
},
"facet_fields": {},
"facet_dates": {},
"facet_ranges": {},
"facet_intervals": {},
"facet_heatmaps": {},
"facet_pivot": {
"cat,inStock": [
{
"field": "cat",
"value": "electronics",
"count": 12,
"queries": {
"{!tag=q1}manufacturedate_dt:[2006-01-01T00:00:00Z TO NOW]": 9,
"{!tag=q1}price:[0 TO 100]": 4
},
"pivot": [
{
"field": "inStock",
"value": true,
"count": 8,
"queries": {
"{!tag=q1}manufacturedate_dt:[2006-01-01T00:00:00Z TO NOW]": 6,
"{!tag=q1}price:[0 TO 100]": 2
}
},
"..."]}]}}}
In a similar way, in the example below, two range facets are computed for each of the facet.pivot result
hierarchies:
facet=true
facet.range={!tag=r1}manufacturedate_dt
facet.range.start=2006-01-01T00:00:00Z
facet.range.end=NOW/YEAR
facet.range.gap=+1YEAR
facet.pivot={!range=r1}cat,inStock
{"facet_counts":{
"facet_queries":{},
"facet_fields":{},
"facet_dates":{},
"facet_ranges":{
"manufacturedate_dt":{
"counts":[
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"2006-01-01T00:00:00Z",9,
"2007-01-01T00:00:00Z",0,
"2008-01-01T00:00:00Z",0,
"2009-01-01T00:00:00Z",0,
"2010-01-01T00:00:00Z",0,
"2011-01-01T00:00:00Z",0,
"2012-01-01T00:00:00Z",0,
"2013-01-01T00:00:00Z",0,
"2014-01-01T00:00:00Z",0],
"gap":"+1YEAR",
"start":"2006-01-01T00:00:00Z",
"end":"2015-01-01T00:00:00Z"}},
"facet_intervals":{},
"facet_heatmaps":{},
"facet_pivot":{
"cat,inStock":[{
"field":"cat",
"value":"electronics",
"count":12,
"ranges":{
"manufacturedate_dt":{
"counts":[
"2006-01-01T00:00:00Z",9,
"2007-01-01T00:00:00Z",0,
"2008-01-01T00:00:00Z",0,
"2009-01-01T00:00:00Z",0,
"2010-01-01T00:00:00Z",0,
"2011-01-01T00:00:00Z",0,
"2012-01-01T00:00:00Z",0,
"2013-01-01T00:00:00Z",0,
"2014-01-01T00:00:00Z",0],
"gap":"+1YEAR",
"start":"2006-01-01T00:00:00Z",
"end":"2015-01-01T00:00:00Z"}},
"pivot":[{
"field":"inStock",
"value":true,
"count":8,
"ranges":{
"manufacturedate_dt":{
"counts":[
"2006-01-01T00:00:00Z",6,
"2007-01-01T00:00:00Z",0,
"2008-01-01T00:00:00Z",0,
"2009-01-01T00:00:00Z",0,
"2010-01-01T00:00:00Z",0,
"2011-01-01T00:00:00Z",0,
"2012-01-01T00:00:00Z",0,
"2013-01-01T00:00:00Z",0,
"2014-01-01T00:00:00Z",0],
"gap":"+1YEAR",
"start":"2006-01-01T00:00:00Z",
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"end":"2015-01-01T00:00:00Z"}}},
"..."]}]}}}
Additional Pivot Parameters
Although facet.pivot.mincount deviates in name from the facet.mincount parameter used by field
faceting, many other Field faceting parameters described above can also be used with pivot faceting:
? facet.limit
? facet.offset
? facet.sort
? facet.overrequest.count
? facet.overrequest.ratio
Interval Faceting
Another supported form of faceting is interval faceting. This sounds similar to range faceting, but the
functionality is really closer to doing facet queries with range queries. Interval faceting allows you to set
variable intervals and count the number of documents that have values within those intervals in the
specified field.
Even though the same functionality can be achieved by using a facet query with range queries, the
implementation of these two methods is very different and will provide different performance depending on
the context.
If you are concerned about the performance of your searches you should test with both options. Interval
faceting tends to be better with multiple intervals for the same fields, while facet query tend to be better in
environments where filter cache is more effective (static indexes for example).
This method will use docValues if they are enabled for the field, will use fieldCache otherwise.
The facet.interval parameter
This parameter Indicates the field where interval faceting must be applied. It can be used multiple times in
the same request to indicate multiple fields.
facet.interval=price&facet.interval=size
The facet.interval.set parameter
This parameter is used to set the intervals for the field, it can be specified multiple times to indicate multiple
intervals. This parameter is global, which means that it will be used for all fields indicated with
facet.interval unless there is an override for a specific field. To override this parameter on a specific field
you can use: f.<fieldname>.facet.interval.set , for example:
f.price.facet.interval.set=[0,10]&f.price.facet.interval.set=(10,100]
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Interval Syntax
Intervals must begin with either ‘(‘ or ‘[‘, be followed by the start value, then a comma (‘,‘), the end value,
and finally a closing ‘)‘ or ‘]’.
For example:
? (1,10) → will include values greater than 1 and lower than 10
? [1,10) → will include values greater or equal to 1 and lower than 10
? [1,10] → will include values greater or equal to 1 and lower or equal to 10
The initial and end values cannot be empty.
If the interval needs to be unbounded, the special character can be used for both, start and end
limit. When using this special character, the start syntax options (( anAd [), and end
syntax options () and ]) will be treated the same. [,*] will include all documents with a value in
the field.
The interval limits may be strings but there is no need to add quotes. All the text until the comma will be
treated as the start limit, and the text after that will be the end limit. For example: [Buenos Aires,New
York] . Keep in mind that a string-like comparison will be done to match documents in string intervals (case-
sensitive). The comparator can’t be changed.
Commas, brackets and square brackets can be escaped by using \ in front of them. Whitespaces before and
after the values will be omitted.
The start limit can’t be grater than the end limit. Equal limits are allowed, this allows you to indicate the
specific values that you want to count, like [A,A] , [B,B] and [C,Z] .
Interval faceting supports output key replacement described below. Output keys can be replaced in both the
facet.interval parameter and in the facet.interval.set parameter . For example:
&facet.interval={!key=popularity}some_field
&facet.interval.set={!key=bad}[0,5]
&facet.interval.set={!key=good}[5,*]
&facet=true
Local Parameters for Faceting
The LocalParams syntax allows overriding global settings. It can also provide a method of adding metadata
to other parameter values, much like XML attributes.
Tagging and Excluding Filters
You can tag specific filters and exclude those filters when faceting. This is useful when doing multi-select
faceting.
Consider the following example query with faceting:
q=mainquery&fq=status:public&fq=doctype:pdf&facet=true&facet.field=doctype
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Because everything is already constrained by the filter doctype:pdf , the facet.field=doctype facet
command is currently redundant and will return 0 counts for everything except doctype:pdf .
To implement a multi-select facet for doctype, a GUI may want to still display the other doctype values and
their associated counts, as if the doctype:pdf constraint had not yet been applied. For example:
=== Document Type ===
[ ] Word (42)
[x] PDF (96)
[ ] Excel(11)
[ ] HTML (63)
To return counts for doctype values that are currently not selected, tag filters that directly constrain doctype,
and exclude those filters when faceting on doctype.
q=mainquery&fq=status:public&fq={!tag=dt}doctype:pdf&facet=true&facet.field={!ex=dt}doctype
Filter exclusion is supported for all types of facets. Both the tag and ex local parameters may specify
multiple values by separating them with commas.
Changing the Output Key
To change the output key for a faceting command, specify a new name with the key local parameter. For
example:
facet.field={!ex=dt key=mylabel}doctype
The parameter setting above causes the field facet results for the "doctype" field to be returned using the
key "mylabel" rather than "doctype" in the response. This can be helpful when faceting on the same field
multiple times with different exclusions.
Limiting Facet with Certain Terms
To limit field facet with certain terms specify them comma separated with terms local parameter. Commas
and quotes in terms can be escaped with backslash, as in \, . In this case facet is calculated on a way similar
to facet.method=enum , but ignores facet.enum.cache.minDf . For example:
facet.field={!terms=‘alfa,betta,with\,with\‘,with space‘}symbol
Related Topics
? Heatmap Faceting (Spatial)
BlockJoin Faceting
BlockJoin facets allow you to aggregate children facet counts by their parents.
It is a common requirement that if a parent document has several children documents, all of them need to
increment facet value count only once. This functionality is provided by BlockJoinDocSetFacetComponent ,
and BlockJoinFacetComponent just an alias for compatibility.
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?
This component is considered experimental, and must be explicitly enabled for a request
handler in solrconfig.xml , in the same way as any other search component.
This example shows how you could add this search components to solrconfig.xml and define it in request
handler:
<searchComponent name="bjqFacetComponent" class=
"org.apache.solr.search.join.BlockJoinDocSetFacetComponent"/>
<requestHandler name="/bjqfacet" class="org.apache.solr.handler.component.SearchHandler">
<lst name="defaults">
<str name="shards.qt">/bjqfacet</str>
</lst>
<arr name="last-components">
<str>bjqFacetComponent</str>
</arr>
</requestHandler>
This component can be added into any search request handler. This component work with distributed
search in SolrCloud mode.
Documents should be added in children-parent blocks as described in indexing nested child documents.
Examples:
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Sample document
<add>
<doc>
<field name="id">1</field>
<field name="type_s">parent</field>
<doc>
<field name="id">11</field>
<field name="COLOR_s">Red</field>
<field name="SIZE_s">XL</field>
<field name="PRICE_i">6</field>
</doc>
<doc>
<field name="id">12</field>
<field name="COLOR_s">Red</field>
<field name="SIZE_s">XL</field>
<field name="PRICE_i">7</field>
</doc>
<doc>
<field name="id">13</field>
<field name="COLOR_s">Blue</field>
<field name="SIZE_s">L</field>
<field name="PRICE_i">5</field>
</doc>
</doc>
<doc>
<field name="id">2</field>
<field name="type_s">parent</field>
<doc>
<field name="id">21</field>
<field name="COLOR_s">Blue</field>
<field name="SIZE_s">XL</field>
<field name="PRICE_i">6</field>
</doc>
<doc>
<field name="id">22</field>
<field name="COLOR_s">Blue</field>
<field name="SIZE_s">XL</field>
<field name="PRICE_i">7</field>
</doc>
<doc>
<field name="id">23</field>
<field name="COLOR_s">Red</field>
<field name="SIZE_s">L</field>
<field name="PRICE_i">5</field>
</doc>
</doc>
</add>
Queries are constructed the same way as for a Parent Block Join query. For example:
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http://localhost:8983/solr/bjqfacet?q={!parent
which=type_s:parent}SIZE_s:XL&child.facet.field=COLOR_s
As a result we should have facets for Red(1) and Blue(1), because matches on children id=11 and id=12 are
aggregated into single hit into parent with id=1 . The key components of the request are:
URL Part Meaning
/bjqfacet
The name of the request handler that has been defined with one of block
join facet components enabled.
q={!parent …}..
The mandatory parent query as a main query. The parent query could also
be a subordinate clause in a more complex query.
child.facet.field=…
The child document field, which might be repeated many times with
several fields, as necessary.
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Highlighting
Highlighting in Solr allows fragments of documents that match the user’s query to be included with the
query response.
The fragments are included in a special section of the query response (the highlighting section), and the
client uses the formatting clues also included to determine how to present the snippets to users. Fragments
are a portion of a document field that contains matches from the query and are sometimes also referred to
as "snippets" or "passages".
Highlighting is extremely configurable, perhaps more than any other part of Solr. There are many
parameters each for fragment sizing, formatting, ordering, backup/alternate behavior, and more options
that are hard to categorize. Nonetheless, highlighting is very simple to use.
Usage
You only need to set the hl and often hl.fl parameters to get results. The following table documents these
and some other supported parameters. Note that many highlighting parameters support per-field overrides,
such as: f.title_txt.hl.snippets
Parameter Default Description
hl false Use this parameter to enable or disable highlighting.
hl.method original The highlighting implementation to use. Acceptable values are:
unified , original , fastVector , and postings . See the Choosing a
Highlighter section below for more details on the differences
between the available highlighters.
hl.fl (df=) Specifies a list of fields to highlight. Accepts a comma- or space-
delimited list of fields for which Solr should generate highlighted
snippets. A wildcard of * (asterisk) can be used to match field globs,
such as text_* or even * to highlight on all fields where highlighting
is possible. When using \* , consider adding
hl.requireFieldMatch=true .
hl.q (q=) A query to use for highlighting. This parameter allows you to
highlight different terms than those being used to retrieve
documents.
hl.qparser (defType=) The query parser to use for the hl.q query.
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Parameter Default Description
hl.requireFieldMatc
h
false By default, false, all query terms will be highlighted for each field to
be highlighted ( hl.fl ) no matter what fields the parsed query refer
to. If set to true, only query terms aligning with the field being
highlighted will in turn be highlighted.
Note: if the query references fields different from the field being
highlighted and they have different text analysis, the query may not
highlight query terms it should have and vice versa. The analysis
used is that of the field being highlighted ( hl.fl ), not the query
fields.
hl.usePhraseHighlig
hter
true If set to true, Solr will highlight phrase queries (and other advanced
position-sensitive queries) accurately – as phrases. If false, the parts
of the phrase will be highlighted everywhere instead of only when it
forms the given phrase.
hl.highlightMultiTer
m
true If set to true, Solr will highlight wildcard queries (and other
MultiTermQuery subclasses). If false, they won’t be highlighted at all.
hl.snippets 1 Specifies maximum number of highlighted snippets to generate per
field. It is possible for any number of snippets from zero to this value
to be generated.
hl.fragsize 100 Specifies the approximate size, in characters, of fragments to
consider for highlighting. 0 indicates that no fragmenting should be
considered and the whole field value should be used.
hl.tag.pre <em> ( hl.simple.pre for the Original Highlighter) Specifies the “tag” to
use before a highlighted term. This can be any string, but is most
often an HTML or XML tag.
hl.tag.post </em> ( hl.simple.post for the Original Highlighter) Specifies the “tag” to
use after a highlighted term. This can be any string, but is most often
an HTML or XML tag.
hl.encoder (blank) If blank, the default, then the stored text will be returned without any
escaping/encoding performed by the highlighter. If set to html then
special HMTL/XML characters will be encoded (e.g. & becomes
& ). The pre/post snippet characters are never encoded.
hl.maxAnalyzedChar
s
51200 The character limit to look for highlights, after which no highlighting
will be done. This is mostly only a performance concern for an
analysis based offset source since it’s the slowest. See Schema
Options and Performance Considerations.
There are more parameters supported as well depending on the highlighter (via hl.method ) chosen.
Highlighting in the Query Response
In the response to a query, Solr includes highlighting data in a section separate from the documents. It is up
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to a client to determine how to process this response and display the highlights to users.
Using the example documents included with Solr, we can see how this might work:
In response to a query such as:
http://localhost:8983/solr/gettingstarted/select?hl=on&q=apple&wt=json&hl.fl=manu&fl=id,name,manu
,cat
we get a response such as this (truncated slightly for space):
{
"responseHeader": {
"..."
}
},
"response": {
"numFound": 1,
"start": 0,
"docs": [{
"id": "MA147LL/A",
"name": "Apple 60 GB iPod with Video Playback Black",
"manu": "Apple Computer Inc.",
"cat": [
"electronics",
"music"
]
}]
},
"highlighting": {
"MA147LL/A": {
"manu": [
"<em>Apple</em> Computer Inc."
]
}
}
}
Note the two sections docs and highlighting . The docs section contains the fields of the document
requested with the fl parameter of the query (only "id", "name", "manu", and "cat").
The highlighting section includes the ID of each document, and the field that contains the highlighted
portion. In this example, we used the hl.fl parameter to say we wanted query terms highlighted in the
"manu" field. When there is a match to the query term in that field, it will be included for each document ID
in the list.
Choosing a Highlighter
Solr provides a HighlightComponent (a SearchComponent ) and it’s in the default list of components for
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search handlers. It offers a somewhat unified API over multiple actual highlighting implementations (or
simply "highlighters") that do the business of highlighting.
There are many parameters supported by more than one highlighter, and sometimes the implementation
details and semantics will be a bit different, so don’t expect identical results when switching highlighters.
You should use the hl.method parameter to choose a highlighter but it’s also possible to explicitly configure
an implementation by class name in solrconfig.xml .
There are four highlighters available that can be chosen at runtime with the hl.method parameter, in order
of general recommendation:
Unified Highlighter
( hl.method=unified )
The Unified Highlighter is the newest highlighter (as of Solr 6.4), which stands out as the most flexible
and performant of the options. We recommend that you try this highlighter even though it isn’t the
default (yet).
This highlighter supports the most common highlighting parameters and can handle just about any
query accurately, even SpanQueries (e.g. as seen from the surround parser). A strong benefit to this
highlighter is that you can opt to configure Solr to put more information in the underlying index to speed
up highlighting of large documents; multiple configurations are supported, even on a per-field basis.
There is little or no such flexibility for the other highlighters. More on this below.
Original Highlighter
( hl.method=original , the default)
The Original Highlighter, sometimes called the "Standard Highlighter" or "Default Highlighter", is
Lucene’s original highlighter – a venerable option with a high degree of customization options. Its ability
to highlight just about any query accurately is a strength shared with the Unified Highlighter (they share
some code for this in fact).
The Original Highlighter will normally analyze stored text on the fly in order to highlight. It will use full
term vectors if available, however in this mode it isn’t as fast as the Unified Highlighter or FastVector
Highlighter.
This highlighter is a good choice for a wide variety of search use-cases. Where it falls short is
performance; it’s often twice as slow as the Unified Highlighter. And despite being the most
customizable, it doesn’t have a BreakIterator based fragmenter (all the others do), which could pose a
challenge for some languages.
FastVector Highlighter
( hl.method=fastVector )
The FastVector Highlighter requires full term vector options ( termVectors , termPositions , and
termOffsets ) on the field, and is optimized with that in mind. It is nearly as configurable as the Original
Highlighter with some variability.
This highlighter notably supports multi-colored highlighting such that different query words can be
denoted in the fragment with different marking, usually expressed as an HTML tag with a unique color.
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This highlighter’s query-representation is less advanced than the Original or Unified Highlighters: for
example it will not work well with the surround parser, and there are multiple reported bugs pertaining
to queries with stop-words.
Note that both the FastVector and Original Highlighters can be used in conjunction in a search request to
highlight some fields with one and some the other. In contrast, the other highlighters can only be chosen
exclusively.
The Postings Highlighter
( hl.method=postings )
The Postings Highlighter is the ancestor of the Unified Highlighter, supporting a subset of its options and
none of its index configuration flexibility - it requires storeOffsetsWithPositions on all fields to
highlight. This option is here for backwards compatibility; it’s deprecated. In 7.0, it is internally
implemented as a reconfiguration of the Unified Highlighter. See older reference guide editions for its
options.
The Unified Highlighter is exclusively configured via search parameters. In contrast, some settings for the
Original and FastVector Highlighters are set in solrconfig.xml . There’s a robust example of the latter in the
“ techproducts ” configset.
In addition to further information below, more information can be found in the Solr javadocs.
Schema Options and Performance Considerations
Fundamental to the internals of highlighting are detecting the offsets of the individual words that match the
query. Some of the highlighters can run the stored text through the analysis chain defined in the schema,
some can look them up from postings, and some can look them up from term vectors. These choices have
different trade-offs:
? Analysis: Supported by the Unified and Original Highlighters. If you don’t go out of your way to
configure the other options below, the highlighter will analyze the stored text on the fly (during
highlighting) to calculate offsets.
The benefit of this approach is that your index won’t grow larger with any extra data that isn’t strictly
necessary for highlighting.
The down side is that highlighting speed is roughly linear with the amount of text to process, with a large
factor being the complexity of your analysis chain.
For "short" text, this is a good choice. Or maybe it’s not short but you’re prioritizing a smaller index and
indexing speed over highlighting performance.
? Postings: Supported by the Unified Highlighter. Set storeOffsetsWithPositions to true . This adds a
moderate amount of extra data to the index but it speeds up highlighting tremendously, especially
compared to analysis with longer text fields.
However, wildcard queries will fall back to analysis unless "light" term vectors are added.
? with Term Vectors (light): Supported only by the Unified Highlighter. To enable this mode set
termVectors to true but no other term vector related options on the field being highlighted.
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This adds even more data to the index than just storeOffsetsWithPositions but not as much as
enabling all the extra term vector options. Term Vectors are only accessed by the highlighter when a
wildcard query is used and will prevent a fall back to analysis of the stored text.
This is definitely the fastest option for highlighting wildcard queries on large text fields.
? Term Vectors (full): Supported by the Unified, FastVector, and Original Highlighters. Set termVectors ,
termPositions , and termOffsets to true , and potentially termPayloads for advanced use cases.
This adds substantial weight to the index – similar in size to the compressed stored text. If you are using
the Unified Highlighter then this is not a recommended configuration since it’s slower and heavier than
postings with light term vectors. However, this could make sense if full term vectors are already needed
for another use-case.
The Unified Highlighter
The Unified Highlighter supports these following additional parameters to the ones listed earlier:
Parameter Default Description
hl.offsetSource (blank) By default, the Unified Highlighter will usually pick the right offset
source (see above). However it may be ambiguous such as during a
migration from one offset source to another that hasn’t completed.
The offset source can be explicitly configured to one of: ANALYSIS,
POSTINGS, POSTINGS_WITH_TERM_VECTORS, TERM_VECTORS
hl.tag.ellipsis (blank) By default, each snippet is returned as a separate value (as is done
with the other highlighters). Set this parameter to instead return one
string with this text as the delimiter. Note: this is likely to be removed in
the future.
hl.defaultSummary false If true, use the leading portion of the text as a snippet if a proper
highlighted snippet can’t otherwise be generated.
hl.score.k1 1.2 Specifies BM25 term frequency normalization parameter ‘k1‘. For
example, it can be set to "0" to rank passages solely based on the
number of query terms that match.
hl.score.b 0.75 Specifies BM25 length normalization parameter ‘b‘. For example, it
can be set to "0" to ignore the length of passages entirely when
ranking.
hl.score.pivot 87 Specifies BM25 average passage length in characters.
hl.bs.language (blank) Specifies the breakiterator language for dividing the document into
passages.
hl.bs.country (blank) Specifies the breakiterator country for dividing the document into
passages.
hl.bs.variant (blank) Specifies the breakiterator variant for dividing the document into
passages.
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Parameter Default Description
hl.bs.type SENTENCE Specifies the breakiterator type for dividing the document into
passages. Can be SEPARATOR, SENTENCE, WORD, CHARACTER,
LINE, or WHOLE. SEPARATOR is special value that splits text on a
user-provided character in hl.bs.separator .
hl.bs.separator (blank) Indicates which character to break the text on. Requires
hl.bs.type=SEPARATOR . This is useful when the text has already been
manipulated in advance to have a special delineation character at
desired highlight passage boundaries. This character will still appear
in the text as the last character of a passage.
The Original Highlighter
The Original Highlighter supports these following additional parameters to the ones listed earlier:
Parameter Default Description
hl.mergeContiguous false Instructs Solr to collapse contiguous fragments into a single
fragment. A value of true indicates contiguous fragments will
be collapsed into single fragment. The default value, false, is
also the backward-compatible setting.
hl.maxMultiValuedToExa
mine
Integer.MAX_
VALUE
Specifies the maximum number of entries in a multi-valued field
to examine before stopping. This can potentially return zero
results if the limit is reached before any matches are found. If
used with the maxMultiValuedToMatch , whichever limit is
reached first will determine when to stop looking.
hl.maxMultiValuedToMat
ch
Integer.MAX_
VALUE
Specifies the maximum number of matches in a multi-valued
field that are found before stopping. If
hl.maxMultiValuedToExamine is also defined, whichever limit is
reached first will determine when to stop looking.
hl.alternateField (blank) Specifies a field to be used as a backup default summary if Solr
cannot generate a snippet (i.e., because no terms match).
hl.maxAlternateFieldLeng
th
(unlimited) Specifies the maximum number of characters of the field to
return. Any value less than or equal to 0 means the field’s
length is unlimited. This parameter is only used in conjunction
with the hl.alternateField parameter.
hl.highlightAlternate true If set to true, and hl.alternateFieldName is active, Solr will
show the entire alternate field, with highlighting of occurrences.
If hl.maxAlternateFieldLength=N is used, Solr returns max N
characters surrounding the best matching fragment. If set to
false, or if there is no match in the alternate field either, the
alternate field will be shown without highlighting.
hl.formatter simple Selects a formatter for the highlighted output. Currently the
only legal value is simple, which surrounds a highlighted term
with a customizable pre- and post-text snippet.
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Parameter Default Description
hl.simple.prehl.simple.po
st
<em> and
</em>
Specifies the text that should appear before ( hl.simple.pre )
and after ( hl.simple.post ) a highlighted term, when using the
simple formatter.
hl.fragmenter gap Specifies a text snippet generator for highlighted text. The
standard fragmenter is gap, which creates fixed-sized
fragments with gaps for multi-valued fields. Another option is
regex, which tries to create fragments that resemble a specified
regular expression.
hl.regex.slop 0.6 When using the regex fragmenter ( hl.fragmenter=regex ), this
parameter defines the factor by which the fragmenter can stray
from the ideal fragment size (given by hl.fragsize ) to
accommodate a regular expression. For instance, a slop of 0.2
with hl.fragsize=100 should yield fragments between 80 and
120 characters in length. It is usually good to provide a slightly
smaller hl.fragsize value when using the regex fragmenter.
hl.regex.pattern (blank) Specifies the regular expression for fragmenting. This could be
used to extract sentences.
hl.regex.maxAnalyzedCha
rs
10000 Instructs Solr to analyze only this many characters from a field
when using the regex fragmenter (after which, the fragmenter
produces fixed-sized fragments). Applying a complicated regex
to a huge field is computationally expensive.
hl.preserveMulti false If true, multi-valued fields will return all values in the order they
were saved in the index. If false, only values that match the
highlight request will be returned.
hl.payloads (automatic) When hl.usePhraseHighlighter is true and the indexed field
has payloads but not term vectors (generally quite rare), the
index’s payloads will be read into the highlighter’s memory
index along with the postings. If this may happen and you know
you don’t need them for highlighting (i.e. your queries don’t
filter by payload) then you can save a little memory by setting
this to false.
The Original Highlighter has a plugin architecture that enables new functionality to be registered in
solrconfig.xml . The “ techproducts ” configset shows most of these settings explicitly. You can use it as a
guide to provide your own components to include a SolrFormatter , SolrEncoder , and SolrFragmenter.
The FastVector Highlighter
The FastVector Highlighter (FVH) can be used in conjunction with the Original Highlighter if not all fields
should be highlighted with the FVH. In such a mode, set hl.method=original and
f.yourTermVecField.hl.method=fastVector for all fields that should use the FVH. One annoyance to keep
in mind is that the Original Highlighter uses hl.simple.pre whereas the FVH (and other highlighters) use
hl.tag.pre .
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In addition to the initial listed parameters, the following parameters documented for the Original
Highlighter above are also supported by the FVH:
? hl.alternateField
? hl.maxAlternateFieldLength
? hl.highlightAlternate
And here are additional parameters supported by the FVH:
Parameter Default Description
hl.fragListBuilder weighted The snippet fragmenting algorithm. The weighted fragListBuilder
uses IDF-weights to order fragments. Other options are single, which
returns the entire field contents as one snippet, or simple. You can
select a fragListBuilder with this parameter, or modify an existing
implementation in solrconfig.xml to be the default by adding
"default=true".
hl.fragmentsBuilder default The fragments builder is responsible for formatting the fragments,
which uses <em> and </em> markup (if hl.tag.pre and hl.tag.post
are not defined). Another pre-configured choice is colored, which is
an example of how to use the fragments builder to insert HTML into
the snippets for colored highlights if you choose. You can also
implement your own if you’d like. You can select a fragments builder
with this parameter, or modify an existing implementation in
solrconfig.xml to be the default by adding "default=true".
hl.boundaryScanner See Using Boundary Scanners with the FastVector Highlighter below.
hl.bs.* See Using Boundary Scanners with the FastVector Highlighter below.
hl.phraseLimit 5000 The maximum number of phrases to analyze when searching for the
highest-scoring phrase.
hl.multiValuedSepar
atorChar
" " (space) Text to use to separate one value from the next for a multi-valued
field.
Using Boundary Scanners with the FastVector Highlighter
The FastVector Highlighter will occasionally truncate highlighted words. To prevent this, implement a
boundary scanner in solrconfig.xml , then use the hl.boundaryScanner parameter to specify the boundary
scanner for highlighting.
Solr supports two boundary scanners: breakIterator and simple .
The breakIterator Boundary Scanner
The breakIterator boundary scanner offers excellent performance right out of the box by taking locale and
boundary type into account. In most cases you will want to use the breakIterator boundary scanner. To
implement the breakIterator boundary scanner, add this code to the highlighting section of your
solrconfig.xml file, adjusting the type, language, and country values as appropriate to your application:
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<boundaryScanner name="breakIterator" class="solr.highlight.BreakIteratorBoundaryScanner">
<lst name="defaults">
<str name="hl.bs.type">WORD</str>
<str name="hl.bs.language">en</str>
<str name="hl.bs.country">US</str>
</lst>
</boundaryScanner>
Possible values for the hl.bs.type parameter are WORD, LINE, SENTENCE, and CHARACTER.
The simple Boundary Scanner
The simple boundary scanner scans term boundaries for a specified maximum character value
( hl.bs.maxScan ) and for common delimiters such as punctuation marks ( hl.bs.chars ). The simple
boundary scanner may be useful for some custom To implement the simple boundary scanner, add this
code to the highlighting section of your solrconfig.xml file, adjusting the values as appropriate to your
application:
<boundaryScanner name="simple" class="solr.highlight.SimpleBoundaryScanner" default="true">
<lst name="defaults">
<str name="hl.bs.maxScan">10</str >
<str name="hl.bs.chars">.,!?\t\n</str >
</lst >
</boundaryScanner>
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Spell Checking
The SpellCheck component is designed to provide inline query suggestions based on other, similar, terms.
The basis for these suggestions can be terms in a field in Solr, externally created text files, or fields in other
Lucene indexes.
Configuring the SpellCheckComponent
Define Spell Check in solrconfig.xml
The first step is to specify the source of terms in solrconfig.xml . There are three approaches to spell
checking in Solr, discussed below.
IndexBasedSpellChecker
The IndexBasedSpellChecker uses a Solr index as the basis for a parallel index used for spell checking. It
requires defining a field as the basis for the index terms; a common practice is to copy terms from some
fields (such as title , body , etc.) to another field created for spell checking. Here is a simple example of
configuring solrconfig.xml with the IndexBasedSpellChecker :
<searchComponent name="spellcheck" class="solr.SpellCheckComponent">
<lst name="spellchecker">
<str name="classname">solr.IndexBasedSpellChecker</str>
<str name="spellcheckIndexDir">./spellchecker</str>
<str name="field">content</str>
<str name="buildOnCommit">true</str>
<!-- optional elements with defaults
<str name="distanceMeasure">org.apache.lucene.search.spell.LevensteinDistance</str>
<str name="accuracy">0.5</str>
-->
</lst>
</searchComponent>
The first element defines the searchComponent to use the solr.SpellCheckComponent . The classname is the
specific implementation of the SpellCheckComponent, in this case solr.IndexBasedSpellChecker . Defining
the classname is optional; if not defined, it will default to IndexBasedSpellChecker .
The spellcheckIndexDir defines the location of the directory that holds the spellcheck index, while the
field defines the source field (defined in the Schema) for spell check terms. When choosing a field for the
spellcheck index, it’s best to avoid a heavily processed field to get more accurate results. If the field has
many word variations from processing synonyms and/or stemming, the dictionary will be created with those
variations in addition to more valid spelling data.
Finally, buildOnCommit defines whether to build the spell check index at every commit (that is, every time
new documents are added to the index). It is optional, and can be omitted if you would rather set it to false .
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DirectSolrSpellChecker
The DirectSolrSpellChecker uses terms from the Solr index without building a parallel index like the
IndexBasedSpellChecker . This spell checker has the benefit of not having to be built regularly, meaning
that the terms are always up-to-date with terms in the index. Here is how this might be configured in
solrconfig.xml
<searchComponent name="spellcheck" class="solr.SpellCheckComponent">
<lst name="spellchecker">
<str name="name">default</str>
<str name="field">name</str>
<str name="classname">solr.DirectSolrSpellChecker</str>
<str name="distanceMeasure">internal</str>
<float name="accuracy">0.5</float>
<int name="maxEdits">2</int>
<int name="minPrefix">1</int>
<int name="maxInspections">5</int>
<int name="minQueryLength">4</int>
<float name="maxQueryFrequency">0.01</float>
<float name="thresholdTokenFrequency">.01</float>
</lst>
</searchComponent>
When choosing a field to query for this spell checker, you want one which has relatively little analysis
performed on it (particularly analysis such as stemming). Note that you need to specify a field to use for the
suggestions, so like the IndexBasedSpellChecker , you may want to copy data from fields like title , body ,
etc., to a field dedicated to providing spelling suggestions.
Many of the parameters relate to how this spell checker should query the index for term suggestions. The
distanceMeasure defines the metric to use during the spell check query. The value "internal" uses the
default Levenshtein metric, which is the same metric used with the other spell checker implementations.
Because this spell checker is querying the main index, you may want to limit how often it queries the index
to be sure to avoid any performance conflicts with user queries. The accuracy setting defines the threshold
for a valid suggestion, while maxEdits defines the number of changes to the term to allow. Since most
spelling mistakes are only 1 letter off, setting this to 1 will reduce the number of possible suggestions (the
default, however, is 2); the value can only be 1 or 2. minPrefix defines the minimum number of characters
the terms should share. Setting this to 1 means that the spelling suggestions will all start with the same
letter, for example.
The maxInspections parameter defines the maximum number of possible matches to review before
returning results; the default is 5. minQueryLength defines how many characters must be in the query
before suggestions are provided; the default is 4.
At first, spellchecker analyses incoming query words by looking up them in the index. Only query words,
which are absent in index or too rare ones (below maxQueryFrequency ) are considered as misspelled and
used for finding suggestions. Words which are frequent than maxQueryFrequency bypass spellchecker
unchanged. After suggestions for every misspelled word are found they are filtered for enough frequency
with thresholdTokenFrequency as boundary value. These parameters ( maxQueryFrequency and
thresholdTokenFrequency ) can be a percentage (such as .01, or 1%) or an absolute value (such as 4).
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FileBasedSpellChecker
The FileBasedSpellChecker uses an external file as a spelling dictionary. This can be useful if using Solr as
a spelling server, or if spelling suggestions don’t need to be based on actual terms in the index. In
solrconfig.xml , you would define the searchComponent as so:
<searchComponent name="spellcheck" class="solr.SpellCheckComponent">
<lst name="spellchecker">
<str name="classname">solr.FileBasedSpellChecker</str>
<str name="name">file</str>
<str name="sourceLocation">spellings.txt</str>
<str name="characterEncoding">UTF-8</str>
<str name="spellcheckIndexDir">./spellcheckerFile</str>
<!-- optional elements with defaults
<str name="distanceMeasure">org.apache.lucene.search.spell.LevensteinDistance</str>
<str name="accuracy">0.5</str>
-->
</lst>
</searchComponent>
The differences here are the use of the sourceLocation to define the location of the file of terms and the
use of characterEncoding to define the encoding of the terms file.
?
In the previous example, name is used to name this specific definition of the spellchecker.
Multiple definitions can co-exist in a single solrconfig.xml , and the name helps to
differentiate them. If only defining one spellchecker, no name is required.
WordBreakSolrSpellChecker
WordBreakSolrSpellChecker offers suggestions by combining adjacent query terms and/or breaking terms
into multiple words. It is a SpellCheckComponent enhancement, leveraging Lucene’s
WordBreakSpellChecker . It can detect spelling errors resulting from misplaced whitespace without the use
of shingle-based dictionaries and provides collation support for word-break errors, including cases where
the user has a mix of single-word spelling errors and word-break errors in the same query. It also provides
shard support.
Here is how it might be configured in solrconfig.xml :
<searchComponent name="spellcheck" class="solr.SpellCheckComponent">
<lst name="spellchecker">
<str name="name">wordbreak</str>
<str name="classname">solr.WordBreakSolrSpellChecker</str>
<str name="field">lowerfilt</str>
<str name="combineWords">true</str>
<str name="breakWords">true</str>
<int name="maxChanges">10</int>
</lst>
</searchComponent>
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Some of the parameters will be familiar from the discussion of the other spell checkers, such as name ,
classname , and field . New for this spell checker is combineWords , which defines whether words should be
combined in a dictionary search (default is true); breakWords , which defines if words should be broken
during a dictionary search (default is true); and maxChanges , an integer which defines how many times the
spell checker should check collation possibilities against the index (default is 10).
The spellchecker can be configured with a traditional checker (ie: DirectSolrSpellChecker ). The results are
combined and collations can contain a mix of corrections from both spellcheckers.
Add It to a Request Handler
Queries will be sent to a RequestHandler. If every request should generate a suggestion, then you would
add the following to the requestHandler that you are using:
<str name="spellcheck">true</str>
One of the possible parameters is the spellcheck.dictionary to use, and multiples can be defined. With
multiple dictionaries, all specified dictionaries are consulted and results are interleaved. Collations are
created with combinations from the different spellcheckers, with care taken that multiple overlapping
corrections do not occur in the same collation.
Here is an example with multiple dictionaries:
<requestHandler name="spellCheckWithWordbreak" class=
"org.apache.solr.handler.component.SearchHandler">
<lst name="defaults">
<str name="spellcheck.dictionary">default</str>
<str name="spellcheck.dictionary">wordbreak</str>
<str name="spellcheck.count">20</str>
</lst>
<arr name="last-components">
<str>spellcheck</str>
</arr>
</requestHandler>
Spell Check Parameters
The SpellCheck component accepts the parameters described in the table below.
Parameter Description
spellcheck Turns on or off SpellCheck suggestions for the request. If true, then
spelling suggestions will be generated.
spellcheck.q or q Selects the query to be spellchecked.
spellcheck.build Instructs Solr to build a dictionary for use in spellchecking.
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Parameter Description
spellcheck.collate Causes Solr to build a new query based on the best suggestion for each
term in the submitted query.
spellcheck.maxCollations This parameter specifies the maximum number of collations to return.
spellcheck.maxCollationTries This parameter specifies the number of collation possibilities for Solr to try
before giving up.
spellcheck.maxCollationEvaluat
ions
This parameter specifies the maximum number of word correction
combinations to rank and evaluate prior to deciding which collation
candidates to test against the index.
spellcheck.collateExtendedRes
ults
If true, returns an expanded response detailing the collations found. If
spellcheck.collate is false, this parameter will be ignored.
spellcheck.collateMaxCollectDo
cs
The maximum number of documents to collect when testing potential
Collations
spellcheck.collateParam.* Specifies param=value pairs that can be used to override normal query
params when validating collations
spellcheck.count Specifies the maximum number of spelling suggestions to be returned.
spellcheck.dictionary Specifies the dictionary that should be used for spellchecking.
spellcheck.extendedResults Causes Solr to return additional information about spellcheck results, such
as the frequency of each original term in the index (origFreq) as well as the
frequency of each suggestion in the index (frequency). Note that this
result format differs from the non-extended one as the returned
suggestion for a word is actually an array of lists, where each list holds the
suggested term and its frequency.
spellcheck.onlyMorePopular Limits spellcheck responses to queries that are more popular than the
original query.
spellcheck.maxResultsForSugg
est
The maximum number of hits the request can return in order to both
generate spelling suggestions and set the "correctlySpelled" element to
"false".
spellcheck.alternativeTermCou
nt
The count of suggestions to return for each query term existing in the
index and/or dictionary.
spellcheck.reload Reloads the spellchecker.
spellcheck.accuracy Specifies an accuracy value to help decide whether a result is worthwhile.
spellcheck.<DICT_NAME>.key Specifies a key/value pair for the implementation handling a given
dictionary.
The spellcheck Parameter
This parameter turns on SpellCheck suggestions for the request. If true, then spelling suggestions will be
generated.
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The spellcheck.q or q Parameter
This parameter specifies the query to spellcheck. If spellcheck.q is defined, then it is used; otherwise the
original input query is used. The spellcheck.q parameter is intended to be the original query, minus any
extra markup like field names, boosts, and so on. If the q parameter is specified, then the
SpellingQueryConverter class is used to parse it into tokens; otherwise the WhitespaceTokenizer is used.
The choice of which one to use is up to the application. Essentially, if you have a spelling "ready" version in
your application, then it is probably better to use spellcheck.q . Otherwise, if you just want Solr to do the
job, use the q parameter.
?
The SpellingQueryConverter class does not deal properly with non-ASCII characters. In this
case, you have either to use spellcheck.q , or implement your own QueryConverter.
The spellcheck.build Parameter
If set to true, this parameter creates the dictionary that the SolrSpellChecker will use for spell-checking. In a
typical search application, you will need to build the dictionary before using the SolrSpellChecker. However,
it’s not always necessary to build a dictionary first. For example, you can configure the spellchecker to use a
dictionary that already exists.
The dictionary will take some time to build, so this parameter should not be sent with every request.
The spellcheck.reload Parameter
If set to true, this parameter reloads the spellchecker. The results depend on the implementation of
SolrSpellChecker.reload() . In a typical implementation, reloading the spellchecker means reloading the
dictionary.
The spellcheck.count Parameter
This parameter specifies the maximum number of suggestions that the spellchecker should return for a
term. If this parameter isn’t set, the value defaults to 1. If the parameter is set but not assigned a number,
the value defaults to 5. If the parameter is set to a positive integer, that number becomes the maximum
number of suggestions returned by the spellchecker.
The spellcheck.onlyMorePopular Parameter
If true, Solr will to return suggestions that result in more hits for the query than the existing query. Note
that this will return more popular suggestions even when the given query term is present in the index and
considered "correct".
The spellcheck.maxResultsForSuggest Parameter
For example, if this is set to 5 and the user’s query returns 5 or fewer results, the spellchecker will report
"correctlySpelled=false" and also offer suggestions (and collations if requested). Setting this greater than
zero is useful for creating "did-you-mean?" suggestions for queries that return a low number of hits.
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The spellcheck.alternativeTermCount Parameter
Specify the number of suggestions to return for each query term existing in the index and/or dictionary.
Presumably, users will want fewer suggestions for words with docFrequency>0. Also setting this value turns
"on" context-sensitive spell suggestions.
The spellcheck.extendedResults Parameter
This parameter causes to Solr to include additional information about the suggestion, such as the frequency
in the index.
The spellcheck.collate Parameter
If true, this parameter directs Solr to take the best suggestion for each token (if one exists) and construct a
new query from the suggestions. For example, if the input query was "jawa class lording" and the best
suggestion for "jawa" was "java" and "lording" was "loading", then the resulting collation would be "java
class loading".
The spellcheck.collate parameter only returns collations that are guaranteed to result in hits if re-queried,
even when applying original fq parameters. This is especially helpful when there is more than one
correction per query.
?
This only returns a query to be used. It does not actually run the suggested query.
The spellcheck.maxCollations Parameter
The maximum number of collations to return. The default is 1. This parameter is ignored if
spellcheck.collate is false.
The spellcheck.maxCollationTries Parameter
This parameter specifies the number of collation possibilities for Solr to try before giving up. Lower values
ensure better performance. Higher values may be necessary to find a collation that can return results. The
default value is 0 , which maintains backwards-compatible (Solr 1.4) behavior (do not check collations). This
parameter is ignored if spellcheck.collate is false.
The spellcheck.maxCollationEvaluations Parameter
This parameter specifies the maximum number of word correction combinations to rank and evaluate prior
to deciding which collation candidates to test against the index. This is a performance safety-net in case a
user enters a query with many misspelled words. The default is 10,000 combinations, which should work well
in most situations.
The spellcheck.collateExtendedResults Parameter
If true, this parameter returns an expanded response format detailing the collations Solr found. The default
value is false and this is ignored if spellcheck.collate is false.
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The spellcheck.collateMaxCollectDocs Parameter
This parameter specifies the maximum number of documents that should be collect when testing potential
collations against the index. A value of 0 indicates that all documents should be collected, resulting in exact
hit-counts. Otherwise an estimation is provided as a performance optimization in cases where exact hit-
counts are unnecessary – the higher the value specified, the more precise the estimation.
The default value for this parameter is 0, but when spellcheck.collateExtendedResults is false, the
optimization is always used as if a 1 had been specified.
The spellcheck.collateParam.* Parameter Prefix
This parameter prefix can be used to specify any additional parameters that you wish to the Spellchecker to
use when internally validating collation queries. For example, even if your regular search results allow for
loose matching of one or more query terms via parameters like q.op=OR and mm=20% you can specify
override params such as spellcheck.collateParam.q.op=AND&spellcheck.collateParam.mm=100% to
require that only collations consisting of words that are all found in at least one document may be returned.
The spellcheck.dictionary Parameter
This parameter causes Solr to use the dictionary named in the parameter’s argument. The default setting is
"default". This parameter can be used to invoke a specific spellchecker on a per request basis.
The spellcheck.accuracy Parameter
Specifies an accuracy value to be used by the spell checking implementation to decide whether a result is
worthwhile or not. The value is a float between 0 and 1. Defaults to Float.MIN_VALUE .
The spellcheck.<DICT_NAME>.key Parameter
Specifies a key/value pair for the implementation handling a given dictionary. The value that is passed
through is just key=value ( spellcheck.<DICT_NAME>. is stripped off.
For example, given a dictionary called foo , spellcheck.foo.myKey=myValue would result in myKey=myValue
being passed through to the implementation handling the dictionary foo .
Example
Using Solr’s bin/solr -e techproducts example, this query shows the results of a simple request that
defines a query using the spellcheck.q parameter, and forces the collations to require all input terms must
match:
http://localhost:8983/solr/techproducts/spell?df=text&spellcheck.q=delll+ultra+sharp&spellch
eck=true&spellcheck.collateParam.q.op=AND
Results:
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<lst name="spellcheck">
<lst name="suggestions">
<lst name="delll">
<int name="numFound">1</int>
<int name="startOffset">0</int>
<int name="endOffset">5</int>
<int name="origFreq">0</int>
<arr name="suggestion">
<lst>
<str name="word">dell</str>
<int name="freq">1</int>
</lst>
</arr>
</lst>
<lst name="ultra sharp">
<int name="numFound">1</int>
<int name="startOffset">6</int>
<int name="endOffset">17</int>
<int name="origFreq">0</int>
<arr name="suggestion">
<lst>
<str name="word">ultrasharp</str>
<int name="freq">1</int>
</lst>
</arr>
</lst>
</lst>
<bool name="correctlySpelled">false</bool>
<lst name="collations">
<lst name="collation">
<str name="collationQuery">dell ultrasharp</str>
<int name="hits">1</int>
<lst name="misspellingsAndCorrections">
<str name="delll">dell</str>
<str name="ultra sharp">ultrasharp</str>
</lst>
</lst>
</lst>
</lst>
Distributed SpellCheck
The SpellCheckComponent also supports spellchecking on distributed indexes. If you are using the
SpellCheckComponent on a request handler other than "/select", you must provide the following two
parameters:
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Parameter Description
shards Specifies the shards in your distributed indexing configuration. For more
information about distributed indexing, see Distributed Search with Index
Sharding
shards.qt Specifies the request handler Solr uses for requests to shards. This
parameter is not required for the /select request handler.
For example:
http://localhost:8983/solr/techproducts/spell?spellcheck=true&spellcheck.build=true&spellcheck.q=
toyata&shards.qt=/spell&shards=solr-shard1:8983/solr/techproducts,solr-
shard2:8983/solr/techproducts
In case of a distributed request to the SpellCheckComponent, the shards are requested for at least five
suggestions even if the spellcheck.count parameter value is less than five. Once the suggestions are
collected, they are ranked by the configured distance measure (Levenstein Distance by default) and then by
aggregate frequency.
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Query Re-Ranking
Query Re-Ranking allows you to run a simple query (A) for matching documents and then re-rank the top N
documents using the scores from a more complex query (B).
Since the more costly ranking from query B is only applied to the top N documents it will have less impact on
performance then just using the complex query B by itself. The trade off is that documents which score very
low using the simple query A may not be considered during the re-ranking phase, even if they would score
very highly using query B.
Specifying a Ranking Query
A Ranking query can be specified using the rq request parameter. The rq parameter must specify a query
string that when parsed, produces a RankQuery.
Three rank queries are currently included in the Solr distribution. You can also configure a custom
QParserPlugin you have written, but most users can just use a parser provided with Solr.
Parser QParserPlugin class
rerank ReRankQParserPlugin
xport ExportQParserPlugin
ltr LTRQParserPlugin
ReRank Query Parser
The rerank parser wraps a query specified by an local parameter, along with additional parameters
indicating how many documents should be re-ranked, and how the final scores should be computed:
Parameter Default Description
reRankQuery
(Mandatory) The query string for your complex ranking query - in most cases a
variable will be used to refer to another request parameter.
reRankDocs
200 The number of top N documents from the original query that should
be re-ranked. This number will be treated as a minimum, and may be
increased internally automatically in order to rank enough
documents to satisfy the query (ie: start+rows)
reRankWeight
2.0 A multiplicative factor that will be applied to the score from the
reRankQuery for each of the top matching documents, before that
score is added to the original score
In the example below, the top 1000 documents matching the query "greetings" will be re-ranked using the
query "(hi hello hey hiya)". The resulting scores for each of those 1000 documents will be 3 times their score
from the "(hi hello hey hiya)", plus the score from the original "greetings" query:
q=greetings&rq={!rerank reRankQuery=$rqq reRankDocs=1000 reRankWeight=3}&rqq=(hi+hello+hey+hiya)
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If a document matches the original query, but does not match the re-ranking query, the document’s original
score will remain.
LTR Query Parser
The ltr stands for Learning To Rank, please see Learning To Rank for more detailed information.
Combining Ranking Queries with Other Solr Features
The rq parameter and the re-ranking feature in general works well with other Solr features. For example, it
can be used in conjunction with the collapse parser to re-rank the group heads after they’ve been collapsed.
It also preserves the order of documents elevated by the elevation component. And it even has its own
custom explain so you can see how the re-ranking scores were derived when looking at debug information.
Learning To Rank
With the Learning To Rank (or LTR for short) contrib module you can configure and run machine learned
ranking models in Solr.
The module also supports feature extraction inside Solr. The only thing you need to do outside Solr is train
your own ranking model.
Concepts
Re-Ranking
Re-Ranking allows you to run a simple query for matching documents and then re-rank the top N
documents using the scores from a different, complex query. This page describes the use of LTR complex
queries, information on other rank queries included in the Solr distribution can be found on the Query Re-
Ranking page.
Learning To Rank
In information retrieval systems, Learning to Rank is used to re-rank the top N retrieved documents using
trained machine learning models. The hope is that such sophisticated models can make more nuanced
ranking decisions than standard ranking functions like TF-IDF or BM25.
Model
A ranking model computes the scores used to rerank documents. Irrespective of any particular algorithm or
implementation, a ranking model’s computation can use three types of inputs:
? parameters that represent the scoring algorithm
? features that represent the document being scored
? features that represent the query for which the document is being scored
Feature
A feature is a value, a number, that represents some quantity or quality of the document being scored or of
the query for which documents are being scored. For example documents often have a ‘recency‘ quality and
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‘number of past purchases‘ might be a quantity that is passed to Solr as part of the search query.
Normalizer
Some ranking models expect features on a particular scale. A normalizer can be used to translate arbitrary
feature values into normalized values e.g. on a 0..1 or 0..100 scale.
Training
Feature engineering
The LTR contrib module includes several feature classes as well as support for custom features. Each feature
class’s javadocs contain an example to illustrate use of that class. The process of feature engineering itself is
then entirely up to your domain expertise and creativity.
Feature Class Example parameters External Feature
Information
field length FieldLengthFeature
{"field":"title"}
not (yet) supported
field value FieldValueFeature
{"field":"hits"}
not (yet) supported
original score OriginalScoreFeature
{}
not applicable
solr query SolrFeature {"q":"{!func}
recip(ms(NOW,last_modi
fied) ,3.16e-11,1,1)"}
supported
solr filter query SolrFeature
{"fq":["{!terms
f=category}book"]}
supported
solr query + filter query SolrFeature {"q":"{!func}
recip(ms(NOW,last_modi
fied), 3.16e-11,1,1)",
"fq":["{!terms
f=category}book"]}
supported
value ValueFeature
{"value":"${userFromMo
bile}","required":true
}
supported
(custom) (custom class extending
Feature)
Normalizer Class Example parameters
Identity IdentityNormalizer
{}
MinMax MinMaxNormalizer
{"min":"0", "max":"50" }
Standard StandardNormalizer
{"avg":"42","std":"6"}
(custom) (custom class extending
Normalizer)
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Feature Extraction
The ltr contrib module includes a [features transformer] to support the calculation and return of feature
values for feature extraction purposes including and especially when you do not yet have an actual
reranking model.
Feature Selection and Model Training
Feature selection and model training take place offline and outside Solr. The ltr contrib module supports two
generalized forms of models as well as custom models. Each model class’s javadocs contain an example to
illustrate configuration of that class. In the form of JSON files your trained model or models (e.g. different
models for different customer geographies) can then be directly uploaded into Solr using provided REST
APIs.
General form Class Specific examples
Linear LinearModel RankSVM, Pranking
Multiple Additive Trees MultipleAdditiveTreesModel LambdaMART, Gradient Boosted
Regression Trees (GBRT)
(custom) (custom class extending
LTRScoringModel)
(not applicable)
Quick Start Example
The "techproducts" example included with Solr is pre-configured with the plugins required for learning-to-
rank, but they are disabled by default.
To enable the plugins, please specify the solr.ltr.enabled JVM System Property when running the
example:
bin/solr start -e techproducts -Dsolr.ltr.enabled=true
Uploading Features
To upload features in a /path/myFeatures.json file, please run:
curl -XPUT ‘http://localhost:8983/solr/techproducts/schema/feature-store‘ --data-binary
"@/path/myFeatures.json" -H ‘Content-type:application/json‘
To view the features you just uploaded please open the following URL in a browser:
http://localhost:8983/solr/techproducts/schema/feature-store/_DEFAULT_
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Example: /path/myFeatures.json
[
{
"name" : "documentRecency",
"class" : "org.apache.solr.ltr.feature.SolrFeature",
"params" : {
"q" : "{!func}recip( ms(NOW,last_modified), 3.16e-11, 1, 1)"
}
},
{
"name" : "isBook",
"class" : "org.apache.solr.ltr.feature.SolrFeature",
"params" : {
"fq": ["{!terms f=cat}book"]
}
},
{
"name" : "originalScore",
"class" : "org.apache.solr.ltr.feature.OriginalScoreFeature",
"params" : {}
}
]
Extracting Features
To extract features as part of a query, add [features] to the fl parameter, for example:
http://localhost:8983/solr/techproducts/query?q=test&fl=id,score,%5Bfeatures%5D
The output XML will include feature values as a comma-separated list, resembling the output shown here:
{
"responseHeader":{
"status":0,
"QTime":0,
"params":{
"q":"test",
"fl":"id,score,[features]"}},
"response":{"numFound":2,"start":0,"maxScore":1.959392,"docs":[
{
"id":"GB18030TEST",
"score":1.959392,
"[features]":"documentRecency=0.020893794,isBook=0.0,originalScore=1.959392"},
{
"id":"UTF8TEST",
"score":1.5513437,
"[features]":"documentRecency=0.020893794,isBook=0.0,originalScore=1.5513437"}]
}}
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Uploading a Model
To upload the model in a /path/myModel.json file, please run:
curl -XPUT ‘http://localhost:8983/solr/techproducts/schema/model-store‘ --data-binary
"@/path/myModel.json" -H ‘Content-type:application/json‘
To view the model you just uploaded please open the following URL in a browser:
http://localhost:8983/solr/techproducts/schema/model-store
Example: /path/myModel.json
{
"class" : "org.apache.solr.ltr.model.LinearModel",
"name" : "myModel",
"features" : [
{ "name" : "documentRecency" },
{ "name" : "isBook" },
{ "name" : "originalScore" }
],
"params" : {
"weights" : {
"documentRecency" : 1.0,
"isBook" : 0.1,
"originalScore" : 0.5
}
}
}
Running a Rerank Query
To rerank the results of a query, add the rq parameter to your search, for example:
http://localhost:8983/solr/techproducts/query?q=test&rq=%7B!ltr%20model=myModel%20reRankDocs=100%
7D&fl=id,score[http://localhost:8983/solr/techproducts/query?q=test&rq=\{!ltr model=myModel
reRankDocs=100}&fl=id,score]`
The addition of the rq parameter will not change the output XML of the search.
To obtain the feature values computed during reranking, add [features] to the fl parameter, for example:
http://localhost:8983/solr/techproducts/query?q=test&rq=%7B!ltr%20model=myModel%20reRankDocs=100%
7D&fl=id,score,%5Bfeatures%5D[http://localhost:8983/solr/techproducts/query?q=test&rq=\{!ltr
model=myModel reRankDocs=100}&fl=id,score,[features]]
The output XML will include feature values as a comma-separated list, resembling the output shown here:
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{
"responseHeader":{
"status":0,
"QTime":0,
"params":{
"q":"test",
"fl":"id,score,[features]",
"rq":"{!ltr model=myModel reRankDocs=100}"}},
"response":{"numFound":2,"start":0,"maxScore":1.0005897,"docs":[
{
"id":"GB18030TEST",
"score":1.0005897,
"[features]":"documentRecency=0.020893792,isBook=0.0,originalScore=1.959392"},
{
"id":"UTF8TEST",
"score":0.79656565,
"[features]":"documentRecency=0.020893792,isBook=0.0,originalScore=1.5513437"}]
}}
External Feature Information
The ValueFeature and SolrFeature classes support the use of external feature information, efi for short.
Uploading Features
To upload features in a /path/myEfiFeatures.json file, please run:
curl -XPUT ‘http://localhost:8983/solr/techproducts/schema/feature-store‘ --data-binary
"@/path/myEfiFeatures.json" -H ‘Content-type:application/json‘
To view the features you just uploaded please open the following URL in a browser:
http://localhost:8983/solr/techproducts/schema/feature-store/myEfiFeatureStore
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Example: /path/myEfiFeatures.json
[
{
"store" : "myEfiFeatureStore",
"name" : "isPreferredManufacturer",
"class" : "org.apache.solr.ltr.feature.SolrFeature",
"params" : { "fq" : [ "{!field f=manu}${preferredManufacturer}" ] }
},
{
"store" : "myEfiFeatureStore",
"name" : "userAnswerValue",
"class" : "org.apache.solr.ltr.feature.ValueFeature",
"params" : { "value" : "${answer:42}" }
},
{
"store" : "myEfiFeatureStore",
"name" : "userFromMobileValue",
"class" : "org.apache.solr.ltr.feature.ValueFeature",
"params" : { "value" : "${fromMobile}", "required" : true }
},
{
"store" : "myEfiFeatureStore",
"name" : "userTextCat",
"class" : "org.apache.solr.ltr.feature.SolrFeature",
"params" : { "q" : "{!field f=cat}${text}" }
}
]
As an aside, you may have noticed that the myEfiFeatures.json example uses
"store":"myEfiFeatureStore" attributes: read more about feature store in the Lifecycle section of this
page.
Extracting Features
To extract myEfiFeatureStore features as part of a query, add efi.* parameters to the [features] part of
the fl parameter, for example:
http://localhost:8983/solr/techproducts/query?q=test&fl=id,cat,manu,score,[features
store=myEfiFeatureStore efi.text=test efi.preferredManufacturer=Apache efi.fromMobile=1]
http://localhost:8983/solr/techproducts/query?q=test&fl=id,cat,manu,score,[features
store=myEfiFeatureStore efi.text=test efi.preferredManufacturer=Apache efi.fromMobile=0
efi.answer=13]
Uploading a Model
To upload the model in a /path/myEfiModel.json file, please run:
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curl -XPUT ‘http://localhost:8983/solr/techproducts/schema/model-store‘ --data-binary
"@/path/myEfiModel.json" -H ‘Content-type:application/json‘
To view the model you just uploaded please open the following URL in a browser:
http://localhost:8983/solr/techproducts/schema/model-store
Example: /path/myEfiModel.json
{
"store" : "myEfiFeatureStore",
"name" : "myEfiModel",
"class" : "org.apache.solr.ltr.model.LinearModel",
"features" : [
{ "name" : "isPreferredManufacturer" },
{ "name" : "userAnswerValue" },
{ "name" : "userFromMobileValue" },
{ "name" : "userTextCat" }
],
"params" : {
"weights" : {
"isPreferredManufacturer" : 0.2,
"userAnswerValue" : 1.0,
"userFromMobileValue" : 1.0,
"userTextCat" : 0.1
}
}
}
Running a Rerank Query
To obtain the feature values computed during reranking, add [features] to the fl parameter and efi.*
parameters to the rq parameter, for example:
http://localhost:8983/solr/techproducts/query?q=test&rq=\{!ltr model=myEfiModel efi.text=test
efi.preferredManufacturer=Apache efi.fromMobile=1}&fl=id,cat,manu,score,[features]] link:[]
http://localhost:8983/solr/techproducts/query?q=test&rq=\{!ltr model=myEfiModel efi.text=test
efi.preferredManufacturer=Apache efi.fromMobile=0 efi.answer=13}&fl=id,cat,manu,score,[features]]
Notice the absence of efi.* parameters in the [features] part of the fl parameter.
Extracting Features While Reranking
To extract features for myEfiFeatureStore features while still reranking with myModel :
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http://localhost:8983/solr/techproducts/query?q=test&rq=\{!ltr
model=myModel}&fl=id,cat,manu,score,[features store=myEfiFeatureStore efi.text=test
efi.preferredManufacturer=Apache efi.fromMobile=1]] link:[]
Notice the absence of efi. parameters in the rq parameter (because myModel does not use efi
feature) and the presence of efi. parameters in the [features] part of the fl parameter (because
myEfiFeatureStore contains efi features).
Read more about model evolution in the Lifecycle section of this page.
Training Example
Example training data and a demo ‘train and upload model‘ script can be found in the
solr/contrib/ltr/example folder in the Apache lucene-solr git repository which is mirrored on github.com
(the solr/contrib/ltr/example folder is not shipped in the solr binary release).
Installation
The ltr contrib module requires the dist/solr-ltr-*.jar JARs.
Configuration
Learning-To-Rank is a contrib module and therefore its plugins must be configured in solrconfig.xml .
Minimum requirements
? Include the required contrib JARs. Note that by default paths are relative to the Solr core so they may
need adjustments to your configuration, or an explicit specification of the $solr.install.dir .
<lib dir="${solr.install.dir:../../../..}/dist/" regex="solr-ltr-\d.*\.jar" />
? Declaration of the ltr query parser.
<queryParser name="ltr" class="org.apache.solr.ltr.search.LTRQParserPlugin"/>
? Configuration of the feature values cache.
<cache name="QUERY_DOC_FV"
class="solr.search.LRUCache"
size="4096"
initialSize="2048"
autowarmCount="4096"
regenerator="solr.search.NoOpRegenerator" />
? Declaration of the [features] transformer.
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<transformer name="features" class=
"org.apache.solr.ltr.response.transform.LTRFeatureLoggerTransformerFactory">
<str name="fvCacheName">QUERY_DOC_FV</str>
</transformer>
Advanced Options
LTRThreadModule
A thread module can be configured for the query parser and/or the transformer to parallelize the creation of
feature weights. For details, please refer to the LTRThreadModule javadocs.
Feature Vector Customization
The features transformer returns dense CSV values such as
featureA=0.1,featureB=0.2,featureC=0.3,featureD=0.0 .
For sparse CSV output such as featureA:0.1 featureB:0.2 featureC:0.3 you can customize the feature
logger transformer declaration in solrconfig.xml as follows:
<transformer name="features" class=
"org.apache.solr.ltr.response.transform.LTRFeatureLoggerTransformerFactory">
<str name="fvCacheName">QUERY_DOC_FV</str>
<str name="defaultFormat">sparse</str>
<str name="csvKeyValueDelimiter">:</str>
<str name="csvFeatureSeparator"> </str>
</transformer>
Implementation and Contributions
?
How does Solr Learning-To-Rank work under the hood?
Please refer to the ltr javadocs for an implementation overview.
?
How could i write additional models and/or features?
Contributions for further models, features and normalizers are welcome. Related links:
? LTRScoringModel javadocs
? Feature javadocs
? Normalizer javadocs
? http://wiki.apache.org/solr/HowToContribute
? http://wiki.apache.org/lucene-java/HowToContribute
Lifecycle
Feature Stores
It is recommended that you organise all your features into stores which are akin to namespaces:
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? Features within a store must be named uniquely.
? Across stores identical or similar features can share the same name.
? If no store name is specified then the default _DEFAULT_ feature store will be used.
To discover the names of all your feature stores:
http://localhost:8983/solr/techproducts/schema/feature-store
To inspect the content of the commonFeatureStore feature store:
http://localhost:8983/solr/techproducts/schema/feature-store/commonFeatureStore
Models
? A model uses features from exactly one feature store.
? If no store is specified then the default _DEFAULT_ feature store will be used.
? A model need not use all the features defined in a feature store.
? Multiple models can use the same feature store.
To extract features for `currentFeatureStore’s features:
http://localhost:8983/solr/techproducts/query?q=test&fl=id,score,[features
store=currentFeatureStore]
To extract features for nextFeatureStore features whilst reranking with currentModel based on
currentFeatureStore :
http://localhost:8983/solr/techproducts/query?q=test&rq={!ltr model=currentModel
reRankDocs=100}&fl=id,score,[features store=nextFeatureStore]
To view all models:
http://localhost:8983/solr/techproducts/schema/model-store
To delete the currentModel model:
curl -XDELETE ‘http://localhost:8983/solr/techproducts/schema/model-store/currentModel‘
?
A feature store must be deleted only when there are no models using it.
To delete the currentFeatureStore feature store:
curl -XDELETE ‘http://localhost:8983/solr/techproducts/schema/feature-store/currentFeatureStore‘
Applying Changes
The feature store and the model store are both Managed Resources. Changes made to managed resources
are not applied to the active Solr components until the Solr collection (or Solr core in single server mode) is
reloaded.
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Examples
One Feature Store, Multiple Ranking Models
? leftModel and rightModel both use features from commonFeatureStore and the only different between
the two models is the weights attached to each feature.
? Conventions used:
? commonFeatureStore.json file contains features for the commonFeatureStore feature store
? leftModel.json file contains model named leftModel
? rightModel.json file contains model named rightModel
? The model’s features and weights are sorted alphabetically by name, this makes it easy to see what
the commonalities and differences between the two models are.
? The stores features are sorted alphabetically by name, this makes it easy to lookup features used in
the models
Example: /path/commonFeatureStore.json
[
{
"store" : "commonFeatureStore",
"name" : "documentRecency",
"class" : "org.apache.solr.ltr.feature.SolrFeature",
"params" : {
"q" : "{!func}recip( ms(NOW,last_modified), 3.16e-11, 1, 1)"
}
},
{
"store" : "commonFeatureStore",
"name" : "isBook",
"class" : "org.apache.solr.ltr.feature.SolrFeature",
"params" : {
"fq": [ "{!terms f=category}book" ]
}
},
{
"store" : "commonFeatureStore",
"name" : "originalScore",
"class" : "org.apache.solr.ltr.feature.OriginalScoreFeature",
"params" : {}
}
]
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Example: /path/leftModel.json
{
"store" : "commonFeatureStore",
"name" : "leftModel",
"class" : "org.apache.solr.ltr.model.LinearModel",
"features" : [
{ "name" : "documentRecency" },
{ "name" : "isBook" },
{ "name" : "originalScore" }
],
"params" : {
"weights" : {
"documentRecency" : 0.1,
"isBook" : 1.0,
"originalScore" : 0.5
}
}
}
Example: /path/rightModel.json
{
"store" : "commonFeatureStore",
"name" : "rightModel",
"class" : "org.apache.solr.ltr.model.LinearModel",
"features" : [
{ "name" : "documentRecency" },
{ "name" : "isBook" },
{ "name" : "originalScore" }
],
"params" : {
"weights" : {
"documentRecency" : 1.0,
"isBook" : 0.1,
"originalScore" : 0.5
}
}
}
Model Evolution
? linearModel201701 uses features from featureStore201701
? treesModel201702 uses features from featureStore201702
? linearModel201701 and treesModel201702 and their feature stores can co-exist whilst both are needed.
? When linearModel201701 has been deleted then featureStore201701 can also be deleted.
? Conventions used:
? <store>.json file contains features for the <store> feature store
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? <model>.json file contains model name <model>
? a ‘generation‘ id (e.g. YYYYMM year-month) is part of the feature store and model names
? The model’s features and weights are sorted alphabetically by name, this makes it easy to see what
the commonalities and differences between the two models are.
? The stores features are sorted alphabetically by name, this makes it easy to see what the
commonalities and differences between the two feature stores are.
Example: /path/featureStore201701.json
[
{
"store" : "featureStore201701",
"name" : "documentRecency",
"class" : "org.apache.solr.ltr.feature.SolrFeature",
"params" : {
"q" : "{!func}recip( ms(NOW,last_modified), 3.16e-11, 1, 1)"
}
},
{
"store" : "featureStore201701",
"name" : "isBook",
"class" : "org.apache.solr.ltr.feature.SolrFeature",
"params" : {
"fq": [ "{!terms f=category}book" ]
}
},
{
"store" : "featureStore201701",
"name" : "originalScore",
"class" : "org.apache.solr.ltr.feature.OriginalScoreFeature",
"params" : {}
}
]
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Example: /path/linearModel201701.json
{
"store" : "featureStore201701",
"name" : "linearModel201701",
"class" : "org.apache.solr.ltr.model.LinearModel",
"features" : [
{ "name" : "documentRecency" },
{ "name" : "isBook" },
{ "name" : "originalScore" }
],
"params" : {
"weights" : {
"documentRecency" : 0.1,
"isBook" : 1.0,
"originalScore" : 0.5
}
}
}
Example: /path/featureStore201702.json
[
{
"store" : "featureStore201702",
"name" : "isBook",
"class" : "org.apache.solr.ltr.feature.SolrFeature",
"params" : {
"fq": [ "{!terms f=category}book" ]
}
},
{
"store" : "featureStore201702",
"name" : "originalScore",
"class" : "org.apache.solr.ltr.feature.OriginalScoreFeature",
"params" : {}
}
]
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Example: /path/treesModel201702.json
{
"store" : "featureStore201702",
"name" : "treesModel201702",
"class" : "org.apache.solr.ltr.model.MultipleAdditiveTreesModel",
"features" : [
{ "name" : "isBook" },
{ "name" : "originalScore" }
],
"params" : {
"trees" : [
{
"weight" : "1",
"root" : {
"feature" : "isBook",
"threshold" : "0.5",
"left" : { "value" : "-100" },
"right" : {
"feature" : "originalScore",
"threshold" : "10.0",
"left" : { "value" : "50" },
"right" : { "value" : "75" }
}
}
},
{
"weight" : "2",
"root" : {
"value" : "-10"
}
}
]
}
}
Additional Resources
? "Learning to Rank in Solr" presentation at Lucene/Solr Revolution 2015 in Austin:
? Slides: http://www.slideshare.net/lucidworks/learning-to-rank-in-solr-presented-by-michael-nilsson-
diego-ceccarelli-bloomberg-lp
? Video: https://www.youtube.com/watch?v=M7BKwJoh96s
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Transforming Result Documents
Document Transformers can be used to modify the information returned about each documents in the
results of a query.
Using Document Transformers
When executing a request, a document transformer can be used by including it in the fl parameter using
square brackets, for example:
fl=id,name,score,[shard]
Some transformers allow, or require, local parameters which can be specified as key value pairs inside the
brackets:
fl=id,name,score,[explain style=nl]
As with regular fields, you can change the key used when a Transformer adds a field to a document via a
prefix:
fl=id,name,score,my_val_a:[value v=42 t=int],my_val_b:[value v=7 t=float]
The sections below discuss exactly what these various transformers do.
Available Transformers
[value] - ValueAugmenterFactory
Modifies every document to include the exact same value, as if it were a stored field in every document:
q=*:*&fl=id,greeting:[value v=‘hello‘]
The above query would produce results like the following:
<result name="response" numFound="32" start="0">
<doc>
<str name="id">1</str>
<str name="greeting">hello</str></doc>
</doc>
...
By default, values are returned as a String, but a “ t ” parameter can be specified using a value of int, float,
double, or date to force a specific return type:
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q=*:*&fl=id,my_number:[value v=42 t=int],my_string:[value v=42]
In addition to using these request parameters, you can configure additional named instances of
ValueAugmenterFactory, or override the default behavior of the existing [value] transformer in your
solrconfig.xml file:
<transformer name="mytrans2" class="org.apache.solr.response.transform.ValueAugmenterFactory" >
<int name="value">5</int>
</transformer>
<transformer name="value" class="org.apache.solr.response.transform.ValueAugmenterFactory" >
<double name="defaultValue">5</double>
</transformer>
The “ value ” option forces an explicit value to always be used, while the “ defaultValue ” option provides a
default that can still be overridden using the “ v ” and “ t ” local parameters.
[explain] - ExplainAugmenterFactory
Augments each document with an inline explanation of its score exactly like the information available about
each document in the debug section:
q=features:cache&wt=json&fl=id,[explain style=nl]
Supported values for “ style ” are “ text ”, and “ html ”, and "nl" which returns the information as structured
data:
{ "response":{"numFound":2,"start":0,"docs":[
{
"id":"6H500F0",
"[explain]":{
"match":true,
"value":1.052226,
"description":"weight(features:cache in 2) [DefaultSimilarity], result of:",
"details":[{
...
A default style can be configured by specifying an "args" parameter in your configuration:
<transformer name="explain" class="org.apache.solr.response.transform.ExplainAugmenterFactory" >
<str name="args">nl</str>
</transformer>
[child] - ChildDocTransformerFactory
This transformer returns all descendant documents of each parent document matching your query in a flat
list nested inside the matching parent document. This is useful when you have indexed nested child
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documents and want to retrieve the child documents for the relevant parent documents for any type of
search query.
fl=id,[child parentFilter=doc_type:book childFilter=doc_type:chapter limit=100]
Note that this transformer can be used even though the query itself is not a Block Join query.
When using this transformer, the parentFilter parameter must be specified, and works the same as in all
Block Join Queries, additional optional parameters are:
? childFilter - query to filter which child documents should be included, this can be particularly useful
when you have multiple levels of hierarchical documents (default: all children)
? limit - the maximum number of child documents to be returned per parent document (default: 10)
[shard] - ShardAugmenterFactory
This transformer adds information about what shard each individual document came from in a distributed
request.
ShardAugmenterFactory does not support any request parameters, or configuration options.
[docid] - DocIdAugmenterFactory
This transformer adds the internal Lucene document id to each document – this is primarily only useful for
debugging purposes.
DocIdAugmenterFactory does not support any request parameters, or configuration options.
[elevated] and [excluded]
These transformers are available only when using the Query Elevation Component.
? [elevated] annotates each document to indicate if it was elevated or not.
? [excluded] annotates each document to indicate if it would have been excluded - this is only supported
if you also use the markExcludes parameter.
fl=id,[elevated],[excluded]&excludeIds=GB18030TEST&elevateIds=6H500F0&markExcludes=true
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{ "response":{"numFound":32,"start":0,"docs":[
{
"id":"6H500F0",
"[elevated]":true,
"[excluded]":false},
{
"id":"GB18030TEST",
"[elevated]":false,
"[excluded]":true},
{
"id":"SP2514N",
"[elevated]":false,
"[excluded]":false},
...
[json] / [xml]
These transformers replace field value containing a string representation of a valid XML or JSON structure
with the actual raw XML or JSON structure rather than just the string value. Each applies only to the specific
writer, such that [json] only applies to wt=json and [xml] only applies to wt=xml .
fl=id,source_s:[json]&wt=json
[subquery]
This transformer executes a separate query per transforming document passing document fields as an
input for subquery parameters. It’s usually used with {!join} and {!parent} query parsers, and is intended
to be an improvement for [child] .
? It must be given an unique name: fl=*,children:[subquery]
? There might be a few of them, eg fl=*,sons:[subquery],daughters:[subquery] .
? Every [subquery] occurrence adds a field into a result document with the given name, the value of this
field is a document list, which is a result of executing subquery using document fields as an input.
Here is how it looks like in various formats:
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<result name="response" numFound="2" start="0">
<doc>
<int name="id">1</int>
<arr name="title">
<str>vdczoypirs</str>
</arr>
<result name="children" numFound="1" start="0">
<doc>
<int name="id">2</int>
<arr name="title">
<str>vdczoypirs</str>
</arr>
</doc>
</result>
</doc>
...
{ "response":{
"numFound":2, "start":0,
"docs":[
{
"id":1,
"subject":["parentDocument"],
"title":["xrxvomgu"],
"children":{
"numFound":1, "start":0,
"docs":[
{ "id":2,
"cat":["childDocument"]
}
]
}},
{
"id":4,
...
SolrDocumentList subResults = (SolrDocumentList)doc.getFieldValue("children");
Subquery result fields
To appear in subquery document list, a field should be specified both fl parameters, in main one fl (despite
the main result documents have no this field) and in subquery’s one eg foo.fl . Of course, you can use
wildcard in any or both of these parameters. For example, if field title should appear in categories subquery,
it can be done via one of these ways.
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fl=...title,categories:[subquery]&categories.fl=title&categories.q=...
fl=...title,categories:[subquery]&categories.fl=*&categories.q=...
fl=...*,categories:[subquery]&categories.fl=*&categories.q=...
fl=...*,categories:[subquery]&categories.fl=*&categories.q=...
Subquery Parameters Shift
If subquery is declared as fl=*,foo:[subquery] , subquery parameters are prefixed with the given name
and period. eg
q=:&fl=*,foo:[subquery]&foo.q=to be continued&foo.rows=10&foo.sort=id desc
Document Field as an Input for Subquery Parameters
It’s necessary to pass some document field values as a parameter for subquery. It’s supported via implicit
row.fieldname parameter, and can be (but might not only) referred via Local Parameters syntax:
q=namne:john&fl=name,id,depts:[subquery]&depts.q={!terms f=id v=$row.dept_id}&depts.rows=10
Here departmens are retrieved per every employee in search result. We can say that it’s like SQL join ON
emp.dept_id=dept.id .
Note, when document field has multiple values they are concatenated with comma by default, it can be
changed by local parameter foo:[subquery separator=‘ ‘] , this mimics {!terms} to work smoothly with
it.
To log substituted subquery request parameters, add the corresponding parameter names, as in
depts.logParamsList=q,fl,rows,row.dept_id
Cores and Collections in SolrCloud
Use foo:[subquery fromIndex=departments] to invoke subquery on another core on the same node, it’s
what {!join} does for non-SolrCloud mode. But in case of SolrCloud just (and only) explicitly specify its‘
native parameters like collection, shards for subquery, eg:
q=:&fl=*,foo:[subquery]&foo.q=cloud&foo.collection=departments
?
If subquery collection has a different unique key field name (let’s say foo_id at contrast to
id in primary collection), add the following parameters to accommodate this difference:
foo.fl=id:foo_id&foo.distrib.singlePass=true . Otherwise you’ll get
NullPoniterException from QueryComponent.mergeIds .
[geo] - Geospatial formatter
Formats spatial data from a spatial field using a designated format type name. Two inner parameters are
required: f for the field name, and w for the format name. Example: geojson:[geo f=mySpatialField
w=GeoJSON] .
Normally you’ll simply be consistent in choosing the format type you want by setting the format attribute on
the spatial field type to WKT or GeoJSON – see the section Spatial Search for more information. If you are
consistent, it’ll come out the way you stored it. This transformer offers a convenience to transform the
spatial format to something different on retrieval.
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In addition, this feature is very useful with the RptWithGeometrySpatialField to avoid double-storage of
the potentially large vector geometry. This transformer will detect that field type and fetch the geometry
from an internal compact binary representation on disk (in docValues), and then format it as desired. As
such, you needn’t mark the field as stored, which would be redundant. In a sense this double-storage
between docValues and stored-value storage isn’t unique to spatial but with polygonal geometry it can be a
lot of data, and furthermore you’d like to avoid storing it in a verbose format (like GeoJSON or WKT).
[features] - LTRFeatureLoggerTransformerFactory
The "LTR" prefix stands for Learning To Rank. This transformer returns the values of features and it can be
used for feature extraction and feature logging.
fl=id,[features store=yourFeatureStore]
This will return the values of the features in the yourFeatureStore store.
fl=id,[features]&rq={!ltr model=yourModel}
If you use [features] together with an Learning-To-Rank reranking query then the values of the features in
the reranking model ( yourModel ) will be returned.
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Suggester
The SuggestComponent in Solr provides users with automatic suggestions for query terms.
You can use this to implement a powerful auto-suggest feature in your search application.
Although it is possible to use the Spell Checking functionality to power autosuggest behavior, Solr has a
dedicated SuggestComponent designed for this functionality.
This approach utilizes Lucene’s Suggester implementation and supports all of the lookup implementations
available in Lucene.
The main features of this Suggester are:
? Lookup implementation pluggability
? Term dictionary pluggability, giving you the flexibility to choose the dictionary implementation
? Distributed support
The solrconfig.xml found in Solr’s “ techproducts ” example has the new Suggester implementation
configured already. For more on search components, see the section RequestHandlers and
SearchComponents in SolrConfig.
Configuring Suggester in solrconfig.xml
The “ techproducts ” example solrconfig.xml has a suggest search component and a /suggest request
handler already configured. You can use that as the basis for your configuration, or create it from scratch, as
detailed below.
Adding the Suggest Search Component
The first step is to add a search component to solrconfig.xml and tell it to use the SuggestComponent.
Here is some sample code that could be used.
<searchComponent name="suggest" class="solr.SuggestComponent">
<lst name="suggester">
<str name="name">mySuggester</str>
<str name="lookupImpl">FuzzyLookupFactory</str>
<str name="dictionaryImpl">DocumentDictionaryFactory</str>
<str name="field">cat</str>
<str name="weightField">price</str>
<str name="suggestAnalyzerFieldType">string</str>
<str name="buildOnStartup">false</str>
</lst>
</searchComponent>
Suggester Search Component Parameters
The Suggester search component takes several configuration parameters. The choice of the lookup
implementation ( lookupImpl , how terms are found in the suggestion dictionary) and the dictionary
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implementation ( dictionaryImpl , how terms are stored in the suggestion dictionary) will dictate some of
the parameters required. Below are the main parameters that can be used no matter what lookup or
dictionary implementation is used. In the following sections additional parameters are provided for each
implementation.
Parameter Description
searchComponent name Arbitrary name for the search component.
name A symbolic name for this suggester. You can refer to this name in the URL
parameters and in the SearchHandler configuration. It is possible to have
mutiples of these
lookupImpl Lookup implementation. There are several possible implementations,
described below in the section Lookup Implementations. If not set, the
default lookup is JaspellLookupFactory.
dictionaryImpl The dictionary implementation to use. There are several possible
implementations, described below in the section Dictionary
Implementations . If not set, the default dictionary implementation is
HighFrequencyDictionaryFactory unless a sourceLocation is used, in
which case, the dictionary implementation will be FileDictionaryFactory
field A field from the index to use as the basis of suggestion terms. If
sourceLocation is empty (meaning any dictionary implementation other
than FileDictionaryFactory) then terms from this field in the index will be
used.
To be used as the basis for a suggestion, the field must be stored. You
may want to use copyField rules to create a special ‘suggest‘ field
comprised of terms from other fields in documents. In any event, you
likely want a minimal amount of analysis on the field, so an additional
option is to create a field type in your schema that only uses basic
tokenizers or filters. One option for such a field type is shown here:
<fieldType class="solr.TextField" name="textSuggest"
positionIncrementGap="100">
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.StandardFilterFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
</fieldType>
However, this minimal analysis is not required if you want more analysis to
occur on terms. If using the AnalyzingLookupFactory as your lookupImpl,
however, you have the option of defining the field type rules to use for
index and query time analysis.
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Parameter Description
sourceLocation The path to the dictionary file if using the FileDictionaryFactory. If this
value is empty then the main index will be used as a source of terms and
weights.
storeDir The location to store the dictionary file.
buildOnCommit or
buildOnOptimize
If true then the lookup data structure will be rebuilt after soft-commit. If
false, the default, then the lookup data will be built only when requested
by URL parameter suggest.build=true . Use buildOnCommit to rebuild the
dictionary with every soft-commit, or buildOnOptimize to build the
dictionary only when the index is optimized. Some lookup
implementations may take a long time to build, specially with large
indexes, in such cases, using buildOnCommit or buildOnOptimize,
particularly with a high frequency of softCommits is not recommended,
and it’s recommended instead to build the suggester at a lower frequency
by manually issuing requests with suggest.build=true .
buildOnStartup If true then the lookup data structure will be built when Solr starts or when
the core is reloaded. If this parameter is not specified, the suggester will
check if the lookup data structure is present on disk and build it if not
found. Enabling this to true could lead to the core talking longer to load
(or reload) as the suggester data structure needs to be built, which can
sometimes take a long time. It’s usually preferred to have this setting set
to ‘false‘ and build suggesters manually issuing requests with
suggest.build=true .
Lookup Implementations
The lookupImpl parameter defines the algorithms used to look up terms in the suggest index. There are
several possible implementations to choose from, and some require additional parameters to be configured.
AnalyzingLookupFactory
A lookup that first analyzes the incoming text and adds the analyzed form to a weighted FST, and then does
the same thing at lookup time.
This implementation uses the following additional properties:
? suggestAnalyzerFieldType: The field type to use for the query-time and build-time term suggestion
analysis.
? exactMatchFirst: If true, the default, exact suggestions are returned first, even if they are prefixes or
other strings in the FST have larger weights.
? preserveSep: If true, the default, then a separator between tokens is preserved. This means that
suggestions are sensitive to tokenization (e.g., baseball is different from base ball).
? preservePositionIncrements: If true, the suggester will preserve position increments. This means that
token filters which leave gaps (for example, when StopFilter matches a stopword) the position would be
respected when building the suggester. The default is false.
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FuzzyLookupFactory
This is a suggester which is an extension of the AnalyzingSuggester but is fuzzy in nature. The similarity is
measured by the Levenshtein algorithm.
This implementation uses the following additional properties:
? exactMatchFirst: If true, the default, exact suggestions are returned first, even if they are prefixes or
other strings in the FST have larger weights.
? preserveSep: If true, the default, then a separator between tokens is preserved. This means that
suggestions are sensitive to tokenization (e.g., baseball is different from base ball).
? maxSurfaceFormsPerAnalyzedForm: Maximum number of surface forms to keep for a single analyzed
form. When there are too many surface forms we discard the lowest weighted ones.
? maxGraphExpansions: When building the FST ("index-time"), we add each path through the tokenstream
graph as an individual entry. This places an upper-bound on how many expansions will be added for a
single suggestion. The default is -1 which means there is no limit.
? preservePositionIncrements: If true, the suggester will preserve position increments. This means that
token filters which leave gaps (for example, when StopFilter matches a stopword) the position would be
respected when building the suggester. The default is false.
? maxEdits: The maximum number of string edits allowed. The systems hard limit is 2. The default is 1.
? transpositions: If true, the default, transpositions should be treated as a primitive edit operation.
? nonFuzzyPrefix: The length of the common non fuzzy prefix match which must match a suggestion. The
default is 1.
? minFuzzyLength: The minimum length of query before which any string edits will be allowed. The default
is 3.
? unicodeAware: If true, maxEdits, minFuzzyLength, transpositions and nonFuzzyPrefix parameters will be
measured in unicode code points (actual letters) instead of bytes. The default is false.
AnalyzingInfixLookupFactory
Analyzes the input text and then suggests matches based on prefix matches to any tokens in the indexed
text. This uses a Lucene index for its dictionary.
This implementation uses the following additional properties.
? indexPath: When using AnalyzingInfixSuggester you can provide your own path where the index will get
built. The default is analyzingInfixSuggesterIndexDir and will be created in your collections data
directory.
? minPrefixChars: Minimum number of leading characters before PrefixQuery is used (default is 4).
Prefixes shorter than this are indexed as character ngrams (increasing index size but making lookups
faster).
? allTermsRequired: Boolean option for multiple terms. Default is true - all terms required.
? highlight: Highlight suggest terms. Default is true.
This implementation supports Context Filtering.
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BlendedInfixLookupFactory
An extension of the AnalyzingInfixSuggester which provides additional functionality to weight prefix
matches across the matched documents. You can tell it to score higher if a hit is closer to the start of the
suggestion or vice versa.
This implementation uses the following additional properties:
? blenderType: used to calculate weight coefficient using the position of the first matching word. Can be
one of:
? position_linear: weightFieldValue*(1 - 0.10*position): Matches to the start will be given a higher score
(Default)
? position_reciprocal: weightFieldValue/(1+position): Matches to the end will be given a higher score.
? exponent: an optional configuration variable for the position_reciprocal blenderType used to
control how fast the score will increase or decrease. Default 2.0.
? numFactor: The factor to multiply the number of searched elements from which results will be pruned.
Default is 10.
? indexPath: When using BlendedInfixSuggester you can provide your own path where the index will get
built. The default directory name is blendedInfixSuggesterIndexDir and will be created in your collections
data directory.
? minPrefixChars: Minimum number of leading characters before PrefixQuery is used (default 4). Prefixes
shorter than this are indexed as character ngrams (increasing index size but making lookups faster).
This implementation supports Context Filtering .
FreeTextLookupFactory
It looks at the last tokens plus the prefix of whatever final token the user is typing, if present, to predict the
most likely next token. The number of previous tokens that need to be considered can also be specified. This
suggester would only be used as a fallback, when the primary suggester fails to find any suggestions.
This implementation uses the following additional properties:
? suggestFreeTextAnalyzerFieldType: The analyzer used at "query-time" and "build-time" to analyze
suggestions. This field is required.
? ngrams: The max number of tokens out of which singles will be make the dictionary. The default value is
2. Increasing this would mean you want more than the previous 2 tokens to be taken into consideration
when making the suggestions.
FSTLookupFactory
An automaton-based lookup. This implementation is slower to build, but provides the lowest memory cost.
We recommend using this implementation unless you need more sophisticated matching results, in which
case you should use the Jaspell implementation.
This implementation uses the following additional properties:
? exactMatchFirst: If true, the default, exact suggestions are returned first, even if they are prefixes or
other strings in the FST have larger weights.
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? weightBuckets: The number of separate buckets for weights which the suggester will use while building
its dictionary.
TSTLookupFactory
A simple compact ternary trie based lookup.
WFSTLookupFactory
A weighted automaton representation which is an alternative to FSTLookup for more fine-grained ranking.
WFSTLookup does not use buckets, but instead a shortest path algorithm. Note that it expects weights to be
whole numbers. If weight is missing it’s assumed to be 1.0. Weights affect the sorting of matching
suggestions when spellcheck.onlyMorePopular=true is selected: weights are treated as "popularity"
score, with higher weights preferred over suggestions with lower weights.
JaspellLookupFactory
A more complex lookup based on a ternary trie from the JaSpell project. Use this implementation if you need
more sophisticated matching results.
Dictionary Implementations
The dictionary implementations define how terms are stored. There are several options, and multiple
dictionaries can be used in a single request if necessary.
DocumentDictionaryFactory
A dictionary with terms, weights, and an optional payload taken from the index.
This dictionary implementation takes the following parameters in addition to parameters described for the
Suggester generally and for the lookup implementation:
? weightField: A field that is stored or a numeric DocValue field. This field is optional.
? payloadField: The payloadField should be a field that is stored. This field is optional.
? contextField: Field to be used for context filtering. Note that only some lookup implementations support
filtering.
DocumentExpressionDictionaryFactory
This dictionary implementation is the same as the DocumentDictionaryFactory but allows users to specify an
arbitrary expression into the ‘weightExpression‘ tag.
This dictionary implementation takes the following parameters in addition to parameters described for the
Suggester generally and for the lookup implementation:
? payloadField: The payloadField should be a field that is stored. This field is optional.
? weightExpression: An arbitrary expression used for scoring the suggestions. The fields used must be
numeric fields. This field is required.
? contextField: Field to be used for context filtering. Note that only some lookup implementations support
filtering.
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HighFrequencyDictionaryFactory
This dictionary implementation allows adding a threshold to prune out less frequent terms in cases where
very common terms may overwhelm other terms.
This dictionary implementation takes one parameter in addition to parameters described for the Suggester
generally and for the lookup implementation:
? threshold: A value between zero and one representing the minimum fraction of the total documents
where a term should appear in order to be added to the lookup dictionary.
FileDictionaryFactory
This dictionary implementation allows using an external file that contains suggest entries. Weights and
payloads can also be used.
If using a dictionary file, it should be a plain text file in UTF-8 encoding. You can use both single terms and
phrases in the dictionary file. If adding weights or payloads, those should be separated from terms using the
delimiter defined with the fieldDelimiter property (the default is ‘\t‘, the tab representation). If using
payloads, the first line in the file must specify a payload.
This dictionary implementation takes one parameter in addition to parameters described for the Suggester
generally and for the lookup implementation:
fieldDelimiter
Specify the delimiter to be used separating the entries, weights and payloads. The default is tab (‘\t‘).
Example File
acquire
accidentally 2.0
accommodate 3.0
Multiple Dictionaries
It is possible to include multiple dictionaryImpl definitions in a single SuggestComponent definition.
To do this, simply define separate suggesters, as in this example:
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<searchComponent name="suggest" class="solr.SuggestComponent">
<lst name="suggester">
<str name="name">mySuggester</str>
<str name="lookupImpl">FuzzyLookupFactory</str>
<str name="dictionaryImpl">DocumentDictionaryFactory</str>
<str name="field">cat</str>
<str name="weightField">price</str>
<str name="suggestAnalyzerFieldType">string</str>
</lst>
<lst name="suggester">
<str name="name">altSuggester</str>
<str name="dictionaryImpl">DocumentExpressionDictionaryFactory</str>
<str name="lookupImpl">FuzzyLookupFactory</str>
<str name="field">product_name</str>
<str name="weightExpression">((price * 2) + ln(popularity))</str>
<str name="sortField">weight</str>
<str name="sortField">price</str>
<str name="storeDir">suggest_fuzzy_doc_expr_dict</str>
<str name="suggestAnalyzerFieldType">text_en</str>
</lst>
</searchComponent>
When using these Suggesters in a query, you would define multiple ‘suggest.dictionary‘ parameters in the
request, referring to the names given for each Suggester in the search component definition. The response
will include the terms in sections for each Suggester. See the Examples section below for an example
request and response.
Adding the Suggest Request Handler
After adding the search component, a request handler must be added to solrconfig.xml . This request
handler works the same as any other request handler, and allows you to configure default parameters for
serving suggestion requests. The request handler definition must incorporate the "suggest" search
component defined previously.
<requestHandler name="/suggest" class="solr.SearchHandler" startup="lazy">
<lst name="defaults">
<str name="suggest">true</str>
<str name="suggest.count">10</str>
</lst>
<arr name="components">
<str>suggest</str>
</arr>
</requestHandler>
Suggest Request Handler Parameters
The following parameters allow you to set defaults for the Suggest request handler:
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Parameter Description
suggest=true This parameter should always be true, because we always want to run the
Suggester for queries submitted to this handler.
suggest.dictionary The name of the dictionary component configured in the search
component. This is a mandatory parameter. It can be set in the request
handler, or sent as a parameter at query time.
suggest.q The query to use for suggestion lookups.
suggest.count Specifies the number of suggestions for Solr to return.
suggest.cfq A Context Filter Query used to filter suggestions based on the context field,
if supported by the suggester.
suggest.build If true, it will build the suggester index. This is likely useful only for initial
requests; you would probably not want to build the dictionary on every
request, particularly in a production system. If you would like to keep your
dictionary up to date, you should use the buildOnCommit or
buildOnOptimize parameter for the search component.
suggest.reload If true, it will reload the suggester index.
suggest.buildAll If true, it will build all suggester indexes.
suggest.reloadAll If true, it will reload all suggester indexes.
These properties can also be overridden at query time, or not set in the request handler at all and always
sent at query time.
?
Context Filtering
Context filtering ( suggest.cfq ) is currently only supported by AnalyzingInfixLookupFactory
and BlendedInfixLookupFactory, and only when backed by a Document*Dictionary. All
other implementations will return unfiltered matches as if filtering was not requested.
Example Usages
Get Suggestions with Weights
This is the basic suggestion using a single dictionary and a single Solr core.
Example query:
http://localhost:8983/solr/techproducts/suggest?suggest=true&suggest.build=true&suggest.dictionar
y=mySuggester&wt=json&suggest.q=elec
In this example, we’ve simply requested the string ‘elec‘ with the suggest.q parameter and requested that
the suggestion dictionary be built with suggest.build (note, however, that you would likely not want to build
the index on every query - instead you should use buildOnCommit or buildOnOptimize if you have regularly
changing documents).
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Example response:
{
"responseHeader": {
"status": 0,
"QTime": 35
},
"command": "build",
"suggest": {
"mySuggester": {
"elec": {
"numFound": 3,
"suggestions": [
{
"term": "electronics and computer1",
"weight": 2199,
"payload": ""
},
{
"term": "electronics",
"weight": 649,
"payload": ""
},
{
"term": "electronics and stuff2",
"weight": 279,
"payload": ""
}
]
}
}
}
}
Multiple Dictionaries
If you have defined multiple dictionaries, you can use them in queries.
Example query:
http://localhost:8983/solr/techproducts/suggest?suggest=true& \
suggest.dictionary=mySuggester&suggest.dictionary=altSuggester&wt=json&suggest.q=elec
In this example we have sent the string ‘elec‘ as the suggest.q parameter and named two suggest.dictionary
definitions to be used.
Example response:
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{
"responseHeader": {
"status": 0,
"QTime": 3
},
"suggest": {
"mySuggester": {
"elec": {
"numFound": 1,
"suggestions": [
{
"term": "electronics and computer1",
"weight": 100,
"payload": ""
}
]
}
},
"altSuggester": {
"elec": {
"numFound": 1,
"suggestions": [
{
"term": "electronics and computer1",
"weight": 10,
"payload": ""
}
]
}
}
}
}
Context Filtering
Context filtering lets you filter suggestions by a separate context field, such as category, department or any
other token. The AnalyzingInfixLookupFactory and BlendedInfixLookupFactory currently support this
feature, when backed by DocumentDictionaryFactory.
Add contextField to your suggester configuration. This example will suggest names and allow to filter by
category:
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solrconfig.xml
<searchComponent name="suggest" class="solr.SuggestComponent">
<lst name="suggester">
<str name="name">mySuggester</str>
<str name="lookupImpl">AnalyzingInfixLookupFactory</str>
<str name="dictionaryImpl">DocumentDictionaryFactory</str>
<str name="field">name</str>
<str name="weightField">price</str>
<str name="contextField">cat</str>
<str name="suggestAnalyzerFieldType">string</str>
<str name="buildOnStartup">false</str>
</lst>
</searchComponent>
Example context filtering suggest query:
http://localhost:8983/solr/techproducts/suggest?suggest=true&suggest.build=true& \
suggest.dictionary=mySuggester&wt=json&suggest.q=c&suggest.cfq=memory
The suggester will only bring back suggestions for products tagged with cat=memory.
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MoreLikeThis
The MoreLikeThis search component enables users to query for documents similar to a document in their
result list.
It does this by using terms from the original document to find similar documents in the index.
There are three ways to use MoreLikeThis. The first, and most common, is to use it as a request handler. In
this case, you would send text to the MoreLikeThis request handler as needed (as in when a user clicked on
a "similar documents" link).
The second is to use it as a search component. This is less desirable since it performs the MoreLikeThis
analysis on every document returned. This may slow search results.
The final approach is to use it as a request handler but with externally supplied text. This case, also referred
to as the MoreLikeThisHandler, will supply information about similar documents in the index based on the
text of the input document.
How MoreLikeThis Works
MoreLikeThis constructs a Lucene query based on terms in a document. It does this by pulling terms from
the defined list of fields ( see the mlt.fl parameter, below). For best results, the fields should have stored
term vectors in schema.xml . For example:
<field name="cat" ... termVectors="true" />
If term vectors are not stored, MoreLikeThis will generate terms from stored fields. A uniqueKey must also
be stored in order for MoreLikeThis to work properly.
The next phase filters terms from the original document using thresholds defined with the MoreLikeThis
parameters. Finally, a query is run with these terms, and any other query parameters that have been defined
(see the mlt.qf parameter, below) and a new document set is returned.
Common Parameters for MoreLikeThis
The table below summarizes the MoreLikeThis parameters supported by Lucene/Solr. These parameters
can be used with any of the three possible MoreLikeThis approaches.
Parameter Description
mlt.fl Specifies the fields to use for similarity. If possible, these should have
stored termVectors .
mlt.mintf Specifies the Minimum Term Frequency, the frequency below which terms
will be ignored in the source document.
mlt.mindf Specifies the Minimum Document Frequency, the frequency at which
words will be ignored which do not occur in at least this many documents.
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Parameter Description
mlt.maxdf Specifies the Maximum Document Frequency, the frequency at which
words will be ignored which occur in more than this many documents.
mlt.minwl Sets the minimum word length below which words will be ignored.
mlt.maxwl Sets the maximum word length above which words will be ignored.
mlt.maxqt Sets the maximum number of query terms that will be included in any
generated query.
mlt.maxntp Sets the maximum number of tokens to parse in each example document
field that is not stored with TermVector support.
mlt.boost Specifies if the query will be boosted by the interesting term relevance. It
can be either "true" or "false".
mlt.qf Query fields and their boosts using the same format as that used by the
DisMax Query Parser. These fields must also be specified in mlt.fl .
Parameters for the MoreLikeThisComponent
Using MoreLikeThis as a search component returns similar documents for each document in the response
set. In addition to the common parameters, these additional options are available:
Parameter Description
mlt If set to true, activates the MoreLikeThis component and enables Solr to
return MoreLikeThis results.
mlt.count Specifies the number of similar documents to be returned for each result.
The default value is 5.
Parameters for the MoreLikeThisHandler
The table below summarizes parameters accessible through the MoreLikeThisHandler . It supports faceting,
paging, and filtering using common query parameters, but does not work well with alternate query parsers.
Parameter Description
mlt.match.include Specifies whether or not the response should include the matched
document. If set to false, the response will look like a normal select
response.
mlt.match.offset Specifies an offset into the main query search results to locate the
document on which the MoreLikeThis query should operate. By default,
the query operates on the first result for the q parameter.
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Parameter Description
mlt.interestingTerms Controls how the MoreLikeThis component presents the "interesting"
terms (the top TF/IDF terms) for the query. Supports three settings. The
setting list lists the terms. The setting none lists no terms. The setting
details lists the terms along with the boost value used for each term.
Unless mlt.boost=true , all terms will have boost=1.0 .
More Like This Query Parser
The mlt query parser provides a mechanism to retrieve documents similar to a given document, like the
handler. More information on the usage of the mlt query parser can be found in the section Other Parsers.
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Pagination of Results
In most search applications, the "top" matching results (sorted by score, or some other criteria) are
displayed to some human user.
In many applications the UI for these sorted results are displayed to the user in "pages" containing a fixed
number of matching results, and users don’t typically look at results past the first few pages worth of
results.
Basic Pagination
In Solr, this basic paginated searching is supported using the start and rows parameters, and performance
of this common behaviour can be tuned by utilizing the queryResultCache and adjusting the
queryResultWindowSize configuration options based on your expected page sizes.
Basic Pagination Examples
The easiest way to think about simple pagination, is to simply multiply the page number you want (treating
the "first" page number as "0") by the number of rows per page; such as in the following pseudo-code:
function fetch_solr_page($page_number, $rows_per_page) {
$start = $page_number * $rows_per_page
$params = [ q = $some_query, rows = $rows_per_page, start = $start ]
return fetch_solr($params)
}
How Basic Pagination is Affected by Index Updates
The start param specified in a request to Solr indicates an absolute "offset" in the complete sorted list of
matches that the client wants Solr to use as the beginning of the current "page".
If an index modification (such as adding or removing documents) which affects the sequence of ordered
documents matching a query occurs in between two requests from a client for subsequent pages of results,
then it is possible that these modifications can result in the same document being returned on multiple
pages, or documents being "skipped" as the result set shrinks or grows.
For example, consider an index containing 26 documents like so:
id name
1 A
2 B
…
26 Z
Followed by the following requests & index modifications interleaved:
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? A client requests q=:&rows=5&start=0&sort=name asc
? documents with the ids 1-5 will be returned to the client
? Document id 3 is deleted
? The client requests "page #2" using q=:&rows=5&start=5&sort=name asc
? Documents 7-11 will be returned
? Document 6 has been skipped, since it is now the 5th document in the sorted set of all matching
results – it would be returned on a new request for "page #1"
? 3 new documents are now added with the ids 90 , 91 , and 92 ; All three documents have a name of A
? The client requests "page #3" using q=:&rows=5&start=10&sort=name asc
? Documents 9-13 will be returned
? Documents 9 , 10 , and 11 have now been returned on both page #2 and page #3 since they moved
farther back in the list of sorted results
In typical situations these impacts from index changes on paginated searching don’t significantly affect user
experience?—?either because they happen extremely infrequently in fairly static collections, or because the
users recognize that the collection of data is constantly evolving and expect to see documents shift up and
down in the result sets.
Performance Problems with "Deep Paging"
In some situations, the results of a Solr search are not destined for a simple paginated user interface.
When you wish to fetch a very large number of sorted results from Solr to feed into an external system,
using very large values for the start or rows parameters can be very inefficient. Pagination using start and
rows not only require Solr to compute (and sort) in memory all of the matching documents that should be
fetched for the current page, but also all of the documents that would have appeared on previous pages.
While a request for start=0&rows=1000000 may be obviously inefficient because it requires Solr to maintain
& sort in memory a set of 1 million documents, likewise a request for start=999000&rows=1000 is equally
inefficient for the same reasons. Solr can’t compute which matching document is the 999001st result in
sorted order, without first determining what the first 999000 matching sorted results are.
If the index is distributed, which is common when running in SolrCloud mode, then 1 million documents are
retrieved from each shard. For a ten shard index, ten million entries must be retrieved and sorted to figure
out the 1000 documents that match those query parameters.
Fetching A Large Number of Sorted Results: Cursors
As an alternative to increasing the "start" parameter to request subsequent pages of sorted results, Solr
supports using a "Cursor" to scan through results.
Cursors in Solr are a logical concept that doesn’t involve caching any state information on the server.
Instead the sort values of the last document returned to the client are used to compute a "mark"
representing a logical point in the ordered space of sort values. That "mark" can be specified in the
parameters of subsequent requests to tell Solr where to continue.
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Using Cursors
To use a cursor with Solr, specify a cursorMark parameter with the value of \* . You can think of this being
analogous to start=0 as a way to tell Solr "start at the beginning of my sorted results" except that it also
informs Solr that you want to use a Cursor.
In addition to returning the top N sorted results (where you can control N using the rows parameter) the
Solr response will also include an encoded String named nextCursorMark . You then take the
nextCursorMark String value from the response, and pass it back to Solr as the cursorMark parameter for
your next request. You can repeat this process until you’ve fetched as many docs as you want, or until the
nextCursorMark returned matches the cursorMark you’ve already specified?—?indicating that there are no
more results.
Constraints when using Cursors
There are a few important constraints to be aware of when using cursorMark parameter in a Solr request
1. cursorMark and start are mutually exclusive parameters.
? Your requests must either not include a start parameter, or it must be specified with a value of “ 0 ”.
2. sort clauses must include the uniqueKey field (either asc or desc ).
? If id is your uniqueKey field, then sort params like id asc and name asc, id desc would both work
fine, but name asc by itself would not
3. Sorts including Date Math based functions that involve calculations relative to NOW will cause confusing
results, since every document will get a new sort value on every subsequent request. This can easily
result in cursors that never end, and constantly return the same documents over and over – even if the
documents are never updated.
In this situation, choose & re-use a fixed value for the NOW request param in all of your cursor requests.
Cursor mark values are computed based on the sort values of each document in the result, which means
multiple documents with identical sort values will produce identical Cursor mark values if one of them is the
last document on a page of results. In that situation, the subsequent request using that cursorMark would
not know which of the documents with the identical mark values should be skipped. Requiring that the
uniqueKey field be used as a clause in the sort criteria guarantees that a deterministic ordering will be
returned, and that every cursorMark value will identify a unique point in the sequence of documents.
Cursor Examples
Fetch All Docs
The pseudo-code shown here shows the basic logic involved in fetching all documents matching a query
using a cursor:
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// when fetching all docs, you might as well use a simple id sort
// unless you really need the docs to come back in a specific order
$params = [ q => $some_query, sort => ‘id asc‘, rows => $r, cursorMark => ‘*‘ ]
$done = false
while (not $done) {
$results = fetch_solr($params)
// do something with $results
if ($params[cursorMark] == $results[nextCursorMark]) {
$done = true
}
$params[cursorMark] = $results[nextCursorMark]
}
Using SolrJ, this pseudo-code would be:
SolrQuery q = (new SolrQuery(some_query)).setRows(r).setSort(SortClause.asc("id"));
String cursorMark = CursorMarkParams.CURSOR_MARK_START;
boolean done = false;
while (! done) {
q.set(CursorMarkParams.CURSOR_MARK_PARAM, cursorMark);
QueryResponse rsp = solrServer.query(q);
String nextCursorMark = rsp.getNextCursorMark();
doCustomProcessingOfResults(rsp);
if (cursorMark.equals(nextCursorMark)) {
done = true;
}
cursorMark = nextCursorMark;
}
If you wanted to do this by hand using curl, the sequence of requests would look something like this:
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$ curl ‘...&rows=10&sort=id+asc&cursorMark=*‘
{
"response":{"numFound":32,"start":0,"docs":[
// ... 10 docs here ...
]},
"nextCursorMark":"AoEjR0JQ"}
$ curl ‘...&rows=10&sort=id+asc&cursorMark=AoEjR0JQ‘
{
"response":{"numFound":32,"start":0,"docs":[
// ... 10 more docs here ...
]},
"nextCursorMark":"AoEpVkRCREIxQTE2"}
$ curl ‘...&rows=10&sort=id+asc&cursorMark=AoEpVkRCREIxQTE2‘
{
"response":{"numFound":32,"start":0,"docs":[
// ... 10 more docs here ...
]},
"nextCursorMark":"AoEmbWF4dG9y"}
$ curl ‘...&rows=10&sort=id+asc&cursorMark=AoEmbWF4dG9y‘
{
"response":{"numFound":32,"start":0,"docs":[
// ... 2 docs here because we‘ve reached the end.
]},
"nextCursorMark":"AoEpdmlld3Nvbmlj"}
$ curl ‘...&rows=10&sort=id+asc&cursorMark=AoEpdmlld3Nvbmlj‘
{
"response":{"numFound":32,"start":0,"docs":[
// no more docs here, and note that the nextCursorMark
// matches the cursorMark param we used
]},
"nextCursorMark":"AoEpdmlld3Nvbmlj"}
Fetch First N docs, Based on Post Processing
Since the cursor is stateless from Solr’s perspective, your client code can stop fetching additional results as
soon as you have decided you have enough information:
while (! done) {
q.set(CursorMarkParams.CURSOR_MARK_PARAM, cursorMark);
QueryResponse rsp = solrServer.query(q);
String nextCursorMark = rsp.getNextCursorMark();
boolean hadEnough = doCustomProcessingOfResults(rsp);
if (hadEnough || cursorMark.equals(nextCursorMark)) {
done = true;
}
cursorMark = nextCursorMark;
}
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How Cursors are Affected by Index Updates
Unlike basic pagination, Cursor pagination does not rely on using an absolute "offset" into the completed
sorted list of matching documents. Instead, the cursorMark specified in a request encapsulates information
about the relative position of the last document returned, based on the absolute sort values of that
document. This means that the impact of index modifications is much smaller when using a cursor
compared to basic pagination. Consider the same example index described when discussing basic
pagination:
id name
1 A
2 B
…
26 Z
? A client requests q=:&rows=5&start=0&sort=name asc, id asc&cursorMark=*
? Documents with the ids 1-5 will be returned to the client in order
? Document id 3 is deleted
? The client requests 5 more documents using the nextCursorMark from the previous response
? Documents 6-10 will be returned?—?the deletion of a document that’s already been returned doesn’t
affect the relative position of the cursor
? 3 new documents are now added with the ids 90 , 91 , and 92 ; All three documents have a name of A
? The client requests 5 more documents using the nextCursorMark from the previous response
? Documents 11-15 will be returned?—?the addition of new documents with sort values already past
does not affect the relative position of the cursor
? Document id 1 is updated to change its ‘name‘ to Q
? Document id 17 is updated to change its ‘name‘ to A
? The client requests 5 more documents using the nextCursorMark from the previous response
? The resulting documents are 16,1,18,19,20 in that order
? Because the sort value of document 1 changed so that it is after the cursor position, the document is
returned to the client twice
? Because the sort value of document 17 changed so that it is before the cursor position, the document
has been "skipped" and will not be returned to the client as the cursor continues to progress
In a nutshell: When fetching all results matching a query using cursorMark , the only way index modifications
can result in a document being skipped, or returned twice, is if the sort value of the document changes.
?
One way to ensure that a document will never be returned more then once, is to use the
uniqueKey field as the primary (and therefore: only significant) sort criterion.
In this situation, you will be guaranteed that each document is only returned once, no
matter how it may be be modified during the use of the cursor.
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"Tailing" a Cursor
Because Cursor requests are stateless, and the cursorMark values encapsulate the absolute sort values of
the last document returned from a search, it’s possible to "continue" fetching additional results from a
cursor that has already reached its end. If new documents are added (or existing documents are updated) to
the end of the results.
You can think of this as similar to using something like "tail -f" in Unix. The most common examples of how
this can be useful is when you have a "timestamp" field recording when a document has been
added/updated in your index. Client applications can continuously poll a cursor using a sort=timestamp
asc, id asc for documents matching a query, and always be notified when a document is added or
updated matching the request criteria.
Another common example is when you have uniqueKey values that always increase as new documents are
created, and you can continuously poll a cursor using sort=id asc to be notified about new documents.
The pseudo-code for tailing a cursor is only a slight modification from our early example for processing all
docs matching a query:
while (true) {
$doneForNow = false
while (not $doneForNow) {
$results = fetch_solr($params)
// do something with $results
if ($params[cursorMark] == $results[nextCursorMark]) {
$doneForNow = true
}
$params[cursorMark] = $results[nextCursorMark]
}
sleep($some_configured_delay)
}
?
For certain specialized cases, the /export handler may be an option.
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Collapse and Expand Results
The Collapsing query parser and the Expand component combine to form an approach to grouping
documents for field collapsing in search results.
The Collapsing query parser groups documents (collapsing the result set) according to your parameters,
while the Expand component provides access to documents in the collapsed group for use in results display
or other processing by a client application. Collapse & Expand can together do what the older Result
Grouping ( group=true ) does for most use-cases but not all. Generally, you should prefer Collapse & Expand.
?
In order to use these features with SolrCloud, the documents must be located on the same
shard. To ensure document co-location, you can define the router.name parameter as
compositeId when creating the collection. For more information on this option, see the
section Document Routing.
Collapsing Query Parser
The CollapsingQParser is really a post filter that provides more performant field collapsing than Solr’s
standard approach when the number of distinct groups in the result set is high. This parser collapses the
result set to a single document per group before it forwards the result set to the rest of the search
components. So all downstream components (faceting, highlighting, etc…) will work with the collapsed
result set.
The CollapsingQParser accepts the following local parameters:
Parameter Description Default
field The field that is being collapsed on. The field must be a single
valued String, Int or Float
none
min | max Selects the group head document for each group based on
which document has the min or max value of the specified
numeric field or function query.
At most only one of the min, max, or sort (see below)
parameters may be specified.
If none are specified, the group head document of each group
will be selected based on the highest scoring document in that
group.
none
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Parameter Description Default
sort Selects the group head document for each group based on
which document comes first according to the specified sort
string.
At most only one of the min, max, (see above) or sort
parameters may be specified.
If none are specified, the group head document of each group
will be selected based on the highest scoring document in that
group.
none
nullPolicy There are three null policies:
? ignore: removes documents with a null value in the collapse
field. This is the default.
? expand: treats each document with a null value in the
collapse field as a separate group.
? collapse: collapses all documents with a null value into a
single group using either highest score, or
minimum/maximum.
ignore
hint Currently there is only one hint available: top_fc , which stands
for top level FieldCache. The top_fc hint is only available when
collapsing on String fields. top_fc usually provides the best
query time speed but takes the longest to warm on startup or
following a commit. top_fc will also result in having the
collapsed field cached in memory twice if it’s used for faceting
or sorting. For very high cardinality (high distinct count) fields,
top_fc may not fare so well.
none
size Sets the initial size of the collapse data structures when
collapsing on a numeric field only. The data structures used
for collapsing grow dynamically when collapsing on numeric
fields. Setting the size above the number of results expected in
the result set will eliminate the resizing cost.
100,000
Sample Syntax:
Collapse on group_field selecting the document in each group with the highest scoring document:
fq={!collapse field=group_field}
Collapse on group_field selecting the document in each group with the minimum value of numeric_field :
fq={!collapse field=group_field min=numeric_field}
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Collapse on group_field selecting the document in each group with the maximum value of numeric_field :
fq={!collapse field=group_field max=numeric_field}
Collapse on group_field selecting the document in each group with the maximum value of a function. Note
that the cscore() function can be used with the min/max options to use the score of the current document
being collapsed.
fq={!collapse field=group_field max=sum(cscore(),numeric_field)}
Collapse on group_field with a null policy so that all docs that do not have a value in the group_field will
be treated as a single group. For each group, the selected document will be based first on a numeric_field ,
but ties will be broken by score:
fq={!collapse field=group_field nullPolicy=collapse sort=‘numeric_field asc, score desc‘}
Collapse on group_field with a hint to use the top level field cache:
fq={!collapse field=group_field hint=top_fc}
The CollapsingQParserPlugin fully supports the QueryElevationComponent.
Expand Component
The ExpandComponent can be used to expand the groups that were collapsed by the
CollapsingQParserPlugin.
Example usage with the CollapsingQParserPlugin:
q=foo&fq={!collapse field=ISBN}
In the query above, the CollapsingQParserPlugin will collapse the search results on the ISBN field. The main
search results will contain the highest ranking document from each book.
The ExpandComponent can now be used to expand the results so you can see the documents grouped by
ISBN. For example:
q=foo&fq={!collapse field=ISBN}&expand=true
The “expand=true” parameter turns on the ExpandComponent. The ExpandComponent adds a new section
to the search output labeled “expanded”.
Inside the expanded section there is a map with each group head pointing to the expanded documents that
are within the group. As applications iterate the main collapsed result set, they can access the expanded map
to retrieve the expanded groups.
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The ExpandComponent has the following parameters:
Parameter Description Default
expand.sort Orders the documents within the expanded groups score desc
expand.rows The number of rows to display in each group 5
expand.q Overrides the main q parameter, determines which documents
to include in the main group.
main q
expand.fq Overrides main fq’s, determines which documents to include in
the main group.
main fq’s
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Result Grouping
Result Grouping groups documents with a common field value into groups and returns the top documents
for each group.
For example, if you searched for "DVD" on an electronic retailer’s e-commerce site, you might be returned
three categories such as "TV and Video", "Movies", and "Computers" with three results per category. In this
case, the query term "DVD" appeared in all three categories, so Solr groups them together in order to
increase relevancy for the user.
?
Prefer Collapse & Expand instead
Solr’s Collapse and Expand feature is newer and mostly overlaps with Result Grouping.
There are features unique to both, and they have different performance characteristics.
That said, in most cases Collapse and Expand is preferable to Result Grouping.
Result Grouping is separate from Faceting. Though it is conceptually similar, faceting returns all relevant
results and allows the user to refine the results based on the facet category. For example, if you search for
"shoes" on a footwear retailer’s e-commerce site, Solr would return all results for that query term, along
with selectable facets such as "size," "color," "brand," and so on.
You can however combine grouping with faceting. Grouped faceting supports facet.field and
facet.range but currently doesn’t support date and pivot faceting. The facet counts are computed based on
the first group.field parameter, and other group.field parameters are ignored.
Grouped faceting differs from non grouped facets (sum of all facets) == (total of products with
that property) as shown in the following example:
Object 1
? name: Phaser 4620a
? ppm: 62
? product_range: 6
Object 2
? name: Phaser 4620i
? ppm: 65
? product_range: 6
Object 3
? name: ML6512
? ppm: 62
? product_range: 7
If you ask Solr to group these documents by "product_range", then the total amount of groups is 2, but the
facets for ppm are 2 for 62 and 1 for 65.
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Request Parameters
Result Grouping takes the following request parameters. Any number of these request parameters can be
included in a single request:
Parameter Type Description
group Boolean If true, query results will be grouped.
group.field string The name of the field by which to group results. The field must
be single-valued, and either be indexed or a field type that has a
value source and works in a function query, such as
ExternalFileField . It must also be a string-based field, such
as StrField or TextField
group.func query Group based on the unique values of a function query.
?
This option does not work with distributed
searches.
group.query query Return a single group of documents that match the given
query.
rows integer The number of groups to return. The default value is 10.
start integer Specifies an initial offset for the list of groups.
group.limit integer Specifies the number of results to return for each group. The
default value is 1.
group.offset integer Specifies an initial offset for the document list of each group.
sort sortspec Specifies how Solr sorts the groups relative to each other. For
example, sort=popularity desc will cause the groups to be
sorted according to the highest popularity document in each
group. The default value is score desc .
group.sort sortspec Specifies how Solr sorts documents within each group. The
default behavior if group.sort is not specified is to use the
same effective value as the sort parameter.
group.format grouped/simple If this parameter is set to simple , the grouped documents are
presented in a single flat list, and the start and rows
parameters affect the numbers of documents instead of
groups.
group.main Boolean If true, the result of the first field grouping command is used as
the main result list in the response, using
group.format=simple .
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Parameter Type Description
group.ngroups Boolean If true, Solr includes the number of groups that have matched
the query in the results. The default value is false.
See below for Distributed Result Grouping Caveats when using
sharded indexes
group.truncate Boolean If true, facet counts are based on the most relevant document
of each group matching the query. The default value is false.
group.facet Boolean Determines whether to compute grouped facets for the field
facets specified in facet.field parameters. Grouped facets are
computed based on the first specified group. As with normal
field faceting, fields shouldn’t be tokenized (otherwise counts
are computed for each token). Grouped faceting supports
single and multivalued fields. Default is false.
Warning: There can be a heavy performance cost to this
option.
See below for Distributed Result Grouping Caveats when using
sharded indexes
group.cache.percen
t
integer between 0
and 100
Setting this parameter to a number greater than 0 enables
caching for result grouping. Result Grouping executes two
searches; this option caches the second search. The default
value is 0. Testing has shown that group caching only improves
search time with Boolean, wildcard, and fuzzy queries. For
simple queries like term or "match all" queries, group caching
degrades performance.
Any number of group commands ( group.field , group.func , group.query ) may be specified in a single
request.
Examples
All of the following sample queries work with Solr’s “bin/solr -e techproducts” example.
Grouping Results by Field
In this example, we will group results based on the manu_exact field, which specifies the manufacturer of the
items in the sample dataset.
http://localhost:8983/solr/techproducts/select?wt=json&indent=true&fl=id,name&q=solr+memory&
group=true&group.field=manu_exact
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{
"..."
"grouped":{
"manu_exact":{
"matches":6,
"groups":[{
"groupValue":"Apache Software Foundation",
"doclist":{"numFound":1,"start":0,"docs":[
{
"id":"SOLR1000",
"name":"Solr, the Enterprise Search Server"}]
}},
{
"groupValue":"Corsair Microsystems Inc.",
"doclist":{"numFound":2,"start":0,"docs":[
{
"id":"VS1GB400C3",
"name":"CORSAIR ValueSelect 1GB 184-Pin DDR SDRAM Unbuffered DDR 400 (PC 3200)
System Memory - Retail"}]
}},
{
"groupValue":"A-DATA Technology Inc.",
"doclist":{"numFound":1,"start":0,"docs":[
{
"id":"VDBDB1A16",
"name":"A-DATA V-Series 1GB 184-Pin DDR SDRAM Unbuffered DDR 400 (PC 3200) System
Memory - OEM"}]
}},
{
"groupValue":"Canon Inc.",
"doclist":{"numFound":1,"start":0,"docs":[
{
"id":"0579B002",
"name":"Canon PIXMA MP500 All-In-One Photo Printer"}]
}},
{
"groupValue":"ASUS Computer Inc.",
"doclist":{"numFound":1,"start":0,"docs":[
{
"id":"EN7800GTX/2DHTV/256M",
"name":"ASUS Extreme N7800GTX/2DHTV (256 MB)"}]
}
}]}}}
The response indicates that there are six total matches for our query. For each of the five unique values of
group.field , Solr returns a docList for that groupValue such that the numFound indicates the total number
of documents in that group, and the top documents are returned according to the implicit default
group.limit=1 and group.sort=score desc parameters. The resulting groups are then sorted by the score
of the top document within each group based on the implicit sort=score desc , and the number of groups
returned is limited to the implicit rows=10 .
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We can run the same query with the request parameter group.main=true . This will format the results as a
single flat document list. This flat format does not include as much information as the normal result
grouping query results – notably the numFound in each group – but it may be easier for existing Solr clients to
parse.
http://localhost:8983/solr/techproducts/select?wt=json&indent=true&fl=id,name,manufacturer&q
=solr+memory&group=true&group.field=manu_exact&group.main=true
{
"responseHeader":{
"status":0,
"QTime":1,
"params":{
"fl":"id,name,manufacturer",
"indent":"true",
"q":"solr memory",
"group.field":"manu_exact",
"group.main":"true",
"group":"true",
"wt":"json"}},
"grouped":{},
"response":{"numFound":6,"start":0,"docs":[
{
"id":"SOLR1000",
"name":"Solr, the Enterprise Search Server"},
{
"id":"VS1GB400C3",
"name":"CORSAIR ValueSelect 1GB 184-Pin DDR SDRAM Unbuffered DDR 400 (PC 3200) System
Memory - Retail"},
{
"id":"VDBDB1A16",
"name":"A-DATA V-Series 1GB 184-Pin DDR SDRAM Unbuffered DDR 400 (PC 3200) System Memory
- OEM"},
{
"id":"0579B002",
"name":"Canon PIXMA MP500 All-In-One Photo Printer"},
{
"id":"EN7800GTX/2DHTV/256M",
"name":"ASUS Extreme N7800GTX/2DHTV (256 MB)"}]
}
}
Grouping by Query
In this example, we will use the group.query parameter to find the top three results for "memory" in two
different price ranges: 0.00 to 99.99, and over 100.
http://localhost:8983/solr/techproducts/select?wt=json&indent=true&fl=name,price&q=memory&gr
oup=true&group.query=price:0+TO+99.99&group.query=price:[100+TO+*]&group.limit=3
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{
"responseHeader":{
"status":0,
"QTime":42,
"params":{
"fl":"name,price",
"indent":"true",
"q":"memory",
"group.limit":"3",
"group.query":["price:[0 TO 99.99]",
"price:[100 TO *]"],
"group":"true",
"wt":"json"}},
"grouped":{
"price:[0 TO 99.99]":{
"matches":5,
"doclist":{"numFound":1,"start":0,"docs":[
{
"name":"CORSAIR ValueSelect 1GB 184-Pin DDR SDRAM Unbuffered DDR 400 (PC 3200) System
Memory - Retail",
"price":74.99}]
}},
"price:[100 TO *]":{
"matches":5,
"doclist":{"numFound":3,"start":0,"docs":[
{
"name":"CORSAIR XMS 2GB (2 x 1GB) 184-Pin DDR SDRAM Unbuffered DDR 400 (PC 3200)
Dual Channel Kit System Memory - Retail",
"price":185.0},
{
"name":"Canon PIXMA MP500 All-In-One Photo Printer",
"price":179.99},
{
"name":"ASUS Extreme N7800GTX/2DHTV (256 MB)",
"price":479.95}]
}
}
}
}
In this case, Solr found five matches for "memory," but only returns four results grouped by price. This is
because one result for "memory" did not have a price assigned to it.
Distributed Result Grouping Caveats
Grouping is supported for distributed searches, with some caveats:
? Currently group.func is is not supported in any distributed searches
? group.ngroups and group.facet require that all documents in each group must be co-located on the
same shard in order for accurate counts to be returned. Document routing via composite keys can be a
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useful solution in many situations.
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Result Clustering
The clustering (or cluster analysis) plugin attempts to automatically discover groups of related search hits
(documents) and assign human-readable labels to these groups.
By default in Solr, the clustering algorithm is applied to the search result of each single query -— this is
called an on-line clustering. While Solr contains an extension for full-index clustering (off-line clustering) this
section will focus on discussing on-line clustering only.
Clusters discovered for a given query can be perceived as dynamic facets. This is beneficial when regular
faceting is difficult (field values are not known in advance) or when the queries are exploratory in nature.
Take a look at the Carrot2 project’s demo page to see an example of search results clustering in action (the
groups in the visualization have been discovered automatically in search results to the right, there is no
external information involved).
The query issued to the system was Solr. It seems clear that faceting could not yield a similar set of groups,
although the goals of both techniques are similar—to let the user explore the set of search results and
either rephrase the query or narrow the focus to a subset of current documents. Clustering is also similar to
Result Grouping in that it can help to look deeper into search results, beyond the top few hits.
Preliminary Concepts
Each document passed to the clustering component is composed of several logical parts:
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? a unique identifier,
? origin URL,
? the title,
? the main content,
? a language code of the title and content.
The identifier part is mandatory, everything else is optional but at least one of the text fields (title or content)
will be required to make the clustering process reasonable. It is important to remember that logical
document parts must be mapped to a particular schema and its fields. The content (text) for clustering can
be sourced from either a stored text field or context-filtered using a highlighter, all these options are
explained below in the configuration section.
A clustering algorithm is the actual logic (implementation) that discovers relationships among the
documents in the search result and forms human-readable cluster labels. Depending on the choice of the
algorithm the clusters may (and probably will) vary. Solr comes with several algorithms implemented in the
open source Carrot2 project, commercial alternatives also exist.
Quick Start Example
The “ techproducts ” example included with Solr is pre-configured with all the necessary components for
result clustering?—?but they are disabled by default.
To enable the clustering component contrib and a dedicated search handler configured to use it, specify a
JVM System Property when running the example:
bin/solr start -e techproducts -Dsolr.clustering.enabled=true
You can now try out the clustering handler by opening the following URL in a browser:
http://localhost:8983/solr/techproducts/clustering?q=:&rows=100
The output XML should include search hits and an array of automatically discovered clusters at the end,
resembling the output shown here:
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">299</int>
</lst>
<result name="response" numFound="32" start="0" maxScore="1.0">
<doc>
<str name="id">GB18030TEST</str>
<str name="name">Test with some GB18030 encoded characters</str>
<arr name="features">
<str>No accents here</str>
<str>这是一个功能</str>
<str>This is a feature (translated)</str>
<str>这份文件是很有光泽</str>
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<str>This document is very shiny (translated)</str>
</arr>
<float name="price">0.0</float>
<str name="price_c">0,USD</str>
<bool name="inStock">true</bool>
<long name="_version_">1448955395025403904</long>
<float name="score">1.0</float>
</doc>
<!-- more search hits, omitted -->
</result>
<arr name="clusters">
<lst>
<arr name="labels">
<str>DDR</str>
</arr>
<double name="score">3.9599865057283354</double>
<arr name="docs">
<str>TWINX2048-3200PRO</str>
<str>VS1GB400C3</str>
<str>VDBDB1A16</str>
</arr>
</lst>
<lst>
<arr name="labels">
<str>iPod</str>
</arr>
<double name="score">11.959228467119022</double>
<arr name="docs">
<str>F8V7067-APL-KIT</str>
<str>IW-02</str>
<str>MA147LL/A</str>
</arr>
</lst>
<!-- More clusters here, omitted. -->
<lst>
<arr name="labels">
<str>Other Topics</str>
</arr>
<double name="score">0.0</double>
<bool name="other-topics">true</bool>
<arr name="docs">
<str>adata</str>
<str>apple</str>
<str>asus</str>
<str>ati</str>
<!-- other unassigned document IDs here -->
</arr>
</lst>
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</arr>
</response>
There were a few clusters discovered for this query ( *:* ), separating search hits into various categories:
DDR, iPod, Hard Drive, etc. Each cluster has a label and score that indicates the "goodness" of the cluster.
The score is algorithm-specific and is meaningful only in relation to the scores of other clusters in the same
set. In other words, if cluster A has a higher score than cluster B, cluster A should be of better quality (have a
better label and/or more coherent document set). Each cluster has an array of identifiers of documents
belonging to it. These identifiers correspond to the uniqueKey field declared in the schema.
Depending on the quality of input documents, some clusters may not make much sense. Some documents
may be left out and not be clustered at all; these will be assigned to the synthetic Other Topics group, marked
with the other-topics property set to true (see the XML dump above for an example). The score of the
other topics group is zero.
Installation
The clustering contrib extension requires dist/solr-clustering-*.jar and all JARs under
contrib/clustering/lib .
Configuration
Declaration of the Search Component and Request Handler
Clustering extension is a search component and must be declared in solrconfig.xml . Such a component
can be then appended to a request handler as the last component in the chain (because it requires search
results which must be previously fetched by the search component).
An example configuration could look as shown below.
1. Include the required contrib JARs. Note that by default paths are relative to the Solr core so they may
need adjustments to your configuration, or an explicit specification of the $solr.install.dir .
<lib dir="${solr.install.dir:../../..}/contrib/clustering/lib/" regex=".*\.jar" />
<lib dir="${solr.install.dir:../../..}/dist/" regex="solr-clustering-\d.*\.jar" />
2. Declaration of the search component. Each component can also declare multiple clustering pipelines
("engines"), which can be selected at runtime by passing clustering.engine=(engine name) URL
parameter.
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<searchComponent name="clustering" class="solr.clustering.ClusteringComponent">
<!-- Lingo clustering algorithm -->
<lst name="engine">
<str name="name">lingo</str>
<str name="carrot.algorithm">org.carrot2.clustering.lingo.LingoClusteringAlgorithm</str>
</lst>
<!-- An example definition for the STC clustering algorithm. -->
<lst name="engine">
<str name="name">stc</str>
<str name="carrot.algorithm">org.carrot2.clustering.stc.STCClusteringAlgorithm</str>
</lst>
</searchComponent>
3. A request handler to which we append the clustering component declared above.
<requestHandler name="/clustering"
class="solr.SearchHandler">
<lst name="defaults">
<bool name="clustering">true</bool>
<bool name="clustering.results">true</bool>
<!-- Logical field to physical field mapping. -->
<str name="carrot.url">id</str>
<str name="carrot.title">doctitle</str>
<str name="carrot.snippet">content</str>
<!-- Configure any other request handler parameters. We will cluster the
top 100 search results so bump up the ‘rows‘ parameter. -->
<str name="rows">100</str>
<str name="fl">*,score</str>
</lst>
<!-- Append clustering at the end of the list of search components. -->
<arr name="last-components">
<str>clustering</str>
</arr>
</requestHandler>
Configuration Parameters of the Clustering Component
The table below summarizes parameters of each clustering engine or the entire clustering component
(depending where they are declared).
Parameter Description
clustering
When true , clustering component is enabled.
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Parameter Description
clustering.engine
Declares which clustering engine to use. If not present, the first declared
engine will become the default one.
clustering.results
When true , the component will perform clustering of search results (this
should be enabled).
clustering.collection
When true , the component will perform clustering of the whole document
index (this section does not cover full-index clustering).
At the engine declaration level, the following parameters are supported.
Parameter Description
carrot.algorithm
The algorithm class.
carrot.resourcesDir
Algorithm-specific resources and configuration files (stop words, other
lexical resources, default settings). By default points to
conf/clustering/carrot2/
carrot.outputSubClusters
If true and the algorithm supports hierarchical clustering, sub-clusters will
also be emitted. Default value: true.
carrot.numDescriptions
Maximum number of per-cluster labels to return (if the algorithm assigns
more than one label to a cluster).
The carrot.algorithm parameter should contain a fully qualified class name of an algorithm supported by
the Carrot2 framework. Currently, the following algorithms are available:
? org.carrot2.clustering.lingo.LingoClusteringAlgorithm (open source)
? org.carrot2.clustering.stc.STCClusteringAlgorithm (open source)
? org.carrot2.clustering.kmeans.BisectingKMeansClusteringAlgorithm (open source)
? com.carrotsearch.lingo3g.Lingo3GClusteringAlgorithm (commercial)
For a comparison of characteristics of these algorithms see the following links:
? http://doc.carrot2.org/#section.advanced-topics.fine-tuning.choosing-algorithm
? http://project.carrot2.org/algorithms.html
? http://carrotsearch.com/lingo3g-comparison.html
The question of which algorithm to choose depends on the amount of traffic (STC is faster than Lingo, but
arguably produces less intuitive clusters, Lingo3G is the fastest algorithm but is not free or open source),
expected result (Lingo3G provides hierarchical clusters, Lingo and STC provide flat clusters), and the input
data (each algorithm will cluster the input slightly differently). There is no one answer which algorithm is
"the best".
Contextual and Full Field Clustering
The clustering engine can apply clustering to the full content of (stored) fields or it can run an internal
highlighter pass to extract context-snippets before clustering. Highlighting is recommended when the
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logical snippet field contains a lot of content (this would affect clustering performance). Highlighting can
also increase the quality of clustering because the content passed to the algorithm will be more focused
around the query (it will be query-specific context). The following parameters control the internal
highlighter.
Parameter Description
carrot.produceSummary
When true the clustering component will run a highlighter pass on the
content of logical fields pointed to by carrot.title and carrot.snippet .
Otherwise full content of those fields will be clustered.
carrot.fragSize
The size, in characters, of the snippets (aka fragments) created by the
highlighter. If not specified, the default highlighting fragsize (
hl.fragsize ) will be used.
carrot.summarySnippets
The number of summary snippets to generate for clustering. If not
specified, the default highlighting snippet count ( hl.snippets ) will be
used.
Logical to Document Field Mapping
As already mentioned in Preliminary Concepts, the clustering component clusters "documents" consisting of
logical parts that need to be mapped onto physical schema of data stored in Solr. The field mapping
attributes provide a connection between fields and logical document parts. Note that the content of title and
snippet fields must be stored so that it can be retrieved at search time.
Parameter Description
carrot.title
The field (alternatively comma- or space-separated list of fields) that
should be mapped to the logical document’s title. The clustering
algorithms typically give more weight to the content of the title field
compared to the content (snippet). For best results, the field should
contain concise, noise-free content. If there is no clear title in your data,
you can leave this parameter blank.
carrot.snippet
The field (alternatively comma- or space-separated list of fields) that
should be mapped to the logical document’s main content. If this mapping
points to very large content fields the performance of clustering may drop
significantly. An alternative then is to use query-context snippets for
clustering instead of full field content. See the description of the
carrot.produceSummary parameter for details.
carrot.url
The field that should be mapped to the logical document’s content URL.
Leave blank if not required.
Clustering Multilingual Content
The field mapping specification can include a carrot.lang parameter, which defines the field that stores ISO
639-1 code of the language in which the title and content of the document are written. This information can
be stored in the index based on apriori knowledge of the documents‘ source or a language detection filter
applied at indexing time. All algorithms inside the Carrot2 framework will accept ISO codes of languages
defined in LanguageCode enum.
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The language hint makes it easier for clustering algorithms to separate documents from different languages
on input and to pick the right language resources for clustering. If you do have multi-lingual query results
(or query results in a language different than English), it is strongly advised to map the language field
appropriately.
Parameter Description
carrot.lang
The field that stores ISO 639-1 code of the language of the document’s text
fields.
carrot.lcmap
A mapping of arbitrary strings into ISO 639 two-letter codes used by
carrot.lang . The syntax of this parameter is the same as
langid.map.lcmap , for example: langid.map.lcmap=japanese:ja
polish:pl english:en
The default language can also be set using Carrot2-specific algorithm attributes (in this case the
MultilingualClustering.defaultLanguage attribute).
Tweaking Algorithm Settings
The algorithms that come with Solr are using their default settings which may be inadequate for all data
sets. All algorithms have lexical resources and resources (stop words, stemmers, parameters) that may
require tweaking to get better clusters (and cluster labels). For Carrot2-based algorithms it is probably best
to refer to a dedicated tuning application called Carrot2 Workbench (screenshot below). From this
application one can export a set of algorithm attributes as an XML file, which can be then placed under the
location pointed to by carrot.resourcesDir .
Providing Defaults
The default attributes for all engines (algorithms) declared in the clustering component are placed under
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carrot.resourcesDir and with an expected file name of engineName-attributes.xml . So for an engine
named lingo and the default value of carrot.resourcesDir , the attributes would be read from a file in
conf/clustering/carrot2/lingo-attributes.xml .
An example XML file changing the default language of documents to Polish is shown below.
<attribute-sets default="attributes">
<attribute-set id="attributes">
<value-set>
<label>attributes</label>
<attribute key="MultilingualClustering.defaultLanguage">
<value type="org.carrot2.core.LanguageCode" value="POLISH"/>
</attribute>
</value-set>
</attribute-set>
</attribute-sets>
Tweaking at Query-Time
The clustering component and Carrot2 clustering algorithms can accept query-time attribute overrides. Note
that certain things (for example lexical resources) can only be initialized once (at startup, via the XML
configuration files).
An example query that changes the LingoClusteringAlgorithm.desiredClusterCountBase parameter for
the Lingo algorithm: http://localhost:8983/solr/techproducts/clustering?q=
:&rows=100&LingoClusteringAlgorithm.desiredClusterCountBase=20.
The clustering engine (the algorithm declared in solrconfig.xml ) can also be changed at runtime by
passing clustering.engine=name request attribute: http://localhost:8983/solr/techproducts/clustering?q=
:&rows=100&clustering.engine=kmeans
Performance Considerations
Dynamic clustering of search results comes with two major performance penalties:
? Increased cost of fetching a larger-than-usual number of search results (50, 100 or more documents),
? Additional computational cost of the clustering itself.
For simple queries, the clustering time will usually dominate the fetch time. If the document content is very
long the retrieval of stored content can become a bottleneck. The performance impact of clustering can be
lowered in several ways:
? feed less content to the clustering algorithm by enabling carrot.produceSummary attribute,
? perform clustering on selected fields (titles only) to make the input smaller,
? use a faster algorithm (STC instead of Lingo, Lingo3G instead of STC),
? tune the performance attributes related directly to a specific algorithm.
Some of these techniques are described in Apache SOLR and Carrot2 integration strategies document,
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available at http://carrot2.github.io/solr-integration-strategies. The topic of improving performance is also
included in the Carrot2 manual at http://doc.carrot2.org/#section.advanced-topics.fine-tuning.performance.
Additional Resources
The following resources provide additional information about the clustering component in Solr and its
potential applications.
? Apache Solr and Carrot2 integration strategies: http://carrot2.github.io/solr-integration-strategies
? Clustering and Visualization of Solr search results (video from Berlin BuzzWords conference, 2011):
http://vimeo.com/26616444
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Spatial Search
Solr supports location data for use in spatial/geospatial searches.
Using spatial search, you can:
? Index points or other shapes
? Filter search results by a bounding box or circle or by other shapes
? Sort or boost scoring by distance between points, or relative area between rectangles
? Generate a 2D grid of facet count numbers for heatmap generation or point-plotting.
There are four main field types available for spatial search:
? LatLonPointSpatialField
? LatLonType (now deprecated) and its non-geodetic twin PointType
? SpatialRecursivePrefixTreeFieldType (RPT for short), including RptWithGeometrySpatialField , a
derivative
? BBoxField
LatLonPointSpatialField is the ideal field type for the most common use-cases for lat-lon point data. It
replaces LatLonType which still exists for backwards compatibility. RPT offers some more features for more
advanced/custom use cases and options like polygons and heatmaps.
RptWithGeometrySpatialField is for indexing and searching non-point data though it can do points too. It
can’t do sorting/boosting.
BBoxField is for indexing bounding boxes, querying by a box, specifying a search predicate
(Intersects,Within,Contains,Disjoint,Equals), and a relevancy sort/boost like overlapRatio or simply the area.
Some esoteric details that are not in this guide can be found at http://wiki.apache.org/solr/SpatialSearch.
LatLonPointSpatialField
Here’s how LatLonPointSpatialField (LLPSF) should usually be configured in the schema:
<fieldType name="location" class="solr.LatLonPointSpatialField" docValues="true"/>
LLPSF supports toggling indexed , stored , docValues , and multiValued . LLPSF internally uses a 2-
dimensional Lucene "Points" (BDK tree) index when "indexed" is enabled (the default). When "docValues" is
enabled, a latitude and longitudes pair are bit-interleaved into 64 bits and put into Lucene DocValues. The
accuracy of the docValues data is about a centimeter.
Indexing Points
For indexing geodetic points (latitude and longitude), supply it in "lat,lon" order (comma separated).
For indexing non-geodetic points, it depends. Use x y (a space) if RPT. For PointType however, use x,y (a
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comma).
If you’d rather use a standard industry format, Solr supports WKT and GeoJSON. However it’s much bulkier
than the raw coordinates for such simple data. (Not supported by the deprecated LatLonType or PointType)
Searching with Query Parsers
There are two spatial Solr "query parsers" for geospatial search: geofilt and bbox . They take the following
parameters:
Parameter Description
d the radial distance, usually in kilometers. (RPT & BBoxField can set other
units via the setting distanceUnits )
pt the center point using the format "lat,lon" if latitude & longitude.
Otherwise, "x,y" for PointType or "x y" for RPT field types.
sfield a spatial indexed field
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Parameter Description
score (Advanced option; not supported by LatLonType (deprecated) or
PointType) If the query is used in a scoring context (e.g. as the main query
in q ), this local parameter determines what scores will be produced. Valid
values are:
? none - A fixed score of 1.0. (the default)
? kilometers - distance in kilometers between the field value and the
specified center point
? miles - distance in miles between the field value and the specified
center point
? degrees - distance in degrees between the field value and the specified
center point
? distance - distance between the field value and the specified center
point in the distanceUnits configured for this field
? recipDistance - 1 / the distance
?
Don’t use this for indexed non-point shapes (e.g.
polygons). The results will be erroneous. And with RPT,
it’s only recommended for multi-valued point data, as the
implementation doesn’t scale very well and for single-
valued fields, you should instead use a separate non-RPT
field purely for distance sorting.
When used with BBoxField , additional options are supported:
? overlapRatio - The relative overlap between the indexed shape &
query shape.
? area - haversine based area of the overlapping shapes expressed in
terms of the distanceUnits configured for this field
? area2D - cartesian coordinates based area of the overlapping shapes
expressed in terms of the distanceUnits configured for this field
filter (Advanced option; not supported by LatLonType (deprecated) or
PointType). If you only want the query to score (with the above score local
parameter), not filter, then set this local parameter to false.
geofilt
The geofilt filter allows you to retrieve results based on the geospatial distance (AKA the "great circle
distance") from a given point. Another way of looking at it is that it creates a circular shape filter. For
example, to find all documents within five kilometers of a given lat/lon point, you could enter &q=
:&fq={!geofilt sfield=store}&pt=45.15,-93.85&d=5 . This filter returns all results within a circle of the
given radius around the initial point:
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bbox
The bbox filter is very similar to geofilt except it uses the bounding box of the calculated circle. See the blue
box in the diagram below. It takes the same parameters as geofilt.
Here’s a sample query:
&q=:&fq={!bbox sfield=store}&pt=45.15,-93.85&d=5
The rectangular shape is faster to compute and so it’s sometimes used as an alternative to geofilt when
it’s acceptable to return points outside of the radius. However, if the ideal goal is a circle but you want it to
run faster, then instead consider using the RPT field and try a large distErrPct value like 0.1 (10% radius).
This will return results outside the radius but it will do so somewhat uniformly around the shape.
?
When a bounding box includes a pole, the bounding box ends up being a "bounding bowl"
(a spherical cap) that includes all values north of the lowest latitude of the circle if it touches
the north pole (or south of the highest latitude if it touches the south pole).
Filtering by an Arbitrary Rectangle
Sometimes the spatial search requirement calls for finding everything in a rectangular area, such as the area
covered by a map the user is looking at. For this case, geofilt and bbox won’t cut it. This is somewhat of a
trick, but you can use Solr’s range query syntax for this by supplying the lower-left corner as the start of the
range and the upper-right corner as the end of the range.
Here’s an example:
&q=:&fq=store:[45,-94 TO 46,-93]
LatLonType (deprecated) does not support rectangles that cross the dateline. For RPT and BBoxField, if you
are non-geospatial coordinates ( geo="false" ) then you must quote the points due to the space, e.g. "x y" .
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Optimizing: Cache or Not
It’s most common to put a spatial query into an "fq" parameter – a filter query. By default, Solr will cache the
query in the filter cache.
If you know the filter query (be it spatial or not) is fairly unique and not likely to get a cache hit then specify
cache="false" as a local-param as seen in the following example. The only spatial types which stand to
benefit from this technique are LatLonPointSpatialField and LatLonType (deprecated). Enable docValues on
the field (if it isn’t already). LatLonType (deprecated) additionally requires a cost="100" (or more) local-
param.
&q=…mykeywords…&fq=…someotherfilters…&fq={!geofilt cache=false}&sfield=store&pt=45.15,-
93.85&d=5
LLPSF does not support Solr’s "PostFilter".
Distance Sorting or Boosting (Function Queries)
There are four distance function queries:
? geodist , see below, usually the most appropriate;
? dist , to calculate the p-norm distance between multi-dimensional vectors;
? hsin , to calculate the distance between two points on a sphere;
? sqedist , to calculate the squared Euclidean distance between two points.
For more information about these function queries, see the section on Function Queries.
geodist
geodist is a distance function that takes three optional parameters: (sfield,latitude,longitude) . You
can use the geodist function to sort results by distance or score return results.
For example, to sort your results by ascending distance, enter …&q=
:&fq={!geofilt}&sfield=store&pt=45.15,-93.85&d=50&sort=geodist() asc .
To return the distance as the document score, enter …&q={!func}geodist()&sfield=store&pt=45.15,-
93.85&sort=score+asc .
More Examples
Here are a few more useful examples of what you can do with spatial search in Solr.
Use as a Sub-Query to Expand Search Results
Here we will query for results in Jacksonville, Florida, or within 50 kilometers of 45.15,-93.85 (near Buffalo,
Minnesota):
&q=:&fq=(state:"FL" AND city:"Jacksonville") OR {!geofilt}&sfield=store&pt=45.15,-
93.85&d=50&sort=geodist()+asc
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Facet by Distance
To facet by distance, you can use the Frange query parser:
&q=:&sfield=store&pt=45.15,-93.85&facet.query={!frange l=0
u=5}geodist()&facet.query={!frange l=5.001 u=3000}geodist()
There are other ways to do it too, like using a \{!geofilt} in each facet.query.
Boost Nearest Results
Using the DisMax or Extended DisMax, you can combine spatial search with the boost function to boost the
nearest results:
&q.alt=:&fq={!geofilt}&sfield=store&pt=45.15,-
93.85&d=50&bf=recip(geodist(),2,200,20)&sort=score desc
RPT
RPT refers to either SpatialRecursivePrefixTreeFieldType (aka simply RPT) and an extended version:
RptWithGeometrySpatialField (aka RPT with Geometry). RPT offers several functional improvements over
LatLonPointSpatialField:
? Non-geodetic – geo=false general x & y (not latitude and longitude)
? Query by polygons and other complex shapes, in addition to circles & rectangles
? Ability to index non-point shapes (e.g. polygons) as well as points – see RptWithGeometrySpatialField
? Heatmap grid faceting
RPT shares various features in common with LatLonPointSpatialField . Some are listed here:
? Latitude/Longitude indexed point data; possibly multi-valued
? Fast filtering with geofilt , bbox filters, and range query syntax (dateline crossing is supported)
? Sort/boost via geodist
? Well-Known-Text (WKT) shape syntax (required for specifying polygons & other complex shapes), and
GeoJSON too. In addition to indexing and searching, this works with the wt=geojson (GeoJSON Solr
response-writer) and [geo f=myfield] (geo Solr document-transformer).
Schema Configuration
To use RPT, the field type must be registered and configured in schema.xml . There are many options for this
field type.
Setting Description
name The name of the field type.
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Setting Description
class This should be solr.SpatialRecursivePrefixTreeFieldType . But be
aware that the Lucene spatial module includes some other so-called
"spatial strategies" other than RPT, notably TermQueryPT*, BBox,
PointVector*, and SerializedDV. Solr requires a field type to parallel these
in order to use them. The asterisked ones have them.
spatialContextFactory This is a Java class name to an internal extension point governing support
for shape definitions & parsing. If you require polygon support, set this to
JTS – an alias for
org.locationtech.spatial4j.context.jts.JtsSpatialContextFactory ;
otherwise it can be omitted. See important info below about JTS. (note:
prior to Solr 6, the "org.locationtech.spatial4j" part was
"com.spatial4j.core" and there used to be no convenience JTS alias)
geo If true, the default, latitude and longitude coordinates will be used and the
mathematical model will generally be a sphere. If false, the coordinates
will be generic X & Y on a 2D plane using Euclidean/Cartesian geometry.
format Defines the shape syntax/format to be used. Defaults to WKT but GeoJSON is
another popular format. Spatial4j governs this feature and supports other
formats. If a given shape is parseable as "lat,lon" or "x y" then that is
always supported.
distanceUnits This is used to specify the units for distance measurements used
throughout the use of this field. This can be degrees , kilometers or
miles . It is applied to nearly all distance measurements involving the field:
maxDistErr , distErr , d , geodist and the score when score is distance ,
area , or area2d . However, it doesn’t affect distances embedded in WKT
strings, (eg: “BUFFER(POINT(200 10),0.2)”), which are still in degrees.
distanceUnits defaults to either “ kilometers ” if geo is “ true ”, or
“ degrees ” if geo is “ false ”.
distanceUnits replaces the units attribute; which is now deprecated and
mutually exclusive with this attribute.
distErrPct Defines the default precision of non-point shapes (both index & query), as
a fraction between 0.0 (fully precise) to 0.5. The closer this number is to
zero, the more accurate the shape will be. However, more precise indexed
shapes use more disk space and take longer to index. Bigger distErrPct
values will make queries faster but less accurate. At query time this can be
overridden in the query syntax, such as to 0.0 so as to not approximate the
search shape. The default for the RPT field is 0.025. Note: For
RPTWithGeometrySpatialField (see below), there’s always complete
accuracy with the serialized geometry and so this doesn’t control accuracy
so much as it controls the trade-off of how big the index should be.
distErrPct defaults to 0.15 for that field.
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Setting Description
maxDistErr Defines the highest level of detail required for indexed data. If left blank,
the default is one meter – just a bit less than 0.000009 degrees. This setting
is used internally to compute an appropriate maxLevels (see below).
worldBounds Defines the valid numerical ranges for x and y, in the format of
ENVELOPE(minX, maxX, maxY, minY) . If geo="true" , the standard lat-lon
world boundaries are assumed. If geo=false , you should define your
boundaries.
distCalculator Defines the distance calculation algorithm. If geo=true , "haversine" is the
default. If geo=false , "cartesian" will be the default. Other possible values
are "lawOfCosines", "vincentySphere" and "cartesian^2".
prefixTree Defines the spatial grid implementation. Since a PrefixTree (such as
RecursivePrefixTree) maps the world as a grid, each grid cell is
decomposed to another set of grid cells at the next level. If geo=true then
the default prefix tree is “ geohash ”, otherwise it’s “ quad ”. Geohash has 32
children at each level, quad has 4. Geohash can only be used for geo=true
as it’s strictly geospatial. A third choice is “ packedQuad ”, which is generally
more efficient than plain "quad", provided there are many
levels?—?perhaps 20 or more.
maxLevels Sets the maximum grid depth for indexed data. Instead, it’s usually more
intuitive to compute an appropriate maxLevels by specifying maxDistErr .
And there are others: normWrapLongitude , datelineRule , validationRule , autoIndex , allowMultiOverlap ,
precisionModel . For further info, see notes below about spatialContextFactory implementations
referenced above, especially the link to the JTS based one.
JTS and Polygons
As indicated above, spatialContextFactory must be set to JTS for polygon support, including multi-
polygon.
All other shapes, including even line-strings, are supported without JTS. JTS stands for JTS Topology Suite,
which does not come with Solr due to its LGPL license. You must download it (a JAR file) and put that in a
special location internal to Solr: SOLR_INSTALL/server/solr-webapp/webapp/WEB-INF/lib/ . You can readily
download it here: https://repo1.maven.org/maven2/com/vividsolutions/jts-core/. It will not work if placed in
other more typical Solr lib directories, unfortunately.
When activated, there are additional configuration attributes available; see
org.locationtech.spatial4j.context.jts.JtsSpatialContextFactory for the Javadocs, and remember to look at the
superclass’s options in as well. One option in particular you should most likely enable is autoIndex (i.e., use
JTS’s PreparedGeometry) as it’s been shown to be a major performance boost for non-trivial polygons.
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<fieldType name="location_rpt" class="solr.SpatialRecursivePrefixTreeFieldType"
spatialContextFactory=
"org.locationtech.spatial4j.context.jts.JtsSpatialContextFactory"
autoIndex="true"
validationRule="repairBuffer0"
distErrPct="0.025"
maxDistErr="0.001"
distanceUnits="kilometers" />
Once the field type has been defined, define a field that uses it.
Here’s an example polygon query for a field "geo" that can be either
solr.SpatialRecursivePrefixTreeFieldType or RptWithGeometrySpatialField:
&q=*:*&fq={!field f=geo}Intersects(POLYGON((-10 30, -40 40, -10 -20, 40 20, 0 0, -10 30)))
Inside the parenthesis following the search predicate is the shape definition. The format of that shape is
governed by the format attribute on the field type, defaulting to WKT. If you prefer GeoJSON, you can specify
that instead.
Beyond this Reference Guide and Spatila4j’s docs, there are some details that remain at the Solr Wiki at
http://wiki.apache.org/solr/SolrAdaptersForLuceneSpatial4.
RptWithGeometrySpatialField
The RptWithGeometrySpatialField field type is a derivative of SpatialRecursivePrefixTreeFieldType
that also stores the original geometry internally in Lucene DocValues, which it uses to achieve accurate
search. It can also be used for indexed point fields. The Intersects predicate (the default) is particularly fast,
since many search results can be returned as an accurate hit without requiring a geometry check. This field
type is configured just like RPT except that the default distErrPct is 0.15 (higher than 0.025) because the
grid squares are purely for performance and not to fundamentally represent the shape.
An optional in-memory cache can be defined in solrconfig.xml , which should be done when the data tends
to have shapes with many vertices. Assuming you name your field "geom", you can configure an optional
cache in solrconfig.xml by adding the following – notice the suffix of the cache name:
<cache name="perSegSpatialFieldCache_geom"
class="solr.LRUCache"
size="256"
initialSize="0"
autowarmCount="100%"
regenerator="solr.NoOpRegenerator"/>
When using this field type, you will likely not want to mark the field as stored because it’s redundant with the
DocValues data and surely larger because of the formatting (be it WKT or GeoJSON). To retrieve the spatial
data in search results from DocValues, use the [geo] transformer?—?Transforming Result Documents.
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Heatmap Faceting
The RPT field supports generating a 2D grid of facet counts for documents having spatial data in each grid
cell. For high-detail grids, this can be used to plot points, and for lesser detail it can be used for heatmap
generation. The grid cells are determined at index-time based on RPT’s configuration. At facet counting
time, the indexed cells in the region of interest are traversed and a grid of counters corresponding to each
cell are incremented. Solr can return the data in a straight-forward 2D array of integers or in a PNG which
compresses better for larger data sets but must be decoded.
The heatmap feature is accessed from Solr’s faceting feature. As a part of faceting, it supports the key local
parameter as well as excluding tagged filter queries, just like other types of faceting do. This allows multiple
heatmaps to be returned on the same field with different filters.
Parameter Description
facet Set to true to enable faceting
facet.heatmap The field name of type RPT
facet.heatmap.geom The region to compute the heatmap on, specified using the rectangle-
range syntax or WKT. It defaults to the world. ex: ["-180 -90" TO "180
90"]
facet.heatmap.gridLevel A specific grid level, which determines how big each grid cell is. Defaults to
being computed via distErrPct (or distErr)
facet.heatmap.distErrPct A fraction of the size of geom used to compute gridLevel. Defaults to 0.15.
It’s computed the same as a similarly named parameter for RPT.
facet.heatmap.distErr A cell error distance used to pick the grid level indirectly. It’s computed the
same as a similarly named parameter for RPT.
facet.heatmap.format The format, either ints2D (default) or png .
?
You’ll experiment with different distErrPct values (probably 0.10 - 0.20) with various input
geometries till the default size is what you’re looking for. The specific details of how it’s
computed isn’t important. For high-detail grids used in point-plotting (loosely one cell per
pixel), set distErr to be the number of decimal-degrees of several pixels or so of the map
being displayed. Also, you probably don’t want to use a geohash based grid because the
cell orientation between grid levels flip-flops between being square and rectangle. Quad is
consistent and has more levels, albeit at the expense of a larger index.
Here’s some sample output in JSON (with "…" inserted for brevity):
{gridLevel=6,columns=64,rows=64,minX=-180.0,maxX=180.0,minY=-90.0,maxY=90.0,
counts_ints2D=[[0, 0, 2, 1, ....],[1, 1, 3, 2, ...],...]}
The output shows the gridLevel which is interesting since it’s often computed from other parameters. If an
interface being developed allows an explicit resolution increase/decrease feature then subsequent requests
can specify the gridLevel explicitly.
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The minX , maxX , minY , maxY reports the region where the counts are. This is the minimally enclosing
bounding rectangle of the input geom at the target grid level. This may wrap the dateline. The columns and
rows values are how many columns and rows that the output rectangle is to be divided by evenly. Note:
Don’t divide an on-screen projected map rectangle evenly to plot these rectangles/points since the cell data
is in the coordinate space of decimal degrees if geo=true or whatever units were given if geo=false. This
could be arranged to be the same as an on-screen map but won’t necessarily be.
The counts_ints2D key has a 2D array of integers. The initial outer level is in row order (top-down), then the
inner arrays are the columns (left-right). If any array would be all zeros, a null is returned instead for
efficiency reasons. The entire value is null if there is no matching spatial data.
If format=png then the output key is counts_png . It’s a base-64 encoded string of a 4-byte PNG. The PNG
logically holds exactly the same data that the ints2D format does. Note that the alpha channel byte is flipped
to make it easier to view the PNG for diagnostic purposes, since otherwise counts would have to exceed
2^24 before it becomes non-opague. Thus counts greater than this value will become opaque.
BBoxField
The BBoxField field type indexes a single rectangle (bounding box) per document field and supports
searching via a bounding box. It supports most spatial search predicates, it has enhanced relevancy modes
based on the overlap or area between the search rectangle and the indexed rectangle. It’s particularly
useful for its relevancy modes. To configure it in the schema, use a configuration like this:
<field name="bbox" type="bbox" />
<fieldType name="bbox" class="solr.BBoxField"
geo="true" units="kilometers" numberType="_bbox_coord" storeSubFields="false"/>
<fieldType name="_bbox_coord" class="solr.TrieDoubleField" precisionStep="8" docValues="true"
stored="false"/>
BBoxField is actually based off of 4 instances of another field type referred to by numberType. It also uses a
boolean to flag a dateline cross. Assuming you want to use the relevancy feature, docValues is required.
Some of the attributes are in common with the RPT field like geo, units, worldBounds, and
spatialContextFactory because they share some of the same spatial infrastructure.
To index a box, add a field value to a bbox field that’s a string in the WKT/CQL ENVELOPE syntax. Example:
ENVELOPE(-10, 20, 15, 10) which is minX, maxX, maxY, minY order. The parameter ordering is unintuitive
but that’s what the spec calls for. Alternatively, you could provide a rectangular polygon in WKT (or GeoJSON
if you set set format="GeoJSON" ).
To search, you can use the {!bbox} query parser, or the range syntax e.g. [10,-10 TO 15,20] , or the
ENVELOPE syntax wrapped in parenthesis with a leading search predicate. The latter is the only way to
choose a predicate other than Intersects. For example:
&q={!field f=bbox}Contains(ENVELOPE(-10, 20, 15, 10))
Now to sort the results by one of the relevancy modes, use it like this:
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&q={!field f=bbox score=overlapRatio}Intersects(ENVELOPE(-10, 20, 15, 10))
The score local parameter can be one of overlapRatio , area , and area2D . area scores by the document
area using surface-of-a-sphere (assuming geo=true ) math, while area2D uses simple width * height.
overlapRatio computes a [0-1] ranged score based on how much overlap exists relative to the document’s
area and the query area. The javadocs of BBoxOverlapRatioValueSource have more info on the formula.
There is an additional parameter queryTargetProportion that allows you to weight the query side of the
formula to the index (target) side of the formula. You can also use &debug=results to see useful score
computation info.
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The Terms Component
The Terms Component provides access to the indexed terms in a field and the number of documents that
match each term. This can be useful for building an auto-suggest feature or any other feature that operates
at the term level instead of the search or document level. Retrieving terms in index order is very fast since
the implementation directly uses Lucene’s TermEnum to iterate over the term dictionary.
In a sense, this search component provides fast field-faceting over the whole index, not restricted by the
base query or any filters. The document frequencies returned are the number of documents that match the
term, including any documents that have been marked for deletion but not yet removed from the index.
Configuring the Terms Component
By default, the Terms Component is already configured in solrconfig.xml for each collection.
Defining the Terms Component
Defining the Terms search component is straightforward: simply give it a name and use the class
solr.TermsComponent .
<searchComponent name="terms" class="solr.TermsComponent"/>
This makes the component available for use, but by itself will not be useable until included with a request
handler.
Using the Terms Component in a Request Handler
The terms component is included with the /terms request handler, which is among Solr’s out-of-the-box
request handlers - see Implicit RequestHandlers.
Note that the defaults for this request handler set the parameter "terms" to true, which allows terms to be
returned on request. The parameter "distrib" is set to false, which allows this handler to be used only on a
single Solr core.
You could add this component to another handler if you wanted to, and pass "terms=true" in the HTTP
request in order to get terms back. If it is only defined in a separate handler, you must use that handler
when querying in order to get terms and not regular documents as results.
Terms Component Parameters
The parameters below allow you to control what terms are returned. You can also configure any of these
with the request handler if you’d like to set them permanently. Or, you can add them to the query request.
These parameters are:
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Parameter Required Default Description
terms No false If set to true, enables the Terms Component. By
default, the Terms Component is off.
Example: terms=true
terms.fl Yes null Specifies the field from which to retrieve terms.
Example: terms.fl=title
terms.list No null Fetches the document frequency for a comma
delimited list of terms. Terms are always returned in
index order. If ‘terms.ttf’ is set to true, also returns
their total term frequency. If multiple ‘terms.fl’ are
defined, these statistics will be returned for each
term in each requested field.
Example: terms.list=termA,termB,termC
terms.limit No 10 Specifies the maximum number of terms to return.
The default is 10. If the limit is set to a number less
than 0, then no maximum limit is enforced. Although
this is not required, either this parameter or
terms.upper must be defined.
Example: terms.limit=20
terms.lower No empty string Specifies the term at which to start. If not specified,
the empty string is used, causing Solr to start at the
beginning of the field.
Example: terms.lower=orange
terms.lower.incl No true If set to true, includes the lower-bound term
(specified with terms.lower in the result set.
Example: terms.lower.incl=false
terms.mincount No null Specifies the minimum document frequency to
return in order for a term to be included in a query
response. Results are inclusive of the mincount (that
is, >= mincount).
Example: terms.mincount=5
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Parameter Required Default Description
terms.maxcount No null Specifies the maximum document frequency a term
must have in order to be included in a query
response. The default setting is -1, which sets no
upper bound. Results are inclusive of the maxcount
(that is, ? maxcount).
Example: terms.maxcount=25
terms.prefix No null Restricts matches to terms that begin with the
specified string.
Example: terms.prefix=inter
terms.raw No false If set to true, returns the raw characters of the
indexed term, regardless of whether it is human-
readable. For instance, the indexed form of numeric
numbers is not human-readable.
Example: terms.raw=true
terms.regex No null Restricts matches to terms that match the regular
expression.
Example: terms.regex=.*pedist
terms.regex.flag No null Defines a Java regex flag to use when evaluating the
regular expression defined with terms.regex . See
http://docs.oracle.com/javase/tutorial/essential/
regex/pattern.html for details of each flag. Valid
options are:
? case_insensitive
? comments
? multiline
? literal
? dotall
? unicode_case
? canon_eq
? unix_lines
Example: terms.regex.flag=case_insensitive
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Parameter Required Default Description
terms.stats No null Include index statistics in the results. Currently
returns only the numDocs for a collection. When
combined with terms.list it provides enough
information to compute idf for a list of terms.
terms.sort No count Defines how to sort the terms returned. Valid
options are count, which sorts by the term
frequency, with the highest term frequency first, or
index, which sorts in index order.
Example: terms.sort=index
terms.ttf No false If set to true, returns both ‘df‘ (docFreq) and ‘ttf‘
(totalTermFreq) statistics for each requested term in
‘terms.list’. In this case, the response format is:
<lst name="terms">
<lst name="field">
<lst name="termA">
<long name="df">22</long>
<long name="ttf">73</long>
</lst>
</lst>
</lst>
terms.upper No null Specifies the term to stop at. Although this
parameter is not required, either this parameter or
terms.limit must be defined.
Example: terms.upper=plum
terms.upper.incl No false If set to true, the upper bound term is included in
the result set. The default is false.
Example: terms.upper.incl=true
The output is a list of the terms and their document frequency values. See below for examples.
Examples
All of the following sample queries work with Solr’s “bin/solr -e techproducts” example.
Get Top 10 Terms
This query requests the first ten terms in the name field:
http://localhost:8983/solr/techproducts/terms?terms.fl=name
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Results:
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">2</int>
</lst>
<lst name="terms">
<lst name="name">
<int name="one">5</int>
<int name="184">3</int>
<int name="1gb">3</int>
<int name="3200">3</int>
<int name="400">3</int>
<int name="ddr">3</int>
<int name="gb">3</int>
<int name="ipod">3</int>
<int name="memory">3</int>
<int name="pc">3</int>
</lst>
</lst>
</response>
Get First 10 Terms Starting with Letter ‘a‘
This query requests the first ten terms in the name field, in index order (instead of the top 10 results by
document count):
http://localhost:8983/solr/techproducts/terms?terms.fl=name&terms.lower=a&terms.sort=index
Results:
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<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">0</int>
</lst>
<lst name="terms">
<lst name="name">
<int name="a">1</int>
<int name="all">1</int>
<int name="apple">1</int>
<int name="asus">1</int>
<int name="ata">1</int>
<int name="ati">1</int>
<int name="belkin">1</int>
<int name="black">1</int>
<int name="british">1</int>
<int name="cable">1</int>
</lst>
</lst>
</response>
SolrJ Invocation
SolrQuery query = new SolrQuery();
query.setRequestHandler("/terms");
query.setTerms(true);
query.setTermsLimit(5);
query.setTermsLower("s");
query.setTermsPrefix("s");
query.addTermsField("terms_s");
query.setTermsMinCount(1);
QueryRequest request = new QueryRequest(query);
List<Term> terms = request.process(getSolrClient()).getTermsResponse().getTerms("terms_s");
Using the Terms Component for an Auto-Suggest Feature
If the Suggester doesn’t suit your needs, you can use the Terms component in Solr to build a similar feature
for your own search application. Simply submit a query specifying whatever characters the user has typed so
far as a prefix. For example, if the user has typed "at", the search engine’s interface would submit the
following query:
http://localhost:8983/solr/techproducts/terms?terms.fl=name&terms.prefix=at
Result:
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<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1</int>
</lst>
<lst name="terms">
<lst name="name">
<int name="ata">1</int>
<int name="ati">1</int>
</lst>
</lst>
</response>
You can use the parameter omitHeader=true to omit the response header from the query response, like in
this example, which also returns the response in JSON format:
http://localhost:8983/solr/techproducts/terms?terms.fl=name&terms.prefix=at&indent=true&wt=j
son&omitHeader=true
Result:
{
"terms": {
"name": [
"ata",
1,
"ati",
1
]
}
}
Distributed Search Support
The TermsComponent also supports distributed indexes. For the /terms request handler, you must provide
the following two parameters:
Parameter Description
shards Specifies the shards in your distributed indexing configuration. For more
information about distributed indexing, see Distributed Search with Index
Sharding.
shards.qt Specifies the request handler Solr uses for requests to shards.
More Resources
? TermsComponent javadoc
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The Term Vector Component
The TermVectorComponent is a search component designed to return additional information about
documents matching your search.
For each document in the response, the TermVectorCcomponent can return the term vector, the term
frequency, inverse document frequency, position, and offset information.
Configuration
The TermVectorComponent is not enabled implicitly in Solr - it must be explicitly configured in your
solrconfig.xml file. The examples on this page show how it is configured in Solr’s “ techproducts ”
example:
bin/solr -e techproducts
To enable the this component, you need to configure it using a searchComponent element:
<searchComponent name="tvComponent" class="org.apache.solr.handler.component.TermVectorComponent
"/>
A request handler must then be configured to use this component name. In the techproducts example, the
component is associated with a special request handler named /tvrh , that enables term vectors by default
using the tv=true parameter; but you can associate it with any request handler:
<requestHandler name="/tvrh" class="org.apache.solr.handler.component.SearchHandler">
<lst name="defaults">
<bool name="tv">true</bool>
</lst>
<arr name="last-components">
<str>tvComponent</str>
</arr>
</requestHandler>
Once your handler is defined, you may use in conjunction with any schema (that has a uniqueKeyField) to
fetch term vectors for fields configured with the termVector attribute, such as in the techproducts sample
schema. For example:
<field name="includes"
type="text_general"
indexed="true"
stored="true"
multiValued="true"
termVectors="true"
termPositions="true"
termOffsets="true" />
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Invoking the Term Vector Component
The example below shows an invocation of this component using the above configuration:
http://localhost:8983/solr/techproducts/tvrh?q=:&start=0&rows=10&fl=id,includes
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...
<lst name="termVectors">
<lst name="GB18030TEST">
<str name="uniqueKey">GB18030TEST</str>
</lst>
<lst name="EN7800GTX/2DHTV/256M">
<str name="uniqueKey">EN7800GTX/2DHTV/256M</str>
</lst>
<lst name="100-435805">
<str name="uniqueKey">100-435805</str>
</lst>
<lst name="3007WFP">
<str name="uniqueKey">3007WFP</str>
<lst name="includes">
<lst name="cable"/>
<lst name="usb"/>
</lst>
</lst>
<lst name="SOLR1000">
<str name="uniqueKey">SOLR1000</str>
</lst>
<lst name="0579B002">
<str name="uniqueKey">0579B002</str>
</lst>
<lst name="UTF8TEST">
<str name="uniqueKey">UTF8TEST</str>
</lst>
<lst name="9885A004">
<str name="uniqueKey">9885A004</str>
<lst name="includes">
<lst name="32mb"/>
<lst name="av"/>
<lst name="battery"/>
<lst name="cable"/>
<lst name="card"/>
<lst name="sd"/>
<lst name="usb"/>
</lst>
</lst>
<lst name="adata">
<str name="uniqueKey">adata</str>
</lst>
<lst name="apple">
<str name="uniqueKey">apple</str>
</lst>
</lst>
Request Parameters
The example below shows the available request parameters for this component:
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http://localhost:8983/solr/techproducts/tvrh?q=includes:* TO
*&rows=10&indent=true&tv=true&tv.tf=true&tv.df=true&tv.positions=true&tv.offsets=true&tv.pay
loads=true&tv.fl=includes
Boolean
Parameters
Description Type
tv Should the component run or not boolean
tv.docIds Returns term vectors for the specified list of Lucene document
IDs (not the Solr Unique Key).
comma seperated
integers
tv.fl Returns term vectors for the specified list of fields. If not
specified, the fl parameter is used.
comma seperated
list of field names
tv.all A shortcut that invokes all the boolean parameters listed below. boolean
tv.df Returns the Document Frequency (DF) of the term in the
collection. This can be computationally expensive.
boolean
tv.offsets Returns offset information for each term in the document. boolean
tv.positions Returns position information. boolean
tv.payloads Returns payload information. boolean
tv.tf Returns document term frequency info per term in the
document.
boolean
tv.tf_idf Calculates TF / DF (ie: TF * IDF) for each term. Please note that
this is a literal calculation of "Term Frequency multiplied by
Inverse Document Frequency" and not a classical TF-IDF
similarity measure.
Requires the parameters tv.tf and tv.df to be "true". This can
be computationally expensive. (The results are not shown in
example output)
boolean
To learn more about TermVector component output, see the Wiki page: http://wiki.apache.org/solr/
TermVectorComponentExampleOptions
For schema requirements, see the Wiki page: http://wiki.apache.org/solr/FieldOptionsByUseCase
SolrJ and the Term Vector Component
Neither the SolrQuery class nor the QueryResponse class offer specific method calls to set Term Vector
Component parameters or get the "termVectors" output. However, there is a patch for it: SOLR-949.
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The Stats Component
The Stats component returns simple statistics for numeric, string, and date fields within the document set.
The sample queries in this section assume you are running the “ techproducts ” example included with Solr:
bin/solr -e techproducts
Stats Component Parameters
The Stats Component accepts the following parameters:
Parameter Description
stats If true, then invokes the Stats component.
stats.field Specifies a field for which statistics should be generated. This parameter
may be invoked multiple times in a query in order to request statistics on
multiple fields.
Local Parameters may be used to indicate which subset of the supported
statistics should be computed, and/or that statistics should be computed
over the results of an arbitrary numeric function (or query) instead of a
simple field name. See the examples below.
stats.facet Returns sub-results for values within the specified facet.
This legacy parameter is not recommended for new users - instead please
consider combining stats.field with facet.pivot
stats.calcdistinct If true, the "countDistinct" and "distinctValues" statistics will be computed
and included the response. These calculations can be very expensive for
fields that do not have a tiny cardinality, so they are disabled by default.
This parameter can be specified using per-filed override (ie:
f.<field>.stats.calcdistinct=true ) but users are encouraged to
instead the statistics desired as Local Parameter - As a top level request
parameter, this option is deprecated.
Example
The query below demonstrates computing stats against two different fields numeric fields, as well as stats
over the results of a termfreq() function call using the text field:
http://localhost:8983/solr/techproducts/select?q=:&stats=true&stats.field={!func}termfreq(‘t
ext‘,‘memory‘)&stats.field=price&stats.field=popularity&rows=0&indent=true
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<lst name="stats">
<lst name="stats_fields">
<lst name="termfreq(text,memory)">
<double name="min">0.0</double>
<double name="max">3.0</double>
<long name="count">32</long>
<long name="missing">0</long>
<double name="sum">10.0</double>
<double name="sumOfSquares">22.0</double>
<double name="mean">0.3125</double>
<double name="stddev">0.7803018439949604</double>
<lst name="facets"/>
</lst>
<lst name="price">
<double name="min">0.0</double>
<double name="max">2199.0</double>
<long name="count">16</long>
<long name="missing">16</long>
<double name="sum">5251.270030975342</double>
<double name="sumOfSquares">6038619.175900028</double>
<double name="mean">328.20437693595886</double>
<double name="stddev">536.3536996709846</double>
<lst name="facets"/>
</lst>
<lst name="popularity">
<double name="min">0.0</double>
<double name="max">10.0</double>
<long name="count">15</long>
<long name="missing">17</long>
<double name="sum">85.0</double>
<double name="sumOfSquares">603.0</double>
<double name="mean">5.666666666666667</double>
<double name="stddev">2.943920288775949</double>
<lst name="facets"/>
</lst>
</lst>
</lst>
Statistics Supported
The table below explains the statistics supported by the Stats component. Not all statistics are supported for
all field types, and not all statistics are computed by default (See Local Parameters below for details)
Local
Param
Sample
Input
Description Supported Types Compute
d by
Default
min true The minimum value of the field/function in all
documents in the set.
All Yes
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Local
Param
Sample
Input
Description Supported Types Compute
d by
Default
max true The maximum value of the field/function in all
documents in the set.
All Yes
sum true The sum of all values of the field/function in all
documents in the set.
Numeric & Date Yes
count true The number of values found in all documents in the
set for this field/function.
All Yes
missing true The number of documents in the set which do not
have a value for this field/function.
All Yes
sumOfSq
uares
true Sum of all values squared (a by product of
computing stddev)
Numeric & Date Yes
mean true The average (v1 + v2 …. + vN)/N Numeric & Date Yes
stddev true Standard deviation, measuring how widely spread
the values in the data set are.
Numeric & Date Yes
percentil
es
"1,99,99.9
"
A list of percentile values based on cut-off points
specified by the param value. These values are an
approximation, using the t-digest algorithm.
Numeric No
distinctVa
lues
true The set of all distinct values for the field/function in
all of the documents in the set. This calculation can
be very expensive for fields that do not have a tiny
cardinality.
All No
countDist
inct
true The exact number of distinct values in the
field/function in all of the documents in the set. This
calculation can be very expensive for fields that do
not have a tiny cardinality.
All No
cardinalit
y
"true"
or"0.3"
A statistical approximation (currently using the
HyperLogLog algorithm) of the number of distinct
values in the field/function in all of the documents in
the set. This calculation is much more efficient then
using the ‘countDistinct‘ option, but may not be
100% accurate. Input for this option can be floating
point number between 0.0 and 1.0 indicating how
aggressively the algorithm should try to be accurate:
0.0 means use as little memory as possible; 1.0
means use as much memory as needed to be as
accurate as possible. ‘true‘ is supported as an alias
for "0.3"
All No
Local Parameters
Similar to the Facet Component, the stats.field parameter supports local parameters for:
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? Tagging & Excluding Filters: stats.field={!ex=filterA}price
? Changing the Output Key: stats.field={!key=my_price_stats}price
? Tagging stats for use with facet.pivot : stats.field={!tag=my_pivot_stats}price
Local parameters can also be used to specify individual statistics by name, overriding the set of statistics
computed by default, eg: stats.field={!min=true max=true percentiles=‘99,99.9,99.99‘}price
?
If any supported statistics are specified via local parameters, then the entire set of default
statistics is overridden and only the requested statistics are computed.
Additional "Expert" local params are supported in some cases for affecting the behavior of some statistics:
? percentiles
? tdigestCompression - a positive numeric value defaulting to 100.0 controlling the compression
factor of the T-Digest. Larger values means more accuracy, but also uses more memory.
? cardinality
? hllPreHashed - a boolean option indicating that the statistics are being computed over a "long" field
that has already been hashed at index time – allowing the HLL computation to skip this step.
? hllLog2m - an integer value specifying an explicit "log2m" value to use, overriding the heuristic value
determined by the cardinality local param and the field type – see the java-hll documentation for
more details
? hllRegwidth - an integer value specifying an explicit "regwidth" value to use, overriding the heuristic
value determined by the cardinality local param and the field type – see the java-hll documentation
for more details
? calcDistinct - for backwards compatibility, calcDistinct=true may be specified as an alias for both
countDistinct=true distinctValues=true
Examples
Here we compute some statistics for the price field. The min, max, mean, 90th, and 99th percentile price
values are computed against all products that are in stock ( q=: and fq=inStock:true ), and independently all
of the default statistics are computed against all products regardless of whether they are in stock or not (by
excluding that filter).
http://localhost:8983/solr/techproducts/select?q=:&fq={!tag=stock_check}inStock:true&stats=t
rue&stats.field={!ex=stock_check+key=instock_prices+min=true+max=true+mean=true+percentiles=
‘90,99‘}price&stats.field={!key=all_prices}price&rows=0&indent=true
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<lst name="stats">
<lst name="stats_fields">
<lst name="instock_prices">
<double name="min">0.0</double>
<double name="max">2199.0</double>
<double name="mean">328.20437693595886</double>
<lst name="percentiles">
<double name="90.0">564.9700012207031</double>
<double name="99.0">1966.6484985351556</double>
</lst>
</lst>
<lst name="all_prices">
<double name="min">0.0</double>
<double name="max">2199.0</double>
<long name="count">12</long>
<long name="missing">5</long>
<double name="sum">4089.880027770996</double>
<double name="sumOfSquares">5385249.921747174</double>
<double name="mean">340.823335647583</double>
<double name="stddev">602.3683083752779</double>
</lst>
</lst>
</lst>
The Stats Component and Faceting
Although the stats.facet parameter is no longer recommended, sets of stats.field parameters can be
referenced by ‘tag’ when using Pivot Faceting to compute multiple statistics at every level (i.e.: field) in the
tree of pivot constraints.
For more information and a detailed example, please see Combining Stats Component With Pivots.
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The Query Elevation Component
The Query Elevation Component lets you configure the top results for a given query regardless of the
normal Lucene scoring.
This is sometimes called "sponsored search," "editorial boosting," or "best bets." This component matches
the user query text to a configured map of top results. The text can be any string or non-string IDs, as long
as it’s indexed. Although this component will work with any QueryParser, it makes the most sense to use
with DisMax or eDisMax.
The Query Elevation Component is supported by distributed searching.
All of the sample configuration and queries used in this section assume you are running Solr’s
“ techproducts ” example:
bin/solr -e techproducts
Configuring the Query Elevation Component
You can configure the Query Elevation Component in the solrconfig.xml file. Search components like
QueryElevationComponent may be added to any request handler; a dedicated request handler is used here
for brevity.
<searchComponent name="elevator" class="solr.QueryElevationComponent" >
<!-- pick a fieldType to analyze queries -->
<str name="queryFieldType">string</str>
<str name="config-file">elevate.xml</str>
</searchComponent>
<requestHandler name="/elevate" class="solr.SearchHandler" startup="lazy">
<lst name="defaults">
<str name="echoParams">explicit</str>
</lst>
<arr name="last-components">
<str>elevator</str>
</arr>
</requestHandler>
Optionally, in the Query Elevation Component configuration you can also specify the following to distinguish
editorial results from "normal" results:
<str name="editorialMarkerFieldName">foo</str>
The Query Elevation Search Component takes the following arguments:
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Argument Description
queryFieldType
Specifies which fieldType should be used to analyze the incoming text. For
example, it may be appropriate to use a fieldType with a LowerCaseFilter.
config-file
Path to the file that defines query elevation. This file must exist in
<instanceDir>/conf/<config-file> or <dataDir>/<config-file> . If the
file exists in the /conf/ directory it will be loaded once at startup. If it exists
in the data directory, it will be reloaded for each IndexReader.
forceElevation
By default, this component respects the requested sort parameter: if the
request asks to sort by date, it will order the results by date. If
forceElevation=true (the default), results will first return the boosted
docs, then order by date.
elevate.xml
Elevated query results are configured in an external XML file specified in the config-file argument. An
elevate.xml file might look like this:
<elevate>
<query text="foo bar">
<doc id="1" />
<doc id="2" />
<doc id="3" />
</query>
<query text="ipod">
<doc id="MA147LL/A" /> <!-- put the actual ipod at the top -->
<doc id="IW-02" exclude="true" /> <!-- exclude this cable -->
</query>
</elevate>
In this example, the query "foo bar" would first return documents 1, 2 and 3, then whatever normally
appears for the same query. For the query "ipod", it would first return "MA147LL/A", and would make sure
that "IW-02" is not in the result set.
Using the Query Elevation Component
The enableElevation Parameter
For debugging it may be useful to see results with and without the elevated docs. To hide results, use
enableElevation=false :
http://localhost:8983/solr/techproducts/elevate?q=ipod&df=text&debugQuery=true&enableElevati
on=true
http://localhost:8983/solr/techproducts/elevate?q=ipod&df=text&debugQuery=true&enableElevati
on=false
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The forceElevation Parameter
You can force elevation during runtime by adding forceElevation=true to the query URL:
http://localhost:8983/solr/techproducts/elevate?q=ipod&df=text&debugQuery=true&enableElevati
on=true&forceElevation=true
The exclusive Parameter
You can force Solr to return only the results specified in the elevation file by adding exclusive=true to the
URL:
http://localhost:8983/solr/techproducts/elevate?q=ipod&df=text&debugQuery=true&exclusive=tru
e
Document Transformers and the markExcludes Parameter
The [elevated] Document Transformer can be used to annotate each document with information about
whether or not it was elevated:
http://localhost:8983/solr/techproducts/elevate?q=ipod&df=text&fl=id,[elevated]
Likewise, it can be helpful when troubleshooting to see all matching documents – including documents that
the elevation configuration would normally exclude. This is possible by using the markExcludes=true
parameter, and then using the [excluded] transformer:
http://localhost:8983/solr/techproducts/elevate?q=ipod&df=text&markExcludes=true&fl=id,[elev
ated],[excluded]
The elevateIds and excludeIds Parameters
When the elevation component is in use, the pre-configured list of elevations for a query can be overridden
at request time to use the unique keys specified in these request parameters.
For example, in the request below documents 3007WFP and 9885A004 will be elevated, and document IW-02
will be excluded?—?regardless of what elevations or exclusions are configured for the query "cable" in
elevate.xml:
http://localhost:8983/solr/techproducts/elevate?q=cable&df=text&excludeIds=IW-
02&elevateIds=3007WFP,9885A004
If either one of these parameters is specified at request time, the the entire elevation configuration for the
query is ignored.
For example, in the request below documents IW-02 and F8V7067-APL-KIT will be elevated, and no
documents will be excluded – regardless of what elevations or exclusions are configured for the query
"ipod" in elevate.xml:
http://localhost:8983/solr/techproducts/elevate?q=ipod&df=text&elevateIds=IW-02,F8V7067-APL-
KIT
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The fq Parameter
Query elevation respects the standard filter query ( fq ) parameter. That is, if the query contains the fq
parameter, all results will be within that filter even if elevate.xml adds other documents to the result set.
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Response Writers
A Response Writer generates the formatted response of a search. Solr supports a variety of Response
Writers to ensure that query responses can be parsed by the appropriate language or application.
The wt parameter selects the Response Writer to be used. The table below lists the most common settings
for the wt parameter.
wt Parameter Setting Response Writer Selected
csv CSVResponseWriter
geojson GeoJSONResponseWriter
javabin BinaryResponseWriter
json JSONResponseWriter
php PHPResponseWriter
phps PHPSerializedResponseWriter
python PythonResponseWriter
ruby RubyResponseWriter
smile SmileResponseWriter
velocity VelocityResponseWriter
xlsx XLSXResponseWriter
xml XMLResponseWriter
xslt XSLTResponseWriter
The Standard XML Response Writer
The XML Response Writer is the most general purpose and reusable Response Writer currently included with
Solr. It is the format used in most discussions and documentation about the response of Solr queries.
Note that the XSLT Response Writer can be used to convert the XML produced by this writer to other
vocabularies or text-based formats.
The behavior of the XML Response Writer can be driven by the following query parameters.
The version Parameter
The version parameter determines the XML protocol used in the response. Clients are strongly encouraged
to always specify the protocol version, so as to ensure that the format of the response they receive does not
change unexpectedly if the Solr server is upgraded and a new default format is introduced.
Currently supported version values are:
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XML Version Notes
2.2 The format of the responseHeader changed to use
the same <lst> structure as the rest of the response.
The default value is the latest supported.
The stylesheet Parameter
The stylesheet parameter can be used to direct Solr to include a <?xml-stylesheet type="text/xsl"
href="…"?> declaration in the XML response it returns.
The default behavior is not to return any stylesheet declaration at all.
?
Use of the stylesheet parameter is discouraged, as there is currently no way to specify
external stylesheets, and no stylesheets are provided in the Solr distributions. This is a
legacy parameter, which may be developed further in a future release.
The indent Parameter
If the indent parameter is used, and has a non-blank value, then Solr will make some attempts at indenting
its XML response to make it more readable by humans.
The default behavior is not to indent.
The XSLT Response Writer
The XSLT Response Writer applies an XML stylesheet to output. It can be used for tasks such as formatting
results for an RSS feed.
tr Parameter
The XSLT Response Writer accepts one parameter: the tr parameter, which identifies the XML
transformation to use. The transformation must be found in the Solr conf/xslt directory.
The Content-Type of the response is set according to the <xsl:output> statement in the XSLT transform, for
example: <xsl:output media-type="text/html"/>
Configuration
The example below, from the sample_techproducts_configs config set in the Solr distribution, shows how
the XSLT Response Writer is configured.
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<!--
Changes to XSLT transforms are taken into account
every xsltCacheLifetimeSeconds at most.
-->
<queryResponseWriter name="xslt"
class="org.apache.solr.request.XSLTResponseWriter">
<int name="xsltCacheLifetimeSeconds">5</int>
</queryResponseWriter>
A value of 5 for xsltCacheLifetimeSeconds is good for development, to see XSLT changes quickly. For
production you probably want a much higher value.
JSON Response Writer
A very commonly used Response Writer is the JsonResponseWriter , which formats output in JavaScript
Object Notation (JSON), a lightweight data interchange format specified in specified in RFC 4627. Setting the
wt parameter to json invokes this Response Writer.
Here is a sample response for a simple query like q=id:VS1GB400C3&wt=json :
{
"responseHeader":{
"zkConnected":true,
"status":0,
"QTime":7,
"params":{
"q":"id:VS1GB400C3",
"indent":"on",
"wt":"json"}},
"response":{"numFound":1,"start":0,"maxScore":2.3025851,"docs":[
{
"id":"VS1GB400C3",
"name":["CORSAIR ValueSelect 1GB 184-Pin DDR SDRAM Unbuffered DDR 400 (PC 3200) System
Memory - Retail"],
"manu":["Corsair Microsystems Inc."],
"manu_id_s":"corsair",
"cat":["electronics",
"memory"],
"price":[74.99],
"popularity":[7],
"inStock":[true],
"store":["37.7752,-100.0232"],
"manufacturedate_dt":"2006-02-13T15:26:37Z",
"payloads":["electronics|4.0 memory|2.0"],
"_version_":1549728120626479104}]
}}
The default mime type for the JSON writer is application/json , however this can be overridden in the
solrconfig.xml - such as in this example from the “ techproducts ” configuration:
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<queryResponseWriter name="json" class="solr.JSONResponseWriter">
<!-- For the purposes of the tutorial, JSON response are written as
plain text so that it‘s easy to read in *any* browser.
If you are building applications that consume JSON, just remove
this override to get the default "application/json" mime type.
-->
<str name="content-type">text/plain</str>
</queryResponseWriter>
JSON-Specific Parameters
json.nl
This parameter controls the output format of NamedLists, where order is more important than access by
name. NamedList is currently used for field faceting data.
json.nl Parameter
setting
Example output for NamedList("a"=1,
"bar"="foo", null=3, null=null)
Description
flat (the default)
["a",1, "bar","foo", null,3,
null,null]
NamedList is represented as a flat array,
alternating names and values.
map
{"a":1, "bar":"foo", "":3, "":null}
NamedList is represented as a JSON
object. Although this is the simplest
mapping, a NamedList can have optional
keys, repeated keys, and preserves order.
Using a JSON object (essentially a map or
hash) for a NamedList results in the loss
of some information.
arrarr
[["a",1], ["bar","foo"], [null,3],
[null,null]]
NamedList is represented as an array of
two element arrays.
arrmap [ {"a":1}, {"b":2}, 3, null] NamedList is represented as an array of
JSON objects.
arrntv
[{"name":"a","type":"int","value":1}
,
{"name":"bar","type":"str","value":"
foo"},
{"name":null,"type":"int","value":3}
,
{"name":null,"type":"null","value":n
ull}]
NamedList is represented as an array of
Name Type Value JSON objects.
json.wrf
json.wrf=function adds a wrapper-function around the JSON response, useful in AJAX with dynamic script
tags for specifying a JavaScript callback function.
? http://www.xml.com/pub/a/2005/12/21/json-dynamic-script-tag.html
? http://www.theurer.cc/blog/2005/12/15/web-services-json-dump-your-proxy/
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Binary Response Writer
This is a custom binary format used by Solr for inter-node communication as well as client-server
communication. SolrJ uses this as the default for indexing as well as querying. See Client APIs for more
details.
GeoJSON Response Writer
Returns Solr results in GeoJSON augmented with Solr-specific JSON. To use this, set wt=geojson and
geojson.field to the name of a spatial Solr field. Not all spatial fields types are supported, and you’ll get an
error if you use an unsupported one.
Python Response Writer
Solr has an optional Python response format that extends its JSON output in the following ways to allow the
response to be safely evaluated by the python interpreter:
? true and false changed to True and False
? Python unicode strings are used where needed
? ASCII output (with unicode escapes) is used for less error-prone interoperability
? newlines are escaped
? null changed to None
PHP Response Writer and PHP Serialized Response Writer
Solr has a PHP response format that outputs an array (as PHP code) which can be evaluated. Setting the wt
parameter to php invokes the PHP Response Writer.
Example usage:
$code = file_get_contents(‘http://localhost:8983/solr/techproducts/select?q=iPod&wt=php‘);
eval("$result = " . $code . ";");
print_r($result);
Solr also includes a PHP Serialized Response Writer that formats output in a serialized array. Setting the wt
parameter to phps invokes the PHP Serialized Response Writer.
Example usage:
$serializedResult = file_get_contents(
‘http://localhost:8983/solr/techproducts/select?q=iPod&wt=phps‘);
$result = unserialize($serializedResult);
print_r($result);
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Ruby Response Writer
Solr has an optional Ruby response format that extends its JSON output in the following ways to allow the
response to be safely evaluated by Ruby’s interpreter:
? Ruby’s single quoted strings are used to prevent possible string exploits.
? \ and ‘ are the only two characters escaped.
? Unicode escapes are not used. Data is written as raw UTF-8.
? nil used for null.
? ? is used as the key/value separator in maps.
Here is a simple example of how one may query Solr using the Ruby response format:
require ‘net/http‘
h = Net::HTTP.new(‘localhost‘, 8983)
hresp, data = h.get(‘/solr/techproducts/select?q=iPod&wt=ruby‘, nil)
rsp = eval(data)
puts ‘number of matches = ‘ + rsp[‘response‘][‘numFound‘].to_s
#print out the name field for each returned document
rsp[‘response‘][‘docs‘].each { |doc| puts ‘name field = ‘ + doc[‘name‘\] }
CSV Response Writer
The CSV response writer returns a list of documents in comma-separated values (CSV) format. Other
information that would normally be included in a response, such as facet information, is excluded.
The CSV response writer supports multi-valued fields, as well aspseudo-fields, and the output of this CSV
format is compatible with Solr’s CSV update format.
CSV Parameters
These parameters specify the CSV format that will be returned. You can accept the default values or specify
your own.
Parameter Default Value
csv.encapsulator "
csv.escape None
csv.separator ,
csv.header Defaults to true. If false,
Solr does not print the
column headers
csv.newline \n
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Parameter Default Value
csv.null Defaults to a zero length
string. Use this
parameter when a
document has no value
for a particular field.
Multi-Valued Field CSV Parameters
These parameters specify how multi-valued fields are encoded. Per-field overrides for these values can be
done using f.<fieldname>.csv.separator=| .
Parameter Default Value
csv.mv.encapsulator None
csv.mv.escape \
csv.mv.separator Defaults to the
csv.separator value
Example
http://localhost:8983/solr/techproducts/select?q=ipod&fl=id,cat,name,popularity,price,score&
wt=csv returns:
id,cat,name,popularity,price,score
IW-02,"electronics,connector",iPod & iPod Mini USB 2.0 Cable,1,11.5,0.98867977
F8V7067-APL-KIT,"electronics,connector",Belkin Mobile Power Cord for iPod w/
Dock,1,19.95,0.6523595
MA147LL/A,"electronics,music",Apple 60 GB iPod with Video Playback Black,10,399.0,0.2446348
Velocity Response Writer
The VelocityResponseWriter processes the Solr response and request context through Apache Velocity
templating.
See Velocity Response Writer section for details.
Smile Response Writer
The Smile format is a JSON-compatible binary format, described in detail here: http://wiki.fasterxml.com/
SmileFormat.
XLSX Response Writer
Use this to get the response as a spreadsheet in the .xlsx (Microsoft Excel) format. It accepts parameters in
the form colwidth.<field-name> and colname.<field-name> which helps you customize the column
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widths and column names.
This response writer has been added as part of the extraction library, and will only work if the extraction
contrib is present in the server classpath. Defining the classpath with the lib directive is not sufficient.
Instead, you will need to copy the necessary .jars to the Solr webapp’s lib directory manually. You can run
these commands from your $SOLR_INSTALL directory:
cp contrib/extraction/lib/*.jar server/solr-webapp/webapp/WEB-INF/lib/
cp dist/solr-cell-6.3.0.jar server/solr-webapp/webapp/WEB-INF/lib/
Once the libraries are in place, you can add wt=xlsx to your request, and results will be returned as an XLSX
sheet.
Velocity Response Writer
The VelocityResponseWriter is an optional plugin available in the contrib/velocity directory. It powers the
/browse user interfaces when using configurations such as "basic_configs", "techproducts", and
"example/files".
Its JAR and dependencies must be added (via <lib> or solr/home lib inclusion), and must be registered in
solrconfig.xml like this:
<queryResponseWriter name="velocity" class="solr.VelocityResponseWriter">
<str name="template.base.dir">${velocity.template.base.dir:}</str>
<!--
<str name="init.properties.file">velocity-init.properties</str>
<bool name="params.resource.loader.enabled">true</bool>
<bool name="solr.resource.loader.enabled">false</bool>
<lst name="tools">
<str name="mytool">com.example.MyCustomTool</str>
</lst>
-->
</queryResponseWriter>
The above example shows the optional initialization and custom tool parameters used by
VelocityResponseWriter; these are detailed in the following table. These initialization parameters are only
specified in the writer registration in solrconfig.xml, not as request-time parameters. See further below for
request-time parameters.
Configuration & Usage
VelocityResponseWriter Initialization Parameters
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Parameter Description Default value
template.base.dir If specified and exists as a file system directory, a file resource
loader will be added for this directory. Templates in this
directory will override "solr" resource loader templates.
init.properties.file Specifies a properties file name which must exist in the Solr
conf/ directory (not under a velocity/ subdirectory) or root of
a JAR file in a <lib>.
params.resource.loa
der.enabled
The "params" resource loader allows templates to be specified
in Solr request parameters. For example:
http://localhost:8983/solr/gettingstarted/select?q=*:*
&wt=velocity&v.template=custom&v.template.custom=CUSTO
M%3A%20%23core_name
where v.template=custom says to render a template called
"custom" and `v.template.custom’s value is the actual custom
template. This is disabled by default; it’d be a niche, unusual,
use case to need this enabled.
false
solr.resource.loader
.enabled
The "solr" resource loader is the only template loader
registered by default. Templates are served from resources
visible to the SolrResourceLoader under a velocity/
subdirectory. The VelocityResponseWriter itself has some built-
in templates (in its JAR file, under velocity/) that are available
automatically through this loader. These built-in templates can
be overridden when the same template name is in
conf/velocity/ or by using the template.base.dir option.
true
tools External "tools" can be specified as list of string name/value
(tool name / class name) pairs. Tools, in the Velocity context,
are simply Java objects. Tool classes are constructed using a no-
arg constructor (or a single-SolrCore-arg constructor if it exists)
and added to the Velocity context with the specified name. A
custom registered tool can override the built-in context objects
with the same name, except for $request, $response, $page,
and $debug (these tools are designed to not be overridden).
VelocityResponseWriter Request Parameters
Parameter Description Default value
v.template Specifies the name of the template to render.
v.layout Specifies a template name to use as the layout around the
main, v.template , specified template.
The main template is rendered into a string value included into
the layout rendering as $content .
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Parameter Description Default value
v.layout.enabled Determines if the main template should have a layout wrapped
around it. True by default, but requires v.layout to specified as
well.
true
v.contentType Specifies the content type used in the HTTP response. If not
specified, the default will depend on whether v.json is
specified or not.
without json.wrf:
text/html;charset=U
TF-8
with json.wrf:
application/json;cha
rset=UTF-8
v.json Specifies a function name to wrap around the response
rendered as JSON. If specified, the content type used in the
response will be "application/json;charset=UTF-8", unless
overridden by v.contentType .
Output will be in this format (with v.json=wrf):
wrf("result":"<Velocity generated response string,
with quotes and backslashes escaped>")
v.locale Locale to use with the $resource tool and other LocaleConfig
implementing tools. The default locale is Locale.ROOT .
Localized resources are loaded from standard Java resource
bundles named resources[_locale-code].properties .
Resource bundles can be added by providing a JAR file visible by
the SolrResourceLoader with resource bundles under a velocity
sub-directory. Resource bundles are not loadable under conf/,
as only the class loader aspect of SolrResourceLoader can be
used here.
v.template.<templat
e_name>
When the "params" resource loader is enabled, templates can
be specified as part of the Solr request.
VelocityResponseWriter Context Objects
Context Reference Description
request SolrQueryRequest javadocs
response QueryResponse most of the time, but in some cases where
QueryResponse doesn’t like the request handlers output
(AnalysisRequestHandler, for example, causes a ClassCastException
parsing "response"), the response will be a SolrResponseBase object.
esc A Velocity EscapeTool instance
date A Velocity ComparisonDateTool instance
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Context Reference Description
list A Velocity ListTool instance
math A Velocity MathTool instance
number A Velocity NumberTool instance
sort A Velocity SortTool instance
display A Velocity DisplayTool instance
resource A Velocity ResourceTool instance
engine The current VelocityEngine instance
page An instance of Solr’s PageTool (only included if the response is a
QueryResponse where paging makes sense)
debug A shortcut to the debug part of the response, or null if debug is not on.
This is handy for having debug-only sections in a template using
#if($debug)…#end
content The rendered output of the main template, when rendering the layout
(v.layout.enabled=true and v.layout=<template>).
[custom tool(s)] Tools provided by the optional "tools" list of the VelocityResponseWriter
registration are available by their specified name.
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Near Real Time Searching
Near Real Time (NRT) search means that documents are available for search almost immediately after being
indexed.
This allows additions and updates to documents to be seen in ‘near‘ real time. Solr does not block updates
while a commit is in progress. Nor does it wait for background merges to complete before opening a new
search of indexes and returning.
With NRT, you can modify a commit command to be a soft commit, which avoids parts of a standard commit
that can be costly. You will still want to do standard commits to ensure that documents are in stable storage,
but soft commits let you see a very near real time view of the index in the meantime.
However, pay special attention to cache and autowarm settings as they can have a significant impact on NRT
performance.
Commits and Optimizing
A commit operation makes index changes visible to new search requests. A hard commit uses the
transaction log to get the id of the latest document changes, and also calls fsync on the index files to ensure
they have been flushed to stable storage and no data loss will result from a power failure.
A soft commit is much faster since it only makes index changes visible and does not fsync index files or
write a new index descriptor. If the JVM crashes or there is a loss of power, changes that occurred after the
last hard commit will be lost. Search collections that have NRT requirements (that want index changes to be
quickly visible to searches) will want to soft commit often but hard commit less frequently. A softCommit
may be "less expensive" in terms of time, but not free, since it can slow throughput.
An optimize is like a hard commit except that it forces all of the index segments to be merged into a single
segment first. Depending on the use, this operation should be performed infrequently (e.g., nightly), if at all,
since it involves reading and re-writing the entire index. Segments are normally merged over time anyway
(as determined by the merge policy), and optimize just forces these merges to occur immediately.
Soft commit takes uses two parameters: maxDocs and maxTime .
Parameter Description
maxDocs
Integer. Defines the number of documents to queue before pushing them
to the index. It works in conjunction with the
update_handler_autosoftcommit_max_time parameter in that if either
limit is reached, the documents will be pushed to the index.
maxTime
The number of milliseconds to wait before pushing documents to the
index. It works in conjunction with the
update_handler_autosoftcommit_max_docs parameter in that if either
limit is reached, the documents will be pushed to the index.
Use maxDocs and maxTime judiciously to fine-tune your commit strategies.
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AutoCommits
An autocommit also uses the parameters maxDocs and maxTime . However it’s useful in many strategies to
use both a hard autocommit and autosoftcommit to achieve more flexible commits.
A common configuration is to do a hard autocommit every 1-10 minutes and a autosoftcommit every
second. With this configuration, new documents will show up within about a second of being added, and if
the power goes out, soft commits are lost unless a hard commit has been done.
For example:
<autoSoftCommit>
<maxTime>1000</maxTime>
</autoSoftCommit>
It’s better to use maxTime rather than maxDocs to modify an autoSoftCommit , especially when indexing a
large number of documents through the commit operation. It’s also better to turn off autoSoftCommit for
bulk indexing.
Optional Attributes for commit and optimize
Parameter Valid Attributes Description
waitSearcher
true, false Block until a new searcher is opened and registered as the main
query searcher, making the changes visible. Default is true.
softCommit
true, false Perform a soft commit. This will refresh the view of the index
faster, but without guarantees that the document is stably
stored. Default is false.
expungeDeletes
true, false Valid for commit only. This parameter purges deleted data from
segments. The default is false.
maxSegments
integer Valid for optimize only. Optimize down to at most this number
of segments. The default is 1.
Example of commit and optimize with optional attributes:
<commit waitSearcher="false"/>
<commit waitSearcher="false" expungeDeletes="true"/>
<optimize waitSearcher="false"/>
Passing commit and commitWithin Parameters as Part of the URL
Update handlers can also get commit -related parameters as part of the update URL. This example adds a
small test document and causes an explicit commit to happen immediately afterwards:
http://localhost:8983/solr/my_collection/update?stream.body=<add><doc>
<field name="id">testdoc</field></doc></add>&commit=true
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Alternately, you may want to use this:
http://localhost:8983/solr/my_collection/update?stream.body=<optimize/>
This example causes the index to be optimized down to at most 10 segments, but won’t wait around until
it’s done ( waitFlush=false ):
curl
‘http://localhost:8983/solr/my_collection/update?optimize=true&maxSegments=10&waitFlush=false‘
This example adds a small test document with a commitWithin instruction that tells Solr to make sure the
document is committed no later than 10 seconds later (this method is generally preferred over explicit
commits):
curl http://localhost:8983/solr/my_collection/update?commitWithin=10000
-H "Content-Type: text/xml" --data-binary ‘<add><doc><field
name="id">testdoc</field></doc></add>‘
Changing default commitWithin Behavior
The commitWithin settings allow forcing document commits to happen in a defined time period. This is used
most frequently with Near Real Time Searching, and for that reason the default is to perform a soft commit.
This does not, however, replicate new documents to slave servers in a master/slave environment. If that’s a
requirement for your implementation, you can force a hard commit by adding a parameter, as in this
example:
<commitWithin>
<softCommit>false</softCommit>
</commitWithin>
With this configuration, when you call commitWithin as part of your update message, it will automatically
perform a hard commit every time.
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RealTime Get
For index updates to be visible (searchable), some kind of commit must reopen a searcher to a new point-in-
time view of the index.
The realtime get feature allows retrieval (by unique-key ) of the latest version of any documents without the
associated cost of reopening a searcher. This is primarily useful when using Solr as a NoSQL data store and
not just a search index.
Real Time Get relies on the update log feature, which is enabled by default and can be configured in
solrconfig.xml :
<updateLog>
<str name="dir">${solr.ulog.dir:}</str>
</updateLog>
Real Time Get requests can be performed using the /get handler which exists implicitly in Solr - see Implicit
RequestHandlers - it’s equivalent to the following configuration:
<requestHandler name="/get" class="solr.RealTimeGetHandler">
<lst name="defaults">
<str name="omitHeader">true</str>
<str name="wt">json</str>
<str name="indent">true</str>
</lst>
</requestHandler>
For example, if you started Solr using the bin/solr -e techproducts example command, you could then
index a new document (with out committing it) like so:
curl ‘http://localhost:8983/solr/techproducts/update/json?commitWithin=10000000‘ \
-H ‘Content-type:application/json‘ -d ‘[{"id":"mydoc","name":"realtime-get test!"}]‘
If you do a normal search, this document should not be found yet:
http://localhost:8983/solr/techproducts/query?q=id:mydoc
...
"response":
{"numFound":0,"start":0,"docs":[]}
However if you use the Real Time Get handler exposed at /get , you can still retrieve that document:
http://localhost:8983/solr/techproducts/get?id=mydoc
...
{"doc":{"id":"mydoc","name":"realtime-get test!", "_version_":1487137811571146752}}
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You can also specify multiple documents at once via the ids parameter and a comma separated list of ids, or
by using multiple id parameters. If you specify multiple ids, or use the ids parameter, the response will
mimic a normal query response to make it easier for existing clients to parse.
For example:
http://localhost:8983/solr/techproducts/get?ids=mydoc,IW-02
http://localhost:8983/solr/techproducts/get?id=mydoc&id=IW-02
...
{"response":
{"numFound":2,"start":0,"docs":
[ { "id":"mydoc",
"name":"realtime-get test!",
"_version_":1487137811571146752},
{
"id":"IW-02",
"name":"iPod & iPod Mini USB 2.0 Cable",
...
]
}
}
Real Time Get requests can also be combined with filter queries, specified with an fq parameter, just like
search requests:
http://localhost:8983/solr/techproducts/get?id=mydoc&id=IW-02&fq=name:realtime-get
...
{"response":
{"numFound":1,"start":0,"docs":
[ { "id":"mydoc",
"name":"realtime-get test!",
"_version_":1487137811571146752}
]
}
}
?
Do NOT disable the realtime get handler at /get if you are using SolrCloud otherwise any
leader election will cause a full sync in ALL replicas for the shard in question.
Similarly, a replica recovery will also always fetch the complete index from the leader
because a partial sync will not be possible in the absence of this handler.
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Exporting Result Sets
It’s possible to export fully sorted result sets using a special rank query parser and response writer
specifically designed to work together to handle scenarios that involve sorting and exporting millions of
records.
This feature uses a stream sorting technique that begins to send records within milliseconds and continues
to stream results until the entire result set has been sorted and exported.
The cases where this functionality may be useful include: session analysis, distributed merge joins, time
series roll-ups, aggregations on high cardinality fields, fully distributed field collapsing, and sort based stats.
Field Requirements
All the fields being sorted and exported must have docValues set to true. For more information, see the
section on DocValues.
The /export RequestHandler
The /export request handler with the appropriate configuration is one of Solr’s out-of-the-box request
handlers - see Implicit RequestHandlers for more information.
Note that this request handler’s properties are defined as "invariants", which means they cannot be
overridden by other properties passed at another time (such as at query time).
Requesting Results Export
You can use /export to make requests to export the result set of a query.
All queries must include sort and fl parameters, or the query will return an error. Filter queries are also
supported.
The supported response writers are json and javabin . For backward compatibility reasons wt=xsort is also
supported as input, but wt=xsort behaves same as wt=json . The default output format is json .
Here is an example of an export request of some indexed log data:
http://localhost:8983/solr/core_name/export?q=my-
query&sort=severity+desc,timestamp+desc&fl=severity,timestamp,msg
Specifying the Sort Criteria
The sort property defines how documents will be sorted in the exported result set. Results can be sorted by
any field that has a field type of int,long, float, double, string. The sort fields must be single valued fields.
Up to four sort fields can be specified per request, with the ‘asc‘ or ‘desc‘ properties.
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Specifying the Field List
The fl property defines the fields that will be exported with the result set. Any of the field types that can be
sorted (i.e., int, long, float, double, string, date, boolean) can be used in the field list. The fields can be single
or multi-valued. However, returning scores and wildcards are not supported at this time.
Distributed Support
See the section Streaming Expressions for distributed support.
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Streaming Expressions
Streaming Expressions provide a simple yet powerful stream processing language for Solr Cloud.
Streaming expressions are a suite of functions that can be combined to perform many different parallel
computing tasks. These functions are the basis for the Parallel SQL Interface.
There is a growing library of functions that can be combined to implement:
? Request/response stream processing
? Batch stream processing
? Fast interactive MapReduce
? Aggregations (Both pushed down faceted and shuffling MapReduce)
? Parallel relational algebra (distributed joins, intersections, unions, complements)
? Publish/subscribe messaging
? Distributed graph traversal
? Machine learning and parallel iterative model training
? Anomaly detection
? Recommendation systems
? Retrieve and rank services
? Text classification and feature extraction
? Streaming NLP
Streams from outside systems can be joined with streams originating from Solr and users can add their own
stream functions by following Solr’s Java streaming API.
?
Both streaming expressions and the streaming API are considered experimental, and the
APIs are subject to change.
Stream Language Basics
Streaming Expressions are comprised of streaming functions which work with a Solr collection. They emit a
stream of tuples (key/value Maps).
Many of the provided streaming functions are designed to work with entire result sets rather then the top N
results like normal search. This is supported by the /export handler.
Some streaming functions act as stream sources to originate the stream flow. Other streaming functions act
as stream decorators to wrap other stream functions and perform operations on the stream of tuples. Many
streams functions can be parallelized across a worker collection. This can be particularly powerful for
relational algebra functions.
Streaming Requests and Responses
Solr has a /stream request handler that takes streaming expression requests and returns the tuples as a
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JSON stream. This request handler is implicitly defined, meaning there is nothing that has to be defined in
solrconfig.xml - see Implicit RequestHandlers.
The /stream request handler takes one parameter, expr , which is used to specify the streaming expression.
For example, this curl command encodes and POSTs a simple search() expression to the /stream handler:
curl --data-urlencode ‘expr=search(enron_emails,
q="from:1800flowers*",
fl="from, to",
sort="from asc",
qt="/export")‘ http://localhost:8983/solr/enron_emails/stream
Details of the parameters for each function are included below.
For the above example the /stream handler responded with the following JSON response:
{"result-set":{"docs":[
{"from":"1800flowers.133139412@s2u2.com","to":"lcampbel@enron.com"},
{"from":"1800flowers.93690065@s2u2.com","to":"jtholt@ect.enron.com"},
{"from":"1800flowers.96749439@s2u2.com","to":"alewis@enron.com"},
{"from":"1800flowers@1800flowers.flonetwork.com","to":"lcampbel@enron.com"},
{"from":"1800flowers@1800flowers.flonetwork.com","to":"lcampbel@enron.com"},
{"from":"1800flowers@1800flowers.flonetwork.com","to":"lcampbel@enron.com"},
{"from":"1800flowers@1800flowers.flonetwork.com","to":"lcampbel@enron.com"},
{"from":"1800flowers@1800flowers.flonetwork.com","to":"lcampbel@enron.com"},
{"from":"1800flowers@shop2u.com","to":"ebass@enron.com"},
{"from":"1800flowers@shop2u.com","to":"lcampbel@enron.com"},
{"from":"1800flowers@shop2u.com","to":"lcampbel@enron.com"},
{"from":"1800flowers@shop2u.com","to":"lcampbel@enron.com"},
{"from":"1800flowers@shop2u.com","to":"ebass@enron.com"},
{"from":"1800flowers@shop2u.com","to":"ebass@enron.com"},
{"EOF":true,"RESPONSE_TIME":33}]}
}
Note the last tuple in the above example stream is {"EOF":true,"RESPONSE_TIME":33} . The EOF indicates
the end of the stream. To process the JSON response, you’ll need to use a streaming JSON implementation
because streaming expressions are designed to return the entire result set which may have millions of
records. In your JSON client you’ll need to iterate each doc (tuple) and check for the EOF tuple to determine
the end of stream.
The org.apache.solr.client.solrj.io package provides Java classes that compile streaming expressions
into streaming API objects. These classes can be used to execute streaming expressions from inside a Java
application. For example:
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StreamFactory streamFactory = new StreamFactory().withCollectionZkHost("collection1", zkServer
.getZkAddress())
.withStreamFunction("search", CloudSolrStream.class)
.withStreamFunction("unique", UniqueStream.class)
.withStreamFunction("top", RankStream.class)
.withStreamFunction("group", ReducerStream.class)
.withStreamFunction("parallel", ParallelStream.class);
ParallelStream pstream = (ParallelStream)streamFactory.constructStream("parallel(collection1,
group(search(collection1, q=\"*:*\", fl=\"id,a_s,a_i,a_f\", sort=\"a_s asc,a_f asc\",
partitionKeys=\"a_s\"), by=\"a_s asc\"), workers=\"2\", zkHost=\""+zkHost+"\", sort=\"a_s asc\")
");
Data Requirements
Because streaming expressions relies on the /export handler, many of the field and field type requirements
to use /export are also requirements for /stream , particularly for sort and fl parameters. Please see the
section Exporting Result Sets for details.
Types of Streaming Expressions
About Stream Sources
Stream sources originate streams. The most commonly used one of these is search , which does a query.
A full reference to all available source expressions is available in Stream Sources.
About Stream Decorators
Stream decorators wrap other stream functions or perform operations on a stream.
A full reference to all available decorator expressions is available in Stream Decorators.
About Stream Evaluators
Stream Evaluators can be used to evaluate (calculate) new values based on other values in a tuple. That
newly evaluated value can be put into the tuple (as part of a select(…) clause), used to filter streams (as
part of a having(…) clause), and for other things. Evaluators can contain field names, raw values, or other
evaluators, giving you the ability to create complex evaluation logic, including conditional if/then choices.
In cases where you want to use raw values as part of an evaluation you will need to consider the order of
how evaluators are parsed.
1. If the parameter can be parsed into a valid number, then it is considered a number. For example,
add(3,4.5)
2. If the parameter can be parsed into a valid boolean, then it is considered a boolean. For example,
eq(true,false)
3. If the parameter can be parsed into a valid evaluator, then it is considered an evaluator. For example,
eq(add(10,4),add(7,7))
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4. The parameter is considered a field name, even if it quoted. For example, eq(fieldA,"fieldB")
If you wish to use a raw string as part of an evaluation, you will want to consider using the raw(string)
evaluator. This will always return the raw value, no matter what is entered.
A full reference to all available evaluator expressions is available in Stream Evaluators.
Stream Source Reference
search
The search function searches a SolrCloud collection and emits a stream of tuples that match the query. This
is very similar to a standard Solr query, and uses many of the same parameters.
This expression allows you to specify a request hander using the qt parameter. By default, the /select
handler is used. The /select handler can be used for simple rapid prototyping of expressions. For
production, however, you will most likely want to use the /export handler which is designed to sort and
export entire result sets. The /export handler is not used by default because it has stricter requirements
then the /select handler so it’s not as easy to get started working with. To read more about the /export
handler requirements review the section Exporting Result Sets.
search Parameters
? collection : (Mandatory) the collection being searched.
? q : (Mandatory) The query to perform on the Solr index.
? fl : (Mandatory) The list of fields to return.
? sort : (Mandatory) The sort criteria.
? zkHost : Only needs to be defined if the collection being searched is found in a different zkHost than the
local stream handler.
? qt : Specifies the query type, or request handler, to use. Set this to /export to work with large result sets.
The default is /select .
? rows : (Mandatory with the /select handler) The rows parameter specifies how many rows to return.
This parameter is only needed with the /select handler (which is the default) since the /export handler
always returns all rows.
? partitionKeys : Comma delimited list of keys to partition the search results by. To be used with the
parallel function for parallelizing operations across worker nodes. See the parallel function for details.
search Syntax
expr=search(collection1,
zkHost="localhost:9983",
qt="/export",
q="*:*",
fl="id,a_s,a_i,a_f",
sort="a_f asc, a_i asc")
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jdbc
The jdbc function searches a JDBC datasource and emits a stream of tuples representing the JDBC result set.
Each row in the result set is translated into a tuple and each tuple contains all the cell values for that row.
jdbc Parameters
? connection : (Mandatory) JDBC formatted connection string to whatever driver you are using.
? sql : (Mandatory) query to pass off to the JDBC endpoint
? sort : (Mandatory) The sort criteria indicating how the data coming out of the JDBC stream is sorted
? driver : The name of the JDBC driver used for the connection. If provided then the driver class will
attempt to be loaded into the JVM. If not provided then it is assumed that the driver is already loaded
into the JVM. Some drivers require explicit loading so this option is provided.
? [driverProperty] : One or more properties to pass to the JDBC driver during connection. The format is
propertyName="propertyValue" . You can provide as many of these properties as you’d like and they will
all be passed to the connection.
Connections and Drivers
Because some JDBC drivers require explicit loading the driver parameter can be used to provide the driver
class name. If provided, then during stream construction the driver will be loaded. If the driver cannot be
loaded because the class is not found on the classpath, then stream construction will fail.
When the JDBC stream is opened it will validate that a driver can be found for the provided connection
string. If a driver cannot be found (because it hasn’t been loaded) then the open will fail.
Datatypes
Due to the inherent differences in datatypes across JDBC sources the following datatypes are supported. The
table indicates what Java type will be used for a given JDBC type. Types marked as requiring conversion will
go through a conversion for each value of that type. For performance reasons the cell data types are only
considered when the stream is opened as this is when the converters are created.
JDBC Type Java Type Requires Conversion
String String No
Short Long Yes
Integer Long Yes
Long Long No
Float Double Yes
Double Double No
Boolean Boolean No
jdbc Syntax
A basic jdbc expression:
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jdbc(
connection="jdbc:hsqldb:mem:.",
sql="select NAME, ADDRESS, EMAIL, AGE from PEOPLE where AGE > 25 order by AGE, NAME DESC",
sort="AGE asc, NAME desc",
driver="org.hsqldb.jdbcDriver"
)
A jdbc expression that passes a property to the driver:
// get_column_name is a property to pass to the hsqldb driver
jdbc(
connection="jdbc:hsqldb:mem:.",
sql="select NAME as FIRST_NAME, ADDRESS, EMAIL, AGE from PEOPLE where AGE > 25 order by AGE,
NAME DESC",
sort="AGE asc, NAME desc",
driver="org.hsqldb.jdbcDriver",
get_column_name="false"
)
echo
The echo function returns a single Tuple echoing its text parameter. Echo is the simplest stream source
designed to provide text to a text analyzing stream decorator.
echo Syntax
echo("Hello world")
facet
The facet function provides aggregations that are rolled up over buckets. Under the covers the facet
function pushes down the aggregation into the search engine using Solr’s JSON Facet API. This provides
sub-second performance for many use cases. The facet function is appropriate for use with a low to
moderate number of distinct values in the bucket fields. To support high cardinality aggregations see the
rollup function.
facet Parameters
? collection : (Mandatory) Collection the facets will be aggregated from.
? q : (Mandatory) The query to build the aggregations from.
? buckets : (Mandatory) Comma separated list of fields to rollup over. The comma separated list represents
the dimensions in a multi-dimensional rollup.
? bucketSorts : Comma separated list of sorts to apply to each dimension in the buckets parameters. Sorts
can be on the computed metrics or on the bucket values.
? bucketSizeLimit : The number of buckets to include. This value is applied to each dimension.
? metrics : List of metrics to compute for the buckets. Currently supported metrics are sum(col) ,
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avg(col) , min(col) , max(col) , count(*) .
facet Syntax
Example 1:
facet(collection1,
q="*:*",
buckets="a_s",
bucketSorts="sum(a_i) desc",
bucketSizeLimit=100,
sum(a_i),
sum(a_f),
min(a_i),
min(a_f),
max(a_i),
max(a_f),
avg(a_i),
avg(a_f),
count(*))
The example above shows a facet function with rollups over a single bucket, where the buckets are returned
in descending order by the calculated value of the sum(a_i) metric.
Example 2:
facet(collection1,
q="*:*",
buckets="year_i, month_i, day_i",
bucketSorts="year_i desc, month_i desc, day_i desc",
bucketSizeLimit=100,
sum(a_i),
sum(a_f),
min(a_i),
min(a_f),
max(a_i),
max(a_f),
avg(a_i),
avg(a_f),
count(*))
The example above shows a facet function with rollups over three buckets, where the buckets are returned
in descending order by bucket value.
features
The features function extracts the key terms from a text field in a classification training set stored in a
SolrCloud collection. It uses an algorithm known as * Information Gain* , to select the important terms from
the training set. The features function was designed to work specifically with the train function, which uses
the extracted features to train a text classifier.
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The features function is designed to work with a training set that provides both positive and negative
examples of a class. It emits a tuple for each feature term that is extracted along with the inverse document
frequency (IDF) for the term in the training set.
The features function uses a query to select the training set from a collection. The IDF for each selected
feature is calculated relative to the training set matching the query. This allows multiple training sets to be
stored in the same SolrCloud collection without polluting the IDF across training sets.
features Parameters
? collection : (Mandatory) The collection that holds the training set
? q : (Mandatory) The query that defines the training set. The IDF for the features will be generated specific
to the result set matching the query.
? featureSet : (Mandatory) The name of the feature set. This can be used to retrieve the features if they
are stored in a SolrCloud collection.
? field : (Mandatory) The text field to extract the features from.
? outcome : (Mandatory) The field that defines the class, positive or negative
? numTerms : (Mandatory) How many feature terms to extract.
? positiveLabel : (defaults to 1) The value in the outcome field that defines a postive outcome.
features Syntax
features(collection1,
q="*:*",
featureSet="features1",
field="body",
outcome="out_i",
numTerms=250)
gatherNodes
The gatherNodes function provides breadth-first graph traversal. For details, see the section Graph
Traversal.
model
The model function retrieves and caches logistic regression text classification models that are stored in a
SolrCloud collection. The model function is designed to work with models that are created by the train
function, but can also be used to retrieve text classification models trained outside of Solr, as long as they
conform to the specified format. After the model is retrieved it can be used by the classify function to classify
documents.
A single model tuple is fetched and returned based on the id parameter. The model is retrieved by matching
the id parameter with a model name in the index. If more then one iteration of the named model is stored in
the index, the highest iteration is selected.
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Caching with model
The model function has an internal LRU (least-recently-used) cache so models do not have to be retrieved
with each invocation of the model function. The time to cache for each model ID can be passed as a
parameter to the function call. Retrieving a cached model does not reset the time for expiring the model ID
in the cache.
Model Storage
The storage format of the models in Solr is below. The train function outputs the format below so you only
need to know schema details if you plan to use the model function with logistic regression models trained
outside of Solr.
? name_s (Single value, String, Stored): The name of the model.
? iteration_i (Single value, Integer, Stored): The iteration number of the model. Solr can store all
iterations of the models generated by the train function.
? terms_ss (Multi value, String, Stored: The array of terms/features of the model.
? weights_ds (Multi value, double, Stored): The array of term weights. Each weight corresponds by array
index to a term.
? idfs_ds (Multi value, double, Stored): The array of term IDFs (Inverse document frequency). Each IDF
corresponds by array index to a term.
model Parameters
? collection : (Mandatory) The collection where the model is stored.
? id : (Mandatory) The id/name of the model. The model function always returns one model. If there are
multiple iterations of the name, the highest iteration is returned.
? cacheMillis : (Optional) The amount of time to cache the model in the LRU cache.
model Syntax
model(modelCollection,
id="myModel"
cacheMillis="200000")
random
The random function searches a SolrCloud collection and emits a pseudo-random set of results that match
the query. Each invocation of random will return a different pseudo-random result set.
random Parameters
? collection : (Mandatory) The collection the stats will be aggregated from.
? q : (Mandatory) The query to build the aggregations from.
? rows : (Mandatory) The number of pseudo-random results to return.
? fl: (Mandatory) The field list to return.
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? fq : (Optional) Filter query
random Syntax
random(baskets,
q="productID:productX",
rows="100",
fl="basketID")
In the example above the random function is searching the baskets collections for all rows where
"productID:productX". It will return 100 pseudo-random results. The field list returned is the basketID.
significantTerms
The significantTerms function queries a SolrCloud collection, but instead of returning documents, it
returns significant terms found in documents in the result set. The significantTerms function scores terms
based on how frequently they appear in the result set and how rarely they appear in the entire corpus. The
significantTerms function emits a tuple for each term which contains the term, the score, the foreground
count and the background count. The foreground count is how many documents the term appears in in the
result set. The background count is how many documents the term appears in in the entire corpus. The
foreground and background counts are global for the collection.
significantTerms Parameters
? collection : (Mandatory) The collection that the function is run on.
? q : (Mandatory) The query that describes the foreground document set.
? field : (Mandatory) The field to extract the terms from.
? limit : (Optional, Default 20) The max number of terms to return.
? minDocFreq : (Optional, Defaults to 5 documents) The minimum number of documents the term must
appear in on a shard. This is a float value. If greater then 1.0 then it’s considered the absolute number of
documents. If less then 1.0 it’s treated as a percentage of documents.
? maxDocFreq : (Optional, Defaults to 30% of documents) The maximum number of documents the term
can appear in on a shard. This is a float value. If greater then 1.0 then it’s considered the absolute
number of documents. If less then 1.0 it’s treated as a percentage of documents.
? minTermLength : (Optional, Default 4) The minimum length of the term to be considered significant.
significantTerms Syntax
significantTerms(collection1,
q="body:Solr",
field="author",
limit="50",
minDocFreq="10",
maxDocFreq=".20",
minTermLength="5")
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In the example above the significantTerms function is querying collection1 and returning at most 50
significant terms from the authors field that appear in 10 or more documents but not more then 20% of the
corpus.
shortestPath
The shortestPath function is an implementation of a shortest path graph traversal. The shortestPath
function performs an iterative breadth-first search through an unweighted graph to find the shortest paths
between two nodes in a graph. The shortestPath function emits a tuple for each path found. Each tuple
emitted will contain a path key which points to a List of nodeIDs comprising the path.
shortestPath Parameters
? collection : (Mandatory) The collection that the topic query will be run on.
? from : (Mandatory) The nodeID to start the search from
? to : (Mandatory) The nodeID to end the search at
? edge : (Mandatory) Syntax: from_field=to_field . The from_field defines which field to search from.
The to_field defines which field to search to. See example below for a detailed explanation.
? threads : (Optional : Default 6) The number of threads used to perform the partitioned join in the
traversal.
? partitionSize : (Optional : Default 250) The number of nodes in each partition of the join.
? fq : (Optional) Filter query
? maxDepth : (Mandatory) Limits to the search to a maximum depth in the graph.
shortestPath Syntax
shortestPath(collection,
from="john@company.com",
to="jane@company.com",
edge="from_address=to_address",
threads="6",
partitionSize="300",
fq="limiting query",
maxDepth="4")
The expression above performs a breadth-first search to find the shortest paths in an unweighted, directed
graph.
The search starts from the nodeID "john@company.com" in the from_address field and searches for the
nodeID "jane@company.com" in the to_address field. This search is performed iteratively until the
maxDepth has been reached. Each level in the traversal is implemented as a parallel partitioned nested loop
join across the entire collection. The threads parameter controls the number of threads performing the join
at each level, while the partitionSize parameter controls the of number of nodes in each join partition. The
maxDepth parameter controls the number of levels to traverse. fq is a limiting query applied to each level in
the traversal.
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shuffle
The shuffle expression sorts and exports entire result sets. The shuffle expression is similar to the search
expression except that under the covers shuffle always uses the /export handler. The shuffle expression is
designed to be combined with the relational algebra decorators that require complete, sorted result sets.
Shuffled result sets can be partitioned across worker nodes with the parallel stream decorator to perform
parallel relational algebra. When used in parallel mode the partitionKeys parameter must be provided.
shuffle Parameters
? collection : (Mandatory) the collection being searched.
? q : (Mandatory) The query to perform on the Solr index.
? fl : (Mandatory) The list of fields to return.
? sort : (Mandatory) The sort criteria.
? zkHost : Only needs to be defined if the collection being searched is found in a different zkHost than the
local stream handler.
? partitionKeys : Comma delimited list of keys to partition the search results by. To be used with the
parallel function for parallelizing operations across worker nodes. See the parallel function for details.
shuffle Syntax
shuffle(collection1,
q="*:*",
fl="id,a_s,a_i,a_f",
sort="a_f asc, a_i asc")
stats
The stats function gathers simple aggregations for a search result set. The stats function does not support
rollups over buckets, so the stats stream always returns a single tuple with the rolled up stats. Under the
covers the stats function pushes down the generation of the stats into the search engine using the
StatsComponent. The stats function currently supports the following metrics: count(*) , sum() , avg() , min() ,
and max() .
stats Parameters
? collection : (Mandatory) Collection the stats will be aggregated from.
? q : (Mandatory) The query to build the aggregations from.
? metrics : (Mandatory) The metrics to include in the result tuple. Current supported metrics are sum(col) ,
avg(col) , min(col) , max(col) and count(*)
stats Syntax
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stats(collection1,
q=*:*,
sum(a_i),
sum(a_f),
min(a_i),
min(a_f),
max(a_i),
max(a_f),
avg(a_i),
avg(a_f),
count(*))
timeseries
The timeseries function builds a time series aggregation. Under the covers the timeseries function uses
the JSON Facet API as its high performance aggregation engine.
timeseries Parameters
? collection : (Mandatory) Collection the stats will be aggregated from.
? q : (Mandatory) The query to build the aggregations from.
? field : (Mandatory) The date field for the time series.
? start : (Mandatory) The start of the time series expressed in Solr date or date math syntax.
? end : (Mandatory) The end of the time series expressed in Solr date or date math syntax.
? gap : (Mandatory) The time gap between time series aggregation points expressed in Solr date math
syntax.
? metrics : (Mandatory) The metrics to include in the result tuple. Current supported metrics are sum(col) ,
avg(col) , min(col) , max(col) and count(*)
timeseries Syntax
timeseries(collection1,
q=*:*,
field="rec_dt"
start="NOW-30DAYS",
end="NOW",
gap="+1DAY",
sum(a_i),
max(a_i),
max(a_f),
avg(a_i),
avg(a_f),
count(*))
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train
The train function trains a Logistic Regression text classifier on a training set stored in a SolrCloud
collection. It uses a parallel iterative, batch Gradient Descent approach to train the model. The training
algorithm is embedded inside Solr so with each iteration only the model is streamed across the network.
The train function wraps a features function which provides the terms and inverse document frequency
(IDF) used to train the model. The train function operates over the same training set as the features
function, which includes both positive and negative examples of the class.
With each iteration the train function emits a tuple with the model. The model contains the feature terms,
weights, and the confusion matrix for the model. The optimized model can then be used to classify
documents based on their feature terms.
train Parameters
? collection : (Mandatory) Collection that holds the training set
? q : (Mandatory) The query that defines the training set. The IDF for the features will be generated on the
? name : (Mandatory) The name of model. This can be used to retrieve the model if they stored in a Solr
Cloud collection.
? field : (Mandatory) The text field to extract the features from.
? outcome : (Mandatory) The field that defines the class, positive or negative
? maxIterations : (Mandatory) How many training iterations to perform.
? positiveLabel : (defaults to 1) The value in the outcome field that defines a positive outcome.
train Syntax
train(collection1,
features(collection1, q="*:*", featureSet="first", field="body", outcome="out_i",
numTerms=250),
q="*:*",
name="model1",
field="body",
outcome="out_i",
maxIterations=100)
topic
The topic function provides publish/subscribe messaging capabilities built on top of SolrCloud. The topic
function allows users to subscribe to a query. The function then provides one-time delivery of new or
updated documents that match the topic query. The initial call to the topic function establishes the
checkpoints for the specific topic ID. Subsequent calls to the same topic ID will return documents added or
updated after the initial checkpoint. Each run of the topic query updates the checkpoints for the topic ID.
Setting the initialCheckpoint parameter to 0 will cause the topic to process all documents in the index that
match the topic query.
?
The topic function should be considered in beta until SOLR-8709 is committed and released.
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topic Parameters
? checkpointCollection : (Mandatory) The collection where the topic checkpoints are stored.
? collection : (Mandatory) The collection that the topic query will be run on.
? id : (Mandatory) The unique ID for the topic. The checkpoints will be saved under this id.
? q : (Mandatory) The topic query.
? fl : (Mandatory) The field list returned by the topic function.
? initialCheckpoint : (Optional) Sets the initial Solr _version_ number to start reading from the queue. If
not set, it defaults to the highest version in the index. Setting to 0 will process all records that match
query in the index.
topic Syntax
topic(checkpointCollection,
collection,
id="uniqueId",
q="topic query",
fl="id, name, country")
tuple
The tuple function emits a single Tuple with name/value pairs. The values can be set to variables assigned
in a let expression, literals, Stream Evaluators or Stream Expressions. In the case of Stream Evaluators the
tuple will output the return value from the evaluator. This could be a numeric, list or map. If a value is set to
a Stream Expression, the tuple function will flatten the tuple stream from the Stream Expression into a list
of Tuples.
tuple Parameters
? name=value pairs
tuple Syntax
tuple(a=add(1,1),
b=search(collection1, q="cat:a", fl="a, b, c", sort="a desc"))
Stream Decorator Reference
classify
The classify function classifies tuples using a logistic regression text classification model. It was designed
specifically to work with models trained using the train function. The classify function uses the model
function to retrieve a stored model and then scores a stream of tuples using the model. The tuples read by
the classifier must contain a text field that can be used for classification. The classify function uses a Lucene
analyzer to extract the features from the text so the model can be applied. By default the classify function
looks for the analyzer using the name of text field in the tuple. If the Solr schema on the worker node does
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not contain this field, the analyzer can be looked up in another field by specifying the analyzerField
parameter.
Each tuple that is classified is assigned two scores:
? probability_d* : A float between 0 and 1 which describes the probability that the tuple belongs to the
class. This is useful in the classification use case.
? score_d* : The score of the document that has not be squashed between 0 and 1. The score may be
positive or negative. The higher the score the better the document fits the class. This un-squashed score
will be useful in query re-ranking and recommendation use cases. This score is particularly useful when
multiple high ranking documents have a probability_d score of 1, which won’t provide a meaningful
ranking between documents.
classify Parameters
? model expression : (Mandatory) Retrieves the stored logistic regression model.
? field : (Mandatory) The field in the tuples to apply the classifier to. By default the analyzer for this field in
the schema will be used extract the features.
? analyzerField : (Optional) Specifies a different field to find the analyzer from in the schema.
classify Syntax
classify(model(modelCollection,
id="model1",
cacheMillis=5000),
search(contentCollection,
q="id:(a b c)",
fl="text_t, id",
sort="id asc"),
field="text_t")
In the example above the classify expression is retrieving the model using the model function. It is then
classifying tuples returned by the search function. The text_t field is used for the text classification and the
analyzer for the text_t field in the Solr schema is used to analyze the text and extract the features.
commit
The commit function wraps a single stream (A) and given a collection and batch size will send commit
messages to the collection when the batch size is fulfilled or the end of stream is reached. A commit stream
is used most frequently with an update stream and as such the commit will take into account possible
summary tuples coming from the update stream. All tuples coming into the commit stream will be returned
out of the commit stream - no tuples will be dropped and no tuples will be added.
commit Parameters
? collection : The collection to send commit messages to (required)
? batchSize : The commit batch size, sends commit message when batch size is hit. If not provided (or
provided as value 0) then a commit is only sent at the end of the incoming stream.
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? waitFlush : The value passed directly to the commit handler (true/false, default: false)
? waitSearcher : The value passed directly to the commit handler (true/false, default: false)
? softCommit : The value passed directly to the commit handler (true/false, default: false)
? StreamExpression for StreamA (required)
commit Syntax
commit(
destinationCollection,
batchSize=2,
update(
destinationCollection,
batchSize=5,
search(collection1, q=*:*, fl="id,a_s,a_i,a_f,s_multi,i_multi", sort="a_f asc, a_i asc")
)
)
complement
The complement function wraps two streams (A and B) and emits tuples from A which do not exist in B. The
tuples are emitted in the order in which they appear in stream A. Both streams must be sorted by the fields
being used to determine equality (using the on parameter).
complement Parameters
? StreamExpression for StreamA
? StreamExpression for StreamB
? on : Fields to be used for checking equality of tuples between A and B. Can be of the format
on="fieldName" , on="fieldNameInLeft=fieldNameInRight" , or on="fieldName,
otherFieldName=rightOtherFieldName" .
complement Syntax
complement(
search(collection1, q=a_s:(setA || setAB), fl="id,a_s,a_i", sort="a_i asc, a_s asc"),
search(collection1, q=a_s:(setB || setAB), fl="id,a_s,a_i", sort="a_i asc"),
on="a_i"
)
complement(
search(collection1, q=a_s:(setA || setAB), fl="id,a_s,a_i", sort="a_i asc, a_s asc"),
search(collection1, q=a_s:(setB || setAB), fl="id,a_s,a_i", sort="a_i asc, a_s asc"),
on="a_i,a_s"
)
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daemon
The daemon function wraps another function and runs it at intervals using an internal thread. The daemon
function can be used to provide both continuous push and pull streaming.
Continuous Push Streaming
With continuous push streaming the daemon function wraps another function and is then sent to the
/stream handler for execution. The /stream handler recognizes the daemon function and keeps it resident in
memory, so it can run its internal function at intervals.
In order to facilitate the pushing of tuples, the daemon function must wrap another stream decorator that
pushes the tuples somewhere. One example of this is the update function, which wraps a stream and sends
the tuples to another SolrCloud collection for indexing.
daemon Syntax
daemon(id="uniqueId",
runInterval="1000",
terminate="true",
update(destinationCollection,
batchSize=100,
topic(checkpointCollection,
topicCollection,
q="topic query",
fl="id, title, abstract, text",
id="topicId",
initialCheckpoint=0)
)
)
The sample code above shows a daemon function wrapping an update function, which is wrapping a topic
function. When this expression is sent to the /stream handler, the /stream hander sees the daemon function
and keeps it in memory where it will run at intervals. In this particular example, the daemon function will run
the update function every second. The update function is wrapping a topic function, which will stream
tuples that match the topic function query in batches. Each subsequent call to the topic will return the next
batch of tuples for the topic. The update function will send all the tuples matching the topic to another
collection to be indexed. The terminate parameter tells the daemon to terminate when the topic function
stops sending tuples.
The effect of this is to push documents that match a specific query into another collection. Custom push
functions can be plugged in that push documents out of Solr and into other systems, such as Kafka or an
email system.
Push streaming can also be used for continuous background aggregation scenarios where aggregates are
rolled up in the background at intervals and pushed to other Solr collections. Another use case is continuous
background machine learning model optimization, where the optimized model is pushed to another Solr
collection where it can be integrated into queries.
The /stream handler supports a small set commands for listing and controlling daemon functions:
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http://localhost:8983/collection/stream?action=list
This command will provide a listing of the current daemon’s running on the specific node along with there
current state.
http://localhost:8983/collection/stream?action=stop&id=daemonId
This command will stop a specific daemon function but leave it resident in memory.
http://localhost:8983/collection/stream?action=start&id=daemonId
This command will start a specific daemon function that has been stopped.
http://localhost:8983/collection/stream?action=kill&id=daemonId
This command will stop a specific daemon function and remove it from memory.
Continuous Pull Streaming
The DaemonStream java class (part of the SolrJ libraries) can also be embedded in a java application to
provide continuous pull streaming. Sample code:
StreamContext context = new StreamContext()
SolrClientCache cache = new SolrClientCache();
context.setSolrClientCache(cache);
Map topicQueryParams = new HashMap();
topicQueryParams.put("q","hello"); // The query for the topic
topicQueryparams.put("rows", "500"); // How many rows to fetch during each run
topicQueryparams.put("fl", "id", "title"); // The field list to return with the documents
TopicStream topicStream = new TopicStream(zkHost, // Host address for the zookeeper
service housing the collections
"checkpoints", // The collection to store the topic
checkpoints
"topicData", // The collection to query for the topic
records
"topicId", // The id of the topic
-1, // checkpoint every X tuples, if set -1
it will checkpoint after each run.
topicQueryParams); // The query parameters for the
TopicStream
DaemonStream daemonStream = new DaemonStream(topicStream, // The underlying stream to
run.
"daemonId", // The id of the daemon
1000, // The interval at which to
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run the internal stream
500); // The internal queue size
for the daemon stream. Tuples will be placed in the queue
// as they are read by the
internal internal thread.
// Calling read() on the
daemon stream reads records from the internal queue.
daemonStream.setStreamContext(context);
daemonStream.open();
//Read until it‘s time to shutdown the DaemonStream. You can define the shutdown criteria.
while(!shutdown()) {
Tuple tuple = daemonStream.read() // This will block until tuples become available from the
underlying stream (TopicStream)
// The EOF tuple (signaling the end of the stream) will
never occur until the DaemonStream has been shutdown.
//Do something with the tuples
}
// Shutdown the DaemonStream.
daemonStream.shutdown();
//Read the DaemonStream until the EOF Tuple is found.
//This allows the underlying stream to perform an orderly shutdown.
while(true) {
Tuple tuple = daemonStream.read();
if(tuple.EOF) {
break;
} else {
//Do something with the tuples.
}
}
//Finally close the stream
daemonStream.close();
eval
The eval function allows for use cases where new streaming expressions are generated on the fly and then
evaluated. The eval function wraps a streaming expression and reads a single tuple from the underlying
stream. The eval function then retrieves a string Streaming Expressions from the expr_s field of the tuple.
The eval function then compiles the string Streaming Expression and emits the tuples.
eval Parameters
? StreamExpression : (Mandatory) The stream which provides the streaming expression to be evaluated.
eval Syntax
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eval(expr)
In the example above the eval expression reads the first tuple from the underlying expression. It then
compiles and executes the string Streaming Expression in the epxr_s field.
executor
The executor function wraps a stream source that contains streaming expressions, and executes the
expressions in parallel. The executor function looks for the expression in the expr_s field in each tuple. The
executor function has an internal thread pool that runs tasks that compile and run expressions in parallel
on the same worker node. This function can also be parallelized across worker nodes by wrapping it in the
parallel function to provide parallel execution of expressions across a cluster.
The executor function does not do anything specific with the output of the expressions that it runs.
Therefore the expressions that are executed must contain the logic for pushing tuples to their destination.
The update function can be included in the expression being executed to send the tuples to a SolrCloud
collection for storage.
This model allows for asynchronous execution of jobs where the output is stored in a SolrCloud collection
where it can be accessed as the job progresses.
executor Parameters
? threads : (Optional) The number of threads in the executors thread pool for executing expressions.
? StreamExpression : (Mandatory) The stream source which contains the Streaming Expressions to
execute.
executor Syntax
daemon(id="myDaemon",
terminate="true",
executor(threads=10,
topic(checkpointCollection
storedExpressions,
q="*:*",
fl="id, expr_s",
initialCheckPoint=0,
id="myTopic")))
In the example above a daemon wraps an executor, which wraps a topic that is returning tuples with
expressions to execute. When sent to the stream handler, the daemon will call the executor at intervals
which will cause the executor to read from the topic and execute the expressions found in the expr_s field.
The daemon will repeatedly call the executor until all the tuples that match the topic have been iterated,
then it will terminate. This is the approach for executing batches of streaming expressions from a topic
queue.
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fetch
The fetch function iterates a stream and fetches additional fields and adds them to the tuples. The fetch
function fetches in batches to limit the number of calls back to Solr. Tuples streamed from the fetch
function will contain the original fields and the additional fields that were fetched. The fetch function
supports one-to-one fetches. Many-to-one fetches, where the stream source contains duplicate keys, will
also work, but one-to-many fetches are currently not supported by this function.
fetch Parameters
? Collection : (Mandatory) The collection to fetch the fields from.
? StreamExpression : (Mandatory) The stream source for the fetch function.
? fl : (Mandatory) The fields to be fetched.
? on : Fields to be used for checking equality of tuples between stream source and fetched records.
Formatted as on="fieldNameInTuple=fieldNameInCollection" .
? batchSize : (Optional) The batch fetch size.
fetch Syntax
fetch(addresses,
search(people, q="*:*", fl="username, firstName, lastName", sort="username asc"),
fl="streetAddress, city, state, country, zip",
on="username=userId")
The example above fetches addresses for users by matching the username in the tuple with the userId field
in the addresses collection.
having
The having expression wraps a stream and applies a boolean operation to each tuple. It emits only tuples
for which the boolean operation returns true.
having Parameters
? StreamExpression : (Mandatory) The stream source for the having function.
? booleanEvaluator : (Madatory) The following boolean operations are supported: eq (equals), gt (greater
than), lt (less than), gteq (greater than or equal to), lteq (less than or equal to), and, or, eor (exclusive
or), and not. Boolean evaluators can be nested with other evaluators to form complex boolean logic.
The comparison evaluators compare the value in a specific field with a value, whether a string, number, or
boolean. For example: eq(field1, 10), returns true if field1 is equal to 10.
having Syntax
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having(rollup(over=a_s,
sum(a_i),
search(collection1,
q=*:*,
fl="id,a_s,a_i,a_f",
sort="a_s asc")),
and(gt(sum(a_i), 100), lt(sum(a_i), 110)))
In this example, the having expression iterates the aggregated tuples from the rollup expression and emits
all tuples where the field sum(a_i) is greater then 100 and less then 110.
leftOuterJoin
The leftOuterJoin function wraps two streams, Left and Right, and emits tuples from Left. If there is a tuple
in Right equal (as defined by on ) then the values in that tuple will be included in the emitted tuple. An equal
tuple in Right need not exist for the Left tuple to be emitted. This supports one-to-one, one-to-many, many-
to-one, and many-to-many left outer join scenarios. The tuples are emitted in the order in which they appear
in the Left stream. Both streams must be sorted by the fields being used to determine equality (using the on
parameter). If both tuples contain a field of the same name then the value from the Right stream will be
used in the emitted tuple.
You can wrap the incoming streams with a select function to be specific about which field values are
included in the emitted tuple.
leftOuterJoin Parameters
? StreamExpression for StreamLeft
? StreamExpression for StreamRight
? on : Fields to be used for checking equality of tuples between Left and Right. Can be of the format
on="fieldName" , on="fieldNameInLeft=fieldNameInRight" , or on="fieldName,
otherFieldName=rightOtherFieldName" .
leftOuterJoin Syntax
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leftOuterJoin(
search(people, q=*:*, fl="personId,name", sort="personId asc"),
search(pets, q=type:cat, fl="personId,petName", sort="personId asc"),
on="personId"
)
leftOuterJoin(
search(people, q=*:*, fl="personId,name", sort="personId asc"),
search(pets, q=type:cat, fl="ownerId,petName", sort="ownerId asc"),
on="personId=ownerId"
)
leftOuterJoin(
search(people, q=*:*, fl="personId,name", sort="personId asc"),
select(
search(pets, q=type:cat, fl="ownerId,name", sort="ownerId asc"),
ownerId,
name as petName
),
on="personId=ownerId"
)
hashJoin
The hashJoin function wraps two streams, Left and Right, and for every tuple in Left which exists in Right
will emit a tuple containing the fields of both tuples. This supports one-to-one, one-to-many, many-to-one,
and many-to-many inner join scenarios. The tuples are emitted in the order in which they appear in the Left
stream. The order of the streams does not matter. If both tuples contain a field of the same name then the
value from the Right stream will be used in the emitted tuple.
You can wrap the incoming streams with a select function to be specific about which field values are
included in the emitted tuple.
The hashJoin function can be used when the tuples of Left and Right cannot be put in the same order.
Because the tuples are out of order this stream functions by reading all values from the Right stream during
the open operation and will store all tuples in memory. The result of this is a memory footprint equal to the
size of the Right stream.
hashJoin Parameters
? StreamExpression for StreamLeft
? hashed=StreamExpression for StreamRight
? on : Fields to be used for checking equality of tuples between Left and Right. Can be of the format
on="fieldName" , on="fieldNameInLeft=fieldNameInRight" , or on="fieldName,
otherFieldName=rightOtherFieldName" .
hashJoin Syntax
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hashJoin(
search(people, q=*:*, fl="personId,name", sort="personId asc"),
hashed=search(pets, q=type:cat, fl="personId,petName", sort="personId asc"),
on="personId"
)
hashJoin(
search(people, q=*:*, fl="personId,name", sort="personId asc"),
hashed=search(pets, q=type:cat, fl="ownerId,petName", sort="ownerId asc"),
on="personId=ownerId"
)
hashJoin(
search(people, q=*:*, fl="personId,name", sort="personId asc"),
hashed=select(
search(pets, q=type:cat, fl="ownerId,name", sort="ownerId asc"),
ownerId,
name as petName
),
on="personId=ownerId"
)
innerJoin
Wraps two streams, Left and Right. For every tuple in Left which exists in Right a tuple containing the fields
of both tuples will be emitted. This supports one-to-one, one-to-many, many-to-one, and many-to-many
inner join scenarios. The tuples are emitted in the order in which they appear in the Left stream. Both
streams must be sorted by the fields being used to determine equality (the ‘on‘ parameter). If both tuples
contain a field of the same name then the value from the Right stream will be used in the emitted tuple. You
can wrap the incoming streams with a select(…) expression to be specific about which field values are
included in the emitted tuple.
innerJoin Parameters
? StreamExpression for StreamLeft
? StreamExpression for StreamRight
? on : Fields to be used for checking equality of tuples between Left and Right. Can be of the format
on="fieldName" , on="fieldNameInLeft=fieldNameInRight" , or on="fieldName,
otherFieldName=rightOtherFieldName" .
innerJoin Syntax
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innerJoin(
search(people, q=*:*, fl="personId,name", sort="personId asc"),
search(pets, q=type:cat, fl="personId,petName", sort="personId asc"),
on="personId"
)
innerJoin(
search(people, q=*:*, fl="personId,name", sort="personId asc"),
search(pets, q=type:cat, fl="ownerId,petName", sort="ownerId asc"),
on="personId=ownerId"
)
innerJoin(
search(people, q=*:*, fl="personId,name", sort="personId asc"),
select(
search(pets, q=type:cat, fl="ownerId,name", sort="ownerId asc"),
ownerId,
name as petName
),
on="personId=ownerId"
)
intersect
The intersect function wraps two streams, A and B, and emits tuples from A which DO exist in B. The tuples
are emitted in the order in which they appear in stream A. Both streams must be sorted by the fields being
used to determine equality (the on parameter). Only tuples from A are emitted.
intersect Parameters
? StreamExpression for StreamA
? StreamExpression for StreamB
? on : Fields to be used for checking equality of tuples between A and B. Can be of the format
on="fieldName" , on="fieldNameInLeft=fieldNameInRight" , or on="fieldName,
otherFieldName=rightOtherFieldName" .
intersect Syntax
intersect(
search(collection1, q=a_s:(setA || setAB), fl="id,a_s,a_i", sort="a_i asc, a_s asc"),
search(collection1, q=a_s:(setB || setAB), fl="id,a_s,a_i", sort="a_i asc"),
on="a_i"
)
intersect(
search(collection1, q=a_s:(setA || setAB), fl="id,a_s,a_i", sort="a_i asc, a_s asc"),
search(collection1, q=a_s:(setB || setAB), fl="id,a_s,a_i", sort="a_i asc, a_s asc"),
on="a_i,a_s"
)
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merge
The merge function merges two or more streaming expressions and maintains the ordering of the
underlying streams. Because the order is maintained, the sorts of the underlying streams must line up with
the on parameter provided to the merge function.
merge Parameters
? StreamExpression A
? StreamExpression B
? Optional StreamExpression C,D,….Z
? on : Sort criteria for performing the merge. Of the form fieldName order where order is asc or desc .
Multiple fields can be provided in the form fieldA order, fieldB order .
merge Syntax
# Merging two stream expressions together
merge(
search(collection1,
q="id:(0 3 4)",
fl="id,a_s,a_i,a_f",
sort="a_f asc"),
search(collection1,
q="id:(1)",
fl="id,a_s,a_i,a_f",
sort="a_f asc"),
on="a_f asc")
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# Merging four stream expressions together. Notice that while the sorts of each stream are not
identical they are
# comparable. That is to say the first N fields in each stream‘s sort matches the N fields in the
merge‘s on clause.
merge(
search(collection1,
q="id:(0 3 4)",
fl="id,fieldA,fieldB,fieldC",
sort="fieldA asc, fieldB desc"),
search(collection1,
q="id:(1)",
fl="id,fieldA",
sort="fieldA asc"),
search(collection2,
q="id:(10 11 13)",
fl="id,fieldA,fieldC",
sort="fieldA asc"),
search(collection3,
q="id:(987)",
fl="id,fieldA,fieldC",
sort="fieldA asc"),
on="fieldA asc")
null
The null expression is a useful utility function for understanding bottlenecks when performing parallel
relational algebra (joins, intersections, rollups etc.). The null function reads all the tuples from an underlying
stream and returns a single tuple with the count and processing time. Because the null stream adds minimal
overhead of it’s own, it can be used to isolate the performance of Solr’s /export handler. If the /export
handlers performance is not the bottleneck, then the bottleneck is likely occurring in the workers where the
stream decorators are running.
The null expression can be wrapped by the parallel function and sent to worker nodes. In this scenario each
worker will return one tuple with the count of tuples processed on the worker and the timing information
for that worker. This gives valuable information such as:
1. As more workers are added does the performance of the /export handler improve or not.
2. Are tuples being evenly distributed across the workers, or is the hash partitioning sending more
documents to a single worker.
3. Are all workers processing data at the same speed, or is one of the workers the source of the bottleneck.
null Parameters
? StreamExpression : (Mandatory) The expression read by the null function.
null Syntax
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parallel(workerCollection,
null(search(collection1, q=*:*, fl="id,a_s,a_i,a_f", sort="a_s desc", qt="/export",
partitionKeys="a_s")),
workers="20",
zkHost="localhost:9983",
sort="a_s desc")
The expression above shows a parallel function wrapping a null function. This will cause the null function to
be run in parallel across 20 worker nodes. Each worker will return a single tuple with number of tuples
processed and time it took to iterate the tuples.
outerHashJoin
The outerHashJoin function wraps two streams, Left and Right, and emits tuples from Left. If there is a tuple
in Right equal (as defined by the on parameter) then the values in that tuple will be included in the emitted
tuple. An equal tuple in Right need not exist for the Left tuple to be emitted. This supports one-to-one, one-
to-many, many-to-one, and many-to-many left outer join scenarios. The tuples are emitted in the order in
which they appear in the Left stream. The order of the streams does not matter. If both tuples contain a field
of the same name then the value from the Right stream will be used in the emitted tuple.
You can wrap the incoming streams with a select function to be specific about which field values are
included in the emitted tuple.
The outerHashJoin stream can be used when the tuples of Left and Right cannot be put in the same order.
Because the tuples are out of order, this stream functions by reading all values from the Right stream during
the open operation and will store all tuples in memory. The result of this is a memory footprint equal to the
size of the Right stream.
outerHashJoin Parameters
? StreamExpression for StreamLeft
? hashed=StreamExpression for StreamRight
? on : Fields to be used for checking equality of tuples between Left and Right. Can be of the format
on="fieldName" , on="fieldNameInLeft=fieldNameInRight" , or on="fieldName,
otherFieldName=rightOtherFieldName" .
outerHashJoin Syntax
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outerHashJoin(
search(people, q=*:*, fl="personId,name", sort="personId asc"),
hashed=search(pets, q=type:cat, fl="personId,petName", sort="personId asc"),
on="personId"
)
outerHashJoin(
search(people, q=*:*, fl="personId,name", sort="personId asc"),
hashed=search(pets, q=type:cat, fl="ownerId,petName", sort="ownerId asc"),
on="personId=ownerId"
)
outerHashJoin(
search(people, q=*:*, fl="personId,name", sort="personId asc"),
hashed=select(
search(pets, q=type:cat, fl="ownerId,name", sort="ownerId asc"),
ownerId,
name as petName
),
on="personId=ownerId"
)
parallel
The parallel function wraps a streaming expression and sends it to N worker nodes to be processed in
parallel.
The parallel function requires that the partitionKeys parameter be provided to the underlying searches.
The partitionKeys parameter will partition the search results (tuples) across the worker nodes. Tuples with
the same values in the partitionKeys field will be shuffled to the same worker nodes.
The parallel function maintains the sort order of the tuples returned by the worker nodes, so the sort criteria
of the parallel function must match up with the sort order of the tuples returned by the workers.
?
Worker Collections
The worker nodes can be from the same collection as the data, or they can be a different
collection entirely, even one that only exists for parallel streaming expressions. A worker
collection can be any SolrCloud collection that has the /stream handler configured. Unlike
normal SolrCloud collections, worker collections don’t have to hold any data. Worker
collections can be empty collections that exist only to execute streaming expressions.
parallel Parameters
? collection : Name of the worker collection to send the StreamExpression to.
? StreamExpression : Expression to send to the worker collection.
? workers : Number of workers in the worker collection to send the expression to.
? zkHost : (Optional) The ZooKeeper connect string where the worker collection resides.
? sort : The sort criteria for ordering tuples returned by the worker nodes.
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parallel Syntax
parallel(workerCollection,
reduce(search(collection1, q=*:*, fl="id,a_s,a_i,a_f", sort="a_s desc",
partitionKeys="a_s"),
by="a_s",
group(sort="a_f desc", n="4")),
workers="20",
zkHost="localhost:9983",
sort="a_s desc")
The expression above shows a parallel function wrapping a reduce function. This will cause the reduce
function to be run in parallel across 20 worker nodes.
priority
The priority function is a simple priority scheduler for the executor function. The executor function doesn’t
directly have a concept of task prioritization; instead it simply executes tasks in the order that they are read
from it’s underlying stream. The priority function provides the ability to schedule a higher priority task
ahead of lower priority tasks that were submitted earlier.
The priority function wraps two topics that are both emitting tuples that contain streaming expressions to
execute. The first topic is considered the higher priority task queue.
Each time the priority function is called, it checks the higher priority task queue to see if there are any
tasks to execute. If tasks are waiting in the higher priority queue then the priority function will emit the
higher priority tasks. If there are no high priority tasks to run, the lower priority queue tasks are emitted.
The priority function will only emit a batch of tasks from one of the queues each time it is called. This
ensures that no lower priority tasks are executed until the higher priority queue has no tasks to run.
Parameters
? topic expression : (Mandatory) the high priority task queue
? topic expression : (Mandatory) the lower priority task queue
Syntax
daemon(id="myDaemon",
executor(threads=10,
priority(topic(checkpointCollection, storedExpressions, q="priority:high",
fl="id, expr_s", initialCheckPoint=0,id="highPriorityTasks"),
topic(checkpointCollection, storedExpressions, q="priority:low", fl="id,
expr_s", initialCheckPoint=0,id="lowPriorityTasks"))))
In the example above the daemon function is calling the executor iteratively. Each time it’s called, the
executor function will execute the tasks emitted by the priority function. The priority function wraps two
topics. The first topic is the higher priority task queue, the second topics is the lower priority topic.
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reduce
The reduce function wraps an internal stream and groups tuples by common fields.
Each tuple group is operated on as a single block by a pluggable reduce operation. The group operation
provided with Solr implements distributed grouping functionality. The group operation also serves as an
example reduce operation that can be referred to when building custom reduce operations.
?
The reduce function relies on the sort order of the underlying stream. Accordingly the sort
order of the underlying stream must be aligned with the group by field.
reduce Parameters
? StreamExpression : (Mandatory)
? by : (Mandatory) A comma separated list of fields to group by.
? Reduce Operation : (Mandatory)
reduce Syntax
reduce(search(collection1, q=*:*, fl="id,a_s,a_i,a_f", sort="a_s asc, a_f asc"),
by="a_s",
group(sort="a_f desc", n="4")
)
rollup
The rollup function wraps another stream function and rolls up aggregates over bucket fields. The rollup
function relies on the sort order of the underlying stream to rollup aggregates one grouping at a time.
Accordingly, the sort order of the underlying stream must match the fields in the over parameter of the
rollup function.
The rollup function also needs to process entire result sets in order to perform its aggregations. When the
underlying stream is the search function, the /export handler can be used to provide full sorted result sets
to the rollup function. This sorted approach allows the rollup function to perform aggregations over very
high cardinality fields. The disadvantage of this approach is that the tuples must be sorted and streamed
across the network to a worker node to be aggregated. For faster aggregation over low to moderate
cardinality fields, the facet function can be used.
rollup Parameters
? StreamExpression (Mandatory)
? over : (Mandatory) A list of fields to group by.
? metrics : (Mandatory) The list of metrics to compute. Currently supported metrics are sum(col) ,
avg(col) , min(col) , max(col) , count(*) .
rollup Syntax
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rollup(
search(collection1, q=*:*, fl="a_s,a_i,a_f", qt="/export", sort="a_s asc"),
over="a_s",
sum(a_i),
sum(a_f),
min(a_i),
min(a_f),
max(a_i),
max(a_f),
avg(a_i),
avg(a_f),
count(*)
)
The example about shows the rollup function wrapping the search function. Notice that search function is
using the /export handler to provide the entire result set to the rollup stream. Also notice that the search
function’s sort param matches up with the rollup’s over parameter. This allows the rollup function to rollup
the over the a_s field, one group at a time.
scoreNodes
See section in graph traversal.
select
The select function wraps a streaming expression and outputs tuples containing a subset or modified set of
fields from the incoming tuples. The list of fields included in the output tuple can contain aliases to
effectively rename fields. The select stream supports both operations and evaluators. One can provide a list
of operations and evaluators to perform on any fields, such as replace, add, if , etc….
select Parameters
? StreamExpression
? fieldName : name of field to include in the output tuple (can include multiple of these), such as
outputTuple[fieldName] = inputTuple[fieldName]
? fieldName as aliasFieldName : aliased field name to include in the output tuple (can include multiple of
these), such as outputTuple[aliasFieldName] = incomingTuple[fieldName]
? replace(fieldName, value, withValue=replacementValue) : if incomingTuple[fieldName] == value
then outgoingTuple[fieldName] will be set to replacementValue . value can be the string "null" to
replace a null value with some other value.
? replace(fieldName, value, withField=otherFieldName) : if incomingTuple[fieldName] == value
then outgoingTuple[fieldName] will be set to the value of incomingTuple[otherFieldName] . value can
be the string "null" to replace a null value with some other value.
select Syntax
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// output tuples with fields teamName, wins, losses, and winPercentages where a null value for
wins or losses is translated to the value of 0
select(
search(collection1, fl="id,teamName_s,wins,losses", q="*:*", sort="id asc"),
teamName_s as teamName,
wins,
losses,
replace(wins,null,withValue=0),
replace(losses,null,withValue=0),
if(eq(0,wins), 0, div(add(wins,losses), wins)) as winPercentage
)
sort
The sort function wraps a streaming expression and re-orders the tuples. The sort function emits all
incoming tuples in the new sort order. The sort function reads all tuples from the incoming stream, re-
orders them using an algorithm with O(nlog(n)) performance characteristics, where n is the total number
of tuples in the incoming stream, and then outputs the tuples in the new sort order. Because all tuples are
read into memory, the memory consumption of this function grows linearly with the number of tuples in the
incoming stream.
sort Parameters
? StreamExpression
? by : Sort criteria for re-ordering the tuples
sort Syntax
The expression below finds dog owners and orders the results by owner and pet name. Notice that it uses
an efficient innerJoin by first ordering by the person/owner id and then re-orders the final output by the
owner and pet names.
sort(
innerJoin(
search(people, q=*:*, fl="id,name", sort="id asc"),
search(pets, q=type:dog, fl="owner,petName", sort="owner asc"),
on="id=owner"
),
by="name asc, petName asc"
)
top
The top function wraps a streaming expression and re-orders the tuples. The top function emits only the top
N tuples in the new sort order. The top function re-orders the underlying stream so the sort criteria does
not have to match up with the underlying stream.
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top Parameters
? n : Number of top tuples to return.
? StreamExpression
? sort : Sort criteria for selecting the top N tuples.
top Syntax
The expression below finds the top 3 results of the underlying search. Notice that it reverses the sort order.
The top function re-orders the results of the underlying stream.
top(n=3,
search(collection1,
q="*:*",
qt="/export",
fl="id,a_s,a_i,a_f",
sort="a_f desc, a_i desc"),
sort="a_f asc, a_i asc")
unique
The unique function wraps a streaming expression and emits a unique stream of tuples based on the over
parameter. The unique function relies on the sort order of the underlying stream. The over parameter must
match up with the sort order of the underlying stream.
The unique function implements a non-co-located unique algorithm. This means that records with the same
unique over field do not need to be co-located on the same shard. When executed in the parallel, the
partitionKeys parameter must be the same as the unique over field so that records with the same keys will
be shuffled to the same worker.
unique Parameters
? StreamExpression
? over : The unique criteria.
unique Syntax
unique(
search(collection1,
q="*:*",
qt="/export",
fl="id,a_s,a_i,a_f",
sort="a_f asc, a_i asc"),
over="a_f")
update
The update function wraps another functions and sends the tuples to a SolrCloud collection for indexing.
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update Parameters
? destinationCollection : (Mandatory) The collection where the tuples will indexed.
? batchSize : (Mandatory) The indexing batch size.
? StreamExpression : (Mandatory)
update Syntax
update(destinationCollection,
batchSize=500,
search(collection1,
q=*:*,
fl="id,a_s,a_i,a_f,s_multi,i_multi",
sort="a_f asc, a_i asc"))
The example above sends the tuples returned by the search function to the destinationCollection to be
indexed.
Stream Evaluator Reference
abs
The abs function will return the absolute value of the provided single parameter. The abs function will fail to
execute if the value is non-numeric. If a null value is found then null will be returned as the result.
abs Parameters
? Field Name | Raw Number | Number Evaluator
abs Syntax
The expressions below show the various ways in which you can use the abs evaluator. Only one parameter is
accepted. Returns a numeric value.
abs(1) // 1, not really a good use case for it
abs(-1) // 1, not really a good use case for it
abs(add(fieldA,fieldB)) // absolute value of fieldA + fieldB
abs(fieldA) // absolute value of fieldA
add
The add function will take 2 or more numeric values and add them together. The add function will fail to
execute if any of the values are non-numeric. If a null value is found then null will be returned as the result.
add Parameters
? Field Name | Raw Number | Number Evaluator
? Field Name | Raw Number | Number Evaluator
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? ……
? Field Name | Raw Number | Number Evaluator
add Syntax
The expressions below show the various ways in which you can use the add evaluator. The number and
order of these parameters do not matter and is not limited except that at least two parameters are required.
Returns a numeric value.
add(1,2,3,4) // 1 + 2 + 3 + 4 == 10
add(1,fieldA) // 1 + value of fieldA
add(fieldA,1.4) // value of fieldA + 1.4
add(fieldA,fieldB,fieldC) // value of fieldA + value of fieldB + value of fieldC
add(fieldA,div(fieldA,fieldB)) // value of fieldA + (value of fieldA / value of fieldB)
add(fieldA,if(gt(fieldA,fieldB),fieldA,fieldB)) // if fieldA > fieldB then fieldA + fieldA, else
fieldA + fieldB
div
The div function will take two numeric values and divide them. The function will fail to execute if any of the
values are non-numeric or null, or the 2nd value is 0. Returns a numeric value.
div Parameters
? Field Name | Raw Number | Number Evaluator
? Field Name | Raw Number | Number Evaluator
div Syntax
The expressions below show the various ways in which you can use the div evaluator. The first value will be
divided by the second and as such the second cannot be 0.
div(1,2) // 1 / 2
div(1,fieldA) // 1 / fieldA
div(fieldA,1.4) // fieldA / 1.4
div(fieldA,add(fieldA,fieldB)) // fieldA / (fieldA + fieldB)
log
The log function will return the natural log of the provided single parameter. The log function will fail to
execute if the value is non-numeric. If a null value is found, then null will be returned as the result.
log Parameters
? Field Name | Raw Number | Number Evaluator
log Syntax
The expressions below show the various ways in which you can use the log evaluator. Only one parameter is
accepted. Returns a numeric value.
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log(100)
log(add(fieldA,fieldB))
log(fieldA)
mult
The mult function will take two or more numeric values and multiply them together. The mult function will
fail to execute if any of the values are non-numeric. If a null value is found then null will be returned as the
result.
mult Parameters
? Field Name | Raw Number | Number Evaluator
? Field Name | Raw Number | Number Evaluator
? ……
? Field Name | Raw Number | Number Evaluator
mult Syntax
The expressions below show the various ways in which you can use the mult evaluator. The number and
order of these parameters do not matter and is not limited except that at least two parameters are required.
Returns a numeric value.
mult(1,2,3,4) // 1 * 2 * 3 * 4
mult(1,fieldA) // 1 * value of fieldA
mult(fieldA,1.4) // value of fieldA * 1.4
mult(fieldA,fieldB,fieldC) // value of fieldA * value of fieldB * value of fieldC
mult(fieldA,div(fieldA,fieldB)) // value of fieldA * (value of fieldA / value of fieldB)
mult(fieldA,if(gt(fieldA,fieldB),fieldA,fieldB)) // if fieldA > fieldB then fieldA * fieldA, else
fieldA * fieldB
sub
The sub function will take 2 or more numeric values and subtract them, from left to right. The sub function
will fail to execute if any of the values are non-numeric. If a null value is found then null will be returned as
the result.
sub Parameters
? Field Name | Raw Number | Number Evaluator
? Field Name | Raw Number | Number Evaluator
? ……
? Field Name | Raw Number | Number Evaluator
sub Syntax
The expressions below show the various ways in which you can use the sub evaluator. The number of these
parameters does not matter and is not limited except that at least two parameters are required. Returns a
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numeric value.
sub(1,2,3,4) // 1 - 2 - 3 - 4
sub(1,fieldA) // 1 - value of fieldA
sub(fieldA,1.4) // value of fieldA - 1.4
sub(fieldA,fieldB,fieldC) // value of fieldA - value of fieldB - value of fieldC
sub(fieldA,div(fieldA,fieldB)) // value of fieldA - (value of fieldA / value of fieldB)
if(gt(fieldA,fieldB),sub(fieldA,fieldB),sub(fieldB,fieldA)) // if fieldA > fieldB then fieldA -
fieldB, else fieldB - field
and
The and function will return the logical AND of at least 2 boolean parameters. The function will fail to execute
if any parameters are non-boolean or null. Returns a boolean value.
and Parameters
? Field Name | Raw Boolean | Boolean Evaluator
? Field Name | Raw Boolean | Boolean Evaluator
? ……
? Field Name | Raw Boolean | Boolean Evaluator
and Syntax
The expressions below show the various ways in which you can use the and evaluator. At least two
parameters are required, but there is no limit to how many you can use.
and(true,fieldA) // true && fieldA
and(fieldA,fieldB) // fieldA && fieldB
and(or(fieldA,fieldB),fieldC) // (fieldA || fieldB) && fieldC
and(fieldA,fieldB,fieldC,or(fieldD,fieldE),fieldF)
eq
The eq function will return whether all the parameters are equal, as per Java’s standard equals(…) function.
The function accepts parameters of any type, but will fail to execute if all the parameters are not of the same
type. That is, all are Boolean, all are String, all are Numeric. If any any parameters are null and there is at
least one parameter that is not null then false will be returned. Returns a boolean value.
eq Parameters
? Field Name | Raw Value | Evaluator
? Field Name | Raw Value | Evaluator
? ……
? Field Name | Raw Value | Evaluator
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eq Syntax
The expressions below show the various ways in which you can use the eq evaluator.
eq(1,2) // 1 == 2
eq(1,fieldA) // 1 == fieldA
eq(fieldA,val(foo)) fieldA == "foo"
eq(add(fieldA,fieldB),6) // fieldA + fieldB == 6
eor
The eor function will return the logical exclusive or of at least two boolean parameters. The function will fail
to execute if any parameters are non-boolean or null. Returns a boolean value.
eor Parameters
? Field Name | Raw Boolean | Boolean Evaluator
? Field Name | Raw Boolean | Boolean Evaluator
? ……
? Field Name | Raw Boolean | Boolean Evaluator
eor Syntax
The expressions below show the various ways in which you can use the eor evaluator. At least two
parameters are required, but there is no limit to how many you can use.
eor(true,fieldA) // true iff fieldA is false
eor(fieldA,fieldB) // true iff either fieldA or fieldB is true but not both
eor(eq(fieldA,fieldB),eq(fieldC,fieldD)) // true iff either fieldA == fieldB or fieldC == fieldD
but not both
gteq
The gteq function will return whether the first parameter is greater than or equal to the second parameter.
The function accepts numeric and string parameters, but will fail to execute if all the parameters are not of
the same type. That is, all are String or all are Numeric. If any any parameters are null then an error will be
raised. Returns a boolean value.
gteq Parameters
? Field Name | Raw Value | Evaluator
? Field Name | Raw Value | Evaluator
gteq Syntax
The expressions below show the various ways in which you can use the gteq evaluator.
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gteq(1,2) // 1 >= 2
gteq(1,fieldA) // 1 >= fieldA
gteq(fieldA,val(foo)) fieldA >= "foo"
gteq(add(fieldA,fieldB),6) // fieldA + fieldB >= 6
gt
The gt function will return whether the first parameter is greater than the second parameter. The function
accepts numeric or string parameters, but will fail to execute if all the parameters are not of the same type.
That is, all are String or all are Numeric. If any any parameters are null then an error will be raised. Returns a
boolean value.
gt Parameters
? Field Name | Raw Value | Evaluator
? Field Name | Raw Value | Evaluator
gt Syntax
The expressions below show the various ways in which you can use the gt evaluator.
gt(1,2) // 1 > 2
gt(1,fieldA) // 1 > fieldA
gt(fieldA,val(foo)) // fieldA > "foo"
gt(add(fieldA,fieldB),6) // fieldA + fieldB > 6
if
The if function works like a standard conditional if/then statement. If the first parameter is true, then the
second parameter will be returned, else the third parameter will be returned. The function accepts a
boolean as the first parameter and anything as the second and third parameters. An error will occur if the
first parameter is not a boolean or is null.
if Parameters
? Field Name | Raw Value | Boolean Evaluator
? Field Name | Raw Value | Evaluator
? Field Name | Raw Value | Evaluator
if Syntax
The expressions below show the various ways in which you can use the if evaluator.
if(fieldA,fieldB,fieldC) // if fieldA is true then fieldB else fieldC
if(gt(fieldA,5), fieldA, 5) // if fieldA > 5 then fieldA else 5
if(eq(fieldB,null), null, div(fieldA,fieldB)) // if fieldB is null then null else fieldA / fieldB
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lteq
The lteq function will return whether the first parameter is less than or equal to the second parameter. The
function accepts numeric and string parameters, but will fail to execute if all the parameters are not of the
same type. That is, all are String or all are Numeric. If any any parameters are null then an error will be
raised. Returns a boolean value.
lteq Parameters
? Field Name | Raw Value | Evaluator
? Field Name | Raw Value | Evaluator
lteq Syntax
The expressions below show the various ways in which you can use the lteq evaluator.
lteq(1,2) // 1 <= 2
lteq(1,fieldA) // 1 <= fieldA
lteq(fieldA,val(foo)) fieldA <= "foo"
lteq(add(fieldA,fieldB),6) // fieldA + fieldB <= 6
lt
The lt function will return whether the first parameter is less than the second parameter. The function
accepts numeric or string parameters, but will fail to execute if all the parameters are not of the same type.
That is, all are String or all are Numeric. If any any parameters are null then an error will be raised. Returns a
boolean value.
lt Parameters
? Field Name | Raw Value | Evaluator
? Field Name | Raw Value | Evaluator
lt Syntax
The expressions below show the various ways in which you can use the lt evaluator.
lt(1,2) // 1 < 2
lt(1,fieldA) // 1 < fieldA
lt(fieldA,val(foo)) fieldA < "foo"
lt(add(fieldA,fieldB),6) // fieldA + fieldB < 6
not
The not function will return the logical NOT of a single boolean parameter. The function will fail to execute if
the parameter is non-boolean or null. Returns a boolean value.
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not Parameters
? Field Name | Raw Boolean | Boolean Evaluator
not Syntax
The expressions below show the various ways in which you can use the not evaluator. Only one parameter is
allowed.
not(true) // false
not(fieldA) // true if fieldA is false else false
not(eq(fieldA,fieldB)) // true if fieldA != fieldB
or
The or function will return the logical OR of at least 2 boolean parameters. The function will fail to execute if
any parameters are non-boolean or null. Returns a boolean value.
or Parameters
? Field Name | Raw Boolean | Boolean Evaluator
? Field Name | Raw Boolean | Boolean Evaluator
? ……
? Field Name | Raw Boolean | Boolean Evaluator
or Syntax
The expressions below show the various ways in which you can use the or evaluator. At least two
parameters are required, but there is no limit to how many you can use.
or(true,fieldA) // true || fieldA
or(fieldA,fieldB) // fieldA || fieldB
or(and(fieldA,fieldB),fieldC) // (fieldA && fieldB) || fieldC
or(fieldA,fieldB,fieldC,and(fieldD,fieldE),fieldF)
raw
The raw function will return whatever raw value is the parameter. This is useful for cases where you want to
use a string as part of another evaluator.
raw Parameters
? Raw Value
raw Syntax
The expressions below show the various ways in which you can use the raw evaluator. Whatever is inside will
be returned as-is. Internal evaluators are considered strings and are not evaluated.
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raw(foo) // "foo"
raw(count(*)) // "count(*)"
raw(45) // 45
raw(true) // "true" (note: this returns the string "true" and not the boolean true)
eq(raw(fieldA), fieldA) // true if the value of fieldA equals the string "fieldA"
Graph Traversal
Graph traversal with streaming expressions uses the gatherNodes function to perform a breadth-first graph
traversal.
The gatherNodes function can be combined with the scoreNodes function to provide recommendations.
gatherNodes can also be combined with the wider streaming expression library to perform complex
operations on gathered node sets.
gatherNodes traversals are distributed within a SolrCloud collection and can span collections.
gatherNodes is designed for use cases that involve zooming into a neighborhood in the graph and
performing precise traversals to gather node sets and aggregations. In these types of use cases
gatherNodes will often provide sub-second performance. Some sample use cases are provided later in the
document.
?
This document assumes a basic understanding of graph terminology and streaming
expressions. You can begin exploring graph traversal concepts with this Wikipedia article.
More details about streaming expressions are available in this Guide, in the section
Streaming Expressions.
Basic Syntax
We’ll start with the most basic syntax and slowly build up more complexity. The most basic syntax for
gatherNodes is:
gatherNodes(emails,
walk="johndoe@apache.org->from",
gather="to")
Let’s break down this simple expression.
The first parameter, emails , is the collection being traversed. The second parameter, walk , maps a hard-
coded node ID ("johndoe@apache.org") to a field in the index ( from ). This will return all the edges in the
index that have johndoe@apache.org in the from field.
The gather parameter tells the function to gather the values in the `to `field. The values that are gathered
are the node IDs emitted by the function.
In the example above the nodes emitted will be all of the people that "johndoe@apache.org" has emailed.
The walk parameter also accepts a list of root node IDs:
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gatherNodes(emails,
walk="johndoe@apache.org, janesmith@apache.org->from",
gather="to")
The gatherNodes function above finds all the edges with "johndoe@apache.org" or "
janesmith@apache.org" in the from field and gathers the to field.
Like all Streaming Expressions, you can execute a gatherNodes expression by sending it to the /stream
handler. For example:
curl --data-urlencode ‘expr=gatherNodes(emails,
walk="johndoe@apache.org, janesmith@apache.org->from",
gather="to")‘ http://localhost:8983/solr/emails/stream
The output of this expression would look like this:
{
"result-set": {
"docs": [
{
"node": "slist@campbell.com",
"collection": "emails",
"field": "to",
"level": 1
},
{
"node": "catherine.pernot@enron.com",
"collection": "emails",
"field": "to",
"level": 1
},
{
"node": "airam.arteaga@enron.com",
"collection": "emails",
"field": "to",
"level": 1
},
{
"EOF": true,
"RESPONSE_TIME": 44
}
]
}
}
All of the tuples returned have the node field. The node field contains the node IDs gathered by the function.
The collection , field , and level of the traversal are also included in the output.
Notice that the level is "1" for each tuple in the example. The root nodes are level 0 (in the example above,
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the root nodes are "johndoe@apache.org, janesmith@apache.org") By default the gatherNodes function
emits only the *leaf nodes* of the traversal, which is the outer-most node set. To emit the root nodes you
can specify the scatter parameter:
gatherNodes(emails,
walk="johndoe@apache.org->from",
gather="to",
scatter="branches, leaves")
The scatter parameter controls whether to emit the branches with the leaves. The root nodes are
considered "branches" because they are not the outer-most level of the traversal.
When scattering both branches and leaves the output would like this:
{
"result-set": {
"docs": [
{
"node": "johndoe@apache.org",
"collection": "emails",
"field": "node",
"level": 0
},
{
"node": "slist@campbell.com",
"collection": "emails",
"field": "to",
"level": 1
},
{
"node": "catherine.pernot@enron.com",
"collection": "emails",
"field": "to",
"level": 1
},
{
"node": "airam.arteaga@enron.com",
"collection": "emails",
"field": "to",
"level": 1
},
{
"EOF": true,
"RESPONSE_TIME": 44
}
]
}
}
Now the level 0 root node is included in the output.
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Aggregations
gatherNodes also supports aggregations. For example:
gatherNodes(emails,
walk="johndoe@apache.org, janesmith@apache.org->from",
gather="to",
count(*))
The expression above finds the edges with "johndoe@apache.org" or "janesmith@apache.org" in the from
field and gathers the values from the to field. It also aggregates the count for each node ID gathered.
A gathered node could have a count of 2 if both "johndoe@apache.org" and "janesmith@apache.org" have
emailed the same person. Node sets contain a unique set of nodes, so the same person won’t appear twice
in the node set, but the count will reflect that it appeared twice during the traversal.
Edges are uniqued as part of the traversal so the count will not reflect the number of times
"johndoe@apache.org" emailed the same person. For example, personA might have emailed personB 100
times. These edges would get uniqued and only be counted once. But if person personC also emailed
personB this would increment the count for personB.
The aggregation functions supported are count(*) , sum(field) , min(field) , max(field) , and avg(field) .
The fields being aggregated should be present in the edges collected during the traversal. Later examples
(below) will show aggregations can be a powerful tool for providing recommendations and limiting the
scope of traversals.
Nesting gatherNodes functions
The gatherNodes function can be nested to traverse deeper into the graph. For example:
gatherNodes(emails,
gatherNodes(emails,
walk="johndoe@apache.org->from",
gather="to"),
walk="node->from",
gather="to")
In the example above the outer gatherNodes function operates on the node set collected from the inner
gatherNodes function.
Notice that the inner gatherNodes function behaves exactly as the examples already discussed. But the walk
parameter of the outer gatherNodes function behaves differently.
In the outer gatherNodes function the walk parameter works with tuples coming from an internal streaming
expression. In this scenario the walk parameter maps the node field to the from field. Remember that the
node IDs collected from the inner gatherNodes expression are placed in the node field.
Put more simply, the inner expression gathers all the people that "johndoe@apache.org" has emailed. We
can call this group the "friends of johndoe@apache.org". The outer expression gathers all the people that
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the "friends of johndoe@apache.org" have emailed. This is a basic friends-of-friends traversal.
This construct of nesting gatherNodes functions is the basic technique for doing a controlled traversal
through the graph.
Cycle Detection
The gatherNodes function performs cycle detection across the entire traversal. This ensures that nodes that
have already been visited are not traversed again. Cycle detection is important for both limiting the size of
traversals and gathering accurate aggregations. Without cycle detection the size of the traversal could grow
exponentially with each hop in the traversal. With cycle detection only new nodes encountered are
traversed.
Cycle detection does not cross collection boundaries. This is because internally the collection name is part of
the node ID. For example the node ID "johndoe@apache.org", is really emails/johndoe@apache.org . When
traversing to another collection "johndoe@apache.org" will be traversed.
Filtering the Traversal
Each level in the traversal can be filtered with a filter query. For example:
gatherNodes(emails,
walk="johndoe@apache.org->from",
fq="body:(solr rocks)",
gather="to")
In the example above only emails that match the filter query will be included in the traversal. Any Solr query
can be included here. So you can do fun things like geospatial queries, apply any of the available query
parsers, or even write custom query parsers to limit the traversal.
Root Streams
Any streaming expression can be used to provide the root nodes for a traversal. For example:
gatherNodes(emails,
search(emails, q="body:(solr rocks)", fl="to", sort="score desc", rows="20")
walk="to->from",
gather="to")
The example above provides the root nodes through a search expression. You can also provide arbitrarily
complex, nested streaming expressions with joins, etc., to specify the root nodes.
Notice that the walk parameter maps a field from the tuples generated by the inner stream. In this case it
maps the to field from the inner stream to the from field.
Skipping High Frequency Nodes
It’s often desirable to skip traversing high frequency nodes in the graph. This is similar in nature to a search
term stop list. The best way to describe this is through an example use case.
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Let’s say that you want to recommend content for a user based on a collaborative filter. Below is one
approach for a simple collaborative filter:
1. Find all content userA has read.
2. Find users whose reading list is closest to userA. These are users with similar tastes as userA.
3. Recommend content based on what the users in step 2 have read, that userA has not yet read.
Look closely at step 2. In large graphs, step 2 can lead to a very large traversal. This is because userA may
have viewed content that has been viewed by millions of other people. We may want to skip these high
frequency nodes for two reasons:
1. A large traversal that visit millions of unique nodes is slow and takes a lot of memory because cycle
detection is tracked in memory.
2. High frequency nodes are also not useful in determining users with similar tastes. The content that fewer
people have viewed provides a more precise recommendation.
The gatherNodes function has the maxDocFreq param to allow for filtering out high frequency nodes. The
sample code below shows steps 1 and 2 of the recommendation:
gatherNodes(logs,
search(logs, q="userID:user1", fl="articleID", sort="articleID asc",
fq="action:view", qt="/export"),
walk="articleID->articleID",
gather="userID",
fq="action:view",
maxDocFreq="10000",
count(*)))
In the example above, the inner search expression searches the logs collection and returning all the articles
viewed by "user1". The outer gatherNodes expression takes all the articles emitted from the inner search
expression and finds all the records in the logs collection for those articles. It then gathers and aggregates
the users that have read the articles. The maxDocFreq parameter limits the articles returned to those that
appear in no more then 10,000 log records (per shard). This guards against returning articles that have been
viewed by millions of users.
Tracking the Traversal
By default the gatherNodes function only tracks enough information to do cycle detection. This provides
enough information to output the nodes and aggregations in the graph.
For some use cases, such as graph visualization, we also need to output the edges. Setting
trackTraversal="true" tells gatherNodes to track the connections between nodes, so the edges can be
constructed. When trackTraversal is enabled a new ancestors property will appear with each node. The
ancestors property contains a list of node IDs that pointed to the node.
Below is a sample gatherNodes expression with trackTraversal set to true:
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gatherNodes(emails,
gatherNodes(emails,
walk="johndoe@apache.org->from",
gather="to",
trackTraversal="true"),
walk="node->from",
trackTraversal="true",
gather="to")
Cross-Collection Traversals
Nested gatherNodes functions can operate on different SolrCloud collections. This allow traversals to "walk"
from one collection to another to gather nodes. Cycle detection does not cross collection boundaries, so
nodes collected in one collection will be traversed in a different collection. This was done deliberately to
support cross-collection traversals. Note that the output from a cross-collection traversal will likely contain
duplicate nodes with different collection attributes.
Below is a sample gatherNodes expression that traverses from the "emails" collection to the "logs"
collection:
gatherNodes(logs,
gatherNodes(emails,
search(emails, q="body:(solr rocks)", fl="from", sort="score desc",
rows="20")
walk="from->from",
gather="to",
scatter="leaves, branches"),
walk="node->user",
fq="action:edit",
gather="contentID")
The example above finds all people who sent emails with a body that contains "solr rocks". It then finds all
the people these people have emailed. Then it traverses to the logs collection and gathers all the content IDs
that these people have edited.
Combining gatherNodes With Other Streaming Expressions
The gatherNodes function can act as both a stream source and a stream decorator. The connection with the
wider stream expression library provides tremendous power and flexibility when performing graph
traversals. Here is an example of using the streaming expression library to intersect two friend networks:
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intersect(on="node",
sort(by="node asc",
gatherNodes(emails,
gatherNodes(emails,
walk="johndoe@apache.org->from",
gather="to"),
walk="node->from",
gather="to",
scatter="branches,leaves")),
sort(by="node asc",
gatherNodes(emails,
gatherNodes(emails,
walk="janedoe@apache.org->from",
gather="to"),
walk="node->from",
gather="to",
scatter="branches,leaves")))
The example above gathers two separate friend networks, one rooted with "johndoe@apache.org" and
another rooted with "janedoe@apache.org". The friend networks are then sorted by the node field, and
intersected. The resulting node set will be the intersection of the two friend networks.
Sample Use Cases
Calculate Market Basket Co-occurrence
It is often useful to know which products are most frequently purchased with a particular product. This
example uses a simple market basket table (indexed in Solr) to store past shopping baskets. The schema for
the table is very simple with each row containing a basketID and a productID . This can be seen as a graph
with each row in the table representing an edge. And it can be traversed very quickly to calculate basket co-
occurrence, even when the graph contains billions of edges.
Here is the sample syntax:
top(n="5",
sort="count(*) desc",
gatherNodes(baskets,
random(baskets, q="productID:ABC", fl="basketID", rows="500"),
walk="basketID->basketID",
fq="-productID:ABC",
gather="productID",
count(*)))
Let’s break down exactly what this traversal is doing.
1. The first expression evaluated is the inner random expression, which returns 500 random basketIDs, from
the baskets collection, that have the productID "ABC". The random expression is very useful for
recommendations because it limits the traversal to a fixed set of baskets, and because it adds the
element of surprise into the recommendation. Using the random function you can provide fast sample
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sets from very large graphs.
2. The outer gatherNodes expression finds all the records in the baskets collection for the basketIDs
generated in step 1. It also filters out productID "ABC" so it doesn’t show up in the results. It then
gathers and counts the productID’s across these baskets.
3. The outer top expression ranks the productIDs emitted in step 2 by the count and selects the top 5.
In a nutshell this expression finds the products that most frequently co-occur with product "ABC" in past
shopping baskets.
Using the scoreNodes Function to Make a Recommendation
This use case builds on the market basket example above that calculates which products co-occur most
frequently with productID:ABC. The ranked co-occurrence counts provide candidates for a recommendation.
The scoreNodes function can be used to score the candidates to find the best recommendation.
Before diving into the syntax of the scoreNodes function it’s useful to understand why the raw co-
occurrence counts may not produce the best recommendation. The reason is that raw co-occurrence counts
favor items that occur frequently across all baskets. A better recommendation would find the product that
has the most significant relationship with productID ABC. The scoreNodes function uses a term frequency-
inverse document frequency (TF-IDF) algorithm to find the most significant relationship.
How It Works
The scoreNodes function assigns a score to each node emitted by the gatherNodes expression. By default
the scoreNodes function uses the count(*) aggregation, which is the co-occurrence count, as the TF value.
The IDF value for each node is fetched from the collection where the node was gathered. Each node is then
scored using the TF*IDF formula, which provides a boost to nodes with a lower frequency across all market
baskets.
Combining the co-occurrence count with the IDF provides a score that shows how important the relationship
is between productID ABC and the recommendation candidates.
The scoreNodes function adds the score to each node in the nodeScore field.
Example Syntax
top(n="1",
sort="nodeScore desc",
scoreNodes(top(n="50",
sort="count(*) desc",
gatherNodes(baskets,
random(baskets, q="productID:ABC", fl="basketID", rows="500"),
walk="basketID->basketID",
fq="-productID:ABC",
gather="productID",
count(*)))))
This example builds on the earlier example "Calculate market basket co-occurrence".
1. Notice that the inner-most top function is taking the top 50 products that co-occur most frequently with
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productID ABC. This provides 50 candidate recommendations.
2. The scoreNodes function then assigns a score to the candidates based on the TF*IDF of each node.
3. The outer top expression selects the highest scoring node. This is the recommendation.
Recommend Content Based on Collaborative Filter
In this example we’ll recommend content for a user based on a collaborative filter. This recommendation is
made using log records that contain the userID and articleID and the action performed. In this scenario
each log record can be viewed as an edge in a graph. The userID and articleID are the nodes and the action
is an edge property used to filter the traversal.
Here is the sample syntax:
top(n="5",
sort="count(*) desc",
gatherNodes(logs,
top(n="30",
sort="count(*) desc",
gatherNodes(logs,
search(logs, q="userID:user1", fl="articleID", sort="articleID
asc", fq="action:read", qt="/export"),
walk="articleID->articleID",
gather="userID",
fq="action:read",
maxDocFreq="10000",
count(*))),
walk="node->userID",
gather="articleID",
fq="action:read",
count(*)))
Let’s break down the expression above step-by-step.
1. The first expression evaluated is the inner search expression. This expression searches the logs
collection for all records matching "user1". This is the user we are making the recommendation for.
There is a filter applied to pull back only records where the "action:read". It returns the articleID for
each record found. In other words, this expression returns all the articles "user1" has read.
2. The inner gatherNodes expression operates over the articleIDs returned from step 1. It takes each
articleID found and searches them against the articleID field.
Note that it skips high frequency nodes using the maxDocFreq param to filter out articles that appear
over 10,000 times in the logs. It gathers userIDs and aggregates the counts for each user. This step finds
the users that have read the same articles that "user1" has read and counts how many of the same
articles they have read.
3. The inner top expression ranks the users emitted from step 2. It will emit the top 30 users who have the
most overlap with user1’s reading list.
4. The outer gatherNodes expression gathers the reading list for the users emitted from step 3. It counts
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the articleIDs that are gathered.
Any article selected in step 1 (user1 reading list), will not appear in this step due to cycle detection. So
this step returns the articles read by the users with the most similar readings habits to "user1" that
"user1" has not read yet. It also counts the number of times each article has been read across this user
group.
5. The outer top expression takes the top articles emitted from step 4. This is the recommendation.
Protein Pathway Traversal
In recent years, scientists have become increasingly able to rationally design drugs that target the mutated
proteins, called oncogenes, responsible for some cancers. Proteins typically act through long chains of
chemical interactions between multiple proteins, called pathways, and, while the oncogene in the pathway
may not have a corresponding drug, another protein in the pathway may. Graph traversal on a protein
collection that records protein interactions and drugs may yield possible candidates. (Thanks to Lewis Geer
of the NCBI, for providing this example).
The example below illustrates a protein pathway traversal:
gatherNodes(proteins,
gatherNodes(proteins,
walk="NRAS->name",
gather="interacts"),
walk="node->name",
gather="drug")
Let’s break down exactly what this traversal is doing.
1. The inner gatherNodes expression traverses in the proteins collection. It finds all the edges in the graph
where the name of the protein is "NRAS". Then it gathers the proteins in the interacts field. This
gathers all the proteins that "NRAS" interactions with.
2. The outer gatherNodes expression also works with the proteins collection. It gathers all the drugs that
correspond to proteins emitted from step 1.
3. Using this stepwise approach you can gather the drugs along the pathway of interactions any number of
steps away from the root protein.
Exporting GraphML to Support Graph Visualization
In the examples above, the gatherNodes expression was sent to Solr’s /stream handler like any other
streaming expression. This approach outputs the nodes in the same JSON tuple format as other streaming
expressions so that it can be treated like any other streaming expression. You can use the /stream handler
when you need to operate directly on the tuples, such as in the recommendation use cases above.
There are other graph traversal use cases that involve graph visualization. Solr supports these use cases
with the introduction of the /graph request handler, which takes a gatherNodes expression and outputs the
results in GraphML.
GraphML is an XML format supported by graph visualization tools such as Gephi, which is a sophisticated
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open source tool for statistically analyzing and visualizing graphs. Using a gatherNodes expression, parts of
a larger graph can be exported in GraphML and then imported into tools like Gephi.
There are a few things to keep mind when exporting a graph in GraphML:
1. The /graph handler can export both the nodes and edges in the graph. By default, it only exports the
nodes. To export the edges you must set trackTraversal="true" in the gatherNodes expression.
2. The /graph handler currently accepts an arbitrarily complex streaming expression which includes a
gatherNodes expression. If the streaming expression doesn’t include a gatherNodes expression, the
/graph handler will not properly output GraphML.
3. The /graph handler currently accepts a single arbitrarily complex, nested gatherNodes expression per
request. This means you cannot send in a streaming expression that joins or intersects the node sets
from multiple gatherNodes expressions. The /graph handler does support any level of nesting within a
single gatherNodes expression. The /stream handler does support joining and intersecting node sets,
but the /graph handler currently does not.
Sample Request
curl --data-urlencode ‘expr=gatherNodes(enron_emails,
gatherNodes(enron_emails,
walk="kayne.coulter@enron.com->from",
trackTraversal="true",
gather="to"),
walk="node->from",
scatter="leaves,branches",
trackTraversal="true",
gather="to")‘
http://localhost:8983/solr/enron_emails/graph
Sample GraphML Output
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<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
<graph id="G" edgedefault="directed">
<node id="kayne.coulter@enron.com">
<data key="field">node</data>
<data key="level">0</data>
<data key="count(*)">0.0</data>
</node>
<node id="don.baughman@enron.com">
<data key="field">to</data>
<data key="level">1</data>
<data key="count(*)">1.0</data>
</node>
<edge id="1" source="kayne.coulter@enron.com" target="don.baughman@enron.com"/>
<node id="john.kinser@enron.com">
<data key="field">to</data>
<data key="level">1</data>
<data key="count(*)">1.0</data>
</node>
<edge id="2" source="kayne.coulter@enron.com" target="john.kinser@enron.com"/>
<node id="jay.wills@enron.com">
<data key="field">to</data>
<data key="level">1</data>
<data key="count(*)">1.0</data>
</node>
<edge id="3" source="kayne.coulter@enron.com" target="jay.wills@enron.com"/>
</graph></graphml>
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Parallel SQL Interface
Solr’s Parallel SQL Interface brings the power of SQL to SolrCloud.
The SQL interface seamlessly combines SQL with Solr’s full-text search capabilities. Both MapReduce style
and JSON Facet API aggregations are supported, which means the SQL interface can be used to support
both high query volume and high cardinality use cases.
SQL Architecture
The SQL interface allows sending a SQL query to Solr and getting documents streamed back in response.
Under the covers, Solr’s SQL interface uses the Apache Calcite SQL engine to translate SQL queries to
physical query plans implemented as Streaming Expressions.
Solr Collections and DB Tables
In a standard SELECT statement such as SELECT <expressions> FROM <table> , the table names correspond
to Solr collection names. Table names are case insensitive.
Column names in the SQL query map directly to fields in the Solr index for the collection being queried.
These identifiers are case sensitive. Aliases are supported, and can be referenced in the ORDER BY clause.
The * syntax to indicate all fields is not supported in either limited or unlimited queries. The score field can
be used only with queries that contain a LIMIT clause.
For example, we could index Solr’s sample documents and then construct an SQL query like this:
SELECT manu as mfr, price as retail FROM techproducts
The collection in Solr we are using is "techproducts", and we’ve asked for the "manu" and "price" fields to
be returned and aliased with new names. While this example does not use those aliases, we could build on
this to ORDER BY one or more of those fields.
More information about how to structure SQL queries for Solr is included in the section Solr SQL Syntax
below.
Aggregation Modes
The SQL feature of Solr can work with aggregations (grouping of results) in two ways:
? facet : This is the default aggregation mode, which uses the JSON Facet API or StatsComponent for
aggregations. In this scenario the aggregations logic is pushed down into the search engine and only the
aggregates are sent across the network. This is Solr’s normal mode of operation. This is fast when the
cardinality of GROUP BY fields is low to moderate. But it breaks down when you have high cardinality
fields in the GROUP BY field.
? map_reduce : This implementation shuffles tuples to worker nodes and performs the aggregation on the
worker nodes. It involves sorting and partitioning the entire result set and sending it to worker nodes. In
this approach the tuples arrive at the worker nodes sorted by the GROUP BY fields. The worker nodes
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can then rollup the aggregates one group at a time. This allows for unlimited cardinality aggregation,
but you pay the price of sending the entire result set across the network to worker nodes.
These modes are defined with the aggregationMode property when sending the request to Solr.
As noted, the choice between aggregation modes depends on the cardinality of the fields you are working
with. If you have low-to-moderate cardinality in the fields you are grouping by, the ‘facet‘ aggregation mode
will give you a higher performance because only the final groups are returned, very similar to how facets
work today. If, however, you have high cardinality in the fields, the "map_reduce" aggregation mode with
worker nodes provide a much more performant option.
Configuration
The request handlers used for the SQL interface are configured to load implicitly, meaning there is little to
do to start using this feature.
/sql Request Handler
The /sql handler is the front end of the Parallel SQL interface. All SQL queries are sent to the /sql handler
to be processed. The handler also coordinates the distributed MapReduce jobs when running GROUP BY and
SELECT DISTINCT queries in map_reduce mode. By default the /sql handler will choose worker nodes from
its own collection to handle the distributed operations. In this default scenario the collection where the /sql
handler resides acts as the default worker collection for MapReduce queries.
By default, the /sql request handler is configured as an implicit handler, meaning that it is always enabled
in every Solr installation and no further configuration is required.
?
As described below in the section Best Practices, you may want to set up a separate
collection for parallelized SQL queries. If you have high cardinality fields and a large
amount of data, please be sure to review that section and consider using a separate
collection.
/stream and /export Request Handlers
The Streaming API is an extensible parallel computing framework for SolrCloud. Streaming Expressions
provide a query language and a serialization format for the Streaming API.
The Streaming API provides support for fast MapReduce allowing it to perform parallel relational algebra on
extremely large data sets. Under the covers the SQL interface parses SQL queries using the Apache Calcite
SQL Parser. It then translates the queries to the parallel query plan. The parallel query plan is expressed
using the Streaming API and Streaming Expressions.
Like the /sql request handler, the /stream and /export request handlers are configured as implicit
handlers, and no further configuration is required.
Fields
In some cases, fields used in SQL queries must be configured as DocValue fields. If queries are unlimited, all
fields must be DocValue fields. If queries are limited (with the limit clause) then fields do not have to be
have DocValues enabled.
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Sending Queries
The SQL Interface provides a basic JDBC driver and an HTTP interface to perform queries.
JDBC Driver
The JDBC Driver ships with SolrJ. Below is sample code for creating a connection and executing a query with
the JDBC driver:
Connection con = null;
try {
con = DriverManager.getConnection("jdbc:solr://" + zkHost +
"?collection=collection1&aggregationMode=map_reduce&numWorkers=2");
stmt = con.createStatement();
rs = stmt.executeQuery("SELECT a_s, sum(a_f) as sum FROM collection1 GROUP BY a_s ORDER BY
sum desc");
while(rs.next()) {
String a_s = rs.getString("a_s");
double s = rs.getDouble("sum");
}
} finally {
rs.close();
stmt.close();
con.close();
}
The connection URL must contain the zkHost and the collection parameters. The collection must be a valid
SolrCloud collection at the specified ZooKeeper host. The collection must also be configured with the /sql
handler. The aggregationMode and numWorkers parameters are optional.
HTTP Interface
Solr accepts parallel SQL queries through the /sql handler.
Below is a sample curl command performing a SQL aggregate query in facet mode:
curl --data-urlencode ‘stmt=SELECT to, count(*) FROM collection4 GROUP BY to ORDER BY count(*)
desc LIMIT 10‘ http://localhost:8983/solr/collection4/sql?aggregationMode=facet
Below is sample result set:
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{"result-set":{"docs":[
{"count(*)":9158,"to":"pete.davis@enron.com"},
{"count(*)":6244,"to":"tana.jones@enron.com"},
{"count(*)":5874,"to":"jeff.dasovich@enron.com"},
{"count(*)":5867,"to":"sara.shackleton@enron.com"},
{"count(*)":5595,"to":"steven.kean@enron.com"},
{"count(*)":4904,"to":"vkaminski@aol.com"},
{"count(*)":4622,"to":"mark.taylor@enron.com"},
{"count(*)":3819,"to":"kay.mann@enron.com"},
{"count(*)":3678,"to":"richard.shapiro@enron.com"},
{"count(*)":3653,"to":"kate.symes@enron.com"},
{"EOF":"true","RESPONSE_TIME":10}]}
}
Notice that the result set is an array of tuples with key/value pairs that match the SQL column list. The final
tuple contains the EOF flag which signals the end of the stream.
Solr SQL Syntax
Solr supports a broad range of SQL syntax.
?
SQL Parser is Case Insensitive
The SQL parser being used by Solr to translate the SQL statements is case insensitive.
However, for ease of reading, all examples on this page use capitalized keywords.
Escaping Reserved Words
The SQL parser will return an error if a reserved word is used in the SQL query. Reserved words can be
escaped and included in the query using the back tick. For example:
select `from` from emails
SELECT Statements
Solr supports limited and unlimited select queries. The syntax between the two types of queries are identical
except for the LIMIT clause in the SQL statement. However, they have very different execution plans and
different requirements for how the data is stored. The sections below explores both types of queries.
Basic SELECT statement with LIMIT
A limited select query follows this basic syntax:
SELECT fieldA as fa, fieldB as fb, fieldC as fc FROM tableA WHERE fieldC = ‘term1 term2‘ ORDER BY
fa desc LIMIT 100
We’ve covered many syntax options with this example, so let’s walk through what’s possible below.
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WHERE Clause and Boolean Predicates
?
The WHERE clause must have a field on one side of the predicate. Two constants (5 < 10 )
or two fields (fielda > fieldb) is not supported. Subqueries are also not supported.
The WHERE clause allows Solr’s search syntax to be injected into the SQL query. In the example:
WHERE fieldC = ‘term1 term2‘
The predicate above will execute a full text search for the phrase ‘term1 term2‘ in fieldC.
To execute a non-phrase query, simply add parens inside of the single quotes. For example:
WHERE fieldC = ‘(term1 term2)‘
The predicate above searches for term1 OR term2 in fieldC .
The Solr range query syntax can be used as follows:
WHERE fieldC = ‘[0 TO 100]‘
Complex boolean queries can be specified as follows:
WHERE ((fieldC = ‘term1‘ AND fieldA = ‘term2‘) OR (fieldB = ‘term3‘))
To specify NOT queries, you use the AND NOT syntax as follows:
WHERE (fieldA = ‘term1‘) AND NOT (fieldB = ‘term2‘)
Supported WHERE Operators
The parallel SQL interface supports and pushes down most common SQL operators, specifically:
Operator Description Example Solr Query
= Equals
fielda = 10 fielda:10
<> Does not equal
fielda <> 10 -fielda:10
!= Does not equal
fielda != 10 -fielda:10
> Greater than
fielda > 10 fielda:{10 TO *]
>= Greater than or equals
fielda >= 10 fielda:[10 TO *]
< Less than
fielda < 10 fielda:[* TO 10}
<= Less than or equals
fielda <= 10 fielda:[* TO 10]
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Some operators that are not supported are BETWEEN, LIKE and IN. However, there are workarounds for
BETWEEN and LIKE.
? BETWEEN can be supported with a range query, such as field = [50 TO 100] .
? A simplistic LIKE can be used with a wildcard, such as field = ‘sam*‘ .
ORDER BY Clause
The ORDER BY clause maps directly to Solr fields. Multiple ORDER BY fields and directions are supported.
The score field is accepted in the ORDER BY clause in queries where a limit is specified.
If the ORDER BY clause contains the exact fields in the GROUP BY clause, then there is no-limit placed on the
returned results. If the ORDER BY clause contains different fields than the GROUP BY clause, a limit of 100 is
automatically applied. To increase this limit you must specify a value in the LIMIT clause.
Order by fields are case sensitive.
LIMIT Clause
Limits the result set to the specified size. In the example above the clause LIMIT 100 will limit the result set
to 100 records.
There are a few differences to note between limited and unlimited queries:
? Limited queries support score in the field list and ORDER BY . Unlimited queries do not.
? Limited queries allow any stored field in the field list. Unlimited queries require the fields to be stored as
a DocValues field.
? Limited queries allow any indexed field in the ORDER BY list. Unlimited queries require the fields to be
stored as a DocValues field.
SELECT DISTINCT Queries
The SQL interface supports both MapReduce and Facet implementations for SELECT DISTINCT queries.
The MapReduce implementation shuffles tuples to worker nodes where the Distinct operation is performed.
This implementation can perform the Distinct operation over extremely high cardinality fields.
The Facet implementation pushes down the Distinct operation into the search engine using the JSON Facet
API. This implementation is designed for high performance, high QPS scenarios on low-to-moderate
cardinality fields.
The aggregationMode parameter is available in the both the JDBC driver and HTTP interface to choose the
underlying implementation ( map_reduce or facet ). The SQL syntax is identical for both implementations:
SELECT distinct fieldA as fa, fieldB as fb FROM tableA ORDER BY fa desc, fb desc
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Statistics
The SQL interface supports simple statistics calculated on numeric fields. The supported functions are
count(*) , min , max , sum , and avg .
Because these functions never require data to be shuffled, the aggregations are pushed down into the
search engine and are generated by the StatsComponent.
SELECT count(*) as count, sum(fieldB) as sum FROM tableA WHERE fieldC = ‘Hello‘
GROUP BY Aggregations
The SQL interface also supports GROUP BY aggregate queries.
As with SELECT DISTINCT queries, the SQL interface supports both a MapReduce implementation and a
Facet implementation. The MapReduce implementation can build aggregations over extremely high
cardinality fields. The Facet implementations provides high performance aggregation over fields with
moderate levels of cardinality.
Basic GROUP BY with Aggregates
Here is a basic example of a GROUP BY query that requests aggregations:
SELECT fieldA as fa, fieldB as fb, count(*) as count, sum(fieldC) as sum, avg(fieldY) as avg FROM
tableA WHERE fieldC = ‘term1 term2‘
GROUP BY fa, fb HAVING sum > 1000 ORDER BY sum asc LIMIT 100
Let’s break this down into pieces:
Column Identifiers and Aliases
The Column Identifiers can contain both fields in the Solr index and aggregate functions. The supported
aggregate functions are:
? count(*) : Counts the number of records over a set of buckets.
? sum(field) : Sums a numeric field over over a set of buckets.
? avg(field) : Averages a numeric field over a set of buckets.
? min(field) : Returns the min value of a numeric field over a set of buckets.
? max:(field) : Returns the max value of a numerics over a set of buckets.
The non-function fields in the field list determine the fields to calculate the aggregations over.
GROUP BY Clause
The GROUP BY clause can contain up to 4 fields in the Solr index. These fields should correspond with the
non-function fields in the field list.
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HAVING Clause
The HAVING clause may contain any function listed in the field list. Complex HAVING clauses such as this are
supported:
SELECT fieldA, fieldB, count(*), sum(fieldC), avg(fieldY)
FROM tableA
WHERE fieldC = ‘term1 term2‘
GROUP BY fieldA, fieldB
HAVING ((sum(fieldC) > 1000) AND (avg(fieldY) <= 10))
ORDER BY sum(fieldC) asc
LIMIT 100
Best Practices
Separate Collections
It makes sense to create a separate SolrCloud collection just for the /sql handler. This collection can be
created using SolrCloud’s standard collection API.
Since this collection only exists to handle /sql requests and provide a pool of worker nodes, this collection
does not need to hold any data. Worker nodes are selected randomly from the entire pool of available nodes
in the /sql handler’s collection. So to grow this collection dynamically replicas can be added to existing
shards. New replicas will automatically be put to work after they’ve been added.
Parallel SQL Queries
An earlier section describes how the SQL interface translates the SQL statement to a streaming expression.
One of the parameters of the request is the aggregationMode , which defines if the query should use a
MapReduce-like shuffling technique or push the operation down into the search engine.
Parallelized Queries
The Parallel SQL architecture consists of three logical tiers: a SQL tier, a Worker tier, and a Data Table tier.
By default the SQL and Worker tiers are collapsed into the same physical SolrCloud collection.
SQL Tier
The SQL tier is where the /sql handler resides. The /sql handler takes the SQL query and translates it to a
parallel query plan. It then selects worker nodes to execute the plan and sends the query plan to each
worker node to be run in parallel.
Once the query plan has been executed by the worker nodes, the /sql handler then performs the final
merge of the tuples returned by the worker nodes.
Worker Tier
The workers in the worker tier receive the query plan from the /sql handler and execute the parallel query
plan. The parallel execution plan includes the queries that need to be made on the Data Table tier and the
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relational algebra needed to satisfy the query. Each worker node assigned to the query is shuffled 1/N of the
tuples from the Data Tables. The worker nodes execute the query plan and stream tuples back to the worker
nodes.
Data Table Tier
The Data Table tier is where the tables reside. Each table is its own SolrCloud collection. The Data Table layer
receives queries from the worker nodes and emits tuples (search results). The Data Table tier also handles
the initial sorting and partitioning of tuples sent to the workers. This means the tuples are always sorted and
partitioned before they hit the network. The partitioned tuples are sent directly to the correct worker nodes
in the proper sort order, ready to be reduced.
How Parallel SQL Queries are Distributed
The image above shows the three tiers broken out into different SolrCloud collections for clarity. In practice
the /sql handler and worker collection by default share the same collection.
Note: The image shows the network flow for a single Parallel SQL Query (SQL over MapReduce). This
network flow is used when map_reduce aggregation mode is used for GROUP BY aggregations or the SELECT
DISTINCT query. The traditional SolrCloud network flow (without workers) is used when the facet
aggregation mode is used.
Below is a description of the flow:
1. The client sends a SQL query to the /sql handler. The request is handled by a single /sql handler
instance.
2. The /sql handler parses the SQL query and creates the parallel query plan.
3. The query plan is sent to worker nodes (in green).
4. The worker nodes execute the plan in parallel. The diagram shows each worker node contacting a
collection in the Data Table tier (in blue).
5. The collection in the Data Table tier is the table from the SQL query. Notice that the collection has five
shards each with 3 replicas.
6. Notice that each worker contacts one replica from each shard. Because there are 5 workers, each worker
is returned 1/5 of the search results from each shard. The partitioning is done inside of the Data Table
tier so there is no duplication of data across the network.
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7. Also notice with this design ALL replicas in the data layer are shuffling (sorting & partitioning) data
simultaneously. As the number of shards, replicas and workers grows this design allows for a massive
amount of computing power to be applied to a single query.
8. The worker nodes process the tuples returned from the Data Table tier in parallel. The worker nodes
perform the relational algebra needed to satisfy the query plan.
9. The worker nodes stream tuples back to the /sql handler where the final merge is done, and finally the
tuples are streamed back to the client.
SQL Clients and Database Visualization Tools
The SQL interface supports queries sent from SQL clients and database visualization tools such as
DbVisualizer and Apache Zeppelin.
Generic Clients
For most Java based clients, the following jars will need to be placed on the client classpath:
? all .jars found in $SOLR_HOME/dist/solrj-libs
? the SolrJ .jar found at $SOLR_HOME/dist/solr-solrj-<version>.jar
If you are using Maven, the org.apache.solr.solr-solrj artifact contains the required jars.
Once the jars are available on the classpath, the Solr JDBC driver name is
org.apache.solr.client.solrj.io.sql.DriverImpl and a connection can be made with the following
connection string format:
jdbc:solr://SOLR_ZK_CONNECTION_STRING?collection=COLLECTION_NAME
There are other parameters that can be optionally added to the connection string like aggregationMode and
numWorkers .
DbVisualizer
A step-by-step guide for setting up DbVisualizer is in the section Solr JDBC - DbVisualizer.
SQuirreL SQL
A step-by-step guide for setting up SQuirreL SQL is in the section Solr JDBC - SQuirreL SQL.
Apache Zeppelin (incubating)
A step-by-step guide for setting up Apache Zeppelin is in the section Solr JDBC - Apache Zeppelin.
Python/Jython
Examples of using Python and Jython for connecting to Solr with the Solr JDBC driver are available in the
section Solr JDBC - Python/Jython.
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R
Examples of using R for connecting to Solr with the Solr JDBC driver are available in the section Solr JDBC - R .
Solr JDBC - DbVisualizer
Solr’s JDBC driver supports DBVisualizer for querying Solr.
For DbVisualizer, you will need to create a new driver for Solr using the DbVisualizer Driver Manager. This
will add several SolrJ client .jars to the DbVisualizer classpath. The files required are:
? all .jars found in $SOLR_HOME/dist/solrj-lib
? the SolrJ .jar found at $SOLR_HOME/dist/solr-solrj-<version>.jar
Once the driver has been created, you can create a connection to Solr with the connection string format
outlined in the generic section and use the SQL Commander to issue queries.
Setup Driver
Open Driver Manager
From the Tools menu, choose Driver Manager to add a driver.
Create a New Driver
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Name the Driver
Provide a name for the driver, and provide the URL format:
jdbc:solr://<zk_connection_string>/?collection=<collection> . Do not fill in values for the variables
“ zk_connection_string ” and “ collection ”, those will be provided later when the connection to Solr is
configured. The Driver Class will also be automatically added when the driver .jars are added.
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Add Driver Files to Classpath
The driver files to be added are:
? all .jars in $SOLR_HOME/dist/solrj-lib
? the SolrJ .jar found in $SOLR_HOME/dist/solr-solrj-<version>.jar
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Review and Close Driver Manager
Once the driver files have been added, you can close the Driver Manager.
Create a Connection
Next, create a connection to Solr using the driver just created.
Use the Connection Wizard
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Name the Connection
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Select the Solr driver
Specify the Solr URL
Provide the Solr URL, using the ZooKeeper host and port and the collection. For example,
jdbc:solr://localhost:9983?collection=test
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Open and Connect to Solr
Once the connection has been created, double-click on it to open the connection details screen and connect
to Solr.
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Open SQL Commander to Enter Queries
When the connection is established, you can use the SQL Commander to issue queries and view data.
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Solr JDBC - SQuirreL SQL
For SQuirreL SQL, you will need to create a new driver for Solr. This will add several SolrJ client .jars to the
SQuirreL SQL classpath. The files required are:
? all .jars found in $SOLR_HOME/dist/solrj-libs
? the SolrJ .jar found at $SOLR_HOME/dist/solr-solrj-<version>.jar
Once the driver has been created, you can create a connection to Solr with the connection string format
outlined in the generic section and use the editor to issue queries.
Add Solr JDBC Driver
Open Drivers
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Add Driver
Name the Driver
Provide a name for the driver, and provide the URL format:
jdbc:solr://<zk_connection_string>/?collection=<collection> . Do not fill in values for the variables
“ zk_connection_string ” and “ collection ”, those will be defined later when the connection to Solr is
configured.
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Add Solr JDBC jars to Classpath
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Add the Solr JDBC driver class name
After adding the .jars, you will need to additionally define the Class Name
org.apache.solr.client.solrj.io.sql.DriverImpl .
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Create an Alias
To define a JDBC connection, you must define an alias.
Open Aliases
Add an Alias
Configure the Alias
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Connect to the Alias
Querying
Once you’ve successfully connected to Solr, you can use the SQL interface to enter queries and work with
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data.
Solr JDBC - Apache Zeppelin
The Solr JDBC driver can support Apache Zeppelin.
?
This requires Apache Zeppelin 0.6.0 or greater which contains the JDBC interpreter.
To use Apache Zeppelin with Solr, you will need to create a JDBC interpreter for Solr. This will add SolrJ to the
interpreter classpath. Once the interpreter has been created, you can create a notebook to issue queries.
The Apache Zeppelin JDBC interpreter documentation provides additional information about JDBC prefixes
and other features.
Create the Apache Solr JDBC Interpreter
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Click "Interpreter" in the top navigation
Click "Create"
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Enter information about your Solr installation
?
For most installations, Apache Zeppelin configures PostgreSQL as the JDBC interpreter
default driver. The default driver can either be replaced by the Solr driver as outlined above
or you can add a separate JDBC interpreter prefix as outlined in the Apache Zeppelin JDBC
interpreter documentation.
Create a Notebook
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Click Notebook → Create new note
Provide a name and click "Create Note"
Query with the Notebook
?
For some notebooks, the JDBC interpreter will not be bound to the notebook by default.
Instructions on how to bind the JDBC interpreter to a notebook are available here.
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Results of Solr query
The below code block assumes that the Apache Solr driver is setup as the default JDBC interpreter driver. If
that is not the case, instructions for using a different prefix is available here.
%jdbc
select fielda, fieldb, from test limit 10
Solr JDBC - Python/Jython
Solr’s JDBC driver supports Python and Jython.
Python
Python supports accessing JDBC using the JayDeBeApi library. The CLASSPATH variable must be configured
to contain the solr-solrj jar and the supporting solrj-lib jars.
JayDeBeApi
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run.sh
#!/usr/bin/env bash
# Java 8 must already be installed
pip install JayDeBeApi
export CLASSPATH="$(echo $(ls /opt/solr/dist/solr-solrj* /opt/solr/dist/solrj-lib/*) | tr ‘ ‘
‘:‘)"
python solr_jaydebeapi.py
solr_jaydebeapi.py
#!/usr/bin/env python
# https://pypi.python.org/pypi/JayDeBeApi/
import jaydebeapi
import sys
if __name__ == ‘__main__‘:
jdbc_url = "jdbc:solr://localhost:9983?collection=test"
driverName = "org.apache.solr.client.solrj.io.sql.DriverImpl"
statement = "select fielda, fieldb, fieldc, fieldd_s, fielde_i from test limit 10"
conn = jaydebeapi.connect(driverName, jdbc_url)
curs = conn.cursor()
curs.execute(statement)
print(curs.fetchall())
conn.close()
sys.exit(0)
Jython
Jython supports accessing JDBC natively with Java interfaces or with the zxJDBC library. The CLASSPATH
variable must be configured to contain the solr-solrj jar and the supporting solrj-lib jars.
run.sh
#!/usr/bin/env bash
# Java 8 and Jython must already be installed
export CLASSPATH="$(echo $(ls /opt/solr/dist/solr-solrj* /opt/solr/dist/solrj-lib/*) | tr ‘ ‘
‘:‘)"
jython [solr_java_native.py | solr_zxjdbc.py]
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Java Native
solr_java_native.py
#!/usr/bin/env jython
# http://www.jython.org/jythonbook/en/1.0/DatabasesAndJython.html
# https://wiki.python.org/jython/DatabaseExamples#SQLite_using_JDBC
import sys
from java.lang import Class
from java.sql import DriverManager, SQLException
if __name__ == ‘__main__‘:
jdbc_url = "jdbc:solr://localhost:9983?collection=test"
driverName = "org.apache.solr.client.solrj.io.sql.DriverImpl"
statement = "select fielda, fieldb, fieldc, fieldd_s, fielde_i from test limit 10"
dbConn = DriverManager.getConnection(jdbc_url)
stmt = dbConn.createStatement()
resultSet = stmt.executeQuery(statement)
while resultSet.next():
print(resultSet.getString("fielda"))
resultSet.close()
stmt.close()
dbConn.close()
sys.exit(0)
zxJDBC
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solr_zxjdbc.py
#!/usr/bin/env jython
# http://www.jython.org/jythonbook/en/1.0/DatabasesAndJython.html
# https://wiki.python.org/jython/DatabaseExamples#SQLite_using_ziclix
import sys
from com.ziclix.python.sql import zxJDBC
if __name__ == ‘__main__‘:
jdbc_url = "jdbc:solr://localhost:9983?collection=test"
driverName = "org.apache.solr.client.solrj.io.sql.DriverImpl"
statement = "select fielda, fieldb, fieldc, fieldd_s, fielde_i from test limit 10"
with zxJDBC.connect(jdbc_url, None, None, driverName) as conn:
with conn:
with conn.cursor() as c:
c.execute(statement)
print(c.fetchall())
sys.exit(0)
Solr JDBC - R
R supports accessing JDBC using the RJDBC library.
RJDBC
run.sh
#!/usr/bin/env bash
# Java 8 must already be installed and R configured with `R CMD javareconf`
Rscript -e ‘install.packages("RJDBC", dep=TRUE)‘
Rscript solr_rjdbc.R
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solr_rjdbc.R
# https://www.rforge.net/RJDBC/
library("RJDBC")
solrCP <- c(list.files(‘/opt/solr/dist/solrj-lib‘, full.names=TRUE), list.files(‘/opt/solr/dist‘,
pattern=‘solrj‘, full.names=TRUE, recursive = TRUE))
drv <- JDBC("org.apache.solr.client.solrj.io.sql.DriverImpl",
solrCP,
identifier.quote="`")
conn <- dbConnect(drv, "jdbc:solr://localhost:9983?collection=test", "user", "pwd")
dbGetQuery(conn, "select fielda, fieldb, fieldc, fieldd_s, fielde_i from test limit 10")
dbDisconnect(conn)
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The Well-Configured Solr Instance
This section tells you how to fine-tune your Solr instance for optimum performance.
This section covers the following topics:
Configuring solrconfig.xml: Describes how to work with the main configuration file for Solr, solrconfig.xml ,
covering the major sections of the file.
Solr Cores and solr.xml: Describes how to work with solr.xml and core.properties to configure your Solr
core, or multiple Solr cores within a single instance.
Configuration APIs: Describes several APIs used to configure Solr: Blob Store, Config, Request Parameters
and Managed Resources.
Implicit RequestHandlers: Describes various end-points automatically provided by Solr and how to configure
them.
Solr Plugins: Introduces Solr plugins with pointers to more information.
JVM Settings: Gives some guidance on best practices for working with Java Virtual Machines.
?
The focus of this section is generally on configuring a single Solr instance, but for those
interested in scaling a Solr implementation in a cluster environment, see also the section
SolrCloud. There are also options to scale through sharding or replication, described in the
section Legacy Scaling and Distribution.
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Configuring solrconfig.xml
The solrconfig.xml file is the configuration file with the most parameters affecting Solr itself.
While configuring Solr, you’ll work with solrconfig.xml often, either directly or via the Config API to create
"configuration overlays" ( configoverlay.json ) to override the values in solrconfig.xml .
In solrconfig.xml , you configure important features such as:
? request handlers, which process the requests to Solr, such as requests to add documents to the index or
requests to return results for a query
? listeners, processes that "listen" for particular query-related events; listeners can be used to trigger the
execution of special code, such as invoking some common queries to warm-up caches
? the Request Dispatcher for managing HTTP communications
? the Admin Web interface
? parameters related to replication and duplication (these parameters are covered in detail in Legacy
Scaling and Distribution)
The solrconfig.xml file is located in the conf/ directory for each collection. Several well-commented
example files can be found in the server/solr/configsets/ directories demonstrating best practices for
many different types of installations.
We’ve covered the options in the following sections:
? DataDir and DirectoryFactory in SolrConfig
? Lib Directives in SolrConfig
? Schema Factory Definition in SolrConfig
? IndexConfig in SolrConfig
? RequestHandlers and SearchComponents in SolrConfig
? InitParams in SolrConfig
? UpdateHandlers in SolrConfig
? Query Settings in SolrConfig
? RequestDispatcher in SolrConfig
? Update Request Processors
? Codec Factory
Substituting Properties in Solr Config Files
Solr supports variable substitution of property values in config files, which allows runtime specification of
various configuration options in solrconfig.xml . The syntax is ${propertyname[:option default value] }.
This allows defining a default that can be overridden when Solr is launched. If a default value is not
specified, then the property must be specified at runtime or the configuration file will generate an error
when parsed.
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There are multiple methods for specifying properties that can be used in configuration files. Of those below,
strongly consider "config overlay" as the preferred approach, as it stays local to the config set and because
it’s easy to modify.
JVM System Properties
Any JVM System properties, usually specified using the -D flag when starting the JVM, can be used as
variables in any XML configuration file in Solr.
For example, in the sample solrconfig.xml files, you will see this value which defines the locking type to
use:
<lockType>${solr.lock.type:native}</lockType>
Which means the lock type defaults to "native" but when starting Solr, you could override this using a JVM
system property by launching the Solr it with:
bin/solr start -Dsolr.lock.type=none
In general, any Java system property that you want to set can be passed through the bin/solr script using
the standard -Dproperty=value syntax. Alternatively, you can add common system properties to the
SOLR_OPTS environment variable defined in the Solr include file ( bin/solr.in.sh or bin/solr.in.cmd ). For
more information about how the Solr include file works, refer to: Taking Solr to Production.
Config API
The Config API allows you to use an API to modify Solr’s configuration, specifically user defined properties.
Changes made with this API are stored in a file named configoverlay.json . This file should only be edited
with the API, but will look like this example:
{"userProps":{
"dih.db.url":"jdbc:oracle:thin:@localhost:1521",
"dih.db.user":"username",
"dih.db.pass":"password"}}
For more details, see the section Config API.
solrcore.properties
If the configuration directory for a Solr core contains a file named solrcore.properties that file can contain
any arbitrary user defined property names and values using the Java standard properties file format, and
those properties can be used as variables in the XML configuration files for that Solr core.
For example, the following solrcore.properties file could be created in the conf/ directory of a collection
using one of the example configurations, to override the lockType used.
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#conf/solrcore.properties
solr.lock.type=none
?
Deprecation
solrcore.properties won’t work in SolrCloud mode (it is not read from ZooKeeper). This
feature is likely to be removed in the future. Instead, use another mechanism like a config
overlay.
?
The path and name of the solrcore.properties file can be overridden using the
properties property in core.properties .
User-Defined Properties in core.properties
Every Solr core has a core.properties file, automatically created when using the APIs. When you create a
SolrCloud collection, you can pass through custom parameters to go into each core.properties that will be
created, by prefixing the parameter name with "property." as a URL parameter. Example:
http://localhost:8983/solr/admin/collections?action=CREATE&name=gettingstarted&numShards=1&proper
ty.my.custom.prop=edismax
That would create a core.properties file that has at least the following properties (others omitted for
brevity):
#core.properties
name=gettingstarted
my.custom.prop=edismax
The my.custom.prop property can then be used as a variable, such as in solrconfig.xml :
<requestHandler name="/select">
<lst name="defaults">
<str name="defType">${my.custom.prop}</str>
</lst>
</requestHandler>
Implicit Core Properties
Several attributes of a Solr core are available as "implicit" properties that can be used in variable
substitution, independent of where or how they underlying value is initialized. For example: regardless of
whether the name for a particular Solr core is explicitly configured in core.properties or inferred from the
name of the instance directory, the implicit property solr.core.name is available for use as a variable in that
core’s configuration file…
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<requestHandler name="/select">
<lst name="defaults">
<str name="collection_name">${solr.core.name}</str>
</lst>
</requestHandler>
All implicit properties use the solr.core. name prefix, and reflect the runtime value of the equivalent
core.properties property:
? solr.core.name
? solr.core.config
? solr.core.schema
? solr.core.dataDir
? solr.core.transient
? solr.core.loadOnStartup
DataDir and DirectoryFactory in SolrConfig
Where and how Solr stores its indexes are configurable options.
Specifying a Location for Index Data with the dataDir Parameter
By default, Solr stores its index data in a directory called /data under the core’s instance directory
( instanceDir ). If you would like to specify a different directory for storing index data, you can configure the
dataDir in the core.properties file for the core, or use the <dataDir> parameter in the solrconfig.xml
file. You can specify another directory either with an absolute path or a pathname relative to the instanceDir
of the SolrCore. For example:
<dataDir>/solr/data/${solr.core.name}</dataDir>
The ${solr.core.name} substitution will cause the name of the current core to be substituted, which results
in each core’s data being kept in a separate subdirectory.
If you are using replication to replicate the Solr index (as described in Legacy Scaling and Distribution), then
the <dataDir> directory should correspond to the index directory used in the replication configuration.
Specifying the DirectoryFactory For Your Index
The default solr.StandardDirectoryFactory is filesystem based, and tries to pick the best implementation
for the current JVM and platform. You can force a particular implementation and/or config options by
specifying solr.MMapDirectoryFactory , solr.NIOFSDirectoryFactory , or
solr.SimpleFSDirectoryFactory .
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<directoryFactory name="DirectoryFactory"
class="solr.MMapDirectoryFactory">
<bool name="preload">true</bool>
</directoryFactory>
The solr.RAMDirectoryFactory is memory based, not persistent, and does not work with replication. Use
this DirectoryFactory to store your index in RAM.
<directoryFactory class="org.apache.solr.core.RAMDirectoryFactory"/>
?
If you are using Hadoop and would like to store your indexes in HDFS, you should use the
solr.HdfsDirectoryFactory instead of either of the above implementations. For more
details, see the section Running Solr on HDFS.
Lib Directives in SolrConfig
Solr allows loading plugins by defining <lib/> directives in solrconfig.xml .
The plugins are loaded in the order they appear in solrconfig.xml . If there are dependencies, list the
lowest level dependency jar first.
Regular expressions can be used to provide control loading jars with dependencies on other jars in the same
directory. All directories are resolved as relative to the Solr instanceDir .
<lib dir="../../../contrib/extraction/lib" regex=".*\.jar" />
<lib dir="../../../dist/" regex="solr-cell-\d.*\.jar" />
<lib dir="../../../contrib/clustering/lib/" regex=".*\.jar" />
<lib dir="../../../dist/" regex="solr-clustering-\d.*\.jar" />
<lib dir="../../../contrib/langid/lib/" regex=".*\.jar" />
<lib dir="../../../dist/" regex="solr-langid-\d.*\.jar" />
<lib dir="../../../contrib/velocity/lib" regex=".*\.jar" />
<lib dir="../../../dist/" regex="solr-velocity-\d.*\.jar" />
Schema Factory Definition in SolrConfig
Solr’s Schema API enables remote clients to access schema information, and make schema modifications,
through a REST interface.
Other features such as Solr’s Schemaless Mode also work via schema modifications made programatically at
run time.
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?
Using the Managed Schema is required to be able to use the Schema API to modify your
schema. However, using Managed Schema does not by itself mean you are also using Solr
in Schemaless Mode (or "schema guessing" mode).
Schemaless mode requires enabling the Managed Schema if it is not already, but full
schema guessing requires additional configuration as described in the section Schemaless
Mode.
While the "read" features of the Schema API are supported for all schema types, support for making schema
modifications programatically depends on the <schemaFactory/> in use.
Solr Uses Managed Schema by Default
When a <schemaFactory/> is not explicitly declared in a solrconfig.xml file, Solr implicitly uses a
ManagedIndexSchemaFactory , which is by default "mutable" and keeps schema information in a managed-
schema file.
<!-- An example of Solr‘s implicit default behavior if no
no schemaFactory is explicitly defined.
-->
<schemaFactory class="ManagedIndexSchemaFactory">
<bool name="mutable">true</bool>
<str name="managedSchemaResourceName">managed-schema</str>
</schemaFactory>
If you wish to explicitly configure ManagedIndexSchemaFactory the following options are available:
? mutable - controls whether changes may be made to the Schema data. This must be set to true to allow
edits to be made with the Schema API.
? managedSchemaResourceName is an optional parameter that defaults to "managed-schema", and defines
a new name for the schema file that can be anything other than “schema.xml”.
With the default configuration shown above, you can use the Schema API to modify the schema as much as
you want, and then later change the value of mutable to false if you wish to "lock" the schema in place and
prevent future changes.
Classic schema.xml
An alternative to using a managed schema is to explicitly configure a ClassicIndexSchemaFactory .
ClassicIndexSchemaFactory requires the use of a schema.xml configuration file, and disallows any
programatic changes to the Schema at run time. The schema.xml file must be edited manually and is only
loaded only when the collection is loaded.
<schemaFactory class="ClassicIndexSchemaFactory"/>
Switching from schema.xml to Managed Schema
If you have an existing Solr collection that uses ClassicIndexSchemaFactory , and you wish to convert to use
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a managed schema, you can simply modify the solrconfig.xml to specify the use of the
ManagedIndexSchemaFactory .
Once Solr is restarted and it detects that a schema.xml file exists, but the managedSchemaResourceName file
(ie: “managed-schema”) does not exist, the existing schema.xml file will be renamed to schema.xml.bak and
the contents are re-written to the managed schema file. If you look at the resulting file, you’ll see this at the
top of the page:
<!-- Solr managed schema - automatically generated - DO NOT EDIT -->
You are now free to use the Schema API as much as you want to make changes, and remove the
schema.xml.bak .
Switching from Managed Schema to Manually Edited schema.xml
If you have started Solr with managed schema enabled and you would like to switch to manually editing a
schema.xml file, you should take the following steps:
1. Rename the managed-schema file to schema.xml .
2. Modify solrconfig.xml to replace the schemaFactory class.
a. Remove any ManagedIndexSchemaFactory definition if it exists.
b. Add a ClassicIndexSchemaFactory definition as shown above
3. Reload the core(s).
If you are using SolrCloud, you may need to modify the files via ZooKeeper. The bin/solr script provides an
easy way to download the files from ZooKeeper and upload them back after edits. See the section
ZooKeeper Operations for more information.
?
To have full control over your schema.xml file, you may also want to disable schema
guessing, which allows unknown fields to be added to the schema during indexing. The
properties that enable this feature are discussed in the section Schemaless Mode.
IndexConfig in SolrConfig
The <indexConfig> section of solrconfig.xml defines low-level behavior of the Lucene index writers.
By default, the settings are commented out in the sample solrconfig.xml included with Solr, which means
the defaults are used. In most cases, the defaults are fine.
<indexConfig>
...
</indexConfig>
Writing New Segments
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ramBufferSizeMB
Once accumulated document updates exceed this much memory space (defined in megabytes), then the
pending updates are flushed. This can also create new segments or trigger a merge. Using this setting is
generally preferable to maxBufferedDocs . If both maxBufferedDocs and ramBufferSizeMB are set in
solrconfig.xml , then a flush will occur when either limit is reached. The default is 100Mb.
<ramBufferSizeMB>100</ramBufferSizeMB>
maxBufferedDocs
Sets the number of document updates to buffer in memory before they are flushed as a new segment. This
may also trigger a merge. The default Solr configuration sets to flush by RAM usage ( ramBufferSizeMB ).
<maxBufferedDocs>1000</maxBufferedDocs>
useCompoundFile
Controls whether newly written (and not yet merged) index segments should use the Compound File
Segment format. The default is false.
<useCompoundFile>false</useCompoundFile>
Merging Index Segments
mergePolicyFactory
Defines how merging segments is done.
The default in Solr is to use a TieredMergePolicy , which merges segments of approximately equal size,
subject to an allowed number of segments per tier.
Other policies available are the LogByteSizeMergePolicy and LogDocMergePolicy . For more information on
these policies, please see the MergePolicy javadocs.
<mergePolicyFactory class="org.apache.solr.index.TieredMergePolicyFactory">
<int name="maxMergeAtOnce">10</int>
<int name="segmentsPerTier">10</int>
</mergePolicyFactory>
Controlling Segment Sizes: Merge Factors
The most common adjustment users make to the configuration of TieredMergePolicy (or
LogByteSizeMergePolicy) are the "merge factors" to change how many segments should be merged at one
time.
For TieredMergePolicy, this is controlled by setting the <int name="maxMergeAtOnce"> and <int
name="segmentsPerTier"> options, while LogByteSizeMergePolicy has a single <int name="mergeFactor">
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option (all of which default to 10 ).
To understand why these options are important, consider what happens when an update is made to an
index using LogByteSizeMergePolicy: Documents are always added to the most recently opened segment.
When a segment fills up, a new segment is created and subsequent updates are placed there.
If creating a new segment would cause the number of lowest-level segments to exceed the mergeFactor
value, then all those segments are merged together to form a single large segment. Thus, if the merge
factor is 10, each merge results in the creation of a single segment that is roughly ten times larger than each
of its ten constituents. When there are 10 of these larger segments, then they in turn are merged into an
even larger single segment. This process can continue indefinitely.
When using TieredMergePolicy, the process is the same, but instead of a single mergeFactor value, the
segmentsPerTier setting is used as the threshold to decide if a merge should happen, and the
maxMergeAtOnce setting determines how many segments should be included in the merge.
Choosing the best merge factors is generally a trade-off of indexing speed vs. searching speed. Having
fewer segments in the index generally accelerates searches, because there are fewer places to look. It also
can also result in fewer physical files on disk. But to keep the number of segments low, merges will occur
more often, which can add load to the system and slow down updates to the index.
Conversely, keeping more segments can accelerate indexing, because merges happen less often, making an
update is less likely to trigger a merge. But searches become more computationally expensive and will likely
be slower, because search terms must be looked up in more index segments. Faster index updates also
means shorter commit turnaround times, which means more timely search results.
Customizing Merge Policies
If the configuration options for the built-in merge policies do not fully suit your use case, you can customize
them: either by creating a custom merge policy factory that you specify in your configuration, or by
configuring a merge policy wrapper which uses a wrapped.prefix configuration option to control how the
factory it wraps will be configured:
<mergePolicyFactory class="org.apache.solr.index.SortingMergePolicyFactory">
<str name="sort">timestamp desc</str>
<str name="wrapped.prefix">inner</str>
<str name="inner.class">org.apache.solr.index.TieredMergePolicyFactory</str>
<int name="inner.maxMergeAtOnce">10</int>
<int name="inner.segmentsPerTier">10</int>
</mergePolicyFactory>
The example above shows Solr’s SortingMergePolicyFactory being configured to sort documents in
merged segments by "timestamp desc" , and wrapped around a TieredMergePolicyFactory configured to
use the values maxMergeAtOnce=10 and segmentsPerTier=10 via the inner prefix defined by
SortingMergePolicyFactory’s `wrapped.prefix option. For more information on using
SortingMergePolicyFactory , see the segmentTerminateEarly parameter.
mergeScheduler
The merge scheduler controls how merges are performed. The default ConcurrentMergeScheduler
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performs merges in the background using separate threads. The alternative, SerialMergeScheduler , does
not perform merges with separate threads.
<mergeScheduler class="org.apache.lucene.index.ConcurrentMergeScheduler"/>
mergedSegmentWarmer
When using Solr in for Near Real Time Searching a merged segment warmer can be configured to warm the
reader on the newly merged segment, before the merge commits. This is not required for near real-time
search, but will reduce search latency on opening a new near real-time reader after a merge completes.
<mergedSegmentWarmer class="org.apache.lucene.index.SimpleMergedSegmentWarmer"/>
Compound File Segments
Each Lucene segment is typically comprised of a dozen or so files. Lucene can be configured to bundle all of
the files for a segment into a single compound file using a file extension of .cfs ; it’s an abbreviation for
Compound File Segment.
CFS segments may incur a minor performance hit for various reasons, depending on the runtime
environment. For example, filesystem buffers are typically associated with open file descriptors, which may
limit the total cache space available to each index.
On systems where the number of open files allowed per process is limited, CFS may avoid hitting that limit.
The open files limit might also be tunable for your OS with the Linux/Unix ulimit command, or something
similar for other operating systems.
?
CFS: New Segments vs Merged Segments
To configure whether newly written segments should use CFS, see the useCompoundFile
setting described above. To configure whether merged segments use CFS, review the
Javadocs for your mergePolicyFactory .
Many Merge Policy implementations support noCFSRatio and maxCFSSegmentSizeMB
settings with default values that prevent compound files from being used for large
segments, but do use compound files for small segments.
Index Locks
lockType
The LockFactory options specify the locking implementation to use.
The set of valid lock type options depends on the DirectoryFactory you have configured. The values listed
below are are supported by StandardDirectoryFactory (the default):
? native (default) uses NativeFSLockFactory to specify native OS file locking. If a second Solr process
attempts to access the directory, it will fail. Do not use when multiple Solr web applications are
attempting to share a single index.
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? simple uses SimpleFSLockFactory to specify a plain file for locking.
? single (expert) uses SingleInstanceLockFactory. Use for special situations of a read-only index directory,
or when there is no possibility of more than one process trying to modify the index (even sequentially).
This type will protect against multiple cores within the same JVM attempting to access the same index.
WARNING! If multiple Solr instances in different JVMs modify an index, this type will not protect against
index corruption.
? hdfs uses HdfsLockFactory to support reading and writing index and transaction log files to a HDFS
filesystem. See the section Running Solr on HDFS for more details on using this feature.
For more information on the nuances of each LockFactory, see http://wiki.apache.org/lucene-java/
AvailableLockFactories.
<lockType>native</lockType>
writeLockTimeout
The maximum time to wait for a write lock on an IndexWriter. The default is 1000, expressed in milliseconds.
<writeLockTimeout>1000</writeLockTimeout>
Other Indexing Settings
There are a few other parameters that may be important to configure for your implementation. These
settings affect how or when updates are made to an index.
Setting Description
reopenReaders Controls if IndexReaders will be re-opened, instead of closed and then
opened, which is often less efficient. The default is true.
deletionPolicy Controls how commits are retained in case of rollback. The default is
SolrDeletionPolicy , which has sub-parameters for the maximum
number of commits to keep ( maxCommitsToKeep ), the maximum number of
optimized commits to keep ( maxOptimizedCommitsToKeep ), and the
maximum age of any commit to keep ( maxCommitAge ), which supports
DateMathParser syntax.
infoStream The InfoStream setting instructs the underlying Lucene classes to write
detailed debug information from the indexing process as Solr log
messages.
<reopenReaders>true</reopenReaders>
<deletionPolicy class="solr.SolrDeletionPolicy">
<str name="maxCommitsToKeep">1</str>
<str name="maxOptimizedCommitsToKeep">0</str>
<str name="maxCommitAge">1DAY</str>
</deletionPolicy>
<infoStream>false</infoStream>
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RequestHandlers and SearchComponents in SolrConfig
After the <query> section of solrconfig.xml , request handlers and search components are configured.
A request handler processes requests coming to Solr. These might be query requests or index update
requests. You will likely need several of these defined, depending on how you want Solr to handle the
various requests you will make.
A search component is a feature of search, such as highlighting or faceting. The search component is defined
in solrconfig.xml separate from the request handlers, and then registered with a request handler as
needed.
These are often referred to as "requestHandler" and "searchComponent", which is how they are defined in
solrconfig.xml .
Request Handlers
Every request handler is defined with a name and a class. The name of the request handler is referenced
with the request to Solr, typically as a path. For example, if Solr is installed at ` http://localhost:8983/solr/
`and you have a collection named “ gettingstarted ”, you can make a request using URLs like this:
http://localhost:8983/solr/gettingstarted/select?q=solr
This query will be processed by the request handler with the name /select . We’ve only used the "q"
parameter here, which includes our query term, a simple keyword of "solr". If the request handler has more
parameters defined, those will be used with any query we send to this request handler unless they are over-
ridden by the client (or user) in the query itself.
If you have another request handler defined, you would send your request with that name. For example,
/update is a request handler that handles index updates (i.e., sending new documents to the index). By
default, /select is a request handler that handles query requests.
Request handlers can also process requests for nested paths of their names, for example, a request using
/myhandler/extrapath may be processed by a request handler registered with the name /myhandler . If a
request handler is explicitly defined by the name /myhandler/extrapath , that would take precedence over
the nested path. This assumes you are using the request handler classes included with Solr; if you create
your own request handler, you should make sure it includes the ability to handle nested paths if you want to
use them with your custom request handler.
It is also possible to configure defaults for request handlers with a section called initParams . These defaults
can be used when you want to have common properties that will be used by each separate handler. For
example, if you intend to create several request handlers that will all request the same list of fields in the
response, you can configure an initParams section with your list of fields. For more information about
initParams , see the section InitParams in SolrConfig.
SearchHandlers
The primary request handler defined with Solr by default is the "SearchHandler", which handles search
queries. The request handler is defined, and then a list of defaults for the handler are defined with a
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defaults list.
For example, in the default solrconfig.xml , the first request handler defined looks like this:
<requestHandler name="/select" class="solr.SearchHandler">
<lst name="defaults">
<str name="echoParams">explicit</str>
<int name="rows">10</int>
</lst>
</requestHandler>
This example defines the rows parameter, which defines how many search results to return, to "10". The
echoParams parameter defines that the parameters defined in the query should be returned when debug
information is returned. Note also that the way the defaults are defined in the list varies if the parameter is a
string, an integer, or another type.
All of the parameters described in the section Searching can be defined as defaults for any of the
SearchHandlers.
Besides defaults , there are other options for the SearchHandler, which are:
? appends : This allows definition of parameters that are added to the user query. These might be filter
queries, or other query rules that should be added to each query. There is no mechanism in Solr to allow
a client to override these additions, so you should be absolutely sure you always want these parameters
applied to queries.
<lst name="appends">
<str name="fq">inStock:true</str>
</lst>
In this example, the filter query "inStock:true" will always be added to every query.
? invariants : This allows definition of parameters that cannot be overridden by a client. The values
defined in an invariants section will always be used regardless of the values specified by the user, by
the client, in defaults or in appends .
<lst name="invariants">
<str name="facet.field">cat</str>
<str name="facet.field">manu_exact</str>
<str name="facet.query">price:[* TO 500]</str>
<str name="facet.query">price:[500 TO *]</str>
</lst>
In this example, facet fields have been defined which limits the facets that will be returned by Solr. If the
client requests facets, the facets defined with a configuration like this are the only facets they will see.
The final section of a request handler definition is components , which defines a list of search components
that can be used with a request handler. They are only registered with the request handler. How to define a
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search component is discussed further on in the section on Search Components. The components element
can only be used with a request handler that is a SearchHandler.
The solrconfig.xml file includes many other examples of SearchHandlers that can be used or modified as
needed.
UpdateRequestHandlers
The UpdateRequestHandlers are request handlers which process updates to the index.
In this guide, we’ve covered these handlers in detail in the section Uploading Data with Index Handlers.
ShardHandlers
It is possible to configure a request handler to search across shards of a cluster, used with distributed
search. More information about distributed search and how to configure the shardHandler is in the section
Distributed Search with Index Sharding.
Implicit Request Handlers
Solr includes many out-of-the-box request handlers that are not configured in solrconfig.xml , and so are
referred to as "implicit" - see Implicit RequestHandlers.
Search Components
Search components define the logic that is used by the SearchHandler to perform queries for users.
Default Components
There are several default search components that work with all SearchHandlers without any additional
configuration. If no components are defined (with the exception of first-components and last-components
- see below), these are executed by default, in the following order:
Component Name Class Name More Information
query solr.QueryComponent Described in the section Query Syntax and
Parsing.
facet solr.FacetComponent Described in the section Faceting.
mlt solr.MoreLikeThisComponent Described in the section MoreLikeThis.
highlight solr.HighlightComponent Described in the section Highlighting.
stats solr.StatsComponent Described in the section The Stats
Component.
debug solr.DebugComponent Described in the section on Common
Query Parameters.
expand solr.ExpandComponent Described in the section Collapse and
Expand Results.
If you register a new search component with one of these default names, the newly defined component will
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be used instead of the default.
First-Components and Last-Components
It’s possible to define some components as being used before (with first-components ) or after (with last-
components ) the default components listed above.
?
first-components and/or last-components may only be used in conjunction with the
default components. If you define your own components , the default components will not
be executed, and first-components and last-components are disallowed.
<arr name="first-components">
<str>mycomponent</str>
</arr>
<arr name="last-components">
<str>spellcheck</str>
</arr>
Components
If you define components , the default components (see above) will not be executed, and first-components
and last-components are disallowed:
<arr name="components">
<str>mycomponent</str>
<str>query</str>
<str>debug</str>
</arr>
Other Useful Components
Many of the other useful components are described in sections of this Guide for the features they support.
These are:
? SpellCheckComponent , described in the section Spell Checking.
? TermVectorComponent , described in the section The Term Vector Component.
? QueryElevationComponent , described in the section The Query Elevation Component.
? TermsComponent , described in the section The Terms Component.
InitParams in SolrConfig
The <initParams> section of solrconfig.xml allows you to define request handler parameters outside of
the handler configuration.
There are a couple of use cases where this might be desired:
? Some handlers are implicitly defined in code - see Implicit RequestHandlers - and there should be a way
to add/append/override some of the implicitly defined properties.
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? There are a few properties that are used across handlers. This helps you keep only a single definition of
those properties and apply them over multiple handlers.
For example, if you want several of your search handlers to return the same list of fields, you can create an
<initParams> section without having to define the same set of parameters in each request handler
definition. If you have a single request handler that should return different fields, you can define the
overriding parameters in individual <requestHandler> sections as usual.
The properties and configuration of an <initParams> section mirror the properties and configuration of a
request handler. It can include sections for defaults, appends, and invariants, the same as any request
handler.
For example, here is one of the <initParams> sections defined by default in the data_driven_config
example:
<initParams path="/update/**,/query,/select,/tvrh,/elevate,/spell,/browse">
<lst name="defaults">
<str name="df">_text_</str>
</lst>
</initParams>
This sets the default search field ("df") to be "text" for all of the request handlers named in the path section.
If we later want to change the /query request handler to search a different field by default, we could
override the <initParams> by defining the parameter in the <requestHandler> section for /query .
The syntax and semantics are similar to that of a <requestHandler> . The following are the attributes
Property Description
path A comma-separated list of paths which will use the parameters. Wildcards
can be used in paths to define nested paths, as described below.
name The name of this set of parameters. The name can be used directly in a
requestHandler definition if a path is not explicitly named. If you give your
<initParams> a name, you can refer to the params in a <requestHandler>
that is not defined as a path.
For example, if an <initParams> section has the name "myParams", you
can call the name when defining your request handler:
<requestHandler name="/dump1" class="DumpRequestHandler"
initParams="myParams"/>
Wildcards
An <initParams> section can support wildcards to define nested paths that should use the parameters
defined. A single asterisk (*) denotes that a nested path one level deeper should use the parameters.
Double asterisks (**) denote all nested paths no matter how deep should use the parameters.
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For example, if we have an <initParams> that looks like this:
<initParams name="myParams" path="/myhandler,/root/*,/root1/**">
<lst name="defaults">
<str name="fl">_text_</str>
</lst>
<lst name="invariants">
<str name="rows">10</str>
</lst>
<lst name="appends">
<str name="df">title</str>
</lst>
</initParams>
We’ve defined three paths with this section:
? /myhandler declared as a direct path.
? /root/* with a single asterisk to indicate the parameters should apply to paths that are one level deep.
? /root1/** with double asterisks to indicate the parameters should apply to all nested paths, no matter
how deep.
When we define the request handlers, the wildcards will work in the following ways:
<requestHandler name="/myhandler" class="SearchHandler"/>
The /myhandler class was named as a path in the <initParams> so this will use those parameters.
Next we have a request handler named /root/search5 :
<requestHandler name="/root/search5" class="SearchHandler"/>
We defined a wildcard for nested paths that are one level deeper than /root , so this request handler will use
the parameters. This one, however, will not, because /root/search5/test is more than one level deep from
/root :
<requestHandler name="/root/search5/test" class="SearchHandler"/>
If we want to define all levels of nested paths, we should use double asterisks, as in the example path
/root1/** :
<requestHandler name="/root1/search/tests" class="SearchHandler"/>
Any path under /root1 , whether explicitly defined in a request handler or not, will use the parameters
defined in the matching initParams section.
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UpdateHandlers in SolrConfig
The settings in this section are configured in the <updateHandler> element in solrconfig.xml and may
affect the performance of index updates. These settings affect how updates are done internally.
<updateHandler> configurations do not affect the higher level configuration of RequestHandlers that
process client update requests.
<updateHandler class="solr.DirectUpdateHandler2">
...
</updateHandler>
Commits
Data sent to Solr is not searchable until it has been committed to the index. The reason for this is that in
some cases commits can be slow and they should be done in isolation from other possible commit requests
to avoid overwriting data. So, it’s preferable to provide control over when data is committed. Several options
are available to control the timing of commits.
commit and softCommit
In Solr, a commit is an action which asks Solr to "commit" those changes to the Lucene index files. By default
commit actions result in a "hard commit" of all the Lucene index files to stable storage (disk). When a client
includes a commit=true parameter with an update request, this ensures that all index segments affected by
the adds & deletes on an update are written to disk as soon as index updates are completed.
If an additional flag softCommit=true is specified, then Solr performs a ‘soft commit‘, meaning that Solr will
commit your changes to the Lucene data structures quickly but not guarantee that the Lucene index files are
written to stable storage. This is an implementation of Near Real Time storage, a feature that boosts
document visibility, since you don’t have to wait for background merges and storage (to ZooKeeper, if using
SolrCloud) to finish before moving on to something else. A full commit means that, if a server crashes, Solr
will know exactly where your data was stored; a soft commit means that the data is stored, but the location
information isn’t yet stored. The tradeoff is that a soft commit gives you faster visibility because it’s not
waiting for background merges to finish.
For more information about Near Real Time operations, see Near Real Time Searching.
autoCommit
These settings control how often pending updates will be automatically pushed to the index. An alternative
to autoCommit is to use commitWithin , which can be defined when making the update request to Solr (i.e.,
when pushing documents), or in an update RequestHandler.
Setting Description
maxDocs The number of updates that have occurred since the last commit.
maxTime The number of milliseconds since the oldest uncommitted update.
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Setting Description
openSearcher Whether to open a new searcher when performing a commit. If this is
false, the commit will flush recent index changes to stable storage, but
does not cause a new searcher to be opened to make those changes
visible. The default is true.
If either of these maxDocs or maxTime limits are reached, Solr automatically performs a commit operation. If
the autoCommit tag is missing, then only explicit commits will update the index. The decision whether to use
auto-commit or not depends on the needs of your application.
Determining the best auto-commit settings is a tradeoff between performance and accuracy. Settings that
cause frequent updates will improve the accuracy of searches because new content will be searchable more
quickly, but performance may suffer because of the frequent updates. Less frequent updates may improve
performance but it will take longer for updates to show up in queries.
<autoCommit>
<maxDocs>10000</maxDocs>
<maxTime>30000</maxTime>
<openSearcher>false</openSearcher>
</autoCommit>
You can also specify ‘soft‘ autoCommits in the same way that you can specify ‘soft‘ commits, except that
instead of using autoCommit you set the autoSoftCommit tag.
<autoSoftCommit>
<maxTime>60000</maxTime>
</autoSoftCommit>
commitWithin
The commitWithin settings allow forcing document commits to happen in a defined time period. This is used
most frequently with Near Real Time Searching, and for that reason the default is to perform a soft commit.
This does not, however, replicate new documents to slave servers in a master/slave environment. If that’s a
requirement for your implementation, you can force a hard commit by adding a parameter, as in this
example:
<commitWithin>
<softCommit>false</softCommit>
</commitWithin>
With this configuration, when you call commitWithin as part of your update message, it will automatically
perform a hard commit every time.
Event Listeners
The UpdateHandler section is also where update-related event listeners can be configured. These can be
triggered to occur after any commit ( event="postCommit" ) or only after optimize commands
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( event="postOptimize" ).
Users can write custom update event listener classes, but a common use case is to run external executables
via the RunExecutableListener :
Setting Description
exe The name of the executable to run. It should include the path to the file,
relative to Solr home.
dir The directory to use as the working directory. The default is ".".
wait Forces the calling thread to wait until the executable returns a response.
The default is true.
args Any arguments to pass to the program. The default is none.
env Any environment variables to set. The default is none.
Transaction Log
As described in the section RealTime Get, a transaction log is required for that feature. It is configured in the
updateHandler section of solrconfig.xml .
Realtime Get currently relies on the update log feature, which is enabled by default. It relies on an update
log, which is configured in solrconfig.xml , in a section like:
<updateLog>
<str name="dir">${solr.ulog.dir:}</str>
</updateLog>
Three additional expert-level configuration settings affect indexing performance and how far a replica can
fall behind on updates before it must enter into full recovery - see the section on write side fault tolerance
for more information:
Setting Name Type Default Description
numRecordsToKeep int 100 The number of update records to keep per log
maxNumLogsToKeep int 10 The maximum number of logs keep
numVersionBuckets int 65536 The number of buckets used to keep track of max version
values when checking for re-ordered updates; increase
this value to reduce the cost of synchronizing access to
version buckets during high-volume indexing, this
requires (8 bytes (long) * numVersionBuckets) of heap
space per Solr core.
An example, to be included under <config><updateHandler> in solrconfig.xml , employing the above
advanced settings:
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<updateLog>
<str name="dir">${solr.ulog.dir:}</str>
<int name="numRecordsToKeep">500</int>
<int name="maxNumLogsToKeep">20</int>
<int name="numVersionBuckets">65536</int>
</updateLog>
Query Settings in SolrConfig
The settings in this section affect the way that Solr will process and respond to queries.
These settings are all configured in child elements of the <query> element in solrconfig.xml .
<query>
...
</query>
Caches
Solr caches are associated with a specific instance of an Index Searcher, a specific view of an index that
doesn’t change during the lifetime of that searcher. As long as that Index Searcher is being used, any items
in its cache will be valid and available for reuse. Caching in Solr differs from caching in many other
applications in that cached Solr objects do not expire after a time interval; instead, they remain valid for the
lifetime of the Index Searcher.
When a new searcher is opened, the current searcher continues servicing requests while the new one auto-
warms its cache. The new searcher uses the current searcher’s cache to pre-populate its own. When the new
searcher is ready, it is registered as the current searcher and begins handling all new search requests. The
old searcher will be closed once it has finished servicing all its requests.
In Solr, there are three cache implementations: solr.search.LRUCache , solr.search.FastLRUCache, and
solr.search.LFUCache .
The acronym LRU stands for Least Recently Used. When an LRU cache fills up, the entry with the oldest last-
accessed timestamp is evicted to make room for the new entry. The net effect is that entries that are
accessed frequently tend to stay in the cache, while those that are not accessed frequently tend to drop out
and will be re-fetched from the index if needed again.
The FastLRUCache , which was introduced in Solr 1.4, is designed to be lock-free, so it is well suited for caches
which are hit several times in a request.
Both LRUCache and FastLRUCache use an auto-warm count that supports both integers and percentages
which get evaluated relative to the current size of the cache when warming happens.
The LFUCache refers to the Least Frequently Used cache. This works in a way similar to the LRU cache, except
that when the cache fills up, the entry that has been used the least is evicted.
The Statistics page in the Solr Admin UI will display information about the performance of all the active
caches. This information can help you fine-tune the sizes of the various caches appropriately for your
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particular application. When a Searcher terminates, a summary of its cache usage is also written to the log.
Each cache has settings to define its initial size ( initialSize ), maximum size ( size ) and number of items to
use for during warming ( autowarmCount ). The LRU and FastLRU cache implementations can take a
percentage instead of an absolute value for autowarmCount .
FastLRUCache and LFUCache support showItems attribute. This is the number of cache items to display in
the stats page for the cache. It is for debugging.
Details of each cache are described below.
filterCache
This cache is used by SolrIndexSearcher for filters (DocSets) for unordered sets of all documents that
match a query. The numeric attributes control the number of entries in the cache.
The most typical way Solr uses the filterCache is to cache results of each fq search parameter, though
there are some other cases as well. Subsequent queries using the same parameter filter query result in
cache hits and rapid returns of results. See Searching for a detailed discussion of the fq parameter. Another
Solr feature using this cache is the filter(…) syntax in the default Lucene query parser.
Solr also uses this cache for faceting when the configuration parameter facet.method is set to fc . For a
discussion of faceting, see Searching.
<filterCache class="solr.LRUCache"
size="512"
initialSize="512"
autowarmCount="128"/>
queryResultCache
This cache holds the results of previous searches: ordered lists of document IDs (DocList) based on a query,
a sort, and the range of documents requested.
The queryResultCache has an additional (optional) setting to limit the maximum amount of RAM used
( maxRamMB ). This lets you specify the maximum heap size, in megabytes, used by the contents of this cache.
When the cache grows beyond this size, oldest accessed queries will be evicted until the heap usage of the
cache decreases below the specified limit.
<queryResultCache class="solr.LRUCache"
size="512"
initialSize="512"
autowarmCount="128"
maxRamMB="1000"/>
documentCache
This cache holds Lucene Document objects (the stored fields for each document). Since Lucene internal
document IDs are transient, this cache is not auto-warmed. The size for the documentCache should always be
greater than max_results times the max_concurrent_queries , to ensure that Solr does not need to refetch
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a document during a request. The more fields you store in your documents, the higher the memory usage of
this cache will be.
<documentCache class="solr.LRUCache"
size="512"
initialSize="512"
autowarmCount="0"/>
User Defined Caches
You can also define named caches for your own application code to use. You can locate and use your cache
object by name by calling the SolrIndexSearcher methods getCache() , cacheLookup() and cacheInsert() .
<cache name="myUserCache" class="solr.LRUCache"
size="4096"
initialSize="1024"
autowarmCount="1024"
regenerator="org.mycompany.mypackage.MyRegenerator" />
If you want auto-warming of your cache, include a regenerator attribute with the fully qualified name of a
class that implements solr.search.CacheRegenerator . You can also use the NoOpRegenerator , which
simply repopulates the cache with old items. Define it with the regenerator parameter as`:
regenerator="solr.NoOpRegenerator"`.
Query Sizing and Warming
maxBooleanClauses
This sets the maximum number of clauses allowed in a boolean query. This can affect range or prefix
queries that expand to a query with a large number of boolean terms. If this limit is exceeded, an exception
is thrown.
<maxBooleanClauses>1024</maxBooleanClauses>
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This option modifies a global property that effects all Solr cores. If multiple solrconfig.xml
files disagree on this property, the value at any point in time will be based on the last Solr
core that was initialized.
enableLazyFieldLoading
If this parameter is set to true, then fields that are not directly requested will be loaded lazily as needed. This
can boost performance if the most common queries only need a small subset of fields, especially if
infrequently accessed fields are large in size.
<enableLazyFieldLoading>true</enableLazyFieldLoading>
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useFilterForSortedQuery
This parameter configures Solr to use a filter to satisfy a search. If the requested sort does not include
"score", the filterCache will be checked for a filter matching the query. For most situations, this is only
useful if the same search is requested often with different sort options and none of them ever use "score".
<useFilterForSortedQuery>true</useFilterForSortedQuery>
queryResultWindowSize
Used with the queryResultCache , this will cache a superset of the requested number of document IDs. For
example, if the a search in response to a particular query requests documents 10 through 19, and
queryWindowSize is 50, documents 0 through 49 will be cached.
<queryResultWindowSize>20</queryResultWindowSize>
queryResultMaxDocsCached
This parameter sets the maximum number of documents to cache for any entry in the queryResultCache .
<queryResultMaxDocsCached>200</queryResultMaxDocsCached>
useColdSearcher
This setting controls whether search requests for which there is not a currently registered searcher should
wait for a new searcher to warm up (false) or proceed immediately (true). When set to "false", requests will
block until the searcher has warmed its caches.
<useColdSearcher>false</useColdSearcher>
maxWarmingSearchers
This parameter sets the maximum number of searchers that may be warming up in the background at any
given time. Exceeding this limit will raise an error. For read-only slaves, a value of two is reasonable. Masters
should probably be set a little higher.
<maxWarmingSearchers>2</maxWarmingSearchers>
Query-Related Listeners
As described in the section on Caches, new Index Searchers are cached. It’s possible to use the triggers for
listeners to perform query-related tasks. The most common use of this is to define queries to further
"warm" the Index Searchers while they are starting. One benefit of this approach is that field caches are pre-
populated for faster sorting.
Good query selection is key with this type of listener. It’s best to choose your most common and/or heaviest
queries and include not just the keywords used, but any other parameters such as sorting or filtering
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requests.
There are two types of events that can trigger a listener. A firstSearcher event occurs when a new
searcher is being prepared but there is no current registered searcher to handle requests or to gain auto-
warming data from (i.e., on Solr startup). A newSearcher event is fired whenever a new searcher is being
prepared and there is a current searcher handling requests.
The (commented out) examples below can be found in the solrconfig.xml file of the
sample_techproducts_configs config setincluded with Solr, and demonstrate using the
solr.QuerySenderListener class to warm a set of explicit queries:
<listener event="newSearcher" class="solr.QuerySenderListener">
<arr name="queries">
<!--
<lst><str name="q">solr</str><str name="sort">price asc</str></lst>
<lst><str name="q">rocks</str><str name="sort">weight asc</str></lst>
-->
</arr>
</listener>
<listener event="firstSearcher" class="solr.QuerySenderListener">
<arr name="queries">
<lst><str name="q">static firstSearcher warming in solrconfig.xml</str></lst>
</arr>
</listener>
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The above code comes from a sample solrconfig.xml .
A key best practice is to modify these defaults before taking your application to production,
but please note: while the sample queries are commented out in the section for the
"newSearcher", the sample query is not commented out for the "firstSearcher" event.
There is no point in auto-warming your Index Searcher with the query string "static
firstSearcher warming in solrconfig.xml" if that is not relevant to your search application.
RequestDispatcher in SolrConfig
The requestDispatcher element of solrconfig.xml controls the way the Solr HTTP RequestDispatcher
implementation responds to requests.
Included are parameters for defining if it should handle /select urls (for Solr 1.1 compatibility), if it will
support remote streaming, the maximum size of file uploads and how it will respond to HTTP cache headers
in requests.
handleSelect Element
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handleSelect is for legacy back-compatibility; those new to Solr do not need to change
anything about the way this is configured by default.
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The first configurable item is the handleSelect attribute on the <requestDispatcher> element itself. This
attribute can be set to one of two values, either "true" or "false". It governs how Solr responds to requests
such as /select?qt=XXX . The default value "false" will ignore requests to /select if a requestHandler is not
explicitly registered with the name /select . A value of "true" will route query requests to the parser defined
with the qt value.
In recent versions of Solr, a /select requestHandler is defined by default, so a value of "false" will work fine.
See the section RequestHandlers and SearchComponents in SolrConfig for more information.
<requestDispatcher handleSelect="true" >
...
</requestDispatcher>
requestParsers Element
The <requestParsers> sub-element controls values related to parsing requests. This is an empty XML
element that doesn’t have any content, only attributes.
The attribute enableRemoteStreaming controls whether remote streaming of content is allowed. If set to
false , streaming will not be allowed. Setting it to true (the default) lets you specify the location of content
to be streamed using stream.file or stream.url parameters.
If you enable remote streaming, be sure that you have authentication enabled. Otherwise, someone could
potentially gain access to your content by accessing arbitrary URLs. It’s also a good idea to place Solr behind
a firewall to prevent it being accessed from untrusted clients.
The attribute multipartUploadLimitInKB sets an upper limit in kilobytes on the size of a document that may
be submitted in a multi-part HTTP POST request. The value specified is multiplied by 1024 to determine the
size in bytes.
The attribute formdataUploadLimitInKB sets a limit in kilobytes on the size of form data (application/x-
www-form-urlencoded) submitted in a HTTP POST request, which can be used to pass request parameters
that will not fit in a URL.
The attribute addHttpRequestToContext can be used to indicate that the original HttpServletRequest
object should be included in the context map of the SolrQueryRequest using the key httpRequest . This
HttpServletRequest is not used by any Solr component, but may be useful when developing custom
plugins.
<requestParsers enableRemoteStreaming="true"
multipartUploadLimitInKB="2048000"
formdataUploadLimitInKB="2048"
addHttpRequestToContext="false" />
httpCaching Element
The <httpCaching> element controls HTTP cache control headers. Do not confuse these settings with Solr’s
internal cache configuration. This element controls caching of HTTP responses as defined by the W3C HTTP
specifications.
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This element allows for three attributes and one sub-element. The attributes of the <httpCaching> element
control whether a 304 response to a GET request is allowed, and if so, what sort of response it should be.
When an HTTP client application issues a GET, it may optionally specify that a 304 response is acceptable if
the resource has not been modified since the last time it was fetched.
Parameter Description
never304 If present with the value true , then a GET request will never respond with a 304 code,
even if the requested resource has not been modified. When this attribute is set to
true, the next two attributes are ignored. Setting this to true is handy for
development, as the 304 response can be confusing when tinkering with Solr
responses through a web browser or other client that supports cache headers.
lastModFrom This attribute may be set to either openTime (the default) or dirLastMod . The value
openTime indicates that last modification times, as compared to the If-Modified-Since
header sent by the client, should be calculated relative to the time the Searcher
started. Use dirLastMod if you want times to exactly correspond to when the index
was last updated on disk.
etagSeed This value of this attribute is sent as the value of the ETag header. Changing this value
can be helpful to force clients to re-fetch content even when the indexes have not
changed---for example, when you’ve made some changes to the configuration.
<httpCaching never304="false"
lastModFrom="openTime"
etagSeed="Solr">
<cacheControl>max-age=30, public</cacheControl>
</httpCaching>
cacheControl Element
In addition to these attributes, <httpCaching> accepts one child element: <cacheControl> . The content of
this element will be sent as the value of the Cache-Control header on HTTP responses. This header is used to
modify the default caching behavior of the requesting client. The possible values for the Cache-Control
header are defined by the HTTP 1.1 specification in Section 14.9.
Setting the max-age field controls how long a client may re-use a cached response before requesting it
again from the server. This time interval should be set according to how often you update your index and
whether or not it is acceptable for your application to use content that is somewhat out of date. Setting
must-revalidate will tell the client to validate with the server that its cached copy is still good before re-
using it. This will ensure that the most timely result is used, while avoiding a second fetch of the content if it
isn’t needed, at the cost of a request to the server to do the check.
Update Request Processors
Every update request received by Solr is run through a chain of plugins known as Update Request
Processors, or URPs.
This can be useful, for example, to add a field to the document being indexed; to change the value of a
particular field; or to drop an update if the incoming document doesn’t fulfill certain criteria. In fact, a
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surprisingly large number of features in Solr are implemented as Update Processors and therefore it is
necessary to understand how such plugins work and where are they configured.
Anatomy and Lifecycle
An Update Request Processor is created as part of a chain of one or more update processors. Solr creates a
default update request processor chain comprising of a few update request processors which enable
essential Solr features. This default chain is used to process every update request unless a user chooses to
configure and specify a different custom update request processor chain.
The easiest way to describe an Update Request Processor is to look at the Javadocs of the abstract class
UpdateRequestProcessor. Every UpdateRequestProcessor must have a corresponding factory class which
extends UpdateRequestProcessorFactory. This factory class is used by Solr to create a new instance of this
plugin. Such a design provides two benefits:
1. An update request processor need not be thread safe because it is used by one and only one request
thread and destroyed once the request is complete.
2. The factory class can accept configuration parameters and maintain any state that may be required
between requests. The factory class must be thread-safe.
Every update request processor chain is constructed during loading of a Solr core and cached until the core
is unloaded. Each UpdateRequestProcessorFactory specified in the chain is also instantiated and initialized
with configuration that may have been specified in solrconfig.xml .
When an update request is received by Solr, it looks up the update chain to be used for this request. A new
instance of each UpdateRequestProcessor specified in the chain is created using the corresponding factory.
The update request is parsed into corresponding UpdateCommand objects which are run through the chain.
Each UpdateRequestProcessor instance is responsible for invoking the next plugin in the chain. It can
choose to short circuit the chain by not invoking the next processor and even abort further processing by
throwing an exception.
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A single update request may contain a batch of multiple new documents or deletes and
therefore the corresponding processXXX methods of an UpdateRequestProcessor will be
invoked multiple times for every individual update. However, it is guaranteed that a single
thread will serially invoke these methods.
Configuration
Update request processors chains can be created by either creating the whole chain directly in
solrconfig.xml or by creating individual update processors in solrconfig.xml and then dynamically
creating the chain at run-time by specifying all processors via request parameters.
However, before we understand how to configure update processor chains, we must learn about the default
update processor chain because it provides essential features which are needed in most custom request
processor chains as well.
Default Update Request Processor Chain
In case no update processor chains are configured in solrconfig.xml , Solr will automatically create a
default update processor chain which will be used for all update requests. This default update processor
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chain consists of the following processors (in order):
1. LogUpdateProcessorFactory - Tracks the commands processed during this request and logs them
2. DistributedUpdateProcessorFactory - Responsible for distributing update requests to the right node
e.g., routing requests to the leader of the right shard and distributing updates from the leader to each
replica. This processor is activated only in SolrCloud mode.
3. RunUpdateProcessorFactory - Executes the update using internal Solr APIs.
Each of these perform an essential function and as such any custom chain usually contain all of these
processors. The RunUpdateProcessorFactory is usually the last update processor in any custom chain.
Custom Update Request Processor Chain
The following example demonstrates how a custom chain can be configured inside solrconfig.xml .
Example dedupe updateRequestProcessorChain
<updateRequestProcessorChain name="dedupe">
<processor class="solr.processor.SignatureUpdateProcessorFactory">
<bool name="enabled">true</bool>
<str name="signatureField">id</str>
<bool name="overwriteDupes">false</bool>
<str name="fields">name,features,cat</str>
<str name="signatureClass">solr.processor.Lookup3Signature</str>
</processor>
<processor class="solr.LogUpdateProcessorFactory" />
<processor class="solr.RunUpdateProcessorFactory" />
</updateRequestProcessorChain>
In the above example, a new update processor chain named "dedupe" is created with
SignatureUpdateProcessorFactory , LogUpdateProcessorFactory and RunUpdateProcessorFactory in the
chain. The SignatureUpdateProcessorFactory is further configured with different parameters such as
"signatureField", "overwriteDupes", etc. This chain is an example of how Solr can be configured to perform
de-duplication of documents by calculating a signature using the value of name, features, cat fields which is
then used as the "id" field. As you may have noticed, this chain does not specify the
DistributedUpdateProcessorFactory . Because this processor is critical for Solr to operate properly, Solr
will automatically insert DistributedUpdateProcessorFactory in any chain that does not include it just prior
to the RunUpdateProcessorFactory .
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RunUpdateProcessorFactory
Do not forget to add RunUpdateProcessorFactory at the end of any chains you define in
solrconfig.xml . Otherwise update requests processed by that chain will not actually affect
the indexed data.
Configuring Individual Processors as Top-Level Plugins
Update request processors can also be configured independent of a chain in solrconfig.xml .
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updateProcessor Configuration
<updateProcessor class="solr.processor.SignatureUpdateProcessorFactory" name="signature">
<bool name="enabled">true</bool>
<str name="signatureField">id</str>
<bool name="overwriteDupes">false</bool>
<str name="fields">name,features,cat</str>
<str name="signatureClass">solr.processor.Lookup3Signature</str>
</updateProcessor>
<updateProcessor class="solr.RemoveBlankFieldUpdateProcessorFactory" name="remove_blanks"/>
In this case, an instance of SignatureUpdateProcessorFactory is configured with the name "signature" and
a RemoveBlankFieldUpdateProcessorFactory is defined with the name "remove_blanks". Once the above
has been specified in solrconfig.xml , we can be refer to them in update request processor chains in
solrconfig.xml as follows:
updateRequestProcessorChain Configuration
<updateProcessorChain name="custom" processor="remove_blanks,signature">
<processor class="solr.RunUpdateProcessorFactory" />
</updateProcessorChain>
Update Processors in SolrCloud
In a single node, stand-alone Solr, each update is run through all the update processors in a chain exactly
once. But the behavior of update request processors in SolrCloud deserves special consideration.
A critical SolrCloud functionality is the routing and distributing of requests. For update requests this routing
is implemented by the DistributedUpdateRequestProcessor , and this processor is given a special status by
Solr due to its important function.
In SolrCloud mode, all processors in the chain before the DistributedUpdateProcessor are run on the first
node that receives an update from the client, regardless of this node’s status as a leader or replica. The
DistributedUpdateProcessor then forwards the update to the appropriate shard leader for the update (or
to multiple leaders in the event of an update that affects multiple documents, such as a delete by query or
commit). The shard leader uses a transaction log to apply Atomic Updates & Optimistic Concurrency and
then forwards the update to all of the shard replicas. The leader and each replica run all of the processors in
the chain that are listed after the DistributedUpdateProcessor .
For example, consider the "dedupe" chain which we saw in a section above. Assume that a 3-node SolrCloud
cluster exists where node A hosts the leader of shard1, node B hosts the leader of shard2 and node C hosts
the replica of shard2. Assume that an update request is sent to node A which forwards the update to node B
(because the update belongs to shard2) which then distributes the update to its replica node C. Let’s see
what happens at each node:
? Node A: Runs the update through the SignatureUpdateProcessor (which computes the signature and
puts it in the "id" field), then LogUpdateProcessor and then DistributedUpdateProcessor . This
processor determines that the update actually belongs to node B and is forwarded to node B. The
update is not processed further. This is required because the next processor, RunUpdateProcessor , will
execute the update against the local shard1 index which would lead to duplicate data on shard1 and
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shard2.
? Node B: Receives the update and sees that it was forwarded by another node. The update is directly sent
to DistributedUpdateProcessor because it has already been through the SignatureUpdateProcessor
on node A and doing the same signature computation again would be redundant. The
DistributedUpdateProcessor determines that the update indeed belongs to this node, distributes it to
its replica on Node C and then forwards the update further in the chain to RunUpdateProcessor .
? Node C: Receives the update and sees that it was distributed by its leader. The update is directly sent to
DistributedUpdateProcessor which performs some consistency checks and forwards the update
further in the chain to RunUpdateProcessor .
In summary:
1. All processors before DistributedUpdateProcessor are only run on the first node that receives an
update request whether it be a forwarding node (e.g., node A in the above example) or a leader (e.g.,
node B). We call these "pre-processors" or just "processors".
2. All processors after DistributedUpdateProcessor run only on the leader and the replica nodes. They are
not executed on forwarding nodes. Such processors are called "post-processors".
In the previous section, we saw that the updateRequestProcessorChain was configured with
processor="remove_blanks, signature" . This means that such processors are of the #1 kind and are run
only on the forwarding nodes. Similarly, we can configure them as the #2 kind by specifying with the
attribute "post-processor" as follows:
post-processor Configuration
<updateProcessorChain name="custom" processor="signature" post-processor="remove_blanks">
<processor class="solr.RunUpdateProcessorFactory" />
</updateProcessorChain>
However executing a processor only on the forwarding nodes is a great way of distributing an expensive
computation such as de-duplication across a SolrCloud cluster by sending requests randomly via a load
balancer. Otherwise the expensive computation is repeated on both the leader and replica nodes.
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Custom update chain post-processors may never be invoked on a recovering replica
While a replica is in recovery, inbound update requests are buffered to the transaction log.
After recovery has completed successfully, those buffered update requests are replayed. As
of this writing, however, custom update chain post-processors are never invoked for
buffered update requests. See SOLR-8030. To work around this problem until SOLR-8030
has been fixed, avoid specifying post-processors in custom update chains.
Atomic Updates
If the AtomicUpdateProcessorFactory is in the update chain before the DistributedUpdateProcessor , the
incoming document to the chain will be a partial document.
Because DistributedUpdateProcessor is responsible for processing Atomic Updates into full documents on
the leader node, this means that pre-processors which are executed only on the forwarding nodes can only
operate on the partial document. If you have a processor which must process a full document then the only
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choice is to specify it as a post-processor.
Using Custom Chains
update.chain Request Parameter
The update.chain parameter can be used in any update request to choose a custom chain which has been
configured in solrconfig.xml . For example, in order to choose the "dedupe" chain described in a previous
section, one can issue the following request:
Using update.chain
curl "http://localhost:8983/solr/gettingstarted/update/json?update.chain=dedupe&commit=true" -H
‘Content-type: application/json‘ -d ‘
[
{
"name" : "The Lightning Thief",
"features" : "This is just a test",
"cat" : ["book","hardcover"]
},
{
"name" : "The Lightning Thief",
"features" : "This is just a test",
"cat" : ["book","hardcover"]
}
]‘
The above should dedupe the two identical documents and index only one of them.
Processor & Post-Processor Request Parameters
We can dynamically construct a custom update request processor chain using the processor and post-
processor request parameters. Multiple processors can be specified as a comma-separated value for these
two parameters. For example:
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Executing processors configured in solrconfig.xml as (pre)-processors
curl
"http://localhost:8983/solr/gettingstarted/update/json?processor=remove_blanks,signature&commit=t
rue" -H ‘Content-type: application/json‘ -d ‘
[
{
"name" : "The Lightning Thief",
"features" : "This is just a test",
"cat" : ["book","hardcover"]
},
{
"name" : "The Lightning Thief",
"features" : "This is just a test",
"cat" : ["book","hardcover"]
}
]‘
Executing processors configured in solrconfig.xml as pre- and post-processors
curl "http://localhost:8983/solr/gettingstarted/update/json?processor=remove_blanks&post-
processor=signature&commit=true" -H ‘Content-type: application/json‘ -d ‘
[
{
"name" : "The Lightning Thief",
"features" : "This is just a test",
"cat" : ["book","hardcover"]
},
{
"name" : "The Lightning Thief",
"features" : "This is just a test",
"cat" : ["book","hardcover"]
}
]‘
In the first example, Solr will dynamically create a chain which has "signature" and "remove_blanks" as pre-
processors to be executed only on the forwarding node where as in the second example, "remove_blanks"
will be executed as a pre-processor and "signature" will be executed on the leader and replicas as a post-
processor.
Configuring a Custom Chain as a Default
We can also specify a custom chain to be used by default for all requests sent to specific update handlers
instead of specifying the names in request parameters for each request.
This can be done by adding either "update.chain" or "processor" and "post-processor" as default parameter
for a given path which can be done either via initParams or by adding them in a "defaults" section which is
supported by all request handlers.
The following is an initParam defined in the schemaless configuration which applies a custom update chain
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to all request handlers starting with "/update/".
Example initParams
<initParams path="/update/**">
<lst name="defaults">
<str name="update.chain">add-unknown-fields-to-the-schema</str>
</lst>
</initParams>
Alternately, one can achieve a similar effect using the "defaults" as shown in the example below:
Example defaults
<requestHandler name="/update/extract" startup="lazy" class=
"solr.extraction.ExtractingRequestHandler" >
<lst name="defaults">
<str name="update.chain">add-unknown-fields-to-the-schema</str>
</lst>
</requestHandler>
Update Request Processor Factories
What follows are brief descriptions of the currently available update request processors. An
UpdateRequestProcessorFactory can be integrated into an update chain in solrconfig.xml as necessary.
You are strongly urged to examine the Javadocs for these classes; these descriptions are abridged snippets
taken for the most part from the Javadocs.
General Use UpdateProcessorFactories
AddSchemaFieldsUpdateProcessorFactory
This processor will dynamically add fields to the schema if an input document contains one or more fields
that don’t match any field or dynamic field in the schema.
AtomicUpdateProcessorFactory
This processor will convert conventional field-value documents to atomic update documents.
ClassificationUpdateProcessorFactory
This processor uses Lucene’s classification module to provide simple document classification. See
https://wiki.apache.org/solr/SolrClassification for more details on how to use this processor.
CloneFieldUpdateProcessorFactory
Clones the values found in any matching source field into the configured dest field.
DefaultValueUpdateProcessorFactory
A simple processor that adds a default value to any document which does not already have a value in
fieldName.
DocBasedVersionConstraintsProcessorFactory
This Factory generates an UpdateProcessor that helps to enforce version constraints on documents
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based on per-document version numbers using a configured name of a versionField.
DocExpirationUpdateProcessorFactory
Update Processor Factory for managing automatic "expiration" of documents.
FieldNameMutatingUpdateProcessorFactory
Modifies field names by replacing all matches to the configured pattern with the configured
replacement .
IgnoreCommitOptimizeUpdateProcessorFactory
Allows you to ignore commit and/or optimize requests from client applications when running in
SolrCloud mode, for more information, see: Shards and Indexing Data in SolrCloud
RegexpBoostProcessorFactory
A processor which will match content of "inputField" against regular expressions found in
"boostFilename", and if it matches will return the corresponding boost value from the file and output this
to "boostField" as a double value.
SignatureUpdateProcessorFactory
Uses a defined set of fields to generate a hash "signature" for the document. Useful for only indexing
one copy of "similar" documents.
StatelessScriptUpdateProcessorFactory
An update request processor factory that enables the use of update processors implemented as scripts.
TimestampUpdateProcessorFactory
An update processor that adds a newly generated date value of "NOW" to any document being added
that does not already have a value in the specified field.
URLClassifyProcessorFactory
Update processor which examines a URL and outputs to various other fields with characteristics of that
URL, including length, number of path levels, whether it is a top level URL (levels==0), whether it looks like
a landing/index page, a canonical representation of the URL (e.g., stripping index.html), the domain and
path parts of the URL, etc.
UUIDUpdateProcessorFactory
An update processor that adds a newly generated UUID value to any document being added that does
not already have a value in the specified field.
FieldMutatingUpdateProcessorFactory Derived Factories
These factories all provide functionality to modify fields in a document as they’re being indexed. When using
any of these factories, please consult the FieldMutatingUpdateProcessorFactory javadocs for details on the
common options they all support for configuring which fields are modified.
ConcatFieldUpdateProcessorFactory
Concatenates multiple values for fields matching the specified conditions using a configurable delimiter.
CountFieldValuesUpdateProcessorFactory
Replaces any list of values for a field matching the specified conditions with the the count of the number
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of values for that field.
FieldLengthUpdateProcessorFactory
Replaces any CharSequence values found in fields matching the specified conditions with the lengths of
those CharSequences (as an Integer).
FirstFieldValueUpdateProcessorFactory
Keeps only the first value of fields matching the specified conditions.
HTMLStripFieldUpdateProcessorFactory
Strips all HTML Markup in any CharSequence values found in fields matching the specified conditions.
IgnoreFieldUpdateProcessorFactory
Ignores and removes fields matching the specified conditions from any document being added to the
index.
LastFieldValueUpdateProcessorFactory
Keeps only the last value of fields matching the specified conditions.
MaxFieldValueUpdateProcessorFactory
An update processor that keeps only the the maximum value from any selected fields where multiple
values are found.
MinFieldValueUpdateProcessorFactory
An update processor that keeps only the the minimum value from any selected fields where multiple
values are found.
ParseBooleanFieldUpdateProcessorFactory
Attempts to mutate selected fields that have only CharSequence-typed values into Boolean values.
ParseDateFieldUpdateProcessorFactory
Attempts to mutate selected fields that have only CharSequence-typed values into Solr date values.
ParseNumericFieldUpdateProcessorFactory derived classes
ParseDoubleFieldUpdateProcessorFactory
Attempts to mutate selected fields that have only CharSequence-typed values into Double values.
ParseFloatFieldUpdateProcessorFactory
Attempts to mutate selected fields that have only CharSequence-typed values into Float values.
ParseIntFieldUpdateProcessorFactory
Attempts to mutate selected fields that have only CharSequence-typed values into Integer values.
ParseLongFieldUpdateProcessorFactory
Attempts to mutate selected fields that have only CharSequence-typed values into Long values.
PreAnalyzedUpdateProcessorFactory
An update processor that parses configured fields of any document being added using PreAnalyzedField
with the configured format parser.
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RegexReplaceProcessorFactory
An updated processor that applies a configured regex to any CharSequence values found in the selected
fields, and replaces any matches with the configured replacement string.
RemoveBlankFieldUpdateProcessorFactory
Removes any values found which are CharSequence with a length of 0. (ie: empty strings).
TrimFieldUpdateProcessorFactory
Trims leading and trailing whitespace from any CharSequence values found in fields matching the
specified conditions.
TruncateFieldUpdateProcessorFactory
Truncates any CharSequence values found in fields matching the specified conditions to a maximum
character length.
UniqFieldsUpdateProcessorFactory
Removes duplicate values found in fields matching the specified conditions.
Update Processor Factories That Can Be Loaded as Plugins
These processors are included in Solr releases as "contribs", and require additional jars loaded at runtime.
See the README files associated with each contrib for details:
The langid contrib provides
LangDetectLanguageIdentifierUpdateProcessorFactory
Identifies the language of a set of input fields using http://code.google.com/p/language-detection.
TikaLanguageIdentifierUpdateProcessorFactory
Identifies the language of a set of input fields using Tika’s LanguageIdentifier.
The uima contrib provides
UIMAUpdateRequestProcessorFactory
Update document(s) to be indexed with UIMA extracted information.
Update Processor Factories You Should Not Modify or Remove
These are listed for completeness, but are part of the Solr infrastructure, particularly SolrCloud. Other than
insuring you do not remove them when modifying the update request handlers (or any copies you make),
you will rarely, if ever, need to change these.
DistributedUpdateProcessorFactory
Used to distribute updates to all necessary nodes.
NoOpDistributingUpdateProcessorFactory
An alternative No-Op implementation of DistributingUpdateProcessorFactory that always returns
null. Designed for experts who want to bypass distributed updates and use their own custom update
logic.
LogUpdateProcessorFactory
A logging processor. This keeps track of all commands that have passed through the chain and prints
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them on finish().
RunUpdateProcessorFactory
Executes the update commands using the underlying UpdateHandler. Almost all processor chains should
end with an instance of RunUpdateProcessorFactory unless the user is explicitly executing the update
commands in an alternative custom UpdateRequestProcessorFactory .
Update Processors That Can Be Used at Runtime
TemplateUpdateProcessorFactory
The TemplateUpdateProcessorFactory can be used to add new fields to documents based on a template
pattern.
This can be used directly in a request without any configuration. To enable this processor, use the parameter
processor=Template . The template parameter Template.field (multivalued) define the field to add and the
pattern. Templates may contain placeholders which refer to other fields in the document. You can have
multiple Template.field parameters in a single request.
For example:
processor=Template&Template.field=fullName:Mr. ${firstName} ${lastName}
The above example would add a new field to the document called fullName . The fields firstName and
lastName are supplied from the document fields. If either of them is missing, that part is replaced with an
empty string. If those fields are multi-valued, only the first value is used.
Codec Factory
A codecFactory can be specified in solrconfig.xml to determine which Lucene Codec is used when writing
the index to disk.
If not specified, Lucene’s default codec is implicitly used.
Alternatives to the Default Codec
There are two alternatives to Lucene’s default codec.
solr.SchemaCodecFactory
The solr.SchemaCodecFactory supports 2 key features:
? Schema based per-fieldtype configuration for docValuesFormat and postingsFormat - see the Field Type
Properties section for more details.
? A compressionMode option:
? BEST_SPEED (default) is optimized for search speed performance
? BEST_COMPRESSION is optimized for disk space usage
Example:
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<codecFactory class="solr.SchemaCodecFactory">
<str name="compressionMode">BEST_COMPRESSION</str>
</codecFactory>
solr.SimpleTextCodecFactory
This factory for Lucene’s SimpleTextCodecFactory produces a plain text human-readable index format.
?
FOR RECREATIONAL USE ONLY. This codec should never be used in production.
SimpleTextCodec is relatively slow and takes up a large amount of disk space. Its use
should be limited to educational and debugging purposes.
Example:
<codecFactory class="solr.SimpleTextCodecFactory"/>
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Solr Cores and solr.xml
In Solr, the term core is used to refer to a single index and associated transaction log and configuration files
(including the solrconfig.xml and Schema files, among others). Your Solr installation can have multiple
cores if needed, which allows you to index data with different structures in the same server, and maintain
more control over how your data is presented to different audiences. In SolrCloud mode you will be more
familiar with the term collection. Behind the scenes a collection consists of one or more cores.
Cores can be created using bin/solr script or as part of SolrCloud collection creation using the APIs. Core-
specific properties (such as the directories to use for the indexes or configuration files, the core name, and
other options) are defined in a core.properties file. Any core.properties file in any directory of your Solr
installation (or in a directory under where solr_home is defined) will be found by Solr and the defined
properties will be used for the core named in the file.
In standalone mode, solr.xml must reside in solr_home . In SolrCloud mode, solr.xml will be loaded from
ZooKeeper if it exists, with fallback to solr_home .
?
In older versions of Solr, cores had to be predefined as <core> tags in solr.xml in order for
Solr to know about them. Now, however, Solr supports automatic discovery of cores and
they no longer need to be explicitly defined. The recommended way is to dynamically
create cores/collections using the APIs.
The following sections describe these options in more detail.
? Format of solr.xml: Details on how to define solr.xml , including the acceptable parameters for the
solr.xml file
? Defining core.properties: Details on placement of core.properties and available property options.
? CoreAdmin API: Tools and commands for core administration using a REST API.
? Config Sets: How to use configsets to avoid duplicating effort when defining a new core.
Format of solr.xml
The solr.xml file defines some global configuration options that apply to all or many cores.
This section will describe the default solr.xml file included with Solr and how to modify it for your needs.
For details on how to configure core.properties , see the section Defining core.properties.
Defining solr.xml
You can find solr.xml in your $SOLR_HOME directory (usually server/solr ) in standalone mode or in
ZooKeeper when using SolrCloud. The default solr.xml file looks like this:
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<solr>
<solrcloud>
<str name="host">${host:}</str>
<int name="hostPort">${jetty.port:8983}</int>
<str name="hostContext">${hostContext:solr}</str>
<int name="zkClientTimeout">${zkClientTimeout:15000}</int>
<bool name="genericCoreNodeNames">${genericCoreNodeNames:true}</bool>
</solrcloud>
<shardHandlerFactory name="shardHandlerFactory"
class="HttpShardHandlerFactory">
<int name="socketTimeout">${socketTimeout:0}</int>
<int name="connTimeout">${connTimeout:0}</int>
</shardHandlerFactory>
</solr>
As you can see, the discovery Solr configuration is "SolrCloud friendly". However, the presence of the
<solrcloud> element does not mean that the Solr instance is running in SolrCloud mode. Unless the
-DzkHost or -DzkRun are specified at startup time, this section is ignored.
Solr.xml Parameters
The <solr> Element
There are no attributes that you can specify in the <solr> tag, which is the root element of solr.xml . The
tables below list the child nodes of each XML element in solr.xml .
Node Description
adminHandler
If used, this attribute should be set to the FQN (Fully qualified name) of a
class that inherits from CoreAdminHandler. For example, <str
name="adminHandler">com.myorg.MyAdminHandler</str> would
configure the custom admin handler (MyAdminHandler) to handle admin
requests. If this attribute isn’t set, Solr uses the default admin handler,
org.apache.solr.handler.admin.CoreAdminHandler . For more
information on this parameter, see the Solr Wiki at http://wiki.apache.org/
solr/CoreAdmin#cores.
collectionsHandler
As above, for custom CollectionsHandler implementations.
infoHandler
As above, for custom InfoHandler implementations.
coreLoadThreads
Specifies the number of threads that will be assigned to load cores in
parallel.
coreRootDirectory
The root of the core discovery tree, defaults to $SOLR_HOME .
managementPath
Currently non-operational.
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Node Description
sharedLib
Specifies the path to a common library directory that will be shared across
all cores. Any JAR files in this directory will be added to the search path for
Solr plugins. This path is relative to $SOLR_HOME . Custom handlers may be
placed in this directory.
shareSchema
This attribute, when set to true, ensures that the multiple cores pointing to
the same Schema resource file will be referring to the same IndexSchema
Object. Sharing the IndexSchema Object makes loading the core faster. If
you use this feature, make sure that no core-specific property is used in
your Schema file.
transientCacheSize
Defines how many cores with transient=true that can be loaded before
swapping the least recently used core for a new core.
configSetBaseDir
The directory under which configsets for Solr cores can be found. Defaults
to $SOLR_HOME/configsets .
The <solrcloud> Element
This element defines several parameters that relate so SolrCloud. This section is ignored unless theSolr
instance is started with either -DzkRun or -DzkHost
Node Description
distribUpdateConnTimeout
Used to set the underlying "connTimeout" for intra-cluster updates.
distribUpdateSoTimeout
Used to set the underlying "socketTimeout" for intra-cluster updates.
host
The hostname Solr uses to access cores.
hostContext
The url context path.
hostPort
The port Solr uses to access cores. In the default solr.xml file, this is set to
${jetty.port:8983 }, which will use the Solr port defined in Jetty, and
otherwise fall back to 8983.
leaderVoteWait
When SolrCloud is starting up, how long each Solr node will wait for all
known replicas for that shard to be found before assuming that any nodes
that haven’t reported are down.
leaderConflictResolveWait
When trying to elect a leader for a shard, this property sets the maximum
time a replica will wait to see conflicting state information to be resolved;
temporary conflicts in state information can occur when doing rolling
restarts, especially when the node hosting the Overseer is restarted.
Typically, the default value of 180000 (ms) is sufficient for conflicts to be
resolved; you may need to increase this value if you have hundreds or
thousands of small collections in SolrCloud.
zkClientTimeout
A timeout for connection to a ZooKeeper server. It is used with SolrCloud.
zkHost
In SolrCloud mode, the URL of the ZooKeeper host that Solr should use for
cluster state information.
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Node Description
genericCoreNodeNames
If TRUE , node names are not based on the address of the node, but on a
generic name that identifies the core. When a different machine takes over
serving that core things will be much easier to understand.
zkCredentialsProvider & `
zkACLProvider`
Optional parameters that can be specified if you are using ZooKeeper
Access Control.
The <logging> Element
Node Description
class
The class to use for logging. The corresponding JAR file must be available
to Solr, perhaps through a <lib> directive in solrconfig.xml .
enabled
true/false - whether to enable logging or not.
The <logging><watcher> Element
Node Description
size
The number of log events that are buffered.
threshold
The logging level above which your particular logging implementation will
record. For example when using log4j one might specify DEBUG, WARN,
INFO, etc.
The <shardHandlerFactory> Element
Custom shard handlers can be defined in solr.xml if you wish to create a custom shard handler.
<shardHandlerFactory name="ShardHandlerFactory" class="qualified.class.name">
Since this is a custom shard handler, sub-elements are specific to the implementation. The default and only
shard handler provided by Solr is the HttpShardHandlerFactory in which case, the following sub-elements
can be specified:
Node Description
socketTimeout
The read timeout for intra-cluster query and administrative requests. The
default is the same as the distribUpdateSoTimeout specified in the
<solrcloud> section.
connTimeout
The connection timeout for intra-cluster query and administrative
requests. Defaults to the distribUpdateConnTimeout specified in the
<solrcloud> section
urlScheme
URL scheme to be used in distributed search
maxConnectionsPerHost
Maximum connections allowed per host. Defaults to 20
maxConnections
Maximum total connections allowed. Defaults to 10000
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Node Description
corePoolSize
The initial core size of the threadpool servicing requests. Default is 0.
maximumPoolSize
The maximum size of the threadpool servicing requests. Default is
unlimited.
maxThreadIdleTime
The amount of time in seconds that idle threads persist for in the queue,
before being killed. Default is 5 seconds.
sizeOfQueue
If the threadpool uses a backing queue, what is its maximum size to use
direct handoff. Default is to use a SynchronousQueue.
fairnessPolicy
A boolean to configure if the threadpool favours fairness over throughput.
Default is false to favor throughput.
Substituting JVM System Properties in solr.xml
Solr supports variable substitution of JVM system property values in solr.xml , which allows runtime
specification of various configuration options. The syntax is ${propertyname[:option default value] }.
This allows defining a default that can be overridden when Solr is launched. If a default value is not
specified, then the property must be specified at runtime or the solr.xml file will generate an error when
parsed.
Any JVM system properties usually specified using the -D flag when starting the JVM, can be used as
variables in the solr.xml file.
For example, in the solr.xml file shown below, the socketTimeout and connTimeout values are each set to
"0". However, if you start Solr using ‘bin/solr -DsocketTimeout=1000’, the socketTimeout option of the
HttpShardHandlerFactory to be overridden using a value of 1000ms, while the connTimeout option will
continue to use the default property value of "0".
<solr>
<shardHandlerFactory name="shardHandlerFactory"
class="HttpShardHandlerFactory">
<int name="socketTimeout">${socketTimeout:0}</int>
<int name="connTimeout">${connTimeout:0}</int>
</shardHandlerFactory>
</solr>
Defining core.properties
Core discovery means that creating a core is as simple as a core.properties file located on disk.
The core.properties file is a simple Java Properties file where each line is just a key=value pair, e.g.,
name=core1 . Notice that no quotes are required.
A minimal core.properties file looks like the example below. However, it can also be empty, see
information on placement of core.properties below.
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name=my_core_name
Placement of core.properties
Solr cores are configured by placing a file named core.properties in a sub-directory under solr.home .
There are no a-priori limits to the depth of the tree, nor are there limits to the number of cores that can be
defined. Cores may be anywhere in the tree with the exception that cores may not be defined under an
existing core. That is, the following is not allowed:
./cores/core1/core.properties
./cores/core1/coremore/core5/core.properties
In this example, the enumeration will stop at "core1".
The following is legal:
./cores/somecores/core1/core.properties
./cores/somecores/core2/core.properties
./cores/othercores/core3/core.properties
./cores/extracores/deepertree/core4/core.properties
It is possible to segment Solr into multiple cores, each with its own configuration and indices. Cores may be
dedicated to a single application or to very different ones, but all are administered through a common
administration interface. You can create new Solr cores on the fly, shutdown cores, even replace one
running core with another, all without ever stopping or restarting Solr.
Your core.properties file can be empty if necessary. Suppose core.properties is located in
./cores/core1 (relative to solr_home ) but is empty. In this case, the core name is assumed to be "core1".
The instanceDir will be the folder containing core.properties (i.e., ./cores/core1 ). The dataDir will be
../cores/core1/data , etc.
?
You can run Solr without configuring any cores.
Defining core.properties Files
The minimal core.properties file is an empty file, in which case all of the properties are defaulted
appropriately.
Java properties files allow the hash ( # ) or bang ( ! ) characters to specify comment-to-end-of-line.
This table defines the recognized properties:
Property Description
name
The name of the SolrCore. You’ll use this name to reference the SolrCore when
running commands with the CoreAdminHandler.
config
The configuration file name for a given core. The default is solrconfig.xml .
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Property Description
schema
The schema file name for a given core. The default is schema.xml but please
note that if you are using a "managed schema" (the default behavior) then any
value for this property which does not match the effective
managedSchemaResourceName will be read once, backed up, and converted for
managed schema use. See Schema Factory Definition in SolrConfig for details.
dataDir
The core’s data directory (where indexes are stored) as either an absolute
pathname, or a path relative to the value of instanceDir . This is data by
default.
configSet
The name of a defined configset, if desired, to use to configure the core (see the
Config Sets for more details).
properties
The name of the properties file for this core. The value can be an absolute
pathname or a path relative to the value of instanceDir .
transient
If true, the core can be unloaded if Solr reaches the transientCacheSize . The
default if not specified is false. Cores are unloaded in order of least recently
used first. Setting to true is not recommended in SolrCloud mode.
loadOnStartup
If true, the default if it is not specified, the core will loaded when Solr starts.
Setting to false is not recommended in SolrCloud mode.
coreNodeName
Used only in SolrCloud, this is a unique identifier for the node hosting this
replica. By default a coreNodeName is generated automatically, but setting this
attribute explicitly allows you to manually assign a new core to replace an
existing replica. For example: when replacing a machine that has had a
hardware failure by restoring from backups on a new machine with a new
hostname or port..
ulogDir
The absolute or relative directory for the update log for this core (SolrCloud).
shard
The shard to assign this core to (SolrCloud).
collection
The name of the collection this core is part of (SolrCloud).
roles
Future param for SolrCloud or a way for users to mark nodes for their own use.
Additional "user defined" properties may be specified for use as variables. For more information on how to
define local properties, see the section Substituting Properties in Solr Config Files.
CoreAdmin API
The Core Admin API is primarily used under the covers by the Collections API when running a SolrCloud
cluster.
SolrCloud users should not typically use the CoreAdmin API directly, but the API may be useful for users of
single-node or master/slave Solr installations for core maintenance operations.
The CoreAdmin API is implemented by the CoreAdminHandler, which is a special purpose request handler
that is used to manage Solr cores. Unlike other request handlers, the CoreAdminHandler is not attached to a
single core. Instead, there is a single instance of the CoreAdminHandler in each Solr node that manages all
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the cores running in that node and is accessible at the /solr/admin/cores path.
CoreAdmin actions can be executed by via HTTP requests that specify an action request parameter, with
additional action specific arguments provided as additional parameters.
All action names are uppercase, and are defined in depth in the sections below.
STATUS
The STATUS action returns the status of all running Solr cores, or status for only the named core.
admin/cores?action=STATUS&core=core-name
Input
Query Parameters
Parameter Type Required Default Description
core string No The name of a core, as listed in the "name" attribute of a
<core> element in solr.xml .
indexInfo boolean No true If false, information about the index will not be returned
with a core STATUS request. In Solr implementations with
a large number of cores (i.e., more than hundreds),
retrieving the index information for each core can take a
lot of time and isn’t always required.
CREATE
The CREATE action creates a new core and registers it.
If a Solr core with the given name already exists, it will continue to handle requests while the new core is
initializing. When the new core is ready, it will take new requests and the old core will be unloaded.
admin/cores?action=CREATE&name=core-name&instanceDir=
path/to/dir&config=solrconfig.xml&dataDir=data
Note that this command is the only one of the Core Admin API commands that does not support the core
parameter. Instead, the name parameter is required, as shown below.
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?
CREATE must be able to find a configuration!
Your CREATE call must be able to find a configuration, or it will not succeed.
When you are running SolrCloud and create a new core for a collection, the configuration
will be inherited from the collection. Each collection is linked to a configName, which is
stored in the ZooKeeper database. This satisfies the config requirement. There is
something to note, though – if you’re running SolrCloud, you should NOT be using the
CoreAdmin API at all. Use the Collections API.
When you are not running SolrCloud, if you have Config Sets defined, you can use the
configSet parameter as documented below. If there are no config sets, then the instanceDir
specified in the CREATE call must already exist, and it must contain a conf directory which
in turn must contain solrconfig.xml , your schema, which is usually named either
managed-schema or schema.xml , and any files referenced by those configs.
The config and schema filenames can be specified with the config and schema parameters,
but these are expert options. One thing you could do to avoid creating the conf directory is
use config and schema parameters that point at absolute paths, but this can lead to
confusing configurations unless you fully understand what you are doing.
?
CREATE and the core.properties file
The core.properties file is built as part of the CREATE command. If you create a
core.properties file yourself in a core directory and then try to use CREATE to add that core
to Solr, you will get an error telling you that another core is already defined there. The
core.properties file must NOT exist before calling the CoreAdmin API with the CREATE
command.
Input
Query Parameters
Parameter Type Required Default Description
name string Yes N/A The name of the new core. Same as "name" on the <core>
element.
instanceDir string No The value
specified
for
"name"
paramete
r
The directory where files for this SolrCore should be
stored. Same as instanceDir on the <core> element.
config string No Name of the config file (i.e., solrconfig.xml ) relative to
instanceDir .
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Parameter Type Required Default Description
schema string No Name of the schema file to use for the core. Please note
that if you are using a "managed schema" (the default
behavior) then any value for this property which does not
match the effective managedSchemaResourceName will be
read once, backed up, and converted for managed
schema use. See Schema Factory Definition in SolrConfig
for details.
dataDir string No Name of the data directory relative to instanceDir .
configSet string No Name of the configset to use for this core. For more
information, see the section Config Sets.
collection string No The name of the collection to which this core belongs. The
default is the name of the core.
collection.<param>=<value> causes a property of
<param>=<value> to be set if a new collection is being
created. Use collection.configName=<configname> to
point to the configuration for a new collection.
shard string No The shard id this core represents. Normally you want to be
auto-assigned a shard id.
property.name
=value
string No Sets the core property name to value. See the section on
defining core.properties file contents.
async string No Request ID to track this action which will be processed
asynchronously
Use collection.configName=<configname> to point to the config for a new collection.
Example
http://localhost:8983/solr/admin/cores?action=CREATE&name=my_core&collection=my_collection&s
hard=shard2
?
While it’s possible to create a core for a non-existent collection, this approach is not
supported and not recommended. Always create a collection using the Collections API
before creating a core directly for it.
RELOAD
The RELOAD action loads a new core from the configuration of an existing, registered Solr core. While the
new core is initializing, the existing one will continue to handle requests. When the new Solr core is ready, it
takes over and the old core is unloaded.
admin/cores?action=RELOAD&core=core-name
This is useful when you’ve made changes to a Solr core’s configuration on disk, such as adding new field
definitions. Calling the RELOAD action lets you apply the new configuration without having to restart the
Web container.
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?
RELOAD performs "live" reloads of SolrCore, reusing some existing objects. Some
configuration options, such as the dataDir location and IndexWriter -related settings in
solrconfig.xml can not be changed and made active with a simple RELOAD action.
Input
Query Parameters
Parameter Type Required Default Description
core string Yes N/A The name of the core, as listed in the "name" attribute of
a <core> element in solr.xml .
RENAME
The RENAME action changes the name of a Solr core.
admin/cores?action=RENAME&core=core-name&other=other-core-name
Input
Query Parameters
Parameter Type Required Default Description
core string Yes The name of the Solr core to be renamed.
other string Yes The new name for the Solr core. If the persistent attribute
of <solr> is true , the new name will be written to
solr.xml as the name attribute of the <core> attribute.
async string No Request ID to track this action which will be processed
asynchronously
SWAP
SWAP atomically swaps the names used to access two existing Solr cores. This can be used to swap new
content into production. The prior core remains available and can be swapped back, if necessary. Each core
will be known by the name of the other, after the swap.
admin/cores?action=SWAP&core=core-name&other=other-core-name
?
Do not use SWAP with a SolrCloud node. It is not supported and can result in the core being
unusable.
Input
Query Parameters
Parameter Type Required Default Description
core string Yes The name of one of the cores to be swapped.
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Parameter Type Required Default Description
other string Yes The name of one of the cores to be swapped.
async string No Request ID to track this action which will be processed
asynchronously
UNLOAD
The UNLOAD action removes a core from Solr. Active requests will continue to be processed, but no new
requests will be sent to the named core. If a core is registered under more than one name, only the given
name is removed.
admin/cores?action=UNLOAD&core=core-name
The UNLOAD action requires a parameter ( core ) identifying the core to be removed. If the persistent attribute
of <solr> is set to true , the <core> element with this name attribute will be removed from solr.xml .
?
Unloading all cores in a SolrCloud collection causes the removal of that collection’s
metadata from ZooKeeper.
Input
Query Parameters
Parameter Type Required Default Description
core string Yes The name of one of the cores to be removed.
deleteIndex boolean No false If true, will remove the index when unloading the core.
deleteDataDir boolean No false If true, removes the data directory and all sub-directories.
deleteInstance
Dir
boolean No false If true, removes everything related to the core, including
the index directory, configuration files and other related
files.
async string No Request ID to track this action which will be processed
asynchronously
MERGEINDEXES
The MERGEINDEXES action merges one or more indexes to another index. The indexes must have completed
commits, and should be locked against writes until the merge is complete or the resulting merged index
may become corrupted. The target core index must already exist and have a compatible schema with the
one or more indexes that will be merged to it. Another commit on the target core should also be performed
after the merge is complete.
admin/cores?action=MERGEINDEXES&core=new-core-name&indexDir=
path/to/core1/data/index&indexDir=path/to/core2/data/index
In this example, we use the indexDir parameter to define the index locations of the source cores. The core
parameter defines the target index. A benefit of this approach is that we can merge any Lucene-based index
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that may not be associated with a Solr core.
Alternatively, we can instead use a srcCore parameter, as in this example:
admin/cores?action=mergeindexes&core=new-core-name&srcCore=core1-name&srcCore=core2-name
This approach allows us to define cores that may not have an index path that is on the same physical server
as the target core. However, we can only use Solr cores as the source indexes. Another benefit of this
approach is that we don’t have as high a risk for corruption if writes occur in parallel with the source index.
We can make this call run asynchronously by specifying the async parameter and passing a request-id. This
id can then be used to check the status of the already submitted task using the REQUESTSTATUS API.
Input
Query Parameters
Parameter Type Required Default Description
core string Yes The name of the target core/index.
indexDir string Multi-valued, directories that would be merged.
srcCore string Multi-valued, source cores that would be merged.
async string Request ID to track this action which will be processed
asynchronously
SPLIT
The SPLIT action splits an index into two or more indexes. The index being split can continue to handle
requests. The split pieces can be placed into a specified directory on the server’s filesystem or it can be
merged into running Solr cores.
The SPLIT action supports five parameters, which are described in the table below.
Input
Query Parameters
Parameter Type Required Default Description
core string Yes The name of the core to be split.
path string Multi-valued, the directory path in which a piece of the
index will be written.
targetCore string Multi-valued, the target Solr core to which a piece of the
index will be merged
ranges string No A comma-separated list of hash ranges in hexadecimal
format
split.key string No The key to be used for splitting the index
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Parameter Type Required Default Description
async string No Request ID to track this action which will be processed
asynchronously
?
Either path or targetCore parameter must be specified but not both. The ranges and
split.key parameters are optional and only one of the two should be specified, if at all
required.
Examples
The core index will be split into as many pieces as the number of path or targetCore parameters.
Usage with two targetCore parameters:
http://localhost:8983/solr/admin/cores?action=SPLIT&core=core0&targetCore=core1&targetCore=c
ore2
Here the core index will be split into two pieces and merged into the two targetCore indexes.
Usage with two path parameters:
http://localhost:8983/solr/admin/cores?action=SPLIT&core=core0&path=/path/to/index/1&path=/p
ath/to/index/2
The core index will be split into two pieces and written into the two directory paths specified.
Usage with the split.key parameter:
http://localhost:8983/solr/admin/cores?action=SPLIT&core=core0&targetCore=core1&split.key=A!
Here all documents having the same route key as the split.key i.e. ‘A!‘ will be split from the core index and
written to the targetCore .
Usage with ranges parameter:
http://localhost:8983/solr/admin/cores?action=SPLIT&core=core0&targetCore=core1&targetCore=c
ore2&targetCore=core3&ranges=0-1f4,1f5-3e8,3e9-5dc
This example uses the ranges parameter with hash ranges 0-500, 501-1000 and 1001-1500 specified in
hexadecimal. Here the index will be split into three pieces with each targetCore receiving documents
matching the hash ranges specified i.e. core1 will get documents with hash range 0-500, core2 will receive
documents with hash range 501-1000 and finally, core3 will receive documents with hash range 1001-1500.
At least one hash range must be specified. Please note that using a single hash range equal to a route key’s
hash range is NOT equivalent to using the split.key parameter because multiple route keys can hash to
the same range.
The targetCore must already exist and must have a compatible schema with the core index. A commit is
automatically called on the core index before it is split.
This command is used as part of the SPLITSHARD command but it can be used for non-cloud Solr cores as
well. When used against a non-cloud core without split.key parameter, this action will split the source
index and distribute its documents alternately so that each split piece contains an equal number of
documents. If the split.key parameter is specified then only documents having the same route key will be
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split from the source index.
REQUESTSTATUS
Request the status of an already submitted asynchronous CoreAdmin API call.
admin/cores?action=REQUESTSTATUS&requestid=id
Input
Query Parameters
Parameter Type Required Default Description
requestid string Yes The user defined request-id for the Asynchronous request.
The call below will return the status of an already submitted Asynchronous CoreAdmin call.
http://localhost:8983/solr/admin/cores?action=REQUESTSTATUS&requestid=1
REQUESTRECOVERY
The REQUESTRECOVERY action manually asks a core to recover by synching with the leader. This should be
considered an "expert" level command and should be used in situations where the node (SorlCloud replica)
is unable to become active automatically.
admin/cores?action=REQUESTRECOVERY&core=core-name
Input
Query Parameters
Parameter Type Required Default Description
core string Yes The name of the core to re-sync.
Examples
http://localhost:8981/solr/admin/cores?action=REQUESTRECOVERY&core=gettingstarted_shard1_rep
lica1
The core to specify can be found by expanding the appropriate ZooKeeper node via the admin UI.
Config Sets
On a multicore Solr instance, you may find that you want to share configuration between a number of
different cores. You can achieve this using named configsets, which are essentially shared configuration
directories stored under a configurable configset base directory.
To create a configset, simply add a new directory under the configset base directory. The configset will be
identified by the name of this directory. Then into this copy the config directory you want to share. The
structure should look something like this:
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/<configSetBaseDir>
/configset1
/conf
/managed-schema
/solrconfig.xml
/configset2
/conf
/managed-schema
/solrconfig.xml
The default base directory is $SOLR_HOME/configsets , and it can be configured in solr.xml .
To create a new core using a configset, pass configSet as one of the core properties. For example, if you do
this via the core admin API:
http://localhost:8983/admin/cores?action=CREATE&name=mycore&instanceDir=path/to/instance&con
figSet=configset2
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Configuration APIs
Solr includes several APIs that can be used to modify settings in solrconfig.xml .
? Blob Store API
? Config API
? Request Parameters API
? Managed Resources
Blob Store API
The Blob Store REST API provides REST methods to store, retrieve or list files in a Lucene index.
It can be used to upload a jar file which contains standard solr components such as RequestHandlers,
SearchComponents, or other custom code you have written for Solr. Schema components do not yet support
the Blob Store.
When using the blob store, note that the API does not delete or overwrite a previous object if a new one is
uploaded with the same name. It always adds a new version of the blob to the index. Deletes can be
performed with standard REST delete commands.
The blob store is only available when running in SolrCloud mode. Solr in standalone mode does not
support use of a blob store.
The blob store API is implemented as a requestHandler. A special collection named ".system" is used to
store the blobs. This collection can be created in advance, but if it does not exist it will be created
automatically.
About the .system Collection
Before uploading blobs to the blob store, a special collection must be created and it must be named
.system . Solr will automatically create this collection if it does not already exist, but you can also create it
manually if you choose.
The BlobHandler is automatically registered in the .system collection. The solrconfig.xml , Schema, and
other configuration files for the collection are automatically provided by the system and don’t need to be
defined specifically.
If you do not use the -shards or -replicationFactor options, then defaults of numShards=1 and
replicationFactor=3 (or maximum nodes in the cluster) will be used.
You can create the .system collection with the Collections API, as in this example:
curl http://localhost:8983/solr/admin/collections?action=CREATE&name=.system&replicationFactor=2
?
The bin/solr script cannot be used to create the .system collection.
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Upload Files to Blob Store
After the .system collection has been created, files can be uploaded to the blob store with a request similar
to the following:
curl -X POST -H ‘Content-Type: application/octet-stream‘ --data-binary @{filename}
http://localhost:8983/solr/.system/blob/{blobname}
For example, to upload a file named "test1.jar" as a blob named "test", you would make a POST request like:
curl -X POST -H ‘Content-Type: application/octet-stream‘ --data-binary @test1.jar
http://localhost:8983/solr/.system/blob/test
A GET request will return the list of blobs and other details:
curl http://localhost:8983/solr/.system/blob?omitHeader=true
Output:
{
"response":{"numFound":1,"start":0,"docs":[
{
"id":"test/1",
"md5":"20ff915fa3f5a5d66216081ae705c41b",
"blobName":"test",
"version":1,
"timestamp":"2015-02-04T16:45:48.374Z",
"size":13108}]
}
}
Details on individual blobs can be accessed with a request similar to:
curl http://localhost:8983/solr/.system/blob/{blobname}
For example, this request will return only the blob named ‘test‘:
curl http://localhost:8983/solr/.system/blob/test?omitHeader=true
Output:
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{
"response":{"numFound":1,"start":0,"docs":[
{
"id":"test/1",
"md5":"20ff915fa3f5a5d66216081ae705c41b",
"blobName":"test",
"version":1,
"timestamp":"2015-02-04T16:45:48.374Z",
"size":13108}]
}
}
The filestream response writer can return a particular version of a blob for download, as in:
curl http://localhost:8983/solr/.system/blob/{blobname}/{version}?wt=filestream >
{outputfilename}
For the latest version of a blob, the {version} can be omitted,
curl http://localhost:8983/solr/.system/blob/{blobname}?wt=filestream > {outputfilename}
Use a Blob in a Handler or Component
To use the blob as the class for a request handler or search component, you create a request handler in
solrconfig.xml as usual. You will need to define the following parameters:
class
the fully qualified class name. For example, if you created a new request handler class called
CRUDHandler, you would enter org.apache.solr.core.CRUDHandler .
runtimeLib
Set to true to require that this component should be loaded from the classloader that loads the runtime
jars.
For example, to use a blob named test, you would configure solrconfig.xml like this:
<requestHandler name="/myhandler" class="org.apache.solr.core.myHandler" runtimeLib="true"
version="1">
</requestHandler>
If there are parameters available in the custom handler, you can define them in the same way as any other
request handler definition.
Config API
The Config API enables manipulating various aspects of your solrconfig.xml using REST-like API calls.
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This feature is enabled by default and works similarly in both SolrCloud and standalone mode. Many
commonly edited properties (such as cache sizes and commit settings) and request handler definitions can
be changed with this API.
When using this API, solrconfig.xml is not changed. Instead, all edited configuration is stored in a file
called configoverlay.json . The values in configoverlay.json override the values in solrconfig.xml .
API Entry Points
? /config : retrieve or modify the config. GET to retrieve and POST for executing commands
? /config/overlay : retrieve the details in the configoverlay.json alone
? /config/params : allows creating parameter sets that can override or take the place of parameters
defined in solrconfig.xml . See the Request Parameters API section for more details.
Retrieving the config
All configuration items, can be retrieved by sending a GET request to the /config endpoint - the results will
be the effective configuration resulting from merging settings in configoverlay.json with those in
solrconfig.xml :
curl http://localhost:8983/solr/techproducts/config
To restrict the returned results to a top level section, e.g. query , requestHandler or updateHandler , append
the name of the section to the /config endpoint following a slash. E.g. to retrieve configuration for all
request handlers:
curl http://localhost:8983/solr/techproducts/config/requestHandler
To further restrict returned results to a single component within a top level section, use the componentName
request param, e.g. to return configuration for the /select request handler:
curl http://localhost:8983/solr/techproducts/config/requestHandler?componentName=/select
Commands to modify the config
This API uses specific commands to tell Solr what property or type of property to add to
configoverlay.json . The commands are passed as part of the data sent with the request.
The config commands are categorized into 3 different sections which manipulate various data structures in
solrconfig.xml . Each of these is described below.
? Common Properties
? Components
? User-defined properties
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Commands for Common Properties
The common properties are those that are frequently need to be customized in a Solr instance. They are
manipulated with two commands:
? set-property : Set a well known property. The names of the properties are predefined and fixed. If the
property has already been set, this command will overwrite the previous setting.
? unset-property : Remove a property set using the set-property command.
The properties that are configured with these commands are predefined and listed below. The names of
these properties are derived from their XML paths as found in solrconfig.xml .
? updateHandler.autoCommit.maxDocs
? updateHandler.autoCommit.maxTime
? updateHandler.autoCommit.openSearcher
? updateHandler.autoSoftCommit.maxDocs
? updateHandler.autoSoftCommit.maxTime
? updateHandler.commitWithin.softCommit
? updateHandler.indexWriter.closeWaitsForMerges
? query.filterCache.class
? query.filterCache.size
? query.filterCache.initialSize
? query.filterCache.autowarmCount
? query.filterCache.regenerator
? query.queryResultCache.class
? query.queryResultCache.size
? query.queryResultCache.initialSize
? query.queryResultCache.autowarmCount
? query.queryResultCache.regenerator
? query.documentCache.class
? query.documentCache.size
? query.documentCache.initialSize
? query.documentCache.autowarmCount
? query.documentCache.regenerator
? query.fieldValueCache.class
? query.fieldValueCache.size
? query.fieldValueCache.initialSize
? query.fieldValueCache.autowarmCount
? query.fieldValueCache.regenerator
? query.useFilterForSortedQuery
? query.queryResultWindowSize
? query.queryResultMaxDocCached
? query.enableLazyFieldLoading
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? query.boolToFilterOptimizer
? query.maxBooleanClauses
? jmx.agentId
? jmx.serviceUrl
? jmx.rootName
? requestDispatcher.handleSelect
? requestDispatcher.requestParsers.multipartUploadLimitInKB
? requestDispatcher.requestParsers.formdataUploadLimitInKB
? requestDispatcher.requestParsers.enableRemoteStreaming
? requestDispatcher.requestParsers.addHttpRequestToContext
Commands for Custom Handlers and Local Components
Custom request handlers, search components, and other types of localized Solr components (such as
custom query parsers, update processors, etc.) can be added, updated and deleted with specific commands
for the component being modified.
The syntax is similar in each case: add-<component-name> , update-<component-name> , and delete-
<component-name> . The command name is not case sensitive, so Add-RequestHandler , ADD-REQUESTHANDLER
and add-requesthandler are all equivalent.
In each case, add- commands add the new configuration to configoverlay.json , which will override any
other settings for the component in solrconfig.xml ; update- commands overwrite an existing setting in
configoverlay.json ; and delete- commands remove the setting from configoverlay.json .
Settings removed from configoverlay.json are not removed from solrconfig.xml .
The full list of available commands follows below:
General Purpose Commands
These commands are the most commonly used:
? add-requesthandler
? update-requesthandler
? delete-requesthandler
? add-searchcomponent
? update-searchcomponent
? delete-searchcomponent
? add-initparams
? update-initparams
? delete-initparams
? add-queryresponsewriter
? update-queryresponsewriter
? delete-queryresponsewriter
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Advanced Commands
These commands allow registering more advanced customizations to Solr:
? add-queryparser
? update-queryparser
? delete-queryparser
? add-valuesourceparser
? update-valuesourceparser
? delete-valuesourceparser
? add-transformer
? update-transformer
? delete-transformer
? add-updateprocessor
? update-updateprocessor
? delete-updateprocessor
? add-queryconverter
? update-queryconverter
? delete-queryconverter
? add-listener
? update-listener
? delete-listener
? add-runtimelib
? update-runtimelib
? delete-runtimelib
See the section Creating and Updating Request Handlers below for examples of using these commands.
What about updateRequestProcessorChain?
The Config API does not let you create or edit updateRequestProcessorChain elements. However, it is
possible to create updateProcessor entries and can use them by name to create a chain.
example:
curl http://localhost:8983/solr/techproducts/config -H ‘Content-type:application/json‘ -d ‘{
"add-updateprocessor" : { "name" : "firstFld",
"class": "solr.FirstFieldValueUpdateProcessorFactory",
"fieldName":"test_s"}}‘
You can use this directly in your request by adding a parameter in the updateRequestProcessorChain for
the specific update processor called processor=firstFld .
Commands for User-Defined Properties
Solr lets users templatize the solrconfig.xml using the place holder format
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${variable_name:default_val} . You could set the values using system properties, for example,
-Dvariable_name= my_customvalue . The same can be achieved during runtime using these commands:
? set-user-property : Set a user-defined property. If the property has already been set, this command will
overwrite the previous setting.
? unset-user-property : Remove a user-defined property.
The structure of the request is similar to the structure of requests using other commands, in the format of
"command":{"variable_name":"property_value"} . You can add more than one variable at a time if
necessary.
For more information about user-defined properties, see the section User defined properties from
core.properties.
See also the section Creating and Updating User-Defined Properties below for examples of how to use this
type of command.
How to Map solrconfig.xml Properties to JSON
By using this API, you will be generating JSON representations of properties defined in solrconfig.xml . To
understand how properties should be represented with the API, let’s take a look at a few examples.
Here is what a request handler looks like in solrconfig.xml :
<requestHandler name="/query" class="solr.SearchHandler">
<lst name="defaults">
<str name="echoParams">explicit</str>
<str name="wt">json</str>
<str name="indent">true</str>
</lst>
</requestHandler>
The same request handler defined with the Config API would look like this:
{
"add-requesthandler":{
"name":"/query",
"class":"solr.SearchHandler",
"defaults":{
"echoParams":"explicit",
"wt":"json",
"indent":true
}
}
}
The QueryElevationComponent searchComponent in solrconfig.xml looks like this:
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<searchComponent name="elevator" class="solr.QueryElevationComponent" >
<str name="queryFieldType">string</str>
<str name="config-file">elevate.xml</str>
</searchComponent>
And the same searchComponent with the Config API:
{
"add-searchcomponent":{
"name":"elevator",
"class":"QueryElevationComponent",
"queryFieldType":"string",
"config-file":"elevate.xml"
}
}
Removing the searchComponent with the Config API:
{
"delete-searchcomponent":"elevator"
}
A simple highlighter looks like this in solrconfig.xml (example has been truncated for space):
<searchComponent class="solr.HighlightComponent" name="highlight">
<highlighting>
<fragmenter name="gap"
default="true"
class="solr.highlight.GapFragmenter">
<lst name="defaults">
<int name="hl.fragsize">100</int>
</lst>
</fragmenter>
<formatter name="html"
default="true"
class="solr.highlight.HtmlFormatter">
<lst name="defaults">
<str name="hl.simple.pre"><![CDATA[<em>]]></str>
<str name="hl.simple.post"><![CDATA[</em>]]></str>
</lst>
</formatter>
<encoder name="html" class="solr.highlight.HtmlEncoder" />
...
</highlighting>
The same highlighter with the Config API:
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{
"add-searchcomponent": {
"name": "highlight",
"class": "solr.HighlightComponent",
"": {
"gap": {
"default": "true",
"name": "gap",
"class": "solr.highlight.GapFragmenter",
"defaults": {
"hl.fragsize": 100
}
}
},
"html": [{
"default": "true",
"name": "html",
"class": "solr.highlight.HtmlFormatter",
"defaults": {
"hl.simple.pre": "before-",
"hl.simple.post": "-after"
}
}, {
"name": "html",
"class": "solr.highlight.HtmlEncoder"
}]
}
}
Set autoCommit properties in solrconfig.xml :
<autoCommit>
<maxTime>15000</maxTime>
<openSearcher>false</openSearcher>
</autoCommit>
Define the same properties with the Config API:
{
"set-property": {
"updateHandler.autoCommit.maxTime":15000,
"updateHandler.autoCommit.openSearcher":false
}
}
Name Components for the Config API
The Config API always allows changing the configuration of any component by name. However, some
configurations such as listener or initParams do not require a name in solrconfig.xml . In order to be
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able to update and delete of the same item in configoverlay.json , the name attribute becomes
mandatory.
Examples
Creating and Updating Common Properties
This change sets the query.filterCache.autowarmCount to 1000 items and unsets the
query.filterCache.size .
curl http://localhost:8983/solr/techproducts/config -H ‘Content-type:application/json‘ -d‘{
"set-property" : {"query.filterCache.autowarmCount":1000},
"unset-property" :"query.filterCache.size"}‘
Using the /config/overlay endpoint, you can verify the changes with a request like this:
curl http://localhost:8983/solr/gettingstarted/config/overlay?omitHeader=true
And you should get a response like this:
{
"overlay":{
"znodeVersion":1,
"props":{"query":{"filterCache":{
"autowarmCount":1000,
"size":25}}}}}
Creating and Updating Request Handlers
To create a request handler, we can use the add-requesthandler command:
curl http://localhost:8983/solr/techproducts/config -H ‘Content-type:application/json‘ -d ‘{
"add-requesthandler" : {
"name": "/mypath",
"class":"solr.DumpRequestHandler",
"defaults":{ "x":"y" ,"a":"b", "wt":"json", "indent":true },
"useParams":"x"
}
}‘
Make a call to the new request handler to check if it is registered:
curl http://localhost:8983/solr/techproducts/mypath?omitHeader=true
And you should see the following as output:
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{
"params":{
"indent":"true",
"a":"b",
"x":"y",
"wt":"json"},
"context":{
"webapp":"/solr",
"path":"/mypath",
"httpMethod":"GET"}}
To update a request handler, you should use the update-requesthandler command :
curl http://localhost:8983/solr/techproducts/config -H ‘Content-type:application/json‘ -d ‘{
"update-requesthandler": {
"name": "/mypath",
"class":"solr.DumpRequestHandler",
"defaults": {"x":"new value for X", "wt":"json", "indent":true},
"useParams":"x"
}
}‘
As another example, we’ll create another request handler, this time adding the ‘terms‘ component as part
of the definition:
curl http://localhost:8983/solr/techproducts/config -H ‘Content-type:application/json‘ -d ‘{
"add-requesthandler": {
"name": "/myterms",
"class":"solr.SearchHandler",
"defaults": {"terms":true, "distrib":false},
"components": [ "terms" ]
}
}‘
Creating and Updating User-Defined Properties
This command sets a user property.
curl http://localhost:8983/solr/techproducts/config -H‘Content-type:application/json‘ -d ‘{
"set-user-property" : {"variable_name":"some_value"}}‘
Again, we can use the /config/overlay endpoint to verify the changes have been made:
curl http://localhost:8983/solr/techproducts/config/overlay?omitHeader=true
And we would expect to see output like this:
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{"overlay":{
"znodeVersion":5,
"userProps":{
"variable_name":"some_value"}}
}
To unset the variable, issue a command like this:
curl http://localhost:8983/solr/techproducts/config -H‘Content-type:application/json‘ -d
‘{"unset-user-property" : "variable_name"}‘
How It Works
Every core watches the ZooKeeper directory for the configset being used with that core. In standalone
mode, however, there is no watch (because ZooKeeper is not running). If there are multiple cores in the
same node using the same configset, only one ZooKeeper watch is used. For instance, if the configset
‘myconf‘ is used by a core, the node would watch /configs/myconf . Every write operation performed
through the API would ‘touch‘ the directory (sets an empty byte[] to trigger watches) and all watchers are
notified. Every core would check if the Schema file, solrconfig.xml or configoverlay.json is modified by
comparing the znode versions and if modified, the core is reloaded.
If params.json is modified, the params object is just updated without a core reload (see the section Request
Parameters API for more information about params.json ).
Empty Command
If an empty command is sent to the /config endpoint, the watch is triggered on all cores using this
configset. For example:
curl http://localhost:8983/solr/techproducts/config -H‘Content-type:application/json‘ -d ‘{}‘
Directly editing any files without ‘touching‘ the directory will not make it visible to all nodes.
It is possible for components to watch for the configset ‘touch‘ events by registering a listener using
SolrCore#registerConfListener() .
Listening to config Changes
Any component can register a listener using:
SolrCore#addConfListener(Runnable listener)
to get notified for config changes. This is not very useful if the files modified result in core reloads (i.e.,
configoverlay.xml or Schema). Components can use this to reload the files they are interested in.
Request Parameters API
The Request Parameters API allows creating parameter sets, a.k.a. paramsets, that can override or take the
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place of parameters defined in solrconfig.xml .
The parameter sets defined with this API can be used in requests to Solr, or referenced directly in
solrconfig.xml request handler definitions.
It is really another endpoint of the Config API instead of a separate API, and has distinct commands. It does
not replace or modify any sections of solrconfig.xml , but instead provides another approach to handling
parameters used in requests. It behaves in the same way as the Config API, by storing parameters in
another file that will be used at runtime. In this case, the parameters are stored in a file named
params.json . This file is kept in ZooKeeper or in the conf directory of a standalone Solr instance.
The settings stored in params.json are used at query time to override settings defined in solrconfig.xml in
some cases as described below.
When might you want to use this feature?
? To avoid frequently editing your solrconfig.xml to update request parameters that change often.
? To reuse parameters across various request handlers.
? To mix and match parameter sets at request time.
? To avoid a reload of your collection for small parameter changes.
The Request Parameters Endpoint
All requests are sent to the /config/params endpoint of the Config API.
Setting Request Parameters
The request to set, unset, or update request parameters is sent as a set of Maps with names. These objects
can be directly used in a request or a request handler definition.
The available commands are:
? set : Create or overwrite a parameter set map.
? unset : delete a parameter set map.
? update : update a parameter set map. This is equivalent to a map.putAll(newMap) . Both the maps are
merged and if the new map has same keys as old they are overwritten
You can mix these commands into a single request if necessary.
Each map must include a name so it can be referenced later, either in a direct request to Solr or in a request
handler definition.
In the following example, we are setting 2 sets of parameters named ‘myFacets‘ and ‘myQueries‘.
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curl http://localhost:8983/solr/techproducts/config/params -H ‘Content-type:application/json‘ -d
‘{
"set":{
"myFacets":{
"facet":"true",
"facet.limit":5}},
"set":{
"myQueries":{
"defType":"edismax",
"rows":"5",
"df":"text_all"}}
}‘
In the above example all the parameters are equivalent to the "defaults" in solrconfig.xml . It is possible to
add invariants and appends as follows:
curl http://localhost:8983/solr/techproducts/config/params -H ‘Content-type:application/json‘ -d
‘{
"set":{
"my_handler_params":{
"facet.limit":5,
"_invariants_": {
"facet":true,
"wt":"json"
},
"_appends_":{"facet.field":["field1","field2"]
}
}}
}‘
Using Request Parameters with RequestHandlers
After creating the my_handler_params paramset in the above section, it is possible to define a request
handler as follows:
<requestHandler name="/my_handler" class="solr.SearchHandler" useParams="my_handler_params"/>
It will be equivalent to a standard request handler definition such as this one:
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<requestHandler name="/my_handler" class="solr.SearchHandler">
<lst name="defaults">
<int name="facet.limit">5</int>
</lst>
<lst name="invariants">
<str name="wt">json</str>
<bool name="facet">true</bool>
</lst>
<lst name="appends">
<arr name="facet.field">
<str>field1</str>
<str>field2</str>
</arr>
</lst>
</requestHandler>
Implicit RequestHandlers
Solr ships with many out-of-the-box request handlers that may only be configured via the Request
Parameters API, because their configuration is not present in solrconfig.xml . See Implicit RequestHandlers
for the paramset to use when configuring an implicit request handler.
Viewing Expanded Paramsets and Effective Parameters with RequestHandlers
To see the expanded paramset and the resulting effective parameters for a RequestHandler defined with
useParams , use the expandParams request param. E.g. for the /export request handler:
curl
http://localhost:8983/solr/techproducts/config/requestHandler?componentName=/export&expandParams=
true
Viewing Request Parameters
To see the paramsets that have been created, you can use the /config/params endpoint to read the
contents of params.json , or use the name in the request:
curl http://localhost:8983/solr/techproducts/config/params
#Or use the paramset name
curl http://localhost:8983/solr/techproducts/config/params/myQueries
The useParams Parameter
When making a request, the useParams parameter applies the request parameters sent to the request. This
is translated at request time to the actual parameters.
For example (using the names we set up in the earlier examples, please replace with your own name):
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http://localhost/solr/techproducts/select?useParams=myQueries
It is possible to pass more than one parameter set in the same request. For example:
http://localhost/solr/techproducts/select?useParams=myFacets,myQueries
In the above example the param set ‘myQueries‘ is applied on top of ‘myFacets‘. So, values in ‘myQueries‘
take precedence over values in ‘myFacets‘. Additionally, any values passed in the request take precedence
over useParams parameters. This acts like the "defaults" specified in the <requestHandler> definition in
solrconfig.xml .
The parameter sets can be used directly in a request handler definition as follows. Please note that the
useParams specified is always applied even if the request contains useParams .
<requestHandler name="/terms" class="solr.SearchHandler" useParams="myQueries">
<lst name="defaults">
<bool name="terms">true</bool>
<bool name="distrib">false</bool>
</lst>
<arr name="components">
<str>terms</str>
</arr>
</requestHandler>
To summarize, parameters are applied in this order:
? parameters defined in <invariants> in solrconfig.xml .
? parameters applied in invariants in params.json and that is specified in the requesthandler definition
or even in request
? parameters defined in the request directly.
? parameter sets defined in the request, in the order they have been listed with useParams .
? parameter sets defined in params.json that have been defined in the request handler.
? parameters defined in <defaults> in solrconfig.xml .
Public APIs
The RequestParams Object can be accessed using the method SolrConfig#getRequestParams() . Each
paramset can be accessed by their name using the method RequestParams#getRequestParams(String
name) .
Examples
The Solr "films" example demonstrates the use of the parameters API. See https://github.com/apache/
lucene-solr/tree/master/solr/example/films for details.
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Managed Resources
Managed resources expose a REST API endpoint for performing Create-Read-Update-Delete (CRUD)
operations on a Solr object.
Any long-lived Solr object that has configuration settings and/or data is a good candidate to be a managed
resource. Managed resources complement other programmatically manageable components in Solr, such
as the RESTful schema API to add fields to a managed schema.
Consider a Web-based UI that offers Solr-as-a-Service where users need to configure a set of stop words and
synonym mappings as part of an initial setup process for their search application. This type of use case can
easily be supported using the Managed Stop Filter & Managed Synonym Filter Factories provided by Solr, via
the Managed resources REST API.
Users can also write their own custom plugins, that leverage the same internal hooks to make additional
resources REST managed.
All of the examples in this section assume you are running the "techproducts" Solr example:
bin/solr -e techproducts
Overview
Let’s begin learning about managed resources by looking at a couple of examples provided by Solr for
managing stop words and synonyms using a REST API. After reading this section, you’ll be ready to dig into
the details of how managed resources are implemented in Solr so you can start building your own
implementation.
Stop Words
To begin, you need to define a field type that uses the ManagedStopFilterFactory, such as:
<fieldType name="managed_en" positionIncrementGap="100">
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ManagedStopFilterFactory" ①
managed="english" /> ②
</analyzer>
</fieldType>
There are two important things to notice about this field type definition:
①
The filter implementation class is solr.ManagedStopFilterFactory . This is a special implementation of
the StopFilterFactory that uses a set of stop words that are managed from a REST API.
②
The managed=”english” attribute gives a name to the set of managed stop words, in this case indicating
the stop words are for English text.
The REST endpoint for managing the English stop words in the techproducts collection is:
/solr/techproducts/schema/analysis/stopwords/english .
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The example resource path should be mostly self-explanatory. It should be noted that the
ManagedStopFilterFactory implementation determines the /schema/analysis/stopwords part of the path,
which makes sense because this is an analysis component defined by the schema.
It follows that a field type that uses the following filter:
<filter class="solr.ManagedStopFilterFactory"
managed="french" />
would resolve to path: /solr/techproducts/schema/analysis/stopwords/french .
So now let’s see this API in action, starting with a simple GET request:
curl "http://localhost:8983/solr/techproducts/schema/analysis/stopwords/english"
Assuming you sent this request to Solr, the response body is a JSON document:
{
"responseHeader":{
"status":0,
"QTime":1
},
"wordSet":{
"initArgs":{"ignoreCase":true},
"initializedOn":"2014-03-28T20:53:53.058Z",
"managedList":[
"a",
"an",
"and",
"are",
]
}
}
The sample_techproducts_configs configset ships with a pre-built set of managed stop words, however
you should only interact with this file using the API and not edit it directly.
One thing that should stand out to you in this response is that it contains a managedList of words as well as
initArgs . This is an important concept in this framework?—?managed resources typically have configuration
and data. For stop words, the only configuration parameter is a boolean that determines whether to ignore
the case of tokens during stop word filtering (ignoreCase=true|false). The data is a list of words, which is
represented as a JSON array named managedList in the response.
Now, let’s add a new word to the English stop word list using an HTTP PUT:
curl -X PUT -H ‘Content-type:application/json‘ --data-binary ‘["foo"]‘
"http://localhost:8983/solr/techproducts/schema/analysis/stopwords/english"
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Here we’re using cURL to PUT a JSON list containing a single word “foo” to the managed English stop words
set. Solr will return 200 if the request was successful. You can also put multiple words in a single PUT
request.
You can test to see if a specific word exists by sending a GET request for that word as a child resource of the
set, such as:
curl "http://localhost:8983/solr/techproducts/schema/analysis/stopwords/english/foo"
This request will return a status code of 200 if the child resource (foo) exists or 404 if it does not exist the
managed list.
To delete a stop word, you would do:
curl -X DELETE "http://localhost:8983/solr/techproducts/schema/analysis/stopwords/english/foo"
?
PUT/POST is used to add terms to an existing list instead of replacing the list entirely. This
is because it is more common to add a term to an existing list than it is to replace a list
altogether, so the API favors the more common approach of incrementally adding terms
especially since deleting individual terms is also supported.
Synonyms
For the most part, the API for managing synonyms behaves similar to the API for stop words, except instead
of working with a list of words, it uses a map, where the value for each entry in the map is a set of synonyms
for a term. As with stop words, the sample_techproducts_configs configset includes a pre-built set of
synonym mappings suitable for the sample data that is activated by the following field type definition in
schema.xml:
<fieldType name="managed_en" positionIncrementGap="100">
<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.ManagedStopFilterFactory"
managed="english" />
<filter class="solr.ManagedSynonymFilterFactory"
managed="english" />
</analyzer>
</fieldType>
To get the map of managed synonyms, send a GET request to:
curl "http://localhost:8983/solr/techproducts/schema/analysis/synonyms/english"
This request will return a response that looks like:
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{
"responseHeader":{
"status":0,
"QTime":3},
"synonymMappings":{
"initArgs":{
"ignoreCase":true,
"format":"solr"},
"initializedOn":"2014-12-16T22:44:05.33Z",
"managedMap":{
"GB":
["GiB",
"Gigabyte"],
"TV":
["Television"],
"happy":
["glad",
"joyful"]}}}
Managed synonyms are returned under the managedMap property which contains a JSON Map where the
value of each entry is a set of synonyms for a term, such as "happy" has synonyms "glad" and "joyful" in the
example above.
To add a new synonym mapping, you can PUT/POST a single mapping such as:
curl -X PUT -H ‘Content-type:application/json‘ --data-binary ‘{"mad":["angry","upset"]}‘
"http://localhost:8983/solr/techproducts/schema/analysis/synonyms/english"
The API will return status code 200 if the PUT request was successful. To determine the synonyms for a
specific term, you send a GET request for the child resource, such as
/schema/analysis/synonyms/english/mad would return ["angry","upset"] .
You can also PUT a list of symmetric synonyms, which will be expanded into a mapping for each term in the
list. For example, you could PUT the following list of symmetric synonyms using the JSON list syntax instead
of a map:
curl -X PUT -H ‘Content-type:application/json‘ --data-binary ‘["funny", "entertaining",
"whimiscal", "jocular"]‘
"http://localhost:8983/solr/techproducts/schema/analysis/synonyms/english"
Note that the expansion is performed when processing the PUT request so the underlying persistent state is
still a managed map. Consequently, if after sending the previous PUT request, you did a GET for
/schema/analysis/synonyms/english/jocular , then you would receive a list containing ["funny",
"entertaining", "whimiscal"] . Once you’ve created synonym mappings using a list, each term must be
managed separately.
Lastly, you can delete a mapping by sending a DELETE request to the managed endpoint.
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Applying Changes
Changes made to managed resources via this REST API are not applied to the active Solr components until
the Solr collection (or Solr core in single server mode) is reloaded.
For example: after adding or deleting a stop word, you must reload the core/collection before changes
become active; related APIs: CoreAdmin API and Collections API.
This approach is required when running in distributed mode so that we are assured changes are applied to
all cores in a collection at the same time so that behavior is consistent and predictable. It goes without
saying that you don’t want one of your replicas working with a different set of stop words or synonyms than
the others.
One subtle outcome of this apply-changes-at-reload approach is that the once you make changes with the
API, there is no way to read the active data. In other words, the API returns the most up-to-date data from
an API perspective, which could be different than what is currently being used by Solr components.
However, the intent of this API implementation is that changes will be applied using a reload within a short
time frame after making them so the time in which the data returned by the API differs from what is active
in the server is intended to be negligible.
?
Changing things like stop words and synonym mappings typically require re-indexing
existing documents if being used by index-time analyzers. The RestManager framework
does not guard you from this, it simply makes it possible to programmatically build up a set
of stop words, synonyms etc.
RestManager Endpoint
Metadata about registered ManagedResources is available using the /schema/managed endpoint for each
collection.
Assuming you have the managed_en field type shown above defined in your schema.xml, sending a GET
request to the following resource will return metadata about which schema-related resources are being
managed by the RestManager:
curl "http://localhost:8983/solr/techproducts/schema/managed"
The response body is a JSON document containing metadata about managed resources under the /schema
root:
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{
"responseHeader":{
"status":0,
"QTime":3
},
"managedResources":[
{
"resourceId":"/schema/analysis/stopwords/english",
"class":"org.apache.solr.rest.schema.analysis.ManagedWordSetResource",
"numObservers":"1"
},
{
"resourceId":"/schema/analysis/synonyms/english",
"class":"org.apache.solr.rest.schema.analysis.ManagedSynonymFilterFactory$SynonymManager",
"numObservers":"1"
}
]
}
You can also create new managed resource using PUT/POST to the appropriate URL – before ever
configuring anything that uses these resources.
For example, imagine we want to build up a set of German stop words. Before we can start adding stop
words, we need to create the endpoint:
/solr/techproducts/schema/analysis/stopwords/german
To create this endpoint, send the following PUT/POST request to the endpoint we wish to create:
curl -X PUT -H ‘Content-type:application/json‘ --data-binary \
‘{"class":"org.apache.solr.rest.schema.analysis.ManagedWordSetResource"}‘ \
"http://localhost:8983/solr/techproducts/schema/analysis/stopwords/german"
Solr will respond with status code 200 if the request is successful. Effectively, this action registers a new
endpoint for a managed resource in the RestManager. From here you can start adding German stop words
as we saw above:
curl -X PUT -H ‘Content-type:application/json‘ --data-binary ‘["die"]‘ \
"http://localhost:8983/solr/techproducts/schema/analysis/stopwords/german"
For most users, creating resources in this way should never be necessary, since managed resources are
created automatically when configured.
However, You may want to explicitly delete managed resources if they are no longer being used by a Solr
component.
For instance, the managed resource for German that we created above can be deleted because there are no
Solr components that are using it, whereas the managed resource for English stop words cannot be deleted
because there is a token filter declared in schema.xml that is using it.
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curl -X DELETE "http://localhost:8983/solr/techproducts/schema/analysis/stopwords/german"
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Implicit RequestHandlers
Solr ships with many out-of-the-box RequestHandlers, which are called implicit because they are not
configured in solrconfig.xml .
List of Implicitly Available Endpoints
Endpoint Request Handler
class
Paramset Description
/admin/file
ShowFileRequestHa
ndler
_ADMIN_FILE
Returns content of files in ${solr.home} /conf/ .
/admin/loggi
ng
LoggingHandler
_ADMIN_LOGGI
NG
Retrieve/modify registered loggers.
/admin/luke
LukeRequestHandle
r
_ADMIN_LUKE
Expose the internal lucene index.
/admin/mbean
s
SolrInfoMBeanHand
ler
_ADMIN_MBEAN
S
Provide info about all registered SolrInfoMBeans.
/admin/ping
PingRequestHandler _ADMIN_PING Health check.
/admin/plugi
ns
PluginInfoHandler N/A Return info about all registered plugins.
/admin/prope
rties
PropertiesRequestH
andler
_ADMIN_PROPE
RTIES
Return JRE system properties.
/admin/segme
nts
SegmentsInfoReque
stHandler
_ADMIN_SEGME
NTS
Return info on last commit generation Lucene index
segments.
/admin/syste
m
SystemInfoHandler
_ADMIN_SYSTE
M
Return server statistics and settings
/admin/threa
ds
ThreadDumpHandle
r
_ADMIN_THREA
DS
Return info on all JVM threads.
/analysis/do
cument
DocumentAnalysisR
equestHandler
_ANALYSIS_DO
CUMENT
Return a breakdown of the analysis process of the
given document.
/analysis/fi
eld
FieldAnalysisReques
tHandler
_ANALYSIS_FI
ELD
Return index- and query-time analysis over the given
field(s)/field type(s).
/config
SolrConfigHandler
_CONFIG
Retrieve/modify Solr configuration.
/debug/dump
DumpRequestHandl
er
_DEBUG_DUMP
Echo the request contents back to the client.
/export
SearchHandler
_EXPORT
Export full sorted result sets.
/get
RealTimeGetHandle
r
_GET
Real-time get: low-latency retrieval of the latest
version of a document.
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Endpoint Request Handler
class
Paramset Description
/graph
GraphHandler
_ADMIN_GRAPH
Return GraphML formatted output from a gather
Nodes streaming expression.
/replication
ReplicationHandler
_REPLICATION
Replicate indexes for SolrCloud recovery and
Master/Slave index distribution.
/schema
SchemaHandler
_SCHEMA
Retrieve/modify Solr schema.
/sql
SQLHandler
_SQL
Front end of the Parallel SQL interface.
/stream
StreamHandler
_STREAM
Distributed stream processing.
/terms
SearchHandler
_TERMS
Return a field’s indexed terms and the number of
documents containing each term.
/update
UpdateRequestHan
dler
_UPDATE
Add, delete and update indexed documents
formatted as SolrXML, CSV, SolrJSON or javabin.
/update/csv
UpdateRequestHan
dler
_UPDATE_CSV
Add and update CSV-formatted documents.
/update/json
UpdateRequestHan
dler
_UPDATE_JSON
Add, delete and update SolrJSON-formatted
documents.
/update/json
/docs
UpdateRequestHan
dler
_UPDATE_JSON
_DOCS
Add and update custom JSON-formatted documents.
How to View the Configuration
You can see configuration for all request handlers, including the implicit request handlers, via the Config
API. E.g. for the gettingstarted collection:
curl http://localhost:8983/solr/gettingstarted/config/requestHandler
To restrict the results to the configuration for a particular request handler, use the componentName request
param. E.g. to see just the configuration for the /export request handler:
curl "http://localhost:8983/solr/gettingstarted/config/requestHandler?componentName=/export"
To include the expanded paramset in the response, as well as the effective parameters from merging the
paramset params with the built-in params, use the expandParams request param. E.g. for the /export
request handler:
curl
"http://localhost:8983/solr/gettingstarted/config/requestHandler?componentName=/export&expan
dParams=true"
How to Edit the Configuration
Because implicit request handlers are not present in solrconfig.xml , configuration of their associated
default , invariant and appends parameters may be edited via Request Parameters API using the paramset
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listed in the above table. However, other parameters, including SearchHandler components, may not be
modified. The invariants and appends specified in the implicit configuration cannot be overridden.
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Solr Plugins
Solr allows you to load custom code to perform a variety of tasks within Solr, from custom Request Handlers
to process your searches, to custom Analyzers and Token Filters for your text field. You can even load
custom Field Types. These pieces of custom code are called plugins.
Not everyone will need to create plugins for their Solr instances - what’s provided is usually enough for most
applications. However, if there’s something that you need, you may want to review the Solr Wiki
documentation on plugins at SolrPlugins.
If you have a plugin you would like to use, and you are running in SolrCloud mode, you can use the Blob
Store API and the Config API to load the jars to Solr. The commands to use are described in the section
Adding Custom Plugins in SolrCloud Mode.
Adding Custom Plugins in SolrCloud Mode
In SolrCloud mode, custom plugins need to be shared across all nodes of the cluster.
When running Solr in SolrCloud mode and you want to use custom code (such as custom analyzers,
tokenizers, query parsers, and other plugins), it can be cumbersome to add jars to the classpath on all nodes
in your cluster. Using the Blob Store API and special commands with the Config API, you can upload jars to a
special system-level collection and dynamically load plugins from them at runtime with out needing to
restart any nodes.
?
This Feature is Disabled By Default
In addition to requiring that Solr by running in SolrCloud mode, this feature is also disabled
by default unless all Solr nodes are run with the -Denable.runtime.lib=true option on
startup.
Before enabling this feature, users should carefully consider the issues discussed in the
Securing Runtime Libraries section below.
Uploading Jar Files
The first step is to use the Blob Store API to upload your jar files. This will to put your jars in the .system
collection and distribute them across your SolrCloud nodes. These jars are added to a separate classloader
and only accessible to components that are configured with the property runtimeLib=true . These
components are loaded lazily because the .system collection may not be loaded when a particular core is
loaded.
Config API Commands to use Jars as Runtime Libraries
The runtime library feature uses a special set of commands for the Config API to add, update, or remove jar
files currently available in the blob store to the list of runtime libraries.
The following commands are used to manage runtime libs:
? add-runtimelib
? update-runtimelib
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? delete-runtimelib
curl http://localhost:8983/solr/techproducts/config -H ‘Content-type:application/json‘ -d ‘{
"add-runtimelib": { "name":"jarblobname", "version":2 },
"update-runtimelib": { "name":"jarblobname", "version":3 },
"delete-runtimelib": "jarblobname"
}‘
The name to use is the name of the blob that you specified when you uploaded your jar to the blob store.
You should also include the version of the jar found in the blob store that you want to use. These details are
added to configoverlay.json .
The default SolrResourceLoader does not have visibility to the jars that have been defined as runtime
libraries. There is a classloader that can access these jars which is made available only to those components
which are specially annotated.
Every pluggable component can have an optional extra attribute called runtimeLib=true , which means that
the components are not loaded at core load time. Instead, they will be loaded on demand. If all the
dependent jars are not available when the component is loaded, an error is thrown.
This example shows creating a ValueSourceParser using a jar that has been loaded to the Blob store.
curl http://localhost:8983/solr/techproducts/config -H ‘Content-type:application/json‘ -d ‘{
"create-valuesourceparser": {
"name": "nvl",
"runtimeLib": true,
"class": "solr.org.apache.solr.search.function.NvlValueSourceParser,
"nvlFloatValue": 0.0 }
}‘
Securing Runtime Libraries
A drawback of this feature is that it could be used to load malicious executable code into the system.
However, it is possible to restrict the system to load only trusted jars using PKI to verify that the executables
loaded into the system are trustworthy.
The following steps will allow you enable security for this feature. The instructions assume you have started
all your Solr nodes with the -Denable.runtime.lib=true .
Step 1: Generate an RSA Private Key
The first step is to generate an RSA private key. The example below uses a 512-bit key, but you should use
the strength appropriate to your needs.
$ openssl genrsa -out priv_key.pem 512
Step 2: Output the Public Key
The public portion of the key should be output in DER format so Java can read it.
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$ openssl rsa -in priv_key.pem -pubout -outform DER -out pub_key.der
Step 3: Load the Key to ZooKeeper
The .der files that are output from Step 2 should then be loaded to ZooKeeper under a node /keys/exe so
they are available throughout every node. You can load any number of public keys to that node and all are
valid. If a key is removed from the directory, the signatures of that key will cease to be valid. So, before
removing the a key, make sure to update your runtime library configurations with valid signatures with the
update-runtimelib command.
At the current time, you can only use the ZooKeeper zkCli.sh (or zkCli.cmd on Windows) script to issue
these commands (the Solr version has the same name, but is not the same). If you have your own
ZooKeeper ensemble running already, you can find the script in $ZK_INSTALL/bin/zkCli.sh (or zkCli.cmd if
you are using Windows).
?
If you are running the embedded ZooKeeper that is included with Solr, you do not have
this script already; in order to use it, you will need to download a copy of ZooKeeper v3.4.10
from http://zookeeper.apache.org/. Don’t worry about configuring the download, you’re
just trying to get the command line utility script. When you start the script, you will connect
to the embedded ZooKeeper.
To load the keys, you will need to connect to ZooKeeper with zkCli.sh , create the directories, and then
create the key file, as in the following example.
# Connect to ZooKeeper
# Replace the server location below with the correct ZooKeeper connect string for your
installation.
$ .bin/zkCli.sh -server localhost:9983
# After connection, you will interact with the ZK prompt.
# Create the directories
[zk: localhost:9983(CONNECTED) 5] create /keys
[zk: localhost:9983(CONNECTED) 5] create /keys/exe
# Now create the public key file in ZooKeeper
# The second path is the path to the .der file on your local machine
[zk: localhost:9983(CONNECTED) 5] create /keys/exe/pub_key.der /myLocal/pathTo/pub_key.der
After this, any attempt to load a jar will fail. All your jars must be signed with one of your private keys for Solr
to trust it. The process to sign your jars and use the signature is outlined in Steps 4-6.
Step 4: Sign the jar File
Next you need to sign the sha1 digest of your jar file and get the base64 string.
$ openssl dgst -sha1 -sign priv_key.pem myjar.jar | openssl enc -base64
The output of this step will be a string that you will need to add the jar to your classpath in Step 6 below.
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Step 5: Load the jar to the Blob Store
Load your jar to the Blob store, using the Blob Store API. This step does not require a signature; you will
need the signature in Step 6 to add it to your classpath.
curl -X POST -H ‘Content-Type: application/octet-stream‘ --data-binary @{filename}
http://localhost:8983/solr/.system/blob/{blobname}
The blob name that you give the jar file in this step will be used as the name in the next step.
Step 6: Add the jar to the Classpath
Finally, add the jar to the classpath using the Config API as detailed above. In this step, you will need to
provide the signature of the jar that you got in Step 4.
curl http://localhost:8983/solr/techproducts/config -H ‘Content-type:application/json‘ -d ‘{
"add-runtimelib": {
"name":"blobname",
"version":2,
"sig":"mW1Gwtz2QazjfVdrLFHfbGwcr8xzFYgUOLu68LHqWRDvLG0uLcy1McQ+AzVmeZFBf1yLPDEHBWJb5KXr8bdbHN/
PYgUB1nsr9pk4EFyD9KfJ8TqeH/ijQ9waa/vjqyiKEI9U550EtSzruLVZ32wJ7smvV0fj2YYhrUaaPzOn9g0="
}
}‘
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JVM Settings
Optimizing the JVM can be a key factor in getting the most from your Solr installation.
Configuring your JVM can be a complex topic and a full discussion is beyond the scope of this document.
Luckily, most modern JVMs are quite good at making the best use of available resources with default
settings. The following sections contain a few tips that may be helpful when the defaults are not optimal for
your situation.
For more general information about improving Solr performance, see https://wiki.apache.org/solr/
SolrPerformanceFactors.
Choosing Memory Heap Settings
The most important JVM configuration settings are those that determine the amount of memory it is allowed
to allocate. There are two primary command-line options that set memory limits for the JVM. These are -Xms ,
which sets the initial size of the JVM’s memory heap, and -Xmx , which sets the maximum size to which the
heap is allowed to grow.
If your Solr application requires more heap space than you specify with the -Xms option, the heap will grow
automatically. It’s quite reasonable to not specify an initial size and let the heap grow as needed. The only
downside is a somewhat slower startup time since the application will take longer to initialize. Setting the
initial heap size higher than the default may avoid a series of heap expansions, which often results in objects
being shuffled around within the heap, as the application spins up.
The maximum heap size, set with -Xmx , is more critical. If the memory heap grows to this size, object
creation may begin to fail and throw OutOfMemoryException . Setting this limit too low can cause spurious
errors in your application, but setting it too high can be detrimental as well.
It doesn’t always cause an error when the heap reaches the maximum size. Before an error is raised, the
JVM will first try to reclaim any available space that already exists in the heap. Only if all garbage collection
attempts fail will your application see an exception. As long as the maximum is big enough, your app will run
without error, but it may run more slowly if forced garbage collection kicks in frequently.
The larger the heap the longer it takes to do garbage collection. This can mean minor, random pauses or, in
extreme cases, "freeze the world" pauses of a minute or more. As a practical matter, this can become a
serious problem for heap sizes that exceed about two gigabytes, even if far more physical memory is
available. On robust hardware, you may get better results running multiple JVMs, rather than just one with a
large memory heap. Some specialized JVM implementations may have customized garbage collection
algorithms that do better with large heaps. Consult your JVM vendor’s documentation.
When setting the maximum heap size, be careful not to let the JVM consume all available physical memory.
If the JVM process space grows too large, the operating system will start swapping it, which will severely
impact performance. In addition, the operating system uses memory space not allocated to processes for
file system cache and other purposes. This is especially important for I/O-intensive applications, like
Lucene/Solr. The larger your indexes, the more you will benefit from filesystem caching by the OS. It may
require some experimentation to determine the optimal tradeoff between heap space for the JVM and
memory space for the OS to use.
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On systems with many CPUs/cores, it can also be beneficial to tune the layout of the heap and/or the
behavior of the garbage collector. Adjusting the relative sizes of the generational pools in the heap can
affect how often GC sweeps occur and whether they run concurrently. Configuring the various settings of
how the garbage collector should behave can greatly reduce the overall performance impact when it does
run. There is a lot of good information on this topic available on Sun’s website. A good place to start is here:
Oracle’s Java HotSpot Garbage Collection.
Use the Server HotSpot VM
If you are using Sun’s JVM, add the -server command-line option when you start Solr. This tells the JVM that
it should optimize for a long running, server process. If the Java runtime on your system is a JRE, rather than
a full JDK distribution (including javac and other development tools), then it is possible that it may not
support the -server JVM option. Test this by running java -help and look for -server as an available
option in the displayed usage message.
Checking JVM Settings
A great way to see what JVM settings your server is using, along with other useful information, is to use the
admin RequestHandler, solr/admin/system . This request handler will display a wealth of server statistics
and settings.
You can also use any of the tools that are compatible with the Java Management Extensions (JMX). See the
section Using JMX with Solr for more information.
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Managing Solr
This section describes how to run Solr and how to look at Solr when it is running. It contains the following
sections:
Taking Solr to Production: Describes how to install Solr as a service on Linux for production environments.
Securing Solr: How to use the Basic and Kerberos authentication and rule-based authorization plugins for
Solr, and how to enable SSL.
Running Solr on HDFS: How to use HDFS to store your Solr indexes and transaction logs.
Making and Restoring Backups: Describes backup strategies for your Solr indexes.
Configuring Logging: Describes how to configure logging for Solr.
Using JMX with Solr: Describes how to use Java Management Extensions with Solr.
MBean Request Handler: How to use Solr’s MBeans for programmatic access to the system plugins and
stats.
Performance Statistics Reference: Additional information on statistics returned from JMX.
Metrics Reporting: Details of Solr’s metrics registries and Metrics API.
v2 API: Describes a redesigned API framework covering most existing Solr APIs.
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Taking Solr to Production
This section provides guidance on how to setup Solr to run in production on *nix platforms, such as Ubuntu.
Specifically, we’ll walk through the process of setting up to run a single Solr instance on a Linux host and
then provide tips on how to support multiple Solr nodes running on the same host.
Service Installation Script
Solr includes a service installation script ( bin/install_solr_service.sh ) to help you install Solr as a service
on Linux. Currently, the script only supports CentOS, Debian, Red Hat, SUSE and Ubuntu Linux distributions.
Before running the script, you need to determine a few parameters about your setup. Specifically, you need
to decide where to install Solr and which system user should be the owner of the Solr files and process.
Planning Your Directory Structure
We recommend separating your live Solr files, such as logs and index files, from the files included in the Solr
distribution bundle, as that makes it easier to upgrade Solr and is considered a good practice to follow as a
system administrator.
Solr Installation Directory
By default, the service installation script will extract the distribution archive into /opt . You can change this
location using the -i option when running the installation script. The script will also create a symbolic link to
the versioned directory of Solr. For instance, if you run the installation script for Solr 6.6.0, then the following
directory structure will be used:
/opt/solr-6.6.0
/opt/solr -> /opt/solr-6.6.0
Using a symbolic link insulates any scripts from being dependent on the specific Solr version. If, down the
road, you need to upgrade to a later version of Solr, you can just update the symbolic link to point to the
upgraded version of Solr. We’ll use /opt/solr to refer to the Solr installation directory in the remaining
sections of this page.
Separate Directory for Writable Files
You should also separate writable Solr files into a different directory; by default, the installation script uses
/var/solr , but you can override this location using the -d option. With this approach, the files in /opt/solr
will remain untouched and all files that change while Solr is running will live under /var/solr .
Create the Solr User
Running Solr as root is not recommended for security reasons, and the control script start command will
refuse to do so. Consequently, you should determine the username of a system user that will own all of the
Solr files and the running Solr process. By default, the installation script will create the solr user, but you can
override this setting using the -u option. If your organization has specific requirements for creating new
user accounts, then you should create the user before running the script. The installation script will make
the Solr user the owner of the /opt/solr and /var/solr directories.
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You are now ready to run the installation script.
Run the Solr Installation Script
To run the script, you’ll need to download the latest Solr distribution archive and then do the following:
tar xzf solr-6.6.0.tgz solr-6.6.0/bin/install_solr_service.sh --strip-components=2
The previous command extracts the install_solr_service.sh script from the archive into the current
directory. If installing on Red Hat, please make sure lsof is installed before running the Solr installation
script ( sudo yum install lsof ). The installation script must be run as root:
sudo bash ./install_solr_service.sh solr-6.6.0.tgz
By default, the script extracts the distribution archive into /opt , configures Solr to write files into /var/solr ,
and runs Solr as the solr user. Consequently, the following command produces the same result as the
previous command:
sudo bash ./install_solr_service.sh solr-6.6.0.tgz -i /opt -d /var/solr -u solr -s solr -p 8983
You can customize the service name, installation directories, port, and owner using options passed to the
installation script. To see available options, simply do:
sudo bash ./install_solr_service.sh -help
Once the script completes, Solr will be installed as a service and running in the background on your server
(on port 8983). To verify, you can do:
sudo service solr status
If you do not want to start the service immediately, pass the -n option. You can then start the service
manually later, e.g., after completing the configuration setup.
We’ll cover some additional configuration settings you can make to fine-tune your Solr setup in a moment.
Before moving on, let’s take a closer look at the steps performed by the installation script. This gives you a
better overview and will help you understand important details about your Solr installation when reading
other pages in this guide; such as when a page refers to Solr home, you’ll know exactly where that is on your
system.
Solr Home Directory
The Solr home directory (not to be confused with the Solr installation directory) is where Solr manages core
directories with index files. By default, the installation script uses /var/solr/data . If the -d option is used on
the install script, then this will change to the data subdirectory in the location given to the -d option. Take a
moment to inspect the contents of the Solr home directory on your system. If you do not store solr.xml in
ZooKeeper, the home directory must contain a solr.xml file. When Solr starts up, the Solr Control Script
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passes the location of the home directory using the -Dsolr.solr.home=… system property.
Environment Overrides Include File
The service installation script creates an environment specific include file that overrides defaults used by the
bin/solr script. The main advantage of using an include file is that it provides a single location where all of
your environment-specific overrides are defined. Take a moment to inspect the contents of the
/etc/default/solr.in.sh file, which is the default path setup by the installation script. If you used the -s
option on the install script to change the name of the service, then the first part of the filename will be
different. For a service named solr-demo , the file will be named /etc/default/solr-demo.in.sh . There are
many settings that you can override using this file. However, at a minimum, this script needs to define the
SOLR_PID_DIR and SOLR_HOME variables, such as:
SOLR_PID_DIR=/var/solr
SOLR_HOME=/var/solr/data
The SOLR_PID_DIR variable sets the directory where the control script will write out a file containing the Solr
server’s process ID.
Log Settings
Solr uses Apache Log4J for logging. The installation script copies
/opt/solr/server/resources/log4j.properties to /var/solr/log4j.properties . Take a moment to
verify that the Solr include file is configured to send logs to the correct location by checking the following
settings in /etc/default/solr.in.sh :
LOG4J_PROPS=/var/solr/log4j.properties
SOLR_LOGS_DIR=/var/solr/logs
For more information about Log4J configuration, please see: Configuring Logging
init.d Script
When running a service like Solr on Linux, it’s common to setup an init.d script so that system administrators
can control Solr using the service tool, such as: service solr start . The installation script creates a very
basic init.d script to help you get started. Take a moment to inspect the /etc/init.d/solr file, which is the
default script name setup by the installation script. If you used the -s option on the install script to change
the name of the service, then the filename will be different. Notice that the following variables are setup for
your environment based on the parameters passed to the installation script:
SOLR_INSTALL_DIR=/opt/solr
SOLR_ENV=/etc/default/solr.in.sh
RUNAS=solr
The SOLR_INSTALL_DIR and SOLR_ENV variables should be self-explanatory. The RUNAS variable sets the
owner of the Solr process, such as solr ; if you don’t set this value, the script will run Solr as root, which is
not recommended for production. You can use the /etc/init.d/solr script to start Solr by doing the
following as root:
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service solr start
The /etc/init.d/solr script also supports the stop, restart, and status commands. Please keep in mind
that the init script that ships with Solr is very basic and is intended to show you how to setup Solr as a
service. However, it’s also common to use more advanced tools like supervisord or upstart to control Solr
as a service on Linux. While showing how to integrate Solr with tools like supervisord is beyond the scope of
this guide, the init.d/solr script should provide enough guidance to help you get started. Also, the
installation script sets the Solr service to start automatically when the host machine initializes.
Progress Check
In the next section, we cover some additional environment settings to help you fine-tune your production
setup. However, before we move on, let’s review what we’ve achieved thus far. Specifically, you should be
able to control Solr using /etc/init.d/solr . Please verify the following commands work with your setup:
sudo service solr restart
sudo service solr status
The status command should give some basic information about the running Solr node that looks similar to:
Solr process PID running on port 8983
{
"version":"5.0.0 - ubuntu - 2014-12-17 19:36:58",
"startTime":"2014-12-19T19:25:46.853Z",
"uptime":"0 days, 0 hours, 0 minutes, 8 seconds",
"memory":"85.4 MB (%17.4) of 490.7 MB"}
If the status command is not successful, look for error messages in /var/solr/logs/solr.log .
Fine-Tune Your Production Setup
Memory and GC Settings
By default, the bin/solr script sets the maximum Java heap size to 512M (-Xmx512m), which is fine for
getting started with Solr. For production, you’ll want to increase the maximum heap size based on the
memory requirements of your search application; values between 10 and 20 gigabytes are not uncommon
for production servers. When you need to change the memory settings for your Solr server, use the
SOLR_JAVA_MEM variable in the include file, such as:
SOLR_JAVA_MEM="-Xms10g -Xmx10g"
Also, the Solr Control Script comes with a set of pre-configured Java Garbage Collection settings that have
shown to work well with Solr for a number of different workloads. However, these settings may not work
well for your specific use of Solr. Consequently, you may need to change the GC settings, which should also
be done with the GC_TUNE variable in the /etc/default/solr.in.sh include file. For more information about
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tuning your memory and garbage collection settings, see: JVM Settings.
Out-of-Memory Shutdown Hook
The bin/solr script registers the bin/oom_solr.sh script to be called by the JVM if an OutOfMemoryError
occurs. The oom_solr.sh script will issue a kill -9 to the Solr process that experiences the
OutOfMemoryError . This behavior is recommended when running in SolrCloud mode so that ZooKeeper is
immediately notified that a node has experienced a non-recoverable error. Take a moment to inspect the
contents of the /opt/solr/bin/oom_solr.sh script so that you are familiar with the actions the script will
perform if it is invoked by the JVM.
SolrCloud
To run Solr in SolrCloud mode, you need to set the ZK_HOST variable in the include file to point to your
ZooKeeper ensemble. Running the embedded ZooKeeper is not supported in production environments. For
instance, if you have a ZooKeeper ensemble hosted on the following three hosts on the default client port
2181 (zk1, zk2, and zk3), then you would set:
ZK_HOST=zk1,zk2,zk3
When the ZK_HOST variable is set, Solr will launch in "cloud" mode.
ZooKeeper chroot
If you’re using a ZooKeeper instance that is shared by other systems, it’s recommended to isolate the
SolrCloud znode tree using ZooKeeper’s chroot support. For instance, to ensure all znodes created by
SolrCloud are stored under /solr , you can put /solr on the end of your ZK_HOST connection string, such as:
ZK_HOST=zk1,zk2,zk3/solr
Before using a chroot for the first time, you need to create the root path (znode) in ZooKeeper by using the
Solr Control Script. We can use the mkroot command for that:
bin/solr zk mkroot /solr -z <ZK_node>:<ZK_PORT>
?
If you also want to bootstrap ZooKeeper with existing solr_home , you can instead use use
zkcli.sh / zkcli.bat’s `bootstrap command, which will also create the chroot path if it
does not exist. See Command Line Utilities for more info.
Solr Hostname
Use the SOLR_HOST variable in the include file to set the hostname of the Solr server.
SOLR_HOST=solr1.example.com
Setting the hostname of the Solr server is recommended, especially when running in SolrCloud mode, as this
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determines the address of the node when it registers with ZooKeeper.
Override Settings in solrconfig.xml
Solr allows configuration properties to be overridden using Java system properties passed at startup using
the -Dproperty=value syntax. For instance, in solrconfig.xml , the default auto soft commit settings are set
to:
<autoSoftCommit>
<maxTime>${solr.autoSoftCommit.maxTime:-1}</maxTime>
</autoSoftCommit>
In general, whenever you see a property in a Solr configuration file that uses the
${solr.PROPERTY:DEFAULT_VALUE} syntax, then you know it can be overridden using a Java system property.
For instance, to set the maxTime for soft-commits to be 10 seconds, then you can start Solr with
-Dsolr.autoSoftCommit.maxTime=10000 , such as:
bin/solr start -Dsolr.autoSoftCommit.maxTime=10000
The bin/solr script simply passes options starting with -D on to the JVM during startup. For running in
production, we recommend setting these properties in the SOLR_OPTS variable defined in the include file.
Keeping with our soft-commit example, in /etc/default/solr.in.sh , you would do:
SOLR_OPTS="$SOLR_OPTS -Dsolr.autoSoftCommit.maxTime=10000"
Running Multiple Solr Nodes Per Host
The bin/solr script is capable of running multiple instances on one machine, but for a typical installation,
this is not a recommended setup. Extra CPU and memory resources are required for each additional
instance. A single instance is easily capable of handling multiple indexes.
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?
When to ignore the recommendation
For every recommendation, there are exceptions. For the recommendation above, that
exception is mostly applicable when discussing extreme scalability. The best reason for
running multiple Solr nodes on one host is decreasing the need for extremely large heaps.
When the Java heap gets very large, it can result in extremely long garbage collection
pauses, even with the GC tuning that the startup script provides by default. The exact point
at which the heap is considered "very large" will vary depending on how Solr is used. This
means that there is no hard number that can be given as a threshold, but if your heap is
reaching the neighborhood of 16 to 32 gigabytes, it might be time to consider splitting
nodes. Ideally this would mean more machines, but budget constraints might make that
impossible.
There is another issue once the heap reaches 32GB. Below 32GB, Java is able to use
compressed pointers, but above that point, larger pointers are required, which uses more
memory and slows down the JVM.
Because of the potential garbage collection issues and the particular issues that happen at
32GB, if a single instance would require a 64GB heap, performance is likely to improve
greatly if the machine is set up with two nodes that each have a 31GB heap.
If your use case requires multiple instances, at a minimum you will need unique Solr home directories for
each node you want to run; ideally, each home should be on a different physical disk so that multiple Solr
nodes don’t have to compete with each other when accessing files on disk. Having different Solr home
directories implies that you’ll need a different include file for each node. Moreover, if using the
/etc/init.d/solr script to control Solr as a service, then you’ll need a separate script for each node. The
easiest approach is to use the service installation script to add multiple services on the same host, such as:
sudo bash ./install_solr_service.sh solr-6.6.0.tgz -s solr2 -p 8984
The command shown above will add a service named solr2 running on port 8984 using /var/solr2 for
writable (aka "live") files; the second server will still be owned and run by the solr user and will use the Solr
distribution files in /opt . After installing the solr2 service, verify it works correctly by doing:
sudo service solr2 restart
sudo service solr2 status
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Securing Solr
When planning how to secure Solr, you should consider which of the available features or approaches are
right for you.
? Authentication or authorization of users using:
? Kerberos Authentication Plugin
? Basic Authentication Plugin
? Rule-Based Authorization Plugin
? Custom authentication or authorization plugin
? Enabling SSL
? If using SolrCloud, ZooKeeper Access Control
?
No Solr API, including the Admin UI, is designed to be exposed to non-trusted parties. Tune
your firewall so that only trusted computers and people are allowed access. Because of this,
the project will not regard e.g., Admin UI XSS issues as security vulnerabilities. However, we
still ask you to report such issues in JIRA.
Authentication and Authorization Plugins
Solr has security frameworks for supporting authentication and authorization of users. This allows for
verifying a user’s identity and for restricting access to resources in a Solr cluster.
Solr includes some plugins out of the box, and additional plugins can be developed using the authentication
and authorization frameworks described below.
All authentication and authorization plugins can work with Solr whether they are running in SolrCloud mode
or standalone mode. All authentication and authorization configuration, including users and permission
rules, are stored in a file named security.json . When using Solr in standalone mode, this file must be in the
$SOLR_HOME directory (usually server/solr ). When using SolrCloud, this file must be located in ZooKeeper.
The following section describes how to enable plugins with security.json and place them in the proper
locations for your mode of operation.
Enable Plugins with security.json
All of the information required to initialize either type of security plugin is stored in a security.json file.
This file contains 2 sections, one each for authentication and authorization.
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Sample security.json
{
"authentication" : {
"class": "class.that.implements.authentication"
},
"authorization": {
"class": "class.that.implements.authorization"
}
}
The /security.json file needs to be in the proper location before a Solr instance comes up so Solr starts
with the security plugin enabled. See the section Using security.json with Solr below for information on how
to do this.
Depending on the plugin(s) in use, other information will be stored in security.json such as user
information or rules to create roles and permissions. This information is added through the APIs for each
plugin provided by Solr, or, in the case of a custom plugin, the approach designed by you.
Here is a more detailed security.json example. In this, the Basic authentication and rule-based
authorization plugins are enabled, and some data has been added:
{
"authentication":{
"class":"solr.BasicAuthPlugin",
"credentials":{"solr":"IV0EHq1OnNrj6gvRCwvFwTrZ1+z1oBbnQdiVC3otuq0=
Ndd7LKvVBAaZIF0QAVi1ekCfAJXr1GGfLtRUXhgrF8c="}
},
"authorization":{
"class":"solr.RuleBasedAuthorizationPlugin",
"permissions":[{"name":"security-edit",
"role":"admin"}],
"user-role":{"solr":"admin"}
}}
Using security.json with Solr
In SolrCloud Mode
While configuring Solr to use an authentication or authorization plugin, you will need to upload a
security.json file to ZooKeeper. The following command writes the file as it uploads it - you could also
upload a file that you have already created locally.
>server/scripts/cloud-scripts/zkcli.sh -zkhost localhost:2181 -cmd put /security.json
‘{"authentication": {"class": "org.apache.solr.security.KerberosPlugin"}}‘
Note that this example defines the KerberosPlugin for authentication. You will want to modify this section
as appropriate for the plugin you are using.
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This example also defines security.json on the command line, but you can also define a file locally and
upload it to ZooKeeper.
?
Depending on the authentication and authorization plugin that you use, you may have user
information stored in security.json . If so, we highly recommend that you implement
access control in your ZooKeeper nodes. Information about how to enable this is available
in the section ZooKeeper Access Control.
Once security.json has been uploaded to ZooKeeper, you should use the appropriate APIs for the plugins
you’re using to update it. You can edit it manually, but you must take care to remove any version data so it
will be properly updated across all ZooKeeper nodes. The version data is found at the end of the
security.json file, and will appear as the letter "v" followed by a number, such as {"v":138} .
In Standalone Mode
When running Solr in standalone mode, you need to create the security.json file and put it in the
$SOLR_HOME directory for your installation (this is the same place you have located solr.xml and is usually
server/solr ).
If you are using Legacy Scaling and Distribution, you will need to place security.json on each node of the
cluster.
You can use the authentication and authorization APIs, but if you are using the legacy scaling model, you will
need to make the same API requests on each node separately. You can also edit security.json by hand if
you prefer.
Authentication
Authentication plugins help in securing the endpoints of Solr by authenticating incoming requests. A custom
plugin can be implemented by extending the AuthenticationPlugin class.
An authentication plugin consists of two parts:
1. Server-side component, which intercepts and authenticates incoming requests to Solr using a
mechanism defined in the plugin, such as Kerberos, Basic Auth or others.
2. Client-side component, i.e., an extension of HttpClientConfigurer , which enables a SolrJ client to make
requests to a secure Solr instance using the authentication mechanism which the server understands.
Enabling a Plugin
? Specify the authentication plugin in /security.json as in this example:
{
"authentication": {
"class": "class.that.implements.authentication",
"other_data" : "..."}
}
? All of the content in the authentication block of security.json would be passed on as a map to the
plugin during initialization.
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? An authentication plugin can also be used with a standalone Solr instance by passing in
-DauthenticationPlugin=<plugin class name> during startup.
Available Authentication Plugins
Solr has the following implementations of authentication plugins:
? Kerberos Authentication Plugin
? Basic Authentication Plugin
? Hadoop Authentication Plugin
Authorization
An authorization plugin can be written for Solr by extending the AuthorizationPlugin interface.
Loading a Custom Plugin
? Make sure that the plugin implementation is in the classpath.
? The plugin can then be initialized by specifying the same in security.json in the following manner:
{
"authorization": {
"class": "org.apache.solr.security.MockAuthorizationPlugin",
"other_data" : "..."}
}
All of the content in the authorization block of security.json would be passed on as a map to the plugin
during initialization.
?
The authorization plugin is only supported in SolrCloud mode. Also, reloading the plugin
isn’t yet supported and requires a restart of the Solr installation (meaning, the JVM should
be restarted, not simply a core reload).
Available Authorization Plugins
Solr has one implementation of an authorization plugin:
? Rule-Based Authorization Plugin
Securing Inter-Node Requests
There are a lot of requests that originate from the Solr nodes itself. For example, requests from overseer to
nodes, recovery threads, etc. Each Authentication plugin declares whether it is capable of securing inter-
node requests or not. If not, Solr will fall back to using a special internode authentication mechanism where
each Solr node is a super user and is fully trusted by other Solr nodes, described below.
PKIAuthenticationPlugin
The PKIAuthenticationPlugin is used when there is any request going on between two Solr nodes, and the
configured Authentication plugin does not wish to handle inter-node security.
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For each outgoing request PKIAuthenticationPlugin adds a special header ‘SolrAuth‘ which carries the
timestamp and principal encrypted using the private key of that node. The public key is exposed through an
API so that any node can read it whenever it needs it. Any node who gets the request with that header,
would get the public key from the sender and decrypt the information. If it is able to decrypt the data, the
request trusted. It is invalid if the timestamp is more than 5 secs old. This assumes that the clocks of
different nodes in the cluster are synchronized.
The timeout is configurable through a system property called pkiauth.ttl . For example, if you wish to
bump up the time-to-live to 10 seconds (10000 milliseconds), start each node with a property ‘-
Dpkiauth.ttl=10000‘ .
Basic Authentication Plugin
Solr can support Basic authentication for users with the use of the BasicAuthPlugin.
An authorization plugin is also available to configure Solr with permissions to perform various activities in
the system. The authorization plugin is described in the section Rule-Based Authorization Plugin.
Enable Basic Authentication
To use Basic authentication, you must first create a security.json file. This file and where to put it is
described in detail in the section Enable Plugins with security.json.
For Basic authentication, the security.json file must have an authentication part which defines the class
being used for authentication. Usernames and passwords (as a sha256(password+salt) hash) could be
added when the file is created, or can be added later with the Basic authentication API, described below.
The authorization part is not related to Basic authentication, but is a separate authorization plugin
designed to support fine-grained user access control. For more information, see the section Rule-Based
Authorization Plugin.
An example security.json showing both sections is shown below to show how these plugins can work
together:
{
"authentication":{
"blockUnknown": true,
"class":"solr.BasicAuthPlugin",
"credentials":{"solr":"IV0EHq1OnNrj6gvRCwvFwTrZ1+z1oBbnQdiVC3otuq0=
Ndd7LKvVBAaZIF0QAVi1ekCfAJXr1GGfLtRUXhgrF8c="}
},
"authorization":{
"class":"solr.RuleBasedAuthorizationPlugin",
"permissions":[{"name":"security-edit",
"role":"admin"}],
"user-role":{"solr":"admin"}
}}
There are several things defined in this file:
? Basic authentication and rule-based authorization plugins are enabled.
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? A user called ‘solr‘, with a password ‘SolrRocks‘ has been defined.
? The parameter "blockUnknown": true means that unauthenticated requests are not allowed to pass
through.
? The ‘admin‘ role has been defined, and it has permission to edit security settings.
? The ‘solr‘ user has been defined to the ‘admin‘ role.
Save your settings to a file called security.json locally. If you are using Solr in standalone mode, you
should put this file in $SOLR_HOME .
If blockUnknown does not appear in the security.json file, it will default to false . This has the effect of not
requiring authentication at all. In some cases, you may want this; for example, if you want to have
security.json in place but aren’t ready to enable authentication. However, you will want to ensure that this
parameter is set to true in order for authentication to be truly enabled in your system.
If you are using SolrCloud, you must upload security.json to ZooKeeper. You can use this example
command, ensuring that the ZooKeeper port is correct:
bin/solr zk cp file:path_to_local_security.json zk:/security.json -z localhost:9983
Caveats
There are a few things to keep in mind when using the Basic authentication plugin.
? Credentials are sent in plain text by default. It’s recommended to use SSL for communication when Basic
authentication is enabled, as described in the section Enabling SSL.
? A user who has access to write permissions to security.json will be able to modify all the permissions
and how users have been assigned permissions. Special care should be taken to only grant access to
editing security to appropriate users.
? Your network should, of course, be secure. Even with Basic authentication enabled, you should not
unnecessarily expose Solr to the outside world.
Editing Authentication Plugin Configuration
An Authentication API allows modifying user IDs and passwords. The API provides an endpoint with specific
commands to set user details or delete a user.
API Entry Point
admin/authentication
This endpoint is not collection-specific, so users are created for the entire Solr cluster. If users need to be
restricted to a specific collection, that can be done with the authorization rules.
Add a User or Edit a Password
The set-user command allows you to add users and change their passwords. For example, the following
defines two users and their passwords:
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curl --user solr:SolrRocks http://localhost:8983/solr/admin/authentication -H ‘Content-
type:application/json‘ -d ‘{
"set-user": {"tom" : "TomIsCool" ,
"harry":"HarrysSecret"}}‘
Delete a User
The delete-user command allows you to remove a user. The user password does not need to be sent to
remove a user. In the following example, we’ve asked that user IDs ‘tom‘ and ‘harry‘ be removed from the
system.
curl --user solr:SolrRocks http://localhost:8983/solr/admin/authentication -H ‘Content-
type:application/json‘ -d ‘{
"delete-user": ["tom","harry"]}‘
Set a Property
Set arbitrary properties for authentication plugin. The only supported property is ‘blockUnknown‘
curl --user solr:SolrRocks http://localhost:8983/solr/admin/authentication -H ‘Content-
type:application/json‘ -d ‘{
"set-property": {"blockUnknown":false}}‘
Using BasicAuth with SolrJ
In SolrJ, the basic authentication credentials need to be set for each request as in this example:
SolrRequest req ;//create a new request object
req.setBasicAuthCredentials(userName, password);
solrClient.request(req);
Query example:
QueryRequest req = new QueryRequest(new SolrQuery("*:*"));
req.setBasicAuthCredentials(userName, password);
QueryResponse rsp = req.process(solrClient);
Using Command Line scripts with BasicAuth
Add the following line to the solr.in.sh or solr.in.cmd file. This example tells the bin/solr command line
to to use "basic" as the type of authentication, and to pass credentials with the user-name "solr" and
password "SolrRocks":
SOLR_AUTH_TYPE="basic"
SOLR_AUTHENTICATION_OPTS="-Dbasicauth=solr:SolrRocks"
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Hadoop Authentication Plugin
The Hadoop authentication plugin enables Solr to use the Hadoop authentication library for securing Solr
nodes.
This authentication plugin is a thin wrapper that delegates all functionality to the Hadoop authentication
library. All configuration parameters for the library are passed through the plugin.
This plugin can be particularly useful in leveraging an extended set of features or newly available features in
the Hadoop authentication library.
Please note that the version of Hadoop library used by Solr is upgraded periodically. While Solr will ensure
the stability and backwards compatibility of the structure of the plugin configuration (viz., the parameter
names of this plugin), the values of these parameters may change based on the version of Hadoop library.
Please review the Hadoop documentation for the version used by your Solr installation for more details.
For some of the authentication schemes (e.g., Kerberos), Solr provides a native implementation of
authentication plugin. If you require a more stable setup, in terms of configuration, ability to perform rolling
upgrades, backward compatibility, etc., you should consider using such plugin. Please review the section
Authentication and Authorization Plugins for an overview of authentication plugin options in Solr.
There are two plugin classes:
? HadoopAuthPlugin : This can be used with standalone Solr as well as Solrcloud with PKI authentication for
internode communication.
? ConfigurableInternodeAuthHadoopPlugin : This is an extension of HadoopAuthPlugin that allows you to
configure the authentication scheme for internode communication.
?
For most SolrCloud or standalone Solr setups, the HadoopAuthPlugin should suffice.
Plugin Configuration
Parameter Name Required Description
class Yes Should be either solr.HadoopAuthPlugin or
solr.ConfigurableInternodeAuthHadoopPlugin .
type Yes The type of authentication scheme to be configured. See
configuration options.
sysPropPrefix Yes The prefix to be used to define the Java system property for
configuring the authentication mechanism. The name of the Java
system property is defined by appending the configuration
parameter name to this prefix value. For example, if the prefix is
‘solr‘ then the Java system property ‘solr.kerberos.principal‘ defines
the value of configuration parameter ‘kerberos.principal‘.
authConfigs Yes Configuration parameters required by the authentication scheme
defined by the type property. For more details, see Hadoop
configuration options.
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Parameter Name Required Description
defaultConfigs No Default values for the configuration parameters specified by the
authConfigs property. The default values are specified as a collection
of key-value pairs (i.e., property-name:default_value ).
enableDelegationTo
ken
No Enable (or disable) the delegation tokens functionality.
initKerberosZk No For enabling initialization of kerberos before connecting to
ZooKeeper (if applicable).
proxyUserConfigs No Configures proxy users for the underlying Hadoop authentication
mechanism. This configuration is expressed as a collection of key-
value pairs (i.e., property-name:value ).
clientBuilderFactory No The HttpClientBuilderFactory implementation used for the Solr
internal communication. Only applicable for
ConfigurableInternodeAuthHadoopPlugin .
Example Configurations
Kerberos Authentication using Hadoop Authentication Plugin
This example lets you configure Solr to use Kerberos Authentication, similar to how you would use the
Kerberos Authentication Plugin.
After consulting the Hadoop authentication library’s documentation, you can supply per host configuration
parameters using the solr.* prefix. As an example, the Hadoop authentication library expects a parameter
kerberos.principal , which can be supplied as a system property named solr.kerberos.principal when
starting a Solr node. Refer to the section Kerberos Authentication Plugin for other typical configuration
parameters.
Please note that this example uses ConfigurableInternodeAuthHadoopPlugin , and hence you must provide
the clientBuilderFactory implementation. As a result, all internode communication will use the Kerberos
mechanism, instead of PKI authentication.
To setup this plugin, use the following in your security.json file.
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{
"authentication": {
"class": "solr.ConfigurableInternodeAuthHadoopPlugin",
"sysPropPrefix": "solr.",
"type": "kerberos",
"clientBuilderFactory": "org.apache.solr.client.solrj.impl.Krb5HttpClientBuilder",
"initKerberosZk": "true",
"authConfigs": [
"kerberos.principal",
"kerberos.keytab",
"kerberos.name.rules"
],
"defaultConfigs": {
}
}
}
Simple Authentication with Delegation Tokens
Similar to the previous example, this is an example of setting up a Solr cluster that uses delegation tokens.
Refer to the parameters in the Hadoop authentication library’s documentation or refer to the section
Kerberos Authentication Plugin for further details. Please note that this example does not use Kerberos and
the requests made to Solr must contain valid delegation tokens.
To setup this plugin, use the following in your security.json file.
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{
"authentication": {
"class": "solr.HadoopAuthPlugin",
"sysPropPrefix": "solr.",
"type": "simple",
"enableDelegationToken":"true",
"authConfigs": [
"delegation-token.token-kind",
"delegation-token.update-interval.sec",
"delegation-token.max-lifetime.sec",
"delegation-token.renewal-interval.sec",
"delegation-token.removal-scan-interval.sec",
"cookie.domain",
"signer.secret.provider",
"zk-dt-secret-manager.enable",
"zk-dt-secret-manager.znodeWorkingPath",
"signer.secret.provider.zookeeper.path"
],
"defaultConfigs": {
"delegation-token.token-kind": "solr-dt",
"signer.secret.provider": "zookeeper",
"zk-dt-secret-manager.enable": "true",
"token.validity": "36000",
"zk-dt-secret-manager.znodeWorkingPath": "solr/security/zkdtsm",
"signer.secret.provider.zookeeper.path": "/token",
"cookie.domain": "127.0.0.1"
}
}
}
Kerberos Authentication Plugin
If you are using Kerberos to secure your network environment, the Kerberos authentication plugin can be
used to secure a Solr cluster.
This allows Solr to use a Kerberos service principal and keytab file to authenticate with ZooKeeper and
between nodes of the Solr cluster (if applicable). Users of the Admin UI and all clients (such as SolrJ) would
also need to have a valid ticket before being able to use the UI or send requests to Solr.
Support for the Kerberos authentication plugin is available in SolrCloud mode or standalone mode.
?
If you are using Solr with a Hadoop cluster secured with Kerberos and intend to store your
Solr indexes in HDFS, also see the section Running Solr on HDFS for additional steps to
configure Solr for that purpose. The instructions on this page apply only to scenarios where
Solr will be secured with Kerberos. If you only need to store your indexes in a Kerberized
HDFS system, please see the other section referenced above.
How Solr Works With Kerberos
When setting up Solr to use Kerberos, configurations are put in place for Solr to use a service principal, or a
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Kerberos username, which is registered with the Key Distribution Center (KDC) to authenticate requests. The
configurations define the service principal name and the location of the keytab file that contains the
credentials.
security.json
The Solr authentication model uses a file called security.json . A description of this file and how it is
created and maintained is covered in the section Authentication and Authorization Plugins. If this file is
created after an initial startup of Solr, a restart of each node of the system is required.
Service Principals and Keytab Files
Each Solr node must have a service principal registered with the Key Distribution Center (KDC). The Kerberos
plugin uses SPNego to negotiate authentication.
Using HTTP/host1@YOUR-DOMAIN.ORG , as an example of a service principal:
? HTTP indicates the type of requests which this service principal will be used to authenticate. The HTTP/ in
the service principal is a must for SPNego to work with requests to Solr over HTTP.
? host1 is the host name of the machine hosting the Solr node.
? YOUR-DOMAIN.ORG is the organization wide Kerberos realm.
Multiple Solr nodes on the same host may have the same service principal, since the host name is common
to them all.
Along with the service principal, each Solr node needs a keytab file which should contain the credentials of
the service principal used. A keytab file contains encrypted credentials to support passwordless logins while
obtaining Kerberos tickets from the KDC. For each Solr node, the keytab file should be kept in a secure
location and not shared with users of the cluster.
Since a Solr cluster requires internode communication, each node must also be able to make Kerberos
enabled requests to other nodes. By default, Solr uses the same service principal and keytab as a ‘client
principal‘ for internode communication. You may configure a distinct client principal explicitly, but doing so
is not recommended and is not covered in the examples below.
Kerberized ZooKeeper
When setting up a kerberized SolrCloud cluster, it is recommended to enable Kerberos security for
ZooKeeper as well.
In such a setup, the client principal used to authenticate requests with ZooKeeper can be shared for
internode communication as well. This has the benefit of not needing to renew the ticket granting tickets
(TGTs) separately, since the Zookeeper client used by Solr takes care of this. To achieve this, a single JAAS
configuration (with the app name as Client) can be used for the Kerberos plugin as well as for the Zookeeper
client.
See the ZooKeeper Configuration section below for an example of starting ZooKeeper in Kerberos mode.
Browser Configuration
In order for your browser to access the Solr Admin UI after enabling Kerberos authentication, it must be
able to negotiate with the Kerberos authenticator service to allow you access. Each browser supports this
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differently, and some (like Chrome) do not support it at all. If you see 401 errors when trying to access the
Solr Admin UI after enabling Kerberos authentication, it’s likely your browser has not been configured
properly to know how or where to negotiate the authentication request.
Detailed information on how to set up your browser is beyond the scope of this documentation; please see
your system administrators for Kerberos for details on how to configure your browser.
Plugin Configuration
?
Consult Your Kerberos Admins!
Before attempting to configure Solr to use Kerberos authentication, please review each
step outlined below and consult with your local Kerberos administrators on each detail to
be sure you know the correct values for each parameter. Small errors can cause Solr to not
start or not function properly, and are notoriously difficult to diagnose.
Configuration of the Kerberos plugin has several parts:
? Create service principals and keytab files
? ZooKeeper configuration
? Create or update /security.json
? Define jaas-client.conf
? Solr startup parameters
We’ll walk through each of these steps below.
?
Using Hostnames
To use host names instead of IP addresses, use the SOLR_HOST configuration in
bin/solr.in.sh or pass a -Dhost=<hostname> system parameter during Solr startup. This
guide uses IP addresses. If you specify a hostname, replace all the IP addresses in the
guide with the Solr hostname as appropriate.
Get Service Principals and Keytabs
Before configuring Solr, make sure you have a Kerberos service principal for each Solr host and ZooKeeper
(if ZooKeeper has not already been configured) available in the KDC server, and generate a keytab file as
shown below.
This example assumes the hostname is 192.168.0.107 and your home directory is /home/foo/ . This
example should be modified for your own environment.
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root@kdc:/# kadmin.local
Authenticating as principal foo/admin@EXAMPLE.COM with password.
kadmin.local: addprinc HTTP/192.168.0.107
WARNING: no policy specified for HTTP/192.168.0.107@EXAMPLE.COM; defaulting to no policy
Enter password for principal "HTTP/192.168.0.107@EXAMPLE.COM":
Re-enter password for principal "HTTP/192.168.0.107@EXAMPLE.COM":
Principal "HTTP/192.168.0.107@EXAMPLE.COM" created.
kadmin.local: ktadd -k /tmp/107.keytab HTTP/192.168.0.107
Entry for principal HTTP/192.168.0.107 with kvno 2, encryption type aes256-cts-hmac-sha1-96 added
to keytab WRFILE:/tmp/107.keytab.
Entry for principal HTTP/192.168.0.107 with kvno 2, encryption type arcfour-hmac added to keytab
WRFILE:/tmp/107.keytab.
Entry for principal HTTP/192.168.0.107 with kvno 2, encryption type des3-cbc-sha1 added to keytab
WRFILE:/tmp/108.keytab.
Entry for principal HTTP/192.168.0.107 with kvno 2, encryption type des-cbc-crc added to keytab
WRFILE:/tmp/107.keytab.
kadmin.local: quit
Copy the keytab file from the KDC server’s /tmp/107.keytab location to the Solr host at
/keytabs/107.keytab . Repeat this step for each Solr node.
You might need to take similar steps to create a ZooKeeper service principal and keytab if it has not already
been set up. In that case, the example below shows a different service principal for ZooKeeper, so the above
might be repeated with zookeeper/host1 as the service principal for one of the nodes
ZooKeeper Configuration
If you are using a ZooKeeper that has already been configured to use Kerberos, you can skip the ZooKeeper-
related steps shown here.
Since ZooKeeper manages the communication between nodes in a SolrCloud cluster, it must also be able to
authenticate with each node of the cluster. Configuration requires setting up a service principal for
ZooKeeper, defining a JAAS configuration file and instructing ZooKeeper to use both of those items.
The first step is to create a file java.env in ZooKeeper’s conf directory and add the following to it, as in this
example:
export JVMFLAGS="-Djava.security.auth.login.config=/etc/zookeeper/conf/jaas-client.conf"
The JAAS configuration file should contain the following parameters. Be sure to change the principal and
keyTab path as appropriate. The file must be located in the path defined in the step above, with the filename
specified.
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Server {
com.sun.security.auth.module.Krb5LoginModule required
useKeyTab=true
keyTab="/keytabs/zkhost1.keytab"
storeKey=true
doNotPrompt=true
useTicketCache=false
debug=true
principal="zookeeper/host1@EXAMPLE.COM";
};
Finally, add the following lines to the ZooKeeper configuration file zoo.cfg :
authProvider.1=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
jaasLoginRenew=3600000
Once all of the pieces are in place, start ZooKeeper with the following parameter pointing to the JAAS
configuration file:
bin/zkServer.sh start -Djava.security.auth.login.config=/etc/zookeeper/conf/jaas-client.conf
Create security.json
Create the security.json file.
In SolrCloud mode, you can set up Solr to use the Kerberos plugin by uploading the security.json to
ZooKeeper while you create it, as follows:
server/scripts/cloud-scripts/zkcli.sh -zkhost localhost:2181 -cmd put /security.json
‘{"authentication":{"class": "org.apache.solr.security.KerberosPlugin"}}‘
If you are using Solr in standalone mode, you need to create the security.json file and put it in your
$SOLR_HOME directory.
More details on how to use a /security.json file in Solr are available in the section Authentication and
Authorization Plugins.
?
If you already have a /security.json file in ZooKeeper, download the file, add or modify
the authentication section and upload it back to ZooKeeper using the Command Line
Utilities available in Solr.
Define a JAAS Configuration File
The JAAS configuration file defines the properties to use for authentication, such as the service principal and
the location of the keytab file. Other properties can also be set to ensure ticket caching and other features.
The following example can be copied and modified slightly for your environment. The location of the file can
be anywhere on the server, but it will be referenced when starting Solr so it must be readable on the
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filesystem. The JAAS file may contain multiple sections for different users, but each section must have a
unique name so it can be uniquely referenced in each application.
In the below example, we have created a JAAS configuration file with the name and path of /home/foo/jaas-
client.conf . We will use this name and path when we define the Solr start parameters in the next section.
Note that the client principal here is the same as the service principal. This will be used to authenticate
internode requests and requests to ZooKeeper. Make sure to use the correct principal hostname and the
keyTab file path.
Client {
com.sun.security.auth.module.Krb5LoginModule required
useKeyTab=true
keyTab="/keytabs/107.keytab"
storeKey=true
useTicketCache=true
debug=true
principal="HTTP/192.168.0.107@EXAMPLE.COM";
};
The first line of this file defines the section name, which will be used with the solr.kerberos.jaas.appname
parameter, defined below.
The main properties we are concerned with are the keyTab and principal properties, but there are others
which may be required for your environment. The javadocs for the Krb5LoginModule (the class that’s being
used and is called in the second line above) provide a good outline of the available properties, but for
reference the ones in use in the above example are explained here:
? useKeyTab : this boolean property defines if we should use a keytab file (true, in this case).
? keyTab : the location and name of the keytab file for the principal this section of the JAAS configuration
file is for. The path should be enclosed in double-quotes.
? storeKey : this boolean property allows the key to be stored in the private credentials of the user.
? useTicketCache : this boolean property allows the ticket to be obtained from the ticket cache.
? debug : this boolean property will output debug messages for help in troubleshooting.
? principal : the name of the service principal to be used.
Solr Startup Parameters
While starting up Solr, the following host-specific parameters need to be passed. These parameters can be
passed at the command line with the bin/solr start command (see Solr Control Script Reference for details
on how to pass system parameters) or defined in bin/solr.in.sh or bin/solr.in.cmd as appropriate for
your operating system.
Parameter Name Required Description
solr.kerberos.name.rules
No Used to map Kerberos principals to short names. Default
value is DEFAULT . Example of a name rule:
RULE:[1:$1@$0](.*EXAMPLE.COM)s/@.*//
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Parameter Name Required Description
solr.kerberos.cookie.domain
Yes Used to issue cookies and should have the hostname of
the Solr node.
solr.kerberos.cookie.portaware
No When set to true, cookies are differentiated based on host
and port, as opposed to standard cookies which are not
port aware. This should be set if more than one Solr node
is hosted on the same host. The default is false.
solr.kerberos.principal
Yes The service principal.
solr.kerberos.keytab
Yes Keytab file path containing service principal credentials.
solr.kerberos.jaas.appname
No The app name (section name) within the JAAS
configuration file which is required for internode
communication. Default is Client , which is used for
ZooKeeper authentication as well. If different users are
used for ZooKeeper and Solr, they will need to have
separate sections in the JAAS configuration file.
java.security.auth.login.config
Yes Path to the JAAS configuration file for configuring a Solr
client for internode communication.
Here is an example that could be added to bin/solr.in.sh . Make sure to change this example to use the
right hostname and the keytab file path.
SOLR_AUTH_TYPE="kerberos"
SOLR_AUTHENTICATION_OPTS="-Djava.security.auth.login.config=/home/foo/jaas-client.conf
-Dsolr.kerberos.cookie.domain=192.168.0.107 -Dsolr.kerberos.cookie.portaware=true
-Dsolr.kerberos.principal=HTTP/192.168.0.107@EXAMPLE.COM
-Dsolr.kerberos.keytab=/keytabs/107.keytab"
?
KDC with AES-256 encryption
If your KDC uses AES-256 encryption, you need to add the Java Cryptography Extension
(JCE) Unlimited Strength Jurisdiction Policy Files to your JRE before a kerberized Solr can
interact with the KDC.
You will know this when you see an error like this in your Solr logs : "KrbException:
Encryption type AES256 CTS mode with HMAC SHA1-96 is not supported/enabled"
For Java 1.8, this is available here: http://www.oracle.com/technetwork/java/javase/
downloads/jce8-download-2133166.html.
Replace the local_policy.jar present in JAVA_HOME/jre/lib/security/ with the new
local_policy.jar from the downloaded package and restart the Solr node.
Using Delegation Tokens
The Kerberos plugin can be configured to use delegation tokens, which allow an application to reuse the
authentication of an end-user or another application.
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There are a few use cases for Solr where this might be helpful:
? Using distributed clients (such as MapReduce) where each client may not have access to the user’s
credentials.
? When load on the Kerberos server is high. Delegation tokens can reduce the load because they do not
access the server after the first request.
? If requests or permissions need to be delegated to another user.
To enable delegation tokens, several parameters must be defined. These parameters can be passed at the
command line with the bin/solr start command (see Solr Control Script Reference for details on how to
pass system parameters) or defined in bin/solr.in.sh or bin/solr.in.cmd as appropriate for your
operating system.
Parameter Name Required Description
solr.kerberos.delegation.token.enabl
ed
Yes, to
enable
tokens
False by default, set to true to enable delegation
tokens.
solr.kerberos.delegation.token.kind
No Type of delegation tokens. By default this is solr-dt .
Likely this does not need to change. No other option
is available at this time.
solr.kerberos.delegation.token.valid
ity
No Time, in seconds, for which delegation tokens are
valid. The default is 36000 seconds.
solr.kerberos.delegation.token.signe
r.secret.provider
No Where delegation token information is stored
internally. The default is zookeeper which must be
the location for delegation tokens to work across
Solr servers (when running in SolrCloud mode). No
other option is available at this time.
solr.kerberos.delegation.token.signe
r.secret.provider.zookeper.path
No The ZooKeeper path where the secret provider
information is stored. This is in the form of the path
+ /security/token. The path can include the chroot or
the chroot can be omitted if you are not using it. This
example includes the chroot:
server1:9983,server2:9983,server3:9983/solr/s
ecurity/token .
solr.kerberos.delegation.token.secre
t.manager.znode.working.path
No The ZooKeeper path where token information is
stored. This is in the form of the path +
/security/zkdtsm. The path can include the chroot or
the chroot can be omitted if you are not using it. This
example includes the chroot:
server1:9983,server2:9983,server3:9983/solr/s
ecurity/zkdtsm .
Start Solr
Once the configuration is complete, you can start Solr with the bin/solr script, as in the example below,
which is for users in SolrCloud mode only. This example assumes you modified bin/solr.in.sh or
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bin/solr.in.cmd , with the proper values, but if you did not, you would pass the system parameters along
with the start command. Note you also need to customize the -z property as appropriate for the location of
your ZooKeeper nodes.
bin/solr -c -z server1:2181,server2:2181,server3:2181/solr
Test the Configuration
1. Do a kinit with your username. For example, kinit user@EXAMPLE.COM .
2. Try to access Solr using curl . You should get a successful response.
curl --negotiate -u : "http://192.168.0.107:8983/solr/"
Using SolrJ with a Kerberized Solr
To use Kerberos authentication in a SolrJ application, you need the following two lines before you create a
SolrClient:
System.setProperty("java.security.auth.login.config", "/home/foo/jaas-client.conf");
HttpClientUtil.setConfigurer(new Krb5HttpClientConfigurer());
You need to specify a Kerberos service principal for the client and a corresponding keytab in the JAAS client
configuration file above. This principal should be different from the service principal we created for Solr.
Here’s an example:
SolrJClient {
com.sun.security.auth.module.Krb5LoginModule required
useKeyTab=true
keyTab="/keytabs/foo.keytab"
storeKey=true
useTicketCache=true
debug=true
principal="solrclient@EXAMPLE.COM";
};
Delegation Tokens with SolrJ
Delegation tokens are also supported with SolrJ, in the following ways:
? DelegationTokenRequest and DelegationTokenResponse can be used to get, cancel, and renew
delegation tokens.
? HttpSolrClient.Builder includes a withDelegationToken function for creating an HttpSolrClient that
uses a delegation token to authenticate.
Sample code to get a delegation token:
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private String getDelegationToken(final String renewer, final String user, HttpSolrClient
solrClient) throws Exception {
DelegationTokenRequest.Get get = new DelegationTokenRequest.Get(renewer) {
@Override
public SolrParams getParams() {
ModifiableSolrParams params = new ModifiableSolrParams(super.getParams());
params.set("user", user);
return params;
}
};
DelegationTokenResponse.Get getResponse = get.process(solrClient);
return getResponse.getDelegationToken();
}
To create a HttpSolrClient that uses delegation tokens:
HttpSolrClient client = new HttpSolrClient.Builder("http://localhost:8983/solr")
.withDelegationToken(token).build();
To create a CloudSolrClient that uses delegation tokens:
CloudSolrClient client = new CloudSolrClient.Builder()
.withZkHost("http://localhost:2181")
.withLBHttpSolrClientBuilder(new LBHttpSolrClient.Builder()
.withResponseParser(client.getParser())
.withHttpSolrClientBuilder(
new HttpSolrClient.Builder()
.withKerberosDelegationToken(token)
))
.build();
?
Hadoop’s delegation token responses are in JSON map format. A response parser for that
is available in DelegationTokenResponse . Other response parsers may not work well with
Hadoop responses.
Rule-Based Authorization Plugin
Solr allows configuring roles to control user access to the system.
This is accomplished through rule-based permission definitions which are assigned to users. The roles are
fully customizable, and provide the ability to limit access to specific collections, request handlers, request
parameters, and request methods.
The roles can be used with any of the authentication plugins or with a custom authentication plugin if you
have created one. You will only need to ensure that you configure the role-to-user mappings with the proper
user IDs that your authentication system provides.
Once defined through the API, roles are stored in security.json .
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Enable the Authorization Plugin
The plugin must be enabled in security.json . This file and where to put it in your system is described in
detail in the section Enable Plugins with security.json.
This file has two parts, the authentication part and the authorization part. The authentication part
stores information about the class being used for authentication.
The authorization part is not related to Basic authentication, but is a separate authorization plugin
designed to support fine-grained user access control. When creating security.json you can add the
permissions to the file, or you can use the Authorization API described below to add them as needed.
This example security.json shows how the Basic authentication plugin can work with this authorization
plugin:
{
"authentication":{
"class":"solr.BasicAuthPlugin",
"blockUnknown": true,
"credentials":{"solr":"IV0EHq1OnNrj6gvRCwvFwTrZ1+z1oBbnQdiVC3otuq0=
Ndd7LKvVBAaZIF0QAVi1ekCfAJXr1GGfLtRUXhgrF8c="}
},
"authorization":{
"class":"solr.RuleBasedAuthorizationPlugin",
"permissions":[{"name":"security-edit",
"role":"admin"}],
"user-role":{"solr":"admin"}
}}
There are several things defined in this example:
? Basic authentication and rule-based authorization plugins are enabled.
? A user called ‘solr‘, with a password has been defined.
? All requests w/o credentials will be rejected with a 401 error. Set ‘blockUnknown‘ to false (or remove it
altogether) if you wish to let unauthenticated requests to go through. However, if a particular resource is
protected by a rule, they are rejected anyway with a 401 error.
? The ‘admin‘ role has been defined, and it has permission to edit security settings.
? The ‘solr‘ user has been defined to the ‘admin‘ role.
Permission Attributes
Each role is comprised of one or more permissions which define what the user is allowed to do. Each
permission is made up of several attributes that define the allowed activity. There are some pre-defined
permissions which cannot be modified.
The permissions are consulted in order they appear in security.json . The first permission that matches is
applied for each user, so the strictest permissions should be at the top of the list. Permissions order can be
controlled with a parameter of the Authorization API, as described below.
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Predefined Permissions
There are several permissions that are pre-defined. These have fixed default values, which cannot be
modified, and new attributes cannot be added. To use these attributes, simply define a role that includes
this permission, and then assign a user to that role.
The pre-defined permissions are:
? security-edit: this permission is allowed to edit the security configuration, meaning any update action
that modifies security.json through the APIs will be allowed.
? security-read: this permission is allowed to read the security configuration, meaning any action that
reads security.json settings through the APIs will be allowed.
? schema-edit: this permission is allowed to edit a collection’s schema using the Schema API. Note that
this allows schema edit permissions for all collections. If edit permissions should only be applied to
specific collections, a custom permission would need to be created.
? schema-read: this permission is allowed to read a collection’s schema using the Schema API. Note that
this allows schema read permissions for all collections. If read permissions should only be applied to
specific collections, a custom permission would need to be created.
? config-edit: this permission is allowed to edit a collection’s configuration using the Config API, the
Request Parameters API, and other APIs which modify configoverlay.json . Note that this allows
configuration edit permissions for all collections. If edit permissions should only be applied to specific
collections, a custom permission would need to be created.
? core-admin-read : Read operations on the core admin API
? core-admin-edit: Core admin commands that can mutate the system state.
? config-read: this permission is allowed to read a collection’s configuration using the Config API, the
Request Parameters API, and other APIs which modify configoverlay.json . Note that this allows
configuration read permissions for all collections. If read permissions should only be applied to specific
collections, a custom permission would need to be created.
? collection-admin-edit: this permission is allowed to edit a collection’s configuration using the
Collections API. Note that this allows configuration edit permissions for all collections. If edit permissions
should only be applied to specific collections, a custom permission would need to be created. Specifically,
the following actions of the Collections API would be allowed:
? CREATE
? RELOAD
? SPLITSHARD
? CREATESHARD
? DELETESHARD
? CREATEALIAS
? DELETEALIAS
? DELETE
? DELETEREPLICA
? ADDREPLICA
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? CLUSTERPROP
? MIGRATE
? ADDROLE
? REMOVEROLE
? ADDREPLICAPROP
? DELETEREPLICAPROP
? BALANCESHARDUNIQUE
? REBALANCELEADERS
? collection-admin-read: this permission is allowed to read a collection’s configuration using the
Collections API. Note that this allows configuration read permissions for all collections. If read
permissions should only be applied to specific collections, a custom permission would need to be
created. Specifically, the following actions of the Collections API would be allowed:
? LIST
? OVERSEERSTATUS
? CLUSTERSTATUS
? REQUESTSTATUS
? update: this permission is allowed to perform any update action on any collection. This includes sending
documents for indexing (using an update request handler). This applies to all collections by default
( collection:"*" ).
? read: this permission is allowed to perform any read action on any collection. This includes querying
using search handlers (using request handlers) such as /select , /get , /browse , /tvrh , /terms ,
/clustering , /elevate , /export , /spell , /clustering , and /sql . This applies to all collections by default
( collection:"*" ).
? all: Any requests coming to Solr.
Authorization API
API Endpoint
/admin/authorization : takes a set of commands to create permissions, map permissions to roles, and map
roles to users.
Manage Permissions
Three commands control managing permissions:
? set-permission : create a new permission, overwrite an existing permission definition, or assign a pre-
defined permission to a role.
? update-permission : update some attributes of an existing permission definition.
? delete-permission : remove a permission definition.
Permissions need to be created if they are not on the list of pre-defined permissions above.
Several properties can be used to define your custom permission.
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Property Description
name The name of the permission. This is required only if it is a predefined
permission.
collection The collection or collections the permission will apply to.
When the path that will be allowed is collection-specific, such as when
setting permissions to allow useof the Schema API, omitting the collection
property will allow the defined path and/or method for all collections.
However, when the path is one that is non-collection-specific, such as the
Collections API, the collection value must be null . The default value is *
(all collections).
path A request handler name, such as /update or /select . A wild card is
supported, to allow for all paths as appropriate (such as, /update/* ).
method HTTP methods that are allowed for this permission. You could allow only
GET requests, or have a role that allows PUT and POST requests. The
method values that are allowed for this property are GET, POST,
PUT,DELETEand HEAD.
params The names and values of request parameters. This property can be
omitted if all request parameters are to be matched, but will restrict
access only to the values provided if defined.
For example, this property could be used to limit the actions a role is
allowed to perform with the Collections API. If the role should only be
allowed to perform the LIST or CLUSTERSTATUS requests, you would
define this as follows:
"params": {
"action": ["LIST", "CLUSTERSTATUS"]
}
The value of the parameter can be a simple string or it could be a regular
expression. Use the prefix REGEX: to use a regular expression match
instead of a string identity match
If the commands LIST and CLUSTERSTATUS are case insensitive, the above
example should be as follows
"params": {
"action": ["REGEX:(?i)LIST", "REGEX:(?i)CLUSTERSTATUS"]
}
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Property Description
before This property allows ordering of permissions. The value of this property is
the index of the permission that this new permission should be placed
before in security.json . The index is automatically assigned in the order
they are created
role The name of the role(s) to give this permission. This name will be used to
map user IDs to the role to grant these permissions. The value can be
wildcard such as ( * ), which means that any user is OK, but no user is NOT
OK.
The following creates a new permission named "collection-mgr" that is allowed to create and list collections.
The permission will be placed before the "read" permission. Note also that we have defined "collection as
null , this is because requests to the Collections API are never collection-specific.
curl --user solr:SolrRocks -H ‘Content-type:application/json‘ -d ‘{
"set-permission": {"collection": null,
"path":"/admin/collections",
"params":{"action":[LIST, CREATE]},
"before: 3,
"role": "admin"}
}‘ http://localhost:8983/solr/admin/authorization
Apply an update permission on all collections to a role called dev and read permissions to a role called
guest :
curl --user solr:SolrRocks -H ‘Content-type:application/json‘ -d ‘{
"set-permission": {"name": "update, "role":"dev"},
"set-permission": {"name": "read, "role":"guest"},
}‘ http://localhost:8983/solr/admin/authorization
Update or Delete Permissions
Permissions can be accessed using their index in the list. Use the /admin/authorization API to see the
existing permissions and their indices.
The following example updates the ‘role’ attribute of permission at index ‘3’:
curl --user solr:SolrRocks -H ‘Content-type:application/json‘ -d ‘{
"update-permission": {"index": 3,
"role": ["admin", "dev"]}
}‘ http://localhost:8983/solr/admin/authorization
The following example deletes permission at index ‘3’:
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curl --user solr:SolrRocks -H ‘Content-type:application/json‘ -d ‘{
"delete-permission": 3
}‘ http://localhost:8983/solr/admin/authorization
Map Roles to Users
A single command allows roles to be mapped to users:
? set-user-role : map a user to a permission.
To remove a user’s permission, you should set the role to null . There is no command to delete a user role.
The values supplied to the command are simply a user ID and one or more roles the user should have.
For example, the following would grant a user "solr" the "admin" and "dev" roles, and remove all roles from
the user ID "harry":
curl -u solr:SolrRocks -H ‘Content-type:application/json‘ -d ‘{
"set-user-role" : {"solr": ["admin","dev"],
"harry": null}
}‘ http://localhost:8983/solr/admin/authorization
Enabling SSL
Solr can encrypt communications to and from clients, and between nodes in SolrCloud mode, with SSL.
This section describes enabling SSL with the example Jetty server using a self-signed certificate.
For background on SSL certificates and keys, see http://www.tldp.org/HOWTO/SSL-Certificates-HOWTO/.
Basic SSL Setup
Generate a Self-Signed Certificate and a Key
To generate a self-signed certificate and a single key that will be used to authenticate both the server and
the client, we’ll use the JDK keytool command and create a separate keystore. This keystore will also be
used as a truststore below. It’s possible to use the keystore that comes with the JDK for these purposes, and
to use a separate truststore, but those options aren’t covered here.
Run the commands below in the server/etc/ directory in the binary Solr distribution. It’s assumed that you
have the JDK keytool utility on your PATH , and that openssl is also on your PATH . See
https://www.openssl.org/related/binaries.html for OpenSSL binaries for Windows and Solaris.
The -ext SAN=… keytool option allows you to specify all the DNS names and/or IP addresses that will be
allowed during hostname verification (but see below for how to skip hostname verification between Solr
nodes so that you don’t have to specify all hosts here).
In addition to localhost and 127.0.0.1 , this example includes a LAN IP address 192.168.1.3 for the
machine the Solr nodes will be running on:
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keytool -genkeypair -alias solr-ssl -keyalg RSA -keysize 2048 -keypass secret -storepass secret
-validity 9999 -keystore solr-ssl.keystore.jks -ext SAN=DNS:localhost,IP:192.168.1.3,IP:127.0.0.1
-dname "CN=localhost, OU=Organizational Unit, O=Organization, L=Location, ST=State, C=Country"
The above command will create a keystore file named solr-ssl.keystore.jks in the current directory.
Convert the Certificate and Key to PEM Format for Use with cURL
cURL isn’t capable of using JKS formatted keystores, so the JKS keystore needs to be converted to PEM
format, which cURL understands.
First convert the JKS keystore into PKCS12 format using keytool :
keytool -importkeystore -srckeystore solr-ssl.keystore.jks -destkeystore solr-ssl.keystore.p12
-srcstoretype jks -deststoretype pkcs12
The keytool application will prompt you to create a destination keystore password and for the source
keystore password, which was set when creating the keystore ("secret" in the example shown above).
Next convert the PKCS12 format keystore, including both the certificate and the key, into PEM format using
the openssl command:
openssl pkcs12 -in solr-ssl.keystore.p12 -out solr-ssl.pem
If you want to use cURL on OS X Yosemite (10.10), you’ll need to create a certificate-only version of the PEM
format, as follows:
openssl pkcs12 -nokeys -in solr-ssl.keystore.p12 -out solr-ssl.cacert.pem
Set Common SSL-Related System Properties
The Solr Control Script is already setup to pass SSL-related Java system properties to the JVM. To activate the
SSL settings, uncomment and update the set of properties beginning with SOLR_SSL_* in bin/solr.in.sh .
(or bin\solr.in.cmd on Windows).
?
If you setup Solr as a service on Linux using the steps outlined in Taking Solr to Production,
then make these changes in /var/solr/solr.in.sh instead.
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bin/solr.in.sh example SOLR_SSL_* configuration
SOLR_SSL_KEY_STORE=etc/solr-ssl.keystore.jks
SOLR_SSL_KEY_STORE_PASSWORD=secret
SOLR_SSL_TRUST_STORE=etc/solr-ssl.keystore.jks
SOLR_SSL_TRUST_STORE_PASSWORD=secret
# Require clients to authenticate
SOLR_SSL_NEED_CLIENT_AUTH=false
# Enable clients to authenticate (but not require)
SOLR_SSL_WANT_CLIENT_AUTH=false
# Define Key Store type if necessary
SOLR_SSL_KEY_STORE_TYPE=JKS
SOLR_SSL_TRUST_STORE_TYPE=JKS
When you start Solr, the bin/solr script includes the settings in bin/solr.in.sh and will pass these SSL-
related system properties to the JVM.
?
Client Authentication Settings
Enable either SOLR_SSL_NEED_CLIENT_AUTH or SOLR_SSL_WANT_CLIENT_AUTH but not
both at the same time. They are mutually exclusive and Jetty will select one of them which
may not be what you expect.
Similarly, when you start Solr on Windows, the bin\solr.cmd script includes the settings in bin\solr.in.cmd
- uncomment and update the set of properties beginning with SOLR_SSL_* to pass these SSL-related system
properties to the JVM:
bin\solr.in.cmd example SOLR_SSL_* configuration
set SOLR_SSL_KEY_STORE=etc/solr-ssl.keystore.jks
set SOLR_SSL_KEY_STORE_PASSWORD=secret
set SOLR_SSL_TRUST_STORE=etc/solr-ssl.keystore.jks
set SOLR_SSL_TRUST_STORE_PASSWORD=secret
REM Require clients to authenticate
set SOLR_SSL_NEED_CLIENT_AUTH=false
REM Enable clients to authenticate (but not require)
set SOLR_SSL_WANT_CLIENT_AUTH=false
Run Single Node Solr using SSL
Start Solr using the command shown below; by default clients will not be required to authenticate:
*nix command
bin/solr -p 8984
Windows command
bin\solr.cmd -p 8984
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SSL with SolrCloud
This section describes how to run a two-node SolrCloud cluster with no initial collections and a single-node
external ZooKeeper. The commands below assume you have already created the keystore described above.
Configure ZooKeeper
?
ZooKeeper does not support encrypted communication with clients like Solr. There are
several related JIRA tickets where SSL support is being planned/worked on: ZOOKEEPER-
235; ZOOKEEPER-236; ZOOKEEPER-1000; and ZOOKEEPER-2120.
Before you start any SolrCloud nodes, you must configure your solr cluster properties in ZooKeeper, so that
Solr nodes know to communicate via SSL.
This section assumes you have created and started a single-node external ZooKeeper on port 2181 on
localhost - see Setting Up an External ZooKeeper Ensemble.
The urlScheme cluster-wide property needs to be set to https before any Solr node starts up. The example
below uses the zkcli tool that comes with the binary Solr distribution to do this:
*nix command
server/scripts/cloud-scripts/zkcli.sh -zkhost localhost:2181 -cmd clusterprop -name urlScheme
-val https
Windows command
server\scripts\cloud-scripts\zkcli.bat -zkhost localhost:2181 -cmd clusterprop -name urlScheme
-val https
If you have set up your ZooKeeper cluster to use a chroot for Solr , make sure you use the correct zkhost
string with zkcli , e.g. -zkhost localhost:2181/solr .
Run SolrCloud with SSL
Create Solr Home Directories for Two Nodes
Create two copies of the server/solr/ directory which will serve as the Solr home directories for each of
your two SolrCloud nodes:
*nix commands
mkdir cloud
cp -r server/solr cloud/node1
cp -r server/solr cloud/node2
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Windows commands
mkdir cloud
xcopy /E server\solr cloud\node1\
xcopy /E server\solr cloud\node2\
Start the First Solr Node
Next, start the first Solr node on port 8984. Be sure to stop the standalone server first if you started it when
working through the previous section on this page.
*nix command
bin/solr -cloud -s cloud/node1 -z localhost:2181 -p 8984
Windows command
bin\solr.cmd -cloud -s cloud\node1 -z localhost:2181 -p 8984
Notice the use of the -s option to set the location of the Solr home directory for node1.
If you created your SSL key without all DNS names/IP addresses on which Solr nodes will run, you can tell
Solr to skip hostname verification for inter-Solr-node communications by setting the
solr.ssl.checkPeerName system property to false :
*nix command
bin/solr -cloud -s cloud/node1 -z localhost:2181 -p 8984 -Dsolr.ssl.checkPeerName=false
Windows command
bin\solr.cmd -cloud -s cloud\node1 -z localhost:2181 -p 8984 -Dsolr.ssl.checkPeerName=false
Start the Second Solr Node
Finally, start the second Solr node on port 7574 - again, to skip hostname verification, add
-Dsolr.ssl.checkPeerName=false ;
*nix command
bin/solr -cloud -s cloud/node2 -z localhost:2181 -p 7574
Windows command
bin\solr.cmd -cloud -s cloud\node2 -z localhost:2181 -p 7574
Example Client Actions
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?
cURL on OS X Mavericks (10.9) has degraded SSL support. For more information and
workarounds to allow one-way SSL, see http://curl.haxx.se/mail/archive-2013-10/0036.html.
cURL on OS X Yosemite (10.10) is improved - 2-way SSL is possible - see http://curl.haxx.se/
mail/archive-2014-10/0053.html .
The cURL commands in the following sections will not work with the system curl on OS X
Yosemite (10.10). Instead, the certificate supplied with the -E param must be in PKCS12
format, and the file supplied with the --cacert param must contain only the CA certificate,
and no key (see above for instructions on creating this file):
curl -E solr-ssl.keystore.p12:secret --cacert solr-ssl.cacert.pem ...
?
If your operating system does not include cURL, you can download binaries here:
http://curl.haxx.se/download.html
Create a SolrCloud Collection using bin/solr
Create a 2-shard, replicationFactor=1 collection named mycollection using the default configset
(data_driven_schema_configs):
*nix command
bin/solr create -c mycollection -shards 2
Windows command
bin\solr.cmd create -c mycollection -shards 2
The create action will pass the SOLR_SSL_* properties set in your include file to the SolrJ code used to create
the collection.
Retrieve SolrCloud Cluster Status using cURL
To get the resulting cluster status (again, if you have not enabled client authentication, remove the -E solr-
ssl.pem:secret option):
curl -E solr-ssl.pem:secret --cacert solr-ssl.pem
"https://localhost:8984/solr/admin/collections?action=CLUSTERSTATUS&wt=json&indent=on"
You should get a response that looks like this:
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{
"responseHeader":{
"status":0,
"QTime":2041},
"cluster":{
"collections":{
"mycollection":{
"shards":{
"shard1":{
"range":"80000000-ffffffff",
"state":"active",
"replicas":{"core_node1":{
"state":"active",
"base_url":"https://127.0.0.1:8984/solr",
"core":"mycollection_shard1_replica1",
"node_name":"127.0.0.1:8984_solr",
"leader":"true"}}},
"shard2":{
"range":"0-7fffffff",
"state":"active",
"replicas":{"core_node2":{
"state":"active",
"base_url":"https://127.0.0.1:7574/solr",
"core":"mycollection_shard2_replica1",
"node_name":"127.0.0.1:7574_solr",
"leader":"true"}}}},
"maxShardsPerNode":"1",
"router":{"name":"compositeId"},
"replicationFactor":"1"}},
"properties":{"urlScheme":"https"}}}
Index Documents using post.jar
Use post.jar to index some example documents to the SolrCloud collection created above:
cd example/exampledocs
java -Djavax.net.ssl.keyStorePassword=secret -Djavax.net.ssl.keyStore=../../server/etc/solr
-ssl.keystore.jks -Djavax.net.ssl.trustStore=../../server/etc/solr-ssl.keystore.jks
-Djavax.net.ssl.trustStorePassword=secret -Durl=https://localhost:8984/solr/mycollection/update
-jar post.jar *.xml
Query Using cURL
Use cURL to query the SolrCloud collection created above, from a directory containing the PEM formatted
certificate and key created above (e.g. example/etc/ ) - if you have not enabled client authentication (system
property -Djetty.ssl.clientAuth=true) , then you can remove the -E solr-ssl.pem:secret option:
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curl -E solr-ssl.pem:secret --cacert solr-ssl.pem
"https://localhost:8984/solr/mycollection/select?q=*:*&wt=json&indent=on"
Index a document using CloudSolrClient
From a java client using SolrJ, index a document. In the code below, the javax.net.ssl.* system properties
are set programmatically, but you could instead specify them on the java command line, as in the post.jar
example above:
System.setProperty("javax.net.ssl.keyStore", "/path/to/solr-ssl.keystore.jks");
System.setProperty("javax.net.ssl.keyStorePassword", "secret");
System.setProperty("javax.net.ssl.trustStore", "/path/to/solr-ssl.keystore.jks");
System.setProperty("javax.net.ssl.trustStorePassword", "secret");
String zkHost = "127.0.0.1:2181";
CloudSolrClient client = new CloudSolrClient.Builder().withZkHost(zkHost).build();
client.setDefaultCollection("mycollection");
SolrInputDocument doc = new SolrInputDocument();
doc.addField("id", "1234");
doc.addField("name", "A lovely summer holiday");
client.add(doc);
client.commit();
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Running Solr on HDFS
Solr has support for writing and reading its index and transaction log files to the HDFS distributed
filesystem.
This does not use Hadoop MapReduce to process Solr data, rather it only uses the HDFS filesystem for index
and transaction log file storage. To use Hadoop MapReduce to process Solr data, see the
MapReduceIndexerTool in the Solr contrib area.
To use HDFS rather than a local filesystem, you must be using Hadoop 2.x and you will need to instruct Solr
to use the HdfsDirectoryFactory . There are also several additional parameters to define. These can be set
in one of three ways:
? Pass JVM arguments to the bin/solr script. These would need to be passed every time you start Solr
with bin/solr .
? Modify solr.in.sh (or solr.in.cmd on Windows) to pass the JVM arguments automatically when using
bin/solr without having to set them manually.
? Define the properties in solrconfig.xml . These configuration changes would need to be repeated for
every collection, so is a good option if you only want some of your collections stored in HDFS.
Starting Solr on HDFS
Standalone Solr Instances
For standalone Solr instances, there are a few parameters you should be sure to modify before starting Solr.
These can be set in solrconfig.xml (more on that below), or passed to the bin/solr script at startup.
? You need to use an HdfsDirectoryFactory and a data dir of the form hdfs://host:port/path
? You need to specify an UpdateLog location of the form hdfs://host:port/path
? You should specify a lock factory type of ‘hdfs’ or none.
If you do not modify solrconfig.xml , you can instead start Solr on HDFS with the following command:
bin/solr start -Dsolr.directoryFactory=HdfsDirectoryFactory
-Dsolr.lock.type=hdfs
-Dsolr.data.dir=hdfs://host:port/path
-Dsolr.updatelog=hdfs://host:port/path
This example will start Solr in standalone mode, using the defined JVM properties (explained in more detail
below).
SolrCloud Instances
In SolrCloud mode, it’s best to leave the data and update log directories as the defaults Solr comes with and
simply specify the solr.hdfs.home . All dynamically created collections will create the appropriate directories
automatically under the solr.hdfs.home root directory.
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? Set solr.hdfs.home in the form hdfs://host:port/path
? You should specify a lock factory type of ‘hdfs’ or none.
bin/solr start -c -Dsolr.directoryFactory=HdfsDirectoryFactory
-Dsolr.lock.type=hdfs
-Dsolr.hdfs.home=hdfs://host:port/path
This command starts Solr in SolrCloud mode, using the defined JVM properties.
Modifying solr.in.sh (*nix) or solr.in.cmd (Windows)
The examples above assume you will pass JVM arguments as part of the start command every time you use
bin/solr to start Solr. However, bin/solr looks for an include file named solr.in.sh ( solr.in.cmd on
Windows) to set environment variables. By default, this file is found in the bin directory, and you can modify
it to permanently add the HdfsDirectoryFactory settings and ensure they are used every time Solr is
started.
For example, to set JVM arguments to always use HDFS when running in SolrCloud mode (as shown above),
you would add a section such as this:
# Set HDFS DirectoryFactory & Settings
-Dsolr.directoryFactory=HdfsDirectoryFactory \
-Dsolr.lock.type=hdfs \
-Dsolr.hdfs.home=hdfs://host:port/path \
The Block Cache
For performance, the HdfsDirectoryFactory uses a Directory that will cache HDFS blocks. This caching
mechanism is meant to replace the standard file system cache that Solr utilizes so much. By default, this
cache is allocated off heap. This cache will often need to be quite large and you may need to raise the off
heap memory limit for the specific JVM you are running Solr in. For the Oracle/OpenJDK JVMs, the follow is
an example command line parameter that you can use to raise the limit when starting Solr:
-XX:MaxDirectMemorySize=20g
HdfsDirectoryFactory Parameters
The HdfsDirectoryFactory has a number of settings that are defined as part of the directoryFactory
configuration.
Solr HDFS Settings
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Parameter Example Value Default Description
solr.hdfs.home hdfs://host:port/path/solr
N/A A root location in HDFS for Solr to write
collection data to. Rather than specifying
an HDFS location for the data directory or
update log directory, use this to specify
one root location and have everything
automatically created within this HDFS
location.
Block Cache Settings
Parameter Default Description
solr.hdfs.blockcache.enabl
ed
true Enable the blockcache
solr.hdfs.blockcache.read.
enabled
true Enable the read cache
solr.hdfs.blockcache.direc
t.memory.allocation
true Enable direct memory allocation. If this is false, heap is used
solr.hdfs.blockcache.slab.
count
1 Number of memory slabs to allocate. Each slab is 128 MB in
size.
solr.hdfs.blockcache.globa
l
true Enable/Disable using one global cache for all SolrCores. The
settings used will be from the first HdfsDirectoryFactory
created.
NRTCachingDirectory Settings
Parameter Default Description
solr.hdfs.nrtcachingdirect
ory.enable
true Enable the use of NRTCachingDirectory
solr.hdfs.nrtcachingdirect
ory.maxmergesizemb
16 NRTCachingDirectory max segment size for merges
solr.hdfs.nrtcachingdirect
ory.maxcachedmb
192 NRTCachingDirectory max cache size
HDFS Client Configuration Settings
solr.hdfs.confdir pass the location of HDFS client configuration files - needed for HDFS HA for example.
Parameter Default Description
solr.hdfs.confdir
N/A Pass the location of HDFS client configuration files - needed for
HDFS HA for example.
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Kerberos Authentication Settings
Hadoop can be configured to use the Kerberos protocol to verify user identity when trying to access core
services like HDFS. If your HDFS directories are protected using Kerberos, then you need to configure Solr’s
HdfsDirectoryFactory to authenticate using Kerberos in order to read and write to HDFS. To enable Kerberos
authentication from Solr, you need to set the following parameters:
Parameter Default Description
solr.hdfs.security.kerbero
s.enabled
false Set to true to enable Kerberos authentication
solr.hdfs.security.kerbero
s.keytabfile
N/A A keytab file contains pairs of Kerberos principals and
encrypted keys which allows for password-less authentication
when Solr attempts to authenticate with secure Hadoop.
This file will need to be present on all Solr servers at the same
path provided in this parameter.
solr.hdfs.security.kerbero
s.principal
N/A The Kerberos principal that Solr should use to authenticate to
secure Hadoop; the format of a typical Kerberos V5 principal is:
primary/instance@realm
Example
Here is a sample solrconfig.xml configuration for storing Solr indexes on HDFS:
<directoryFactory name="DirectoryFactory" class="solr.HdfsDirectoryFactory">
<str name="solr.hdfs.home">hdfs://host:port/solr</str>
<bool name="solr.hdfs.blockcache.enabled">true</bool>
<int name="solr.hdfs.blockcache.slab.count">1</int>
<bool name="solr.hdfs.blockcache.direct.memory.allocation">true</bool>
<int name="solr.hdfs.blockcache.blocksperbank">16384</int>
<bool name="solr.hdfs.blockcache.read.enabled">true</bool>
<bool name="solr.hdfs.nrtcachingdirectory.enable">true</bool>
<int name="solr.hdfs.nrtcachingdirectory.maxmergesizemb">16</int>
<int name="solr.hdfs.nrtcachingdirectory.maxcachedmb">192</int>
</directoryFactory>
If using Kerberos, you will need to add the three Kerberos related properties to the <directoryFactory>
element in solrconfig.xml, such as:
<directoryFactory name="DirectoryFactory" class="solr.HdfsDirectoryFactory">
...
<bool name="solr.hdfs.security.kerberos.enabled">true</bool>
<str name="solr.hdfs.security.kerberos.keytabfile">/etc/krb5.keytab</str>
<str name="solr.hdfs.security.kerberos.principal">solr/admin@KERBEROS.COM</str>
</directoryFactory>
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Automatically Add Replicas in SolrCloud
One benefit to running Solr in HDFS is the ability to automatically add new replicas when the Overseer
notices that a shard has gone down. Because the "gone" index shards are stored in HDFS, the a new core
will be created and the new core will point to the existing indexes in HDFS.
Collections created using autoAddReplicas=true on a shared file system have automatic addition of replicas
enabled. The following settings can be used to override the defaults in the <solrcloud> section of solr.xml .
Param Default Description
autoReplicaFailoverWorkLoopDelay 10000 The time (in ms) between clusterstate inspections by
the Overseer to detect and possibly act on creation
of a replacement replica.
autoReplicaFailoverWaitAfterExpiration 30000 The minimum time (in ms) to wait for initiating
replacement of a replica after first noticing it not
being live. This is important to prevent false positives
while stoping or starting the cluster.
autoReplicaFailoverBadNodeExpiration 60000 The delay (in ms) after which a replica marked as
down would be unmarked.
Temporarily disable autoAddReplicas for the entire cluster
When doing offline maintenance on the cluster and for various other use cases where an admin would like
to temporarily disable auto addition of replicas, the following APIs will disable and re-enable
autoAddReplicas for all collections in the cluster:
Disable auto addition of replicas cluster wide by setting the cluster property autoAddReplicas to false :
http://localhost:8983/solr/admin/collections?action=CLUSTERPROP&name=autoAddReplicas&val=false
Re-enable auto addition of replicas (for those collections created with autoAddReplica=true) by unsetting the
autoAddReplicas cluster property (when no val param is provided, the cluster property is unset):
http://localhost:8983/solr/admin/collections?action=CLUSTERPROP&name=autoAddReplicas
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Making and Restoring Backups
If you are worried about data loss, and of course you should be, you need a way to back up your Solr indexes
so that you can recover quickly in case of catastrophic failure.
Solr provides two approaches to backing up and restoring Solr cores or collections, depending on how you
are running Solr. If you run in SolrCloud mode, you will use the Collections API. If you run Solr in standalone
mode, you will use the replication handler.
SolrCloud Backups
Support for backups when running SolrCloud is provided with the Collections API. This allows the backups to
be generated across multiple shards, and restored to the same number of shards and replicas as the
original collection.
Two commands are available:
? action=BACKUP : This command backs up Solr indexes and configurations. More information is available
in the section Backup Collection.
? action=RESTORE : This command restores Solr indexes and configurations. More information is available
in the section Restore Collection.
Standalone Mode Backups
Backups and restoration uses Solr’s replication handler. Out of the box, Solr includes implicit support for
replication so this API can be used. Configuration of the replication handler can, however, be customized by
defining your own replication handler in solrconfig.xml . For details on configuring the replication handler,
see the section Configuring the ReplicationHandler.
Backup API
The backup API requires sending a command to the /replication handler to back up the system.
You can trigger a back-up with an HTTP command like this (replace "gettingstarted" with the name of the
core you are working with):
Backup API Example
http://localhost:8983/solr/gettingstarted/replication?command=backup
The backup command is an asynchronous call, and it will represent data from the latest index commit point.
All indexing and search operations will continue to be executed against the index as usual.
Only one backup call can be made against a core at any point in time. While an ongoing backup operation is
happening subsequent calls for restoring will throw an exception.
The backup request can also take the following additional parameters:
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Parameter Description
location The path where the backup will be created. If the path is not absolute then
the backup path will be relative to Solr’s instance directory.
name The snapshot will be created in a directory called snapshot.<name> . If a
name is not specified then the directory name would have the following
format: snapshot.<yyyyMMddHHmmssSSS>
numberToKeep The number of backups to keep. If maxNumberOfBackups has been
specified on the replication handler in solrconfig.xml ,
maxNumberOfBackups is always used and attempts to use numberToKeep
will cause an error. Also, this parameter is not taken into consideration if
the backup name is specified. More information about
maxNumberOfBackups can be found in the section Configuring the
ReplicationHandler.
repository The name of the repository to be used for the backup. If no repository is
specified then the local filesystem repository will be used automatically.
commitName The name of the commit which was used while taking a snapshot using the
CREATESNAPSHOT command.
Backup Status
The backup operation can be monitored to see if it has completed by sending the details command to the
/replication handler, as in this example:
Status API Example
http://localhost:8983/solr/gettingstarted/replication?command=details
Output Snippet
<lst name="backup">
<str name="startTime">Sun Apr 12 16:22:50 DAVT 2015</str>
<int name="fileCount">10</int>
<str name="status">success</str>
<str name="snapshotCompletedAt">Sun Apr 12 16:22:50 DAVT 2015</str>
<str name="snapshotName">my_backup</str>
</lst>
If it failed then a snapShootException will be sent in the response.
Restore API
Restoring the backup requires sending the restore command to the /replication handler, followed by the
name of the backup to restore.
You can restore from a backup with a command like this:
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Example Usage
http://localhost:8983/solr/gettingstarted/replication?command=restore&name=backup_name
This will restore the named index snapshot into the current core. Searches will start reflecting the snapshot
data once the restore is complete.
The restore request can also take these additional parameters:
Parameter Description
location The location of the backup snapshot file. If not specified, it looks for
backups in Solr’s data directory.
name The name of the backed up index snapshot to be restored. If the name is
not provided it looks for backups with snapshot.<timestamp> format in
the location directory. It picks the latest timestamp backup in that case.
repository The name of the repository to be used for the backup. If no repository is
specified then the local filesystem repository will be used automatically.
The restore command is an asynchronous call. Once the restore is complete the data reflected will be of the
backed up index which was restored.
Only one restore call can can be made against a core at one point in time. While an ongoing restore
operation is happening subsequent calls for restoring will throw an exception.
Restore Status API
You can also check the status of a restore operation by sending the restorestatus command to the
/replication handler, as in this example:
Status API Example
http://localhost:8983/solr/gettingstarted/replication?command=restorestatus
Status API Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">0</int>
</lst>
<lst name="restorestatus">
<str name="snapshotName">snapshot.<name></str>
<str name="status">success</str>
</lst>
</response>
The status value can be "In Progress" , "success" or "failed". If it failed then an "exception" will also be sent
in the response.
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Create Snapshot API
The snapshot functionality is different from the backup functionality as the index files aren’t copied
anywhere. The index files are snapshotted in the same index directory and can be referenced while taking
backups.
You can trigger a snapshot command with an HTTP command like this (replace "techproducts" with the
name of the core you are working with):
Create Snapshot API Example
http://localhost:8983/solr/admin/cores?action=CREATESNAPSHOT&core=techproducts&commitName=commit1
The list snapshot request parameters are:
Parameter Description
commitName Specify the commit name to store the snapshot as
core name of the core to perform the snapshot on
async Request ID to track this action which will be processed asynchronously
List Snapshot API
The list snapshot functionality lists all the taken snapshots for a particular core.
You can trigger a list snapshot command with an HTTP command like this (replace "techproducts" with the
name of the core you are working with):
List Snapshot API
http://localhost:8983/solr/admin/cores?action=LISTSNAPSHOTS&core=techproducts&commitName=commit1
The list snapshot request parameters are:
Parameter Description
core name of the core to whose snapshots we want to list
async Request ID to track this action which will be processed asynchronously
Delete Snapshot API
The delete snapshot functionality deletes a particular snapshot for a particular core.
You can trigger a delete snapshot command with an HTTP command like this (replace "techproducts" with
the name of the core you are working with):
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Delete Snapshot API Example
http://localhost:8983/solr/admin/cores?action=DELETESNAPSHOT&core=techproducts&commitName=commit1
The delete snapshot request parameters are:
Parameter Description
commitName Specify the commit name to be deleted
core name of the core whose snapshot we want to delete
async Request ID to track this action which will be
processed asynchronously
Backup/Restore Storage Repositories
Solr provides interfaces to plug different storage systems for backing up and restoring. For example, you
can have a Solr cluster running on a local filesystem like EXT3 but you can backup the indexes to a HDFS
filesystem or vice versa.
The repository interfaces needs to be configured in the solr.xml file . While running backup/restore
commands we can specify the repository to be used.
If no repository is configured then the local filesystem repository will be used automatically.
Example solr.xml section to configure a repository like HDFS:
<backup>
<repository name="hdfs" class="org.apache.solr.core.backup.repository.HdfsBackupRepository"
default="false">
<str name="location">${solr.hdfs.default.backup.path}</str>
<str name="solr.hdfs.home">${solr.hdfs.home:}</str>
<str name="solr.hdfs.confdir">${solr.hdfs.confdir:}</str>
</repository>
</backup>
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Configuring Logging
Solr logs are a key way to know what’s happening in the system. There are several ways to adjust the default
logging configuration.
?
In addition to the logging options described below, there is a way to configure which
request parameters (such as parameters sent as part of queries) are logged with an
additional request parameter called logParamsList . See the section on Common Query
Parameters for more information.
Temporary Logging Settings
You can control the amount of logging output in Solr by using the Admin Web interface. Select the LOGGING
link. Note that this page only lets you change settings in the running system and is not saved for the next
run. (For more information about the Admin Web interface, see Using the Solr Administration User
Interface.)
The Logging Screen
This part of the Admin Web interface allows you to set the logging level for many different log categories.
Fortunately, any categories that are unset will have the logging level of its parent. This makes it possible to
change many categories at once by adjusting the logging level of their parent.
When you select Level, you see the following menu:
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The Log Level Menu
Directories are shown with their current logging levels. The Log Level Menu floats over these. To set a log
level for a particular directory, select it and click the appropriate log level button.
Log levels settings are as follows:
Level Result
FINEST Reports everything.
FINE Reports everything but the least important
messages.
CONFIG Reports configuration errors.
INFO Reports everything but normal status.
WARN Reports all warnings.
SEVERE Reports only the most severe warnings.
OFF Turns off logging.
UNSET Removes the previous log setting.
Multiple settings at one time are allowed.
Log level API
There is also a way of sending REST commands to the logging endpoint to do the same. Example:
# Set the root logger to level WARN
curl -s http://localhost:8983/solr/admin/info/logging --data-binary "set=root:WARN&wt=json"
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Choosing Log Level at Startup
You can temporarily choose a different logging level as you start Solr. There are two ways:
The first way is to set the SOLR_LOG_LEVEL environment variable before you start Solr, or place the same
variable in bin/solr.in.sh or bin/solr.in.cmd . The variable must contain an uppercase string with a
supported log level (see above).
The second way is to start Solr with the -v or -q options, see Solr Control Script Reference for details.
Examples:
# Start with verbose (DEBUG) looging
bin/solr start -f -v
# Start with quiet (WARN) logging
bin/solr start -f -q
Permanent Logging Settings
Solr uses Log4J version 1.2 for logging which is configured using server/resources/log4j.properties .
Take a moment to inspect the contents of the log4j.properties file so that you are familiar with its
structure. By default, Solr log messages will be written to SOLR_LOGS_DIR/solr.log .
When you’re ready to deploy Solr in production, set the variable SOLR_LOGS_DIR to the location where you
want Solr to write log files, such as /var/solr/logs . You may also want to tweak log4j.properties . Note
that if you installed Solr as a service using the instructions provided in Taking Solr to Production, then see
/var/solr/log4j.properties instead of the default server/resources version.
When starting Solr in the foreground ( -f option), all logs will be sent to the console, in addition to solr.log .
When starting Solr in the background, it will write all stdout and stderr output to a log file in solr-<port>-
console.log , and automatically disable the CONSOLE logger configured in log4j.properties , having the
same effect as if you removed the CONSOLE appender from the rootLogger manually.
Also, in log4j.properties the default log rotation size threshold of 4MB is most likely too small for
production servers and should be increased to a larger value (such as 100MB or more).
log4j.appender.file.MaxFileSize=100MB
Java Garbage Collection logs are rotated by the JVM when size hits 20M, for a max of 9 generations. Old GC
logs are moved to SOLR_LOGS_DIR/archived . These settings can only be changed by editing the start scripts.
On every startup of Solr, the start script will clean up old logs and rotate the main solr.log file. If you
changed the log4j.appender.file.MaxBackupIndex setting in log4j.properties , you also need to change
the corresponding setting -rotate_solr_logs 9 in the start script.
You can disable the automatic log rotation at startup by changing the setting SOLR_LOG_PRESTART_ROTATION
found in bin/solr.in.sh or bin/solr.in.cmd to false.
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Logging Slow Queries
For high-volume search applications, logging every query can generate a large amount of logs and,
depending on the volume, potentially impact performance. If you mine these logs for additional insights into
your application, then logging every query request may be useful.
On the other hand, if you’re only concerned about warnings and error messages related to requests, then
you can set the log verbosity to WARN. However, this poses a potential problem in that you won’t know if
any queries are slow, as slow queries are still logged at the INFO level.
Solr provides a way to set your log verbosity threshold to WARN and be able to set a latency threshold above
which a request is considered "slow" and log that request at the WARN level to help you identify slow
queries in your application. To enable this behavior, configure the <slowQueryThresholdMillis> element in
the query section of solrconfig.xml:
<slowQueryThresholdMillis>1000</slowQueryThresholdMillis>
Any queries that take longer than the specified threshold will be logged as "slow" queries at the WARN level.
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Using JMX with Solr
Java Management Extensions (JMX) is a technology that makes it possible for complex systems to be
controlled by tools without the systems and tools having any previous knowledge of each other. In essence,
it is a standard interface by which complex systems can be viewed and manipulated.
Solr, like any other good citizen of the Java universe, can be controlled via a JMX interface. You can enable
JMX support by adding lines to solrconfig.xml . You can use a JMX client, like jconsole, to connect with Solr.
Check out the Wiki page http://wiki.apache.org/solr/SolrJmx for more information. You may also find the
following overview of JMX to be useful: http://docs.oracle.com/javase/8/docs/technotes/guides/
management/agent.html.
Configuring JMX
JMX configuration is provided in solrconfig.xml . Please see the JMX Technology Home Page for more
details.
A rootName attribute can be used when configuring <jmx /> in solrconfig.xml . If this attribute is set, Solr
uses it as the root name for all the MBeans that Solr exposes via JMX. The default name is "solr" followed by
the core name.
?
Enabling/disabling JMX and securing access to MBeanServers is left up to the user by
specifying appropriate JVM parameters and configuration. Please explore the JMX
Technology Home Page for more details.
Configuring an Existing MBeanServer
The command:
<jmx />
enables JMX support in Solr if and only if an existing MBeanServer is found. Use this if you want to configure
JMX with JVM parameters. Remove this to disable exposing Solr configuration and statistics to JMX. If this is
specified, Solr will try to list all available MBeanServers and use the first one to register MBeans.
Configuring an Existing MBeanServer with agentId
The command:
<jmx agentId="myMBeanServer" />
enables JMX support in Solr if and only if an existing MBeanServer is found matching the given agentId. If
multiple servers are found, the first one is used. If none is found, an exception is raised and depending on
the configuration, Solr may refuse to start.
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Configuring a New MBeanServer
The command:
<jmx serviceUrl="service:jmx:rmi:///jndi/rmi://localhost:9999/solrjmx" />
creates a new MBeanServer exposed for remote monitoring at the specific service URL. If the
JMXConnectorServer can’t be started (probably because the serviceUrl is bad), an exception is thrown.
Example
Solr’s sample_techproducts_configs config set uses the simple <jmx /> configuration option. If you start
the example with the necessary JVM system properties to launch an internal MBeanServer, Solr will register
with it and you can connect using a tool like jconsole :
1. Launch the techproducts example with JMX enabled:
bin/solr -e techproducts -Dcom.sun.management.jmxremote
2. Start jconsole (provided with the Sun JDK in the bin directory).
3. Connect to the “start.jar” shown in the list of local processes.
4. Switch to the "MBeans" tab. You should be able to see “solr/techproducts” listed there, at which point
you can drill down and see details of every solr plugin.
Configuring a Remote Connection to Solr JMX
If you need to attach a JMX-enabled Java profiling tool, such as JConsole or VisualVM, to a remote Solr server,
then you need to enable remote JMX access when starting the Solr server. Simply change the
ENABLE_REMOTE_JMX_OPTS property in the include file to true. You’ll also need to choose a port for the JMX
RMI connector to bind to, such as 18983. For example, if your Solr include script sets:
ENABLE_REMOTE_JMX_OPTS=true
RMI_PORT=18983
The JMX RMI connector will allow Java profiling tools to attach to port 18983. When enabled, the following
properties are passed to the JVM when starting Solr:
-Dcom.sun.management.jmxremote \
-Dcom.sun.management.jmxremote.local.only=false \
-Dcom.sun.management.jmxremote.ssl=false \
-Dcom.sun.management.jmxremote.authenticate=false \
-Dcom.sun.management.jmxremote.port=18983 \
-Dcom.sun.management.jmxremote.rmi.port=18983
We don’t recommend enabling remote JMX access in production, but it can sometimes be useful when doing
performance and user-acceptance testing prior to going into production.
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For more information about these settings, see:
http://docs.oracle.com/javase/8/docs/technotes/guides/management/agent.html
?
Making JMX connections into machines running behind NATs (e.g. Amazon’s EC2 service) is
not a simple task. The java.rmi.server.hostname system property may help, but running
jconsole on the server itself and using a remote desktop is often the simplest solution. See
http://web.archive.org/web/20130525022506/http://jmsbrdy.com/monitoring-java-
applications-running-on-ec2-i.
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MBean Request Handler
The MBean Request Handler offers programmatic access to the information provided on the Plugin/Stats
page of the Admin UI.
The MBean Request Handler accepts the following parameters:
Paramet
er
Type Default Description
key multivalued all Restricts results by object key.
cat multivalued all Restricts results by category name.
stats boolean false Specifies whether statistics are returned with results. You can
override the stats parameter on a per-field basis.
wt multivalued xml The output format. This operates the same as the wt parameter
in a query.
Examples
The following examples assume you are running Solr’s techproducts example configuration:
bin/solr start -e techproducts
To return information about the CACHE category only:
http://localhost:8983/solr/techproducts/admin/mbeans?cat=CACHE
To return information and statistics about the CACHE category only, formatted in JSON:
http://localhost:8983/solr/techproducts/admin/mbeans?stats=true&cat=CACHE&indent=true&wt=jso
n
To return information for everything, and statistics for everything except the fieldCache :
http://localhost:8983/solr/techproducts/admin/mbeans?stats=true&f.fieldCache.stats=false
To return information and statistics for the fieldCache only:
http://localhost:8983/solr/techproducts/admin/mbeans?key=fieldCache&stats=true
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Performance Statistics Reference
This page explains some of the JMX statistics that Solr exposes.
The same statistics are also exposed via the MBean Request Handler when statistics are requested.
These statistics are per core. When you are running in SolrCloud mode these statistics would co-relate to
each performance of an individual replica.
Request Handlers
Update Request Handler
The update request handler is an endpoint to send data to Solr. We can see how many update requests are
being fired, how fast is it performing, and other valuable information regarding requests.
Path: /solr/<core>/update
Search Request Handler
Can be useful to measure and track number of search queries , response times etc. If you are not using the
“select” handler then the path needs to be changed appropriately. Similarly if you are using the “sql”
handler or “export” handler , the realtime handler “get”, or any other handler similar statistics can be found
for that as well.
Path: /solr/<core>/select
Both Update Request Handler and Search Request Handler along with handlers like “sql”, “export” and
realtime “get” handler will provide the following attributes in their statistics.
Attribute Description
15minRateReqsPerSecond Requests per second received over the past 15 minutes.
5minRateReqsPerSecond Requests per second received over the past 5 minutes.
75thPcRequestTime Request processing time for the request which belongs to the 75th
Percentile. E.g., if 100 requests are received, then the 75th fastest request
time will be reported by this statistic.
95thPcRequestTime Request processing time in milliseconds for the request which belongs to
the 95th Percentile. E.g., if 80 requests are received, then the 76th fastest
request time will be reported in this statistic.
999thPcRequestTime Request processing time in milliseconds for the request which belongs to
the 99.9th Percentile. E.g., if 1000 requests are received, then the 999th
fastest request time will be reported in this statistic.
99thPcRequestTime Request processing time in milliseconds for the request which belongs to
the 99th Percentile. E.g., if 200 requests are received, then the 198th
fastest request time will be reported in this statistic.
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Attribute Description
avgRequestsPerSecond Average number of requests received per second.
avgTimePerRequest Average time taken for processing the requests. This parameter will decay
over time, with a bias toward activity in the last 5 minutes.
errors Number of error encountered by handler.
clientErrors Number of syntax errors/parse errors made by client while making
requests.
handlerStart Epoch time when the handler was registered.
medianRequestTime Median of all the request processing time.
requests Total number of requests made since the Solr process was started.
serverErrors Number of errors thrown by the server while executing the request.
timeouts Number of responses received with partial results.
totalTime The sum of all request processing times since the Solr process was started.
Update Handler
Update Handler: This section has information on the total number of adds, how many commits have been
fired against a solr core.
Path: /solr/<core>/updateHandler/DirectUpdateHandler2
Attribute Description
adds Total number of “add” requests since last commit.
autocommit maxTime Maximum time between two autocommits execution.
autocommits Total number of auto-commits executed.
cumulative_adds Number of “effective” additions executed over the lifetime. The counter is
incremented when “add‘ command is executed while decremented when
“rollback” is executed.
cumulative_deletesById Number of document deletions executed by ID over the lifetime. The
counter is incremented when “delete” command is executed while
decremented when “rollback” is executed.
cumulative_deletesByQuery Number of document deletions executed by query over the lifetime. The
counter is incremented when “delete” command is executed while
decremented when “rollback” is executed.
cumulative_errors Number of error messages received by Update Handler while performing
addition/deletion action on documents over the lifetime.
deletesById Currently uncommitted deletions by ID.
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Attribute Description
deletesByQuery Currently uncommitted deletions by query.
docsPending Number of documents which are pending commit.
errors Number of error messages received by Update Handler while performing
addition/deletion/commit/rollback action on documents over the lifetime.
expungeDeletes Number of commit commands issued with expunge deletes.
optimizes Number of explicit optimize commands issued
rollbacks Number of rollbacks executed.
soft autocommit maxTime Maximum documents ‘adds’ between two soft auto-commits.
soft autocommits Number of soft commits executed.
transaction_logs_total_number Number of TLogs created from the beginning of the Solr instance. It will be
equivalent to number of Hard commits executed.
transaction_logs_total_size Total size of all the TLogs created so far from the beginning of the Solr
instance.
Caches
Document Cache
This cache holds Lucene Document objects (the stored fields for each document). Since Lucene internal
document IDs are transient, this cache cannot be auto-warmed.
Path: /solr/<cache>/documentCache
Query Result Cache
This cache holds the results of previous searches: ordered lists of document IDs based on a query, a sort,
and the range of documents requested
Path: /solr/<cache>/queryResultCache
Filter Cache
This cache is used for filters for unordered sets of all documents that match a query.
Path: /solr/<cache>/filterCache
Attribute Description
cumulative_evictions Number of cache evictions across all caches since this node has been
running.
cumulative_hitratio Ratio of cache hits to lookups across all the caches since this node has
been running.
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Attribute Description
cumulative_hits Number of cache hits across all the caches since this node has been
running.
cumulative_inserts Number of cache insertions across all the caches since this node has been
running.
cumulative_lookups Number of cache lookups across all the caches since this node has been
running.
evictions Number of cache evictions for the current index searcher.
hitratio Ratio of cache hits to lookups for the current index searcher.
hits Number of hits for the current index searcher.
inserts Number of inserts into the cache.
lookups Number of lookups against the cache.
size Size of the cache at that particular instance (in KBs).
warmupTime Warm-up time for the registered index searcher. This time is taken in
account for the “auto-warming” of caches.
More information on Solr caches is available in the section Query Settings in SolrConfig.
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Metrics Reporting
Solr includes a developer API and instrumentation for the collection of detailed performance-oriented
metrics throughout the life-cycle of Solr service and its various components.
Internally this feature uses the Dropwizard Metrics API, which uses the following classes of meters to
measure events:
? counters - simply count events. They provide a single long value, e.g., the number of requests.
? meters - additionally compute rates of events. Provide a count (as above) and 1-, 5-, and 15-minute
exponentially decaying rates, similar to the Unix system load average.
? histograms - calculate approximate distribution of events according to their values. Provide the
following approximate statistics, with a similar exponential decay as above: mean (arithmetic average),
median, maximum, minimum, standard deviation, and 75
th , 95 th , 98 th , 99 th and 999 th percentiles.
? timers - measure the number and duration of events. They provide a count and histogram of timings.
? gauges - offer instantaneous reading of a current value, e.g., current queue depth, current number of
active connections, free heap size.
Each group of related metrics with unique names is managed in a metric registry. Solr maintains several
such registries, each corresponding to a high-level group such as: jvm , jetty , http , node , and core (see
Metric Registries below).
For each group (and/or for each registry) there can be several reporters, which are components responsible
for communication of metrics from selected registries to external systems. Currently implemented reporters
support emitting metrics via JMX, Ganglia, Graphite and SLF4J.
There is also a dedicated /admin/metrics handler that can be queried to report all or a subset of the current
metrics from multiple registries.
Metric Registries
Solr includes multiple metric registries, which group related metrics.
Metrics are maintained and accumulated through all lifecycles of components from the start of the process
until its shutdown - e.g., metrics for a particular SolrCore are tracked through possibly several load, unload
and/or rename operations, and are deleted only when a core is explicitly deleted. However, metrics are not
persisted across process restarts; restarting Solr will discard all collected metrics.
These are the major groups of metrics that are collected:
JVM Registry ( solr.jvm ):
? direct and mapped buffer pools
? class loading / unloading
? OS memory, CPU time, file descriptors, swap, system load
? GC count and time
? heap, non-heap memory and GC pools
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? number of threads, their states and deadlocks
Node / CoreContainer Registry ( solr.node ):
? handler requests (count, timing): collections, info, admin, configSets, etc.
? number of cores (loaded, lazy, unloaded)
Core (SolrCore) Registry:
The Core (SolrCore) Registry includes solr.core.<collection> , one for each core.
? all common RequestHandler-s report: request timers / counters, timeouts, errors.
? index-level events: meters for minor / major merges, number of merged docs, number of deleted docs,
gauges for currently running merges and their size.
? shard replication and transaction log replay on replicas (TBD, SOLR-9856)
? TBD: caches, update handler details, and other relevant SolrInfoMBean-s
HTTP Registry ( solr.http ):
? open / available / pending connections for shard handler and update handler
Jetty Registry ( solr.jetty ):
? threads and pools,
? connection and request timers,
? meters for responses by HTTP class (1xx, 2xx, etc)
In the future, metrics will be added for shard leaders and cluster nodes, including aggregations from per-
core metrics.
Reporters
Reporter configurations are specified in solr.xml file in <metrics><reporter> sections, for example:
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<solr>
<metrics>
<reporter name="graphite" group="node, jvm" class=
"org.apache.solr.metrics.reporters.SolrGraphiteReporter">
<str name="host">graphite-server</str>
<int name="port">9999</int>
<int name="period">60</int>
</reporter>
<reporter name="collection1Updates" registry="solr.core.collection1" class=
"org.apache.solr.metrics.reporters.SolrSlf4jReporter">
<int name="period">300</int>
<str name="prefix">example</str>
<str name="logger">updatesLogger</str>
<str name="filter">QUERYHANDLER./update</str>
</reporter>
</metrics>
...
</solr>
Reporter Arguments
Reporter plugins use the following arguments:
? name - (required) unique name of the reporter plugin
? class - (required) fully-qualified implementation class of the plugin, must extend SolrMetricReporter
? group - (optional) one or more of the predefined groups (see above)
? registry - (optional) one or more of valid fully-qualified registry names
? If both group and registry attributes are specified only the group attribute is considered. If neither
attribute is specified then the plugin will be used for all groups and registries. Multiple group or registry
names can be specified, separated by comma and/or space.
Additionally, several implementation-specific initialization arguments can be specified in nested elements.
There are some arguments that are common to SLF4J, Ganglia and Graphite reporters:
? period - (optional int) period in seconds between reports. Default value is 60.
? prefix - (optional str) prefix to be added to metric names, may be helpful in logical grouping of related
Solr instances, e.g., machine name or cluster name. Default is empty string, ie. just the registry name and
metric name will be used to form a fully-qualified metric name.
? filter - (optional str) if not empty then only metric names that start with this value will be reported.
Default is no filtering, ie. all metrics from selected registry will be reported.
Reporters are instantiated for every group and registry that they were configured for, at the time when the
respective components are initialized (e.g., on JVM startup or SolrCore load).
When reporters are created their configuration is validated (and e.g., necessary connections are
established). Uncaught errors at this initialization stage cause the reporter to be discarded from the running
configuration.
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Reporters are closed when the corresponding component is being closed (e.g., on SolrCore close, or JVM
shutdown) but metrics that they reported are still maintained in respective registries, as explained in the
previous section.
The following sections provide information on implementation-specific arguments. All implementation
classes provided with Solr can be found under org.apache.solr.metrics.reporters .
JMX Reporter
The JMX Reporter uses the org.apache.solr.metrics.reporters.SolrJmxReporter class.
It takes the following arguments:
? domain - (optional str) JMX domain name. If not specified then registry name will be used.
? serviceUrl - (optional str) service URL for a JMX server. If not specified then the default platform MBean
server will be used.
? agentId - (optional str) agent ID for a JMX server. Note: either serviceUrl or agentId can be specified
but not both - if both are specified then the default MBean server will be used.
Object names created by this reporter are hierarchical, dot-separated but also properly structured to form
corresponding hierarchies in e.g., JConsole. This hierarchy consists of the following elements in the top-
down order:
? registry name (e.g., solr.core.collection1.shard1.replica1 . Dot-separated registry names are also
split into ObjectName hierarchy levels, so that metrics for this registry will be shown under
/solr/core/collection1/shard1/replica1 in JConsole, with each domain part being assigned to dom1,
dom2, … domN property.
? reporter name (the value of reporter’s name attribute)
? category, scope and name for request handlers
? or additional name1, name2, … nameN elements for metrics from other components.
SLF4J Reporter
The SLF4J Reporter uses the org.apache.solr.metrics.reporters.SolrSlf4jReporter class.
It takes the following arguments, in addition to the common arguments above.
? logger - (optional str) name of the logger to use. Default is empty, in which case the group or registry
name will be used if specified in the plugin configuration.
Users can specify logger name (and the corresponding logger configuration in e.g., Log4j configuration) to
output metrics-related logging to separate file(s), which can then be processed by external applications.
Each log line produced by this reporter consists of configuration-specific fields, and a message that follows
this format:
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type=COUNTER, name={}, count={}
type=GAUGE, name={}, value={}
type=TIMER, name={}, count={}, min={}, max={}, mean={}, stddev={}, median={}, p75={}, p95={},
p98={}, p99={}, p999={}, mean_rate={}, m1={}, m5={}, m15={}, rate_unit={}, duration_unit={}
type=METER, name={}, count={}, mean_rate={}, m1={}, m5={}, m15={}, rate_unit={}
type=HISTOGRAM, name={}, count={}, min={}, max={}, mean={}, stddev={}, median={}, p75={}, p95={},
p98={}, p99={}, p999={}
(curly braces added only as placeholders for actual values).
Graphite Reporter
The Graphite Reporter uses the org.apache.solr.metrics.reporters.SolrGraphiteReporter ) class.
It takes the following attributes, in addition to the common attributes above.
? host - (required str) host name where Graphite server is running.
? port - (required int) port number for the server
? pickled - (optional bool) use "pickled" Graphite protocol which may be more efficient. Default is false
(use plain-text protocol).
When plain-text protocol is used ( pickled==false ) it’s possible to use this reporter to integrate with
systems other than Graphite, if they can accept space-separated and line-oriented input over network in the
following format:
dot.separated.metric.name[.and.attribute] value epochTimestamp
For example:
example.solr.node.cores.lazy 0 1482932097
example.solr.node.cores.loaded 1 1482932097
example.solr.jetty.org.eclipse.jetty.server.handler.DefaultHandler.2xx-responses.count 21
1482932097
example.solr.jetty.org.eclipse.jetty.server.handler.DefaultHandler.2xx-responses.m1_rate
2.5474287707930614 1482932097
example.solr.jetty.org.eclipse.jetty.server.handler.DefaultHandler.2xx-responses.m5_rate
3.8003171557510305 1482932097
example.solr.jetty.org.eclipse.jetty.server.handler.DefaultHandler.2xx-responses.m15_rate
4.0623076220244245 1482932097
example.solr.jetty.org.eclipse.jetty.server.handler.DefaultHandler.2xx-responses.mean_rate
0.5698031798408144 1482932097
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Ganglia Reporter
The Ganglia reporter uses the org.apache.solr.metrics.reporters.SolrGangliaReporter class.
It take the following arguments, in addition to the common arguments above.
? host - (required str) host name where Ganglia server is running.
? port - (required int) port number for the server
? multicast - (optional bool) when true use multicast UDP communication, otherwise use UDP unicast.
Default is false.
Core Level Metrics
These metrics are available only on a per-core basis. Metrics that are aggregated across cores are not yet
available.
Index Merge Metrics
These metrics are collected in respective registries for each core (e.g., solr.core.collection1…. ), under
the INDEX category.
Basic metrics are always collected - collection of additional metrics can be turned on using boolean
parameters in the /config/indexConfig/metrics section of solrconfig.xml :
<config>
...
<indexConfig>
<metrics>
<majorMergeDocs>524288</majorMergeDocs>
<bool name="mergeDetails">true</bool>
<bool name="directoryDetails">true</bool>
</metrics>
...
</indexConfig>
...
</config>
The following metrics are collected:
? INDEX.merge.major - timer for merge operations that include at least "majorMergeDocs" (default value
for this parameter is 512k documents).
? INDEX.merge.minor - timer for merge operations that include less than "majorMergeDocs".
? INDEX.merge.errors - counter for merge errors.
? INDEX.flush - meter for index flush operations.
Additionally, the following gauges are reported, which help to monitor the momentary state of index merge
operations:
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? INDEX.merge.major.running - number of running major merge operations (depending on the
implementation of MergeScheduler that is used there can be several concurrently running merge
operations).
? INDEX.merge.minor.running - as above, for minor merge operations.
? INDEX.merge.major.running.docs - total number of documents in the segments being currently
merged in major merge operations.
? INDEX.merge.minor.running.docs - as above, for minor merge operations.
? INDEX.merge.major.running.segments - number of segments being currently merged in major merge
operations.
? INDEX.merge.minor.running.segments - as above, for minor merge operations.
If the boolean flag mergeDetails is true then the following additional metrics are collected:
? INDEX.merge.major.docs - meter for the number of documents merged in major merge operations
? INDEX.merge.major.deletedDocs - meter for the number of deleted documents expunged in major
merge operations
Metrics API
The admin/metrics endpoint provides access to all the metrics for all metric groups.
A few query parameters are available to limit the request:
group
The metric group to retrieve. The default is all to retrieve all metrics for all groups. Other possible values
are: jvm , jetty , node , and core . More than one group can be specified in a request; multiple group
names should be separated by a comma.
type
The type of metric to retrieve. The default is all to retrieve all metric types. Other possible values are
counter , gauge , histogram , meter , and timer . More than one type can be specified in a request; multiple
types should be separated by a comma.
prefix
The first characters of metric name that will filter the metrics returned to those starting with the provided
string. It can be combined with group and/or type parameters. More than one prefix can be specified in a
request; multiple prefixes should be separated by a comma. Prefix matching is also case-sensitive.
compact
When true, a more compact format of the response will be returned. Instead of a response like this:
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"metrics": [
"solr.core.gettingstarted",
{
"CORE.aliases": {
"value": ["gettingstarted"]
},
"CORE.coreName": {
"value": "gettingstarted"
},
"CORE.indexDir": {
"value": "/solr/example/schemaless/solr/gettingstarted/data/index/"
},
"CORE.instanceDir": {
"value": "/solr/example/schemaless/solr/gettingstarted"
},
"CORE.refCount": {
"value": 1
},
"CORE.startTime": {
"value": "2017-03-14T11:43:23.822Z"
}
}
]
The response will look like this:
"metrics": [
"solr.core.gettingstarted",
{
"CORE.aliases": [
"gettingstarted"
],
"CORE.coreName": "gettingstarted",
"CORE.indexDir": "/solr/example/schemaless/solr/gettingstarted/data/index/",
"CORE.instanceDir": "/solr/example/schemaless/solr/gettingstarted",
"CORE.refCount": 1,
"CORE.startTime": "2017-03-14T11:43:23.822Z"
}
]
Like other request handlers, the Metrics API can also take the wt parameter to define the output format.
Examples
Request only "counter" type metrics in the "core" group, returned in JSON:
http://localhost:8983/solr/admin/metrics?wt=json&type=counter&group=core
Request only "core" group metrics that start with "INDEX", returned in JSON:
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http://localhost:8983/solr/admin/metrics?wt=json&prefix=INDEX&group=core
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v2 API
The v2 API is a modernized self-documenting API interface covering most current Solr APIs. It is anticipated
that once the v2 API reaches full coverage, and Solr-internal API usages like SolrJ and the Admin UI have
been converted from the old API to the v2 API, the old API will eventually be retired.
For now the two API styles will coexist, and all the old APIs will continue to work without any change. You can
disable all v2 API endpoints by starting your servers with this system property: -Ddisable.v2.api=true .
The old API and the v2 API differ in three principle ways:
1. Command format: The old API commands and associated parameters are provided through URL request
parameters on HTTP GET requests, while in the v2 API most API commands are provided via a JSON body
POST’ed to v2 API endpoints. The v2 API also supports HTTP methods GET and DELETE where
appropriate.
2. Endpoint structure: The v2 API endpoint structure has been rationalized and regularized.
3. Documentation: The v2 APIs are self-documenting: append /_introspect to any valid v2 API path and
the API specification will be returned in JSON format.
v2 API Path Prefixes
Following are some v2 API URL paths and path prefixes, along with some of the operations that are
supported at these paths and their sub-paths.
Path prefix Some Supported Operations
/v2/collections or equivalently: /v2/c Create, alias, backup, and restore a collection.
/v2/c/collection-name/update
Update requests.
/v2/c/collection-name/config
Configuration requests.
/v2/c/collection-name/schema
Schema requests.
/v2/c/collection-name/handler-name
Handler-specific requests.
/v2/c/collection-name/shards
Split a shard, create a shard, add a replica.
/v2/c/collection-name/shards/shard-name
Delete a shard, force leader election
/v2/c/collection-name/shards/shard-name
/replica-name
Delete a replica.
/v2/cores
Create a core.
/v2/cores/core-name
Reload, rename, delete, and unload a core.
/v2/node
Perform overseer operation, rejoin leader election.
/v2/cluster
Add role, remove role, set cluster property.
/v2/c/.system/blob
Upload and download blobs and metadata.
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Introspect
Append /_introspect to any valid v2 API path and the API specification will be returned in JSON format.
http://localhost:8983/v2/c/_introspect
To limit the introspect output to include just one particular HTTP method, add request param method with
value GET , POST , or DELETE .
http://localhost:8983/v2/c/_introspect?method=POST
Most endpoints support commands provided in a body sent via POST. To limit the introspect output to only
one command, add request param command=command-name .
http://localhost:8983/v2/c/gettingstarted/_introspect?method=POST&command=modify
Interpreting the Introspect Output
Example : http://localhost:8983/v2/c/gettingstarted/get/_introspect
{
"spec":[{
"documentation":"https://cwiki.apache.org/confluence/display/solr/RealTime+Get",
"description":"RealTime Get allows retrieving documents by ID before the documents have
been committed to the index. It is useful when you need access to documents as soon as they are
indexed but your commit times are high for other reasons.",
"methods":["GET"],
"url":{
"paths":["/c/gettingstarted/get"],
"params":{
"id":{
"type":"string",
"description":"A single document ID to retrieve."},
"ids":{
"type":"string",
"description":"One or more document IDs to retrieve. Separate by commas if more than
one ID is specified."},
"fq":{
"type":"string",
"description":"An optional filter query to add to the query. One use case for this is
security filtering, in case users or groups should not be able to retrieve the document ID
requested."}}}}],
"WARNING":"This response format is experimental. It is likely to change in the future.",
"availableSubPaths":{}}
Description of some of the keys in the above example:
? documentation : URL to the online Solr reference guide section for this API
? description : A text description of the feature/variable/command etc.
? spec/methods : HTTP methods supported by this API
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? spec/url/paths : URL paths supported by this API
? spec/url/params : List of supported URL request params
? availableSubPaths : List of valid URL subpaths and the HTTP method(s) each supports
Example of introspect for a POST API:
http://localhost:8983/v2/c/gettingstarted/_introspect?method=POST&command=modify
{
"spec":[{
"documentation":"https://cwiki.apache.org/confluence/display/solr/Collections+API",
"description":"Several collection-level operations are supported with this endpoint: modify
collection attributes; reload a collection; migrate documents to a different collection;
rebalance collection leaders; balance properties across shards; and add or delete a replica
property.",
"methods":["POST"],
"url":{"paths":["/collections/{collection}",
"/c/{collection}"]},
"commands":{"modify":{
"documentation":
"https://cwiki.apache.org/confluence/display/solr/Collections+API#CollectionsAPI-modifycoll",
"description":"Modifies specific attributes of a collection. Multiple attributes can be
changed at one time.",
"type":"object",
"properties":{
"rule":{
"type":"array",
"documentation":"https://cwiki.apache.org/confluence/display/solr/Rule-
based+Replica+Placement",
"description":"Modifies the rules for where replicas should be located in a
cluster.",
"items":{"type":"string"}},
"snitch":{
"type":"array",
"documentation":"https://cwiki.apache.org/confluence/display/solr/Rule-
based+Replica+Placement",
"description":"Details of the snitch provider",
"items":{"type":"string"}},
"autoAddReplicas":{
"type":"boolean",
"description":"When set to true, enables auto addition of replicas on shared file
systems (such as HDFS). See https://cwiki.apache.org/confluence/display/solr/Running+Solr+on+HDFS
for more details on settings and overrides."},
"replicationFactor":{
"type":"string",
"description":"The number of replicas to be created for each shard. Replicas are
physical copies of each shard, acting as failover for the shard. Note that changing this value on
an existing collection does not automatically add more replicas to the collection. However, it
will allow add-replica commands to succeed."},
"maxShardsPerNode":{
"type":"integer",
"description":"When creating collections, the shards and/or replicas are spread
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across all available, live, nodes, and two replicas of the same shard will never be on the same
node. If a node is not live when the collection is created, it will not get any parts of the new
collection, which could lead to too many replicas being created on a single live node. Defining
maxShardsPerNode sets a limit on the number of replicas can be spread to each node. If the entire
collection can not be fit into the live nodes, no collection will be created at all."}}}}}],
"WARNING":"This response format is experimental. It is likely to change in the future.",
"availableSubPaths":{
"/c/gettingstarted/select":["POST", "GET"],
"/c/gettingstarted/config":["POST", "GET"],
"/c/gettingstarted/schema":["POST", "GET"],
"/c/gettingstarted/export":["POST", "GET"],
"/c/gettingstarted/admin/ping":["POST", "GET"],
"/c/gettingstarted/update":["POST"]},
[... more sub-paths ...]
}
The "commands" section in the above example has one entry for each command supported at this endpoint.
The key is the command name and the value is a json object describing the command structure using JSON
schema (see http://json-schema.org/ for a description).
Invocation Examples
For the "gettingstarted" collection, set the replication factor and whether to automatically add replicas (see
above for the introspect output for the "modify" command used here):
$ curl http://localhost:8983/v2/c/gettingstarted -H ‘Content-type:application/json‘ -d ‘
{ modify: { replicationFactor: "3", autoAddReplicas: false } }‘
{"responseHeader":{"status":0,"QTime":842}}
See the state of the cluster:
$ curl http://localhost:8983/v2/cluster
{"responseHeader":{"status":0,"QTime":0},"collections":["gettingstarted",".system"]}
Set a cluster property:
$ curl http://localhost:8983/v2/cluster -H ‘Content-type: application/json‘ -d ‘
{ set-property: { name: autoAddReplicas, val: "false" } }‘
{"responseHeader":{"status":0,"QTime":4}}
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SolrCloud
Apache Solr includes the ability to set up a cluster of Solr servers that combines fault tolerance and high
availability. Called SolrCloud, these capabilities provide distributed indexing and search capabilities,
supporting the following features:
? Central configuration for the entire cluster
? Automatic load balancing and fail-over for queries
? ZooKeeper integration for cluster coordination and configuration.
SolrCloud is flexible distributed search and indexing, without a master node to allocate nodes, shards and
replicas. Instead, Solr uses ZooKeeper to manage these locations, depending on configuration files and
schemas. Queries and updates can be sent to any server. Solr will use the information in the ZooKeeper
database to figure out which servers need to handle the request.
In this section, we’ll cover everything you need to know about using Solr in SolrCloud mode. We’ve split up
the details into the following topics:
? Getting Started with SolrCloud
? How SolrCloud Works
? Shards and Indexing Data in SolrCloud
? Distributed Requests
? Read and Write Side Fault Tolerance
? SolrCloud Configuration and Parameters
? Setting Up an External ZooKeeper Ensemble
? Using ZooKeeper to Manage Configuration Files
? ZooKeeper Access Control
? Collections API
? Parameter Reference
? Command Line Utilities
? SolrCloud with Legacy Configuration Files
? ConfigSets API
? Rule-based Replica Placement
? Cross Data Center Replication (CDCR)
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Getting Started with SolrCloud
SolrCloud is designed to provide a highly available, fault tolerant environment for distributing your indexed
content and query requests across multiple servers.
It’s a system in which data is organized into multiple pieces, or shards, that can be hosted on multiple
machines, with replicas providing redundancy for both scalability and fault tolerance, and a ZooKeeper
server that helps manage the overall structure so that both indexing and search requests can be routed
properly.
This section explains SolrCloud and its inner workings in detail, but before you dive in, it’s best to have an
idea of what it is you’re trying to accomplish.
This page provides a simple tutorial to start Solr in SolrCloud mode, so you can begin to get a sense for how
shards interact with each other during indexing and when serving queries. To that end, we’ll use simple
examples of configuring SolrCloud on a single machine, which is obviously not a real production
environment, which would include several servers or virtual machines. In a real production environment,
you’ll also use the real machine names instead of "localhost" which we’ve used here.
In this section you will learn how to start a SolrCloud cluster using startup scripts and a specific configset.
?
This tutorial assumes that you’re already familiar with the basics of using Solr. If you need a
refresher, please see the Getting Started section to get a grounding in Solr concepts. If you
load documents as part of that exercise, you should start over with a fresh Solr installation
for these SolrCloud tutorials.
SolrCloud Example
Interactive Startup
The bin/solr script makes it easy to get started with SolrCloud as it walks you through the process of
launching Solr nodes in cloud mode and adding a collection. To get started, simply do:
bin/solr -e cloud
This starts an interactive session to walk you through the steps of setting up a simple SolrCloud cluster with
embedded ZooKeeper.
The script starts by asking you how many Solr nodes you want to run in your local cluster, with the default
being 2.
Welcome to the SolrCloud example!
This interactive session will help you launch a SolrCloud cluster on your local workstation.
To begin, how many Solr nodes would you like to run in your local cluster? (specify 1-4 nodes)
[2]
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The script supports starting up to 4 nodes, but we recommend using the default of 2 when starting out.
These nodes will each exist on a single machine, but will use different ports to mimic operation on different
servers.
Next, the script will prompt you for the port to bind each of the Solr nodes to, such as:
Please enter the port for node1 [8983]
Choose any available port for each node; the default for the first node is 8983 and 7574 for the second node.
The script will start each node in order and show you the command it uses to start the server, such as:
solr start -cloud -s example/cloud/node1/solr -p 8983
The first node will also start an embedded ZooKeeper server bound to port 9983. The Solr home for the first
node is in example/cloud/node1/solr as indicated by the -s option.
After starting up all nodes in the cluster, the script prompts you for the name of the collection to create:
Please provide a name for your new collection: [gettingstarted]
The suggested default is "gettingstarted" but you might want to choose a name more appropriate for your
specific search application.
Next, the script prompts you for the number of shards to distribute the collection across. Sharding is
covered in more detail later on, so if you’re unsure, we suggest using the default of 2 so that you can see
how a collection is distributed across multiple nodes in a SolrCloud cluster.
Next, the script will prompt you for the number of replicas to create for each shard. Replication is covered in
more detail later in the guide, so if you’re unsure, then use the default of 2 so that you can see how
replication is handled in SolrCloud.
Lastly, the script will prompt you for the name of a configuration directory for your collection. You can
choose basic_configs, data_driven_schema_configs, or sample_techproducts_configs. The configuration
directories are pulled from server/solr/configsets/ so you can review them beforehand if you wish. The
data_driven_schema_configs configuration (the default) is useful when you’re still designing a schema for
your documents and need some flexiblity as you experiment with Solr.
At this point, you should have a new collection created in your local SolrCloud cluster. To verify this, you can
run the status command:
bin/solr status
If you encounter any errors during this process, check the Solr log files in example/cloud/node1/logs and
example/cloud/node2/logs .
You can see how your collection is deployed across the cluster by visiting the cloud panel in the Solr Admin
UI: http://localhost:8983/solr/#/~cloud. Solr also provides a way to perform basic diagnostics for a collection
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using the healthcheck command:
bin/solr healthcheck -c gettingstarted
The healthcheck command gathers basic information about each replica in a collection, such as number of
docs, current status (active, down, etc), and address (where the replica lives in the cluster).
Documents can now be added to SolrCloud using the Post Tool.
To stop Solr in SolrCloud mode, you would use the bin/solr script and issue the stop command, as in:
bin/solr stop -all
Starting with -noprompt
You can also get SolrCloud started with all the defaults instead of the interactive session using the following
command:
bin/solr -e cloud -noprompt
Restarting Nodes
You can restart your SolrCloud nodes using the bin/solr script. For instance, to restart node1 running on
port 8983 (with an embedded ZooKeeper server), you would do:
bin/solr restart -c -p 8983 -s example/cloud/node1/solr
To restart node2 running on port 7574, you can do:
bin/solr restart -c -p 7574 -z localhost:9983 -s example/cloud/node2/solr
Notice that you need to specify the ZooKeeper address ( -z localhost:9983 ) when starting node2 so that it
can join the cluster with node1.
Adding a node to a cluster
Adding a node to an existing cluster is a bit advanced and involves a little more understanding of Solr. Once
you startup a SolrCloud cluster using the startup scripts, you can add a new node to it by:
mkdir <solr.home for new solr node>
cp <existing solr.xml path> <new solr.home>
bin/solr start -cloud -s solr.home/solr -p <port num> -z <zk hosts string>
Notice that the above requires you to create a Solr home directory. You either need to copy solr.xml to the
solr_home directory, or keep in centrally in ZooKeeper /solr.xml .
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Example (with directory structure) that adds a node to an example started with "bin/solr -e cloud":
mkdir -p example/cloud/node3/solr
cp server/solr/solr.xml example/cloud/node3/solr
bin/solr start -cloud -s example/cloud/node3/solr -p 8987 -z localhost:9983
The previous command will start another Solr node on port 8987 with Solr home set to
example/cloud/node3/solr . The new node will write its log files to example/cloud/node3/logs .
Once you’re comfortable with how the SolrCloud example works, we recommend using the process
described in Taking Solr to Production for setting up SolrCloud nodes in production.
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How SolrCloud Works
The following sections cover provide general information about how various SolrCloud features work. To
understand these features, it’s important to first understand a few key concepts that relate to SolrCloud.
? Shards and Indexing Data in SolrCloud
? Distributed Requests
? Read and Write Side Fault Tolerance
If you are already familiar with SolrCloud concepts and basic functionality, you can skip to the section
covering SolrCloud Configuration and Parameters.
Key SolrCloud Concepts
A SolrCloud cluster consists of some "logical" concepts layered on top of some "physical" concepts.
Logical
? A Cluster can host multiple Collections of Solr Documents.
? A collection can be partitioned into multiple Shards, which contain a subset of the Documents in the
Collection.
? The number of Shards that a Collection has determines:
? The theoretical limit to the number of Documents that Collection can reasonably contain.
? The amount of parallelization that is possible for an individual search request.
Physical
? A Cluster is made up of one or more Solr Nodes, which are running instances of the Solr server process.
? Each Node can host multiple Cores.
? Each Core in a Cluster is a physical Replica for a logical Shard.
? Every Replica uses the same configuration specified for the Collection that it is a part of.
? The number of Replicas that each Shard has determines:
? The level of redundancy built into the Collection and how fault tolerant the Cluster can be in the event
that some Nodes become unavailable.
? The theoretical limit in the number concurrent search requests that can be processed under heavy
load.
Shards and Indexing Data in SolrCloud
When your collection is too large for one node, you can break it up and store it in sections by creating
multiple shards.
A Shard is a logical partition of the collection, containing a subset of documents from the collection, such
that every document in a collection is contained in exactly one Shard. Which shard contains a each
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document in a collection depends on the overall "Sharding" strategy for that collection. For example, you
might have a collection where the "country" field of each document determines which shard it is part of, so
documents from the same country are co-located. A different collection might simply use a "hash" on the
uniqueKey of each document to determine its Shard.
Before SolrCloud, Solr supported Distributed Search, which allowed one query to be executed across
multiple shards, so the query was executed against the entire Solr index and no documents would be
missed from the search results. So splitting an index across shards is not exclusively a SolrCloud concept.
There were, however, several problems with the distributed approach that necessitated improvement with
SolrCloud:
1. Splitting an index into shards was somewhat manual.
2. There was no support for distributed indexing, which meant that you needed to explicitly send
documents to a specific shard; Solr couldn’t figure out on its own what shards to send documents to.
3. There was no load balancing or failover, so if you got a high number of queries, you needed to figure out
where to send them and if one shard died it was just gone.
SolrCloud fixes all those problems. There is support for distributing both the index process and the queries
automatically, and ZooKeeper provides failover and load balancing. Additionally, every shard can also have
multiple replicas for additional robustness.
In SolrCloud there are no masters or slaves. Instead, every shard consists of at least one physical replica,
exactly one of which is a leader. Leaders are automatically elected, initially on a first-come-first-served basis,
and then based on the ZooKeeper process described at
http://zookeeper.apache.org/doc/trunk/recipes.html#sc_leaderElection..
If a leader goes down, one of the other replicas is automatically elected as the new leader.
When a document is sent to a Solr node for indexing, the system first determines which Shard that
document belongs to, and then which node is currently hosting the leader for that shard. The document is
then forwarded to the current leader for indexing, and the leader forwards the update to all of the other
replicas.
Document Routing
Solr offers the ability to specify the router implementation used by a collection by specifying the
router.name parameter when creating your collection.
If you use the (default) “ compositeId ” router, you can send documents with a prefix in the document ID
which will be used to calculate the hash Solr uses to determine the shard a document is sent to for indexing.
The prefix can be anything you’d like it to be (it doesn’t have to be the shard name, for example), but it must
be consistent so Solr behaves consistently. For example, if you wanted to co-locate documents for a
customer, you could use the customer name or ID as the prefix. If your customer is "IBM", for example, with
a document with the ID "12345", you would insert the prefix into the document id field: "IBM!12345". The
exclamation mark (‘!‘) is critical here, as it distinguishes the prefix used to determine which shard to direct
the document to.
Then at query time, you include the prefix(es) into your query with the _route_ parameter (i.e.,
q=solr&_route_=IBM! ) to direct queries to specific shards. In some situations, this may improve query
performance because it overcomes network latency when querying all the shards.
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The compositeId router supports prefixes containing up to 2 levels of routing. For example: a prefix routing
first by region, then by customer: "USA!IBM!12345"
Another use case could be if the customer "IBM" has a lot of documents and you want to spread it across
multiple shards. The syntax for such a use case would be : "shard_key/num!document_id" where the /num
is the number of bits from the shard key to use in the composite hash.
So "IBM/3!12345" will take 3 bits from the shard key and 29 bits from the unique doc id, spreading the
tenant over 1/8th of the shards in the collection. Likewise if the num value was 2 it would spread the
documents across 1/4th the number of shards. At query time, you include the prefix(es) along with the
number of bits into your query with the _route_ parameter (i.e., q=solr&_route_=IBM/3! ) to direct queries
to specific shards.
If you do not want to influence how documents are stored, you don’t need to specify a prefix in your
document ID.
If you created the collection and defined the "implicit" router at the time of creation, you can additionally
define a router.field parameter to use a field from each document to identify a shard where the
document belongs. If the field specified is missing in the document, however, the document will be rejected.
You could also use the _route_ parameter to name a specific shard.
Shard Splitting
When you create a collection in SolrCloud, you decide on the initial number shards to be used. But it can be
difficult to know in advance the number of shards that you need, particularly when organizational
requirements can change at a moment’s notice, and the cost of finding out later that you chose wrong can
be high, involving creating new cores and re-indexing all of your data.
The ability to split shards is in the Collections API. It currently allows splitting a shard into two pieces. The
existing shard is left as-is, so the split action effectively makes two copies of the data as new shards. You can
delete the old shard at a later time when you’re ready.
More details on how to use shard splitting is in the section on the Collection API’s SPLITSHARD command.
Ignoring Commits from Client Applications in SolrCloud
In most cases, when running in SolrCloud mode, indexing client applications should not send explicit commit
requests. Rather, you should configure auto commits with openSearcher=false and auto soft-commits to
make recent updates visible in search requests. This ensures that auto commits occur on a regular schedule
in the cluster.
To enforce a policy where client applications should not send explicit commits, you should update all client
applications that index data into SolrCloud. However, that is not always feasible, so Solr provides the
IgnoreCommitOptimizeUpdateProcessorFactory , which allows you to ignore explicit commits and/or
optimize requests from client applications without having refactor your client application code.
To activate this request processor you’ll need to add the following to your solrconfig.xml :
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<updateRequestProcessorChain name="ignore-commit-from-client" default="true">
<processor class="solr.IgnoreCommitOptimizeUpdateProcessorFactory">
<int name="statusCode">200</int>
</processor>
<processor class="solr.LogUpdateProcessorFactory" />
<processor class="solr.DistributedUpdateProcessorFactory" />
<processor class="solr.RunUpdateProcessorFactory" />
</updateRequestProcessorChain>
As shown in the example above, the processor will return 200 to the client but will ignore the commit /
optimize request. Notice that you need to wire-in the implicit processors needed by SolrCloud as well, since
this custom chain is taking the place of the default chain.
In the following example, the processor will raise an exception with a 403 code with a customized error
message:
<updateRequestProcessorChain name="ignore-commit-from-client" default="true">
<processor class="solr.IgnoreCommitOptimizeUpdateProcessorFactory">
<int name="statusCode">403</int>
<str name="responseMessage">Thou shall not issue a commit!</str>
</processor>
<processor class="solr.LogUpdateProcessorFactory" />
<processor class="solr.DistributedUpdateProcessorFactory" />
<processor class="solr.RunUpdateProcessorFactory" />
</updateRequestProcessorChain>
Lastly, you can also configure it to just ignore optimize and let commits pass thru by doing:
<updateRequestProcessorChain name="ignore-optimize-only-from-client-403">
<processor class="solr.IgnoreCommitOptimizeUpdateProcessorFactory">
<str name="responseMessage">Thou shall not issue an optimize, but commits are OK!</str>
<bool name="ignoreOptimizeOnly">true</bool>
</processor>
<processor class="solr.RunUpdateProcessorFactory" />
</updateRequestProcessorChain>
Distributed Requests
When a Solr node receives a search request, the request is routed behind the scenes to a replica of a shard
that is part of the collection being searched.
The chosen replica acts as an aggregator: it creates internal requests to randomly chosen replicas of every
shard in the collection, coordinates the responses, issues any subsequent internal requests as needed (for
example, to refine facets values, or request additional stored fields), and constructs the final response for
the client.
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Limiting Which Shards are Queried
While one of the advantages of using SolrCloud is the ability to query very large collections distributed
among various shards, in some cases you may know that you are only interested in results from a subset of
your shards. You have the option of searching over all of your data or just parts of it.
Querying all shards for a collection should look familiar; it’s as though SolrCloud didn’t even come into play:
http://localhost:8983/solr/gettingstarted/select?q=*:*
If, on the other hand, you wanted to search just one shard, you can specify that shard by its logical ID, as in:
http://localhost:8983/solr/gettingstarted/select?q=*:*&shards=shard1
If you want to search a group of shard Ids, you can specify them together:
http://localhost:8983/solr/gettingstarted/select?q=*:*&shards=shard1,shard2
In both of the above examples, the shard Id(s) will be used to pick a random replica of that shard.
Alternatively, you can specify the explicit replicas you wish to use in place of a shard Ids:
http://localhost:8983/solr/gettingstarted/select?q=*:*&shards=localhost:7574/solr/gettingstarted,
localhost:8983/solr/gettingstarted
Or you can specify a list of replicas to choose from for a single shard (for load balancing purposes) by using
the pipe symbol (|):
http://localhost:8983/solr/gettingstarted/select?q=*:*&shards=localhost:7574/solr/gettingstarted|
localhost:7500/solr/gettingstarted
And of course, you can specify a list of shards (seperated by commas) each defined by a list of replicas
(seperated by pipes). In this example, 2 shards are queried, the first being a random replica from shard1, the
second being a random replica from the explicit pipe delimited list:
http://localhost:8983/solr/gettingstarted/select?q=*:*&shards=shard1,localhost:7574/solr/gettings
tarted|localhost:7500/solr/gettingstarted
Configuring the ShardHandlerFactory
You can directly configure aspects of the concurrency and thread-pooling used within distributed search in
Solr. This allows for finer grained control and you can tune it to target your own specific requirements. The
default configuration favors throughput over latency.
To configure the standard handler, provide a configuration like this in solrconfig.xml :
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<requestHandler name="standard" class="solr.SearchHandler" default="true">
<!-- other params go here -->
<shardHandler class="HttpShardHandlerFactory">
<int name="socketTimeOut">1000</int>
<int name="connTimeOut">5000</int>
</shardHandler>
</requestHandler>
The parameters that can be specified are as follows:
Parameter Default Explanation
socketTimeout
0 (use OS
default)
The amount of time in ms that a socket is allowed to wait.
connTimeout
0 (use OS
default)
The amount of time in ms that is accepted for binding / connecting a
socket
maxConnectionsPer
Host
20 The maximum number of concurrent connections that is made to
each individual shard in a distributed search.
maxConnections 10000
The total maximum number of concurrent connections in distributed
searches.
corePoolSize
0 The retained lowest limit on the number of threads used in
coordinating distributed search.
maximumPoolSize
Integer.MAX_V
ALUE
The maximum number of threads used for coordinating distributed
search.
maxThreadIdleTime
5 seconds The amount of time to wait for before threads are scaled back in
response to a reduction in load.
sizeOfQueue
-1 If specified, the thread pool will use a backing queue instead of a
direct handoff buffer. High throughput systems will want to
configure this to be a direct hand off (with -1). Systems that desire
better latency will want to configure a reasonable size of queue to
handle variations in requests.
fairnessPolicy
false Chooses the JVM specifics dealing with fair policy queuing, if enabled
distributed searches will be handled in a First in First out fashion at a
cost to throughput. If disabled throughput will be favored over
latency.
Configuring statsCache (Distributed IDF)
Document and term statistics are needed in order to calculate relevancy. Solr provides four implementations
out of the box when it comes to document stats calculation:
? LocalStatsCache : This only uses local term and document statistics to compute relevance. In cases with
uniform term distribution across shards, this works reasonably well.This option is the default if no
<statsCache> is configured.
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? ExactStatsCache : This implementation uses global values (across the collection) for document
frequency.
? ExactSharedStatsCache : This is exactly like the exact stats cache in its functionality but the global stats
are reused for subsequent requests with the same terms.
? LRUStatsCache : This implementation uses an LRU cache to hold global stats, which are shared between
requests.
The implementation can be selected by setting <statsCache> in solrconfig.xml . For example, the following
line makes Solr use the ExactStatsCache implementation:
<statsCache class="org.apache.solr.search.stats.ExactStatsCache"/>
Avoiding Distributed Deadlock
Each shard serves top-level query requests and then makes sub-requests to all of the other shards. Care
should be taken to ensure that the max number of threads serving HTTP requests is greater than the
possible number of requests from both top-level clients and other shards. If this is not the case, the
configuration may result in a distributed deadlock.
For example, a deadlock might occur in the case of two shards, each with just a single thread to service HTTP
requests. Both threads could receive a top-level request concurrently, and make sub-requests to each other.
Because there are no more remaining threads to service requests, the incoming requests will be blocked
until the other pending requests are finished, but they will not finish since they are waiting for the sub-
requests. By ensuring that Solr is configured to handle a sufficient number of threads, you can avoid
deadlock situations like this.
Prefer Local Shards
Solr allows you to pass an optional boolean parameter named preferLocalShards to indicate that a
distributed query should prefer local replicas of a shard when available. In other words, if a query includes
preferLocalShards=true , then the query controller will look for local replicas to service the query instead of
selecting replicas at random from across the cluster. This is useful when a query requests many fields or
large fields to be returned per document because it avoids moving large amounts of data over the network
when it is available locally. In addition, this feature can be useful for minimizing the impact of a problematic
replica with degraded performance, as it reduces the likelihood that the degraded replica will be hit by other
healthy replicas.
Lastly, it follows that the value of this feature diminishes as the number of shards in a collection increases
because the query controller will have to direct the query to non-local replicas for most of the shards. In
other words, this feature is mostly useful for optimizing queries directed towards collections with a small
number of shards and many replicas. Also, this option should only be used if you are load balancing
requests across all nodes that host replicas for the collection you are querying, as Solr’s CloudSolrClient will
do. If not load-balancing, this feature can introduce a hotspot in the cluster since queries won’t be evenly
distributed across the cluster.
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Read and Write Side Fault Tolerance
SolrCloud supports elasticity, high availability, and fault tolerance in reads and writes.
What this means, basically, is that when you have a large cluster, you can always make requests to the
cluster: Reads will return results whenever possible, even if some nodes are down, and Writes will be
acknowledged only if they are durable; i.e., you won’t lose data.
Read Side Fault Tolerance
In a SolrCloud cluster each individual node load balances read requests across all the replicas in collection.
You still need a load balancer on the ‘outside‘ that talks to the cluster, or you need a smart client which
understands how to read and interact with Solr’s metadata in ZooKeeper and only requests the ZooKeeper
ensemble’s address to start discovering to which nodes it should send requests. (Solr provides a smart Java
SolrJ client called CloudSolrClient.)
Even if some nodes in the cluster are offline or unreachable, a Solr node will be able to correctly respond to a
search request as long as it can communicate with at least one replica of every shard, or one replica of every
relevant shard if the user limited the search via the shards or _route_ parameters. The more replicas there
are of every shard, the more likely that the Solr cluster will be able to handle search results in the event of
node failures.
zkConnected
A Solr node will return the results of a search request as long as it can communicate with at least one replica
of every shard that it knows about, even if it can not communicate with ZooKeeper at the time it receives the
request. This is normally the preferred behavior from a fault tolerance standpoint, but may result in stale or
incorrect results if there have been major changes to the collection structure that the node has not been
informed of via ZooKeeper (i.e., shards may have been added or removed, or split into sub-shards)
A zkConnected header is included in every search response indicating if the node that processed the request
was connected with ZooKeeper at the time:
Solr Response with partialResults
{
"responseHeader": {
"status": 0,
"zkConnected": true,
"QTime": 20,
"params": {
"q": "*:*"
}
},
"response": {
"numFound": 107,
"start": 0,
"docs": [ "..." ]
}
}
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shards.tolerant
In the event that one or more shards queried are completely unavailable, then Solr’s default behavior is to
fail the request. However, there are many use-cases where partial results are acceptable and so Solr
provides a boolean shards.tolerant parameter (default false ).
If shards.tolerant=true then partial results may be returned. If the returned response does not contain
results from all the appropriate shards then the response header contains a special flag called
partialResults .
The client can specify ‘ shards.info ‘ along with the shards.tolerant parameter to retrieve more fine-
grained details.
Example response with partialResults flag set to ‘true‘:
Solr Response with partialResults
{
"responseHeader": {
"status": 0,
"zkConnected": true,
"partialResults": true,
"QTime": 20,
"params": {
"q": "*:*"
}
},
"response": {
"numFound": 77,
"start": 0,
"docs": [ "..." ]
}
}
Write Side Fault Tolerance
SolrCloud is designed to replicate documents to ensure redundancy for your data, and enable you to send
update requests to any node in the cluster. That node will determine if it hosts the leader for the appropriate
shard, and if not it will forward the request to the the leader, which will then forward it to all existing
replicas, using versioning to make sure every replica has the most up-to-date version. If the leader goes
down, another replica can take its place. This architecture enables you to be certain that your data can be
recovered in the event of a disaster, even if you are using Near Real Time Searching.
Recovery
A Transaction Log is created for each node so that every change to content or organization is noted. The log
is used to determine which content in the node should be included in a replica. When a new replica is
created, it refers to the Leader and the Transaction Log to know which content to include. If it fails, it retries.
Since the Transaction Log consists of a record of updates, it allows for more robust indexing because it
includes redoing the uncommitted updates if indexing is interrupted.
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If a leader goes down, it may have sent requests to some replicas and not others. So when a new potential
leader is identified, it runs a synch process against the other replicas. If this is successful, everything should
be consistent, the leader registers as active, and normal actions proceed. If a replica is too far out of sync,
the system asks for a full replication/replay-based recovery.
If an update fails because cores are reloading schemas and some have finished but others have not, the
leader tells the nodes that the update failed and starts the recovery procedure.
Achieved Replication Factor
When using a replication factor greater than one, an update request may succeed on the shard leader but
fail on one or more of the replicas. For instance, consider a collection with one shard and a replication factor
of three. In this case, you have a shard leader and two additional replicas. If an update request succeeds on
the leader but fails on both replicas, for whatever reason, the update request is still considered successful
from the perspective of the client. The replicas that missed the update will sync with the leader when they
recover.
Behind the scenes, this means that Solr has accepted updates that are only on one of the nodes (the current
leader). Solr supports the optional min_rf parameter on update requests that cause the server to return the
achieved replication factor for an update request in the response. For the example scenario described
above, if the client application included min_rf >= 1 , then Solr would return rf=1 in the Solr response
header because the request only succeeded on the leader. The update request will still be accepted as the
min_rf parameter only tells Solr that the client application wishes to know what the achieved replication
factor was for the update request. In other words, min_rf does not mean Solr will enforce a minimum
replication factor as Solr does not support rolling back updates that succeed on a subset of replicas.
On the client side, if the achieved replication factor is less than the acceptable level, then the client
application can take additional measures to handle the degraded state. For instance, a client application may
want to keep a log of which update requests were sent while the state of the collection was degraded and
then resend the updates once the problem has been resolved. In short, min_rf is an optional mechanism for
a client application to be warned that an update request was accepted while the collection is in a degraded
state.
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SolrCloud Configuration and Parameters
In this section, we’ll cover the various configuration options for SolrCloud.
The following sections cover these topics:
? Setting Up an External ZooKeeper Ensemble
? Using ZooKeeper to Manage Configuration Files
? ZooKeeper Access Control
? Collections API
? Parameter Reference
? Command Line Utilities
? SolrCloud with Legacy Configuration Files
? ConfigSets API
Setting Up an External ZooKeeper Ensemble
Although Solr comes bundled with Apache ZooKeeper, you should consider yourself discouraged from using
this internal ZooKeeper in production.
Shutting down a redundant Solr instance will also shut down its ZooKeeper server, which might not be quite
so redundant. Because a ZooKeeper ensemble must have a quorum of more than half its servers running at
any given time, this can be a problem.
The solution to this problem is to set up an external ZooKeeper ensemble. Fortunately, while this process
can seem intimidating due to the number of powerful options, setting up a simple ensemble is actually quite
straightforward, as described below.
How Many ZooKeepers?
"For a ZooKeeper service to be active, there must be a majority of non-failing machines that can
communicate with each other. To create a deployment that can tolerate the failure of F machines,
you should count on deploying 2xF+1 machines. Thus, a deployment that consists of three machines can
handle one failure, and a deployment of five machines can handle two failures. Note that a deployment of six
machines can only handle two failures since three machines is not a majority.
For this reason, ZooKeeper deployments are usually made up of an odd number of machines."
— ZooKeeper Administrator‘s Guide, http://zookeeper.apache.org/doc/r3.4.10/zookeeperAdmin.html
When planning how many ZooKeeper nodes to configure, keep in mind that the main principle for a
ZooKeeper ensemble is maintaining a majority of servers to serve requests. This majority is also called a
quorum.
It is generally recommended to have an odd number of ZooKeeper servers in your ensemble, so a majority is
maintained.
For example, if you only have two ZooKeeper nodes and one goes down, 50% of available servers is not a
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majority, so ZooKeeper will no longer serve requests. However, if you have three ZooKeeper nodes and one
goes down, you have 66% of available servers available, and ZooKeeper will continue normally while you
repair the one down node. If you have 5 nodes, you could continue operating with two down nodes if
necessary.
More information on ZooKeeper clusters is available from the ZooKeeper documentation at
http://zookeeper.apache.org/doc/r3.4.10/zookeeperAdmin.html#sc_zkMulitServerSetup.
Download Apache ZooKeeper
The first step in setting up Apache ZooKeeper is, of course, to download the software. It’s available from
http://zookeeper.apache.org/releases.html.
?
When using stand-alone ZooKeeper, you need to take care to keep your version of
ZooKeeper updated with the latest version distributed with Solr. Since you are using it as a
stand-alone application, it does not get upgraded when you upgrade Solr.
Solr currently uses Apache ZooKeeper v3.4.10.
Setting Up a Single ZooKeeper
Create the instance
Creating the instance is a simple matter of extracting the files into a specific target directory. The actual
directory itself doesn’t matter, as long as you know where it is, and where you’d like to have ZooKeeper
store its internal data.
Configure the instance
The next step is to configure your ZooKeeper instance. To do that, create the following file:
<ZOOKEEPER_HOME>/conf/zoo.cfg . To this file, add the following information:
tickTime=2000
dataDir=/var/lib/zookeeper
clientPort=2181
The parameters are as follows:
tickTime
Part of what ZooKeeper does is to determine which servers are up and running at any given time, and the
minimum session time out is defined as two "ticks". The tickTime parameter specifies, in miliseconds,
how long each tick should be.
dataDir
This is the directory in which ZooKeeper will store data about the cluster. This directory should start out
empty.
clientPort
This is the port on which Solr will access ZooKeeper.
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Once this file is in place, you’re ready to start the ZooKeeper instance.
Run the instance
To run the instance, you can simply use the ZOOKEEPER_HOME/bin/zkServer.sh script provided, as with this
command: zkServer.sh start
Again, ZooKeeper provides a great deal of power through additional configurations, but delving into them is
beyond the scope of this tutorial. For more information, see the ZooKeeper Getting Started page. For this
example, however, the defaults are fine.
Point Solr at the instance
Pointing Solr at the ZooKeeper instance you’ve created is a simple matter of using the -z parameter when
using the bin/solr script. For example, in order to point the Solr instance to the ZooKeeper you’ve started on
port 2181, this is what you’d need to do:
Starting cloud example with ZooKeeper already running at port 2181 (with all other defaults):
bin/solr start -e cloud -z localhost:2181 -noprompt
Add a node pointing to an existing ZooKeeper at port 2181:
bin/solr start -cloud -s <path to solr home for new node> -p 8987 -z localhost:2181
?
When you are not using an example to start solr, make sure you upload the configuration
set to ZooKeeper before creating the collection.
Shut down ZooKeeper
To shut down ZooKeeper, use the zkServer script with the "stop" command: zkServer.sh stop .
Setting up a ZooKeeper Ensemble
With an external ZooKeeper ensemble, you need to set things up just a little more carefully as compared to
the Getting Started example.
The difference is that rather than simply starting up the servers, you need to configure them to know about
and talk to each other first. So your original zoo.cfg file might look like this:
dataDir=/var/lib/zookeeperdata/1
clientPort=2181
initLimit=5
syncLimit=2
server.1=localhost:2888:3888
server.2=localhost:2889:3889
server.3=localhost:2890:3890
Here you see three new parameters:
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initLimit
Amount of time, in ticks, to allow followers to connect and sync to a leader. In this case, you have 5 ticks,
each of which is 2000 milliseconds long, so the server will wait as long as 10 seconds to connect and sync
with the leader.
syncLimit
Amount of time, in ticks, to allow followers to sync with ZooKeeper. If followers fall too far behind a
leader, they will be dropped.
server.X
These are the IDs and locations of all servers in the ensemble, the ports on which they communicate with
each other. The server ID must additionally stored in the <dataDir>/myid file and be located in the
dataDir of each ZooKeeper instance. The ID identifies each server, so in the case of this first instance, you
would create the file /var/lib/zookeeperdata/1/myid with the content "1".
Now, whereas with Solr you need to create entirely new directories to run multiple instances, all you need
for a new ZooKeeper instance, even if it’s on the same machine for testing purposes, is a new configuration
file. To complete the example you’ll create two more configuration files.
The <ZOOKEEPER_HOME>/conf/zoo2.cfg file should have the content:
tickTime=2000
dataDir=c:/sw/zookeeperdata/2
clientPort=2182
initLimit=5
syncLimit=2
server.1=localhost:2888:3888
server.2=localhost:2889:3889
server.3=localhost:2890:3890
You’ll also need to create <ZOOKEEPER_HOME>/conf/zoo3.cfg :
tickTime=2000
dataDir=c:/sw/zookeeperdata/3
clientPort=2183
initLimit=5
syncLimit=2
server.1=localhost:2888:3888
server.2=localhost:2889:3889
server.3=localhost:2890:3890
Finally, create your myid files in each of the dataDir directories so that each server knows which instance it
is. The id in the myid file on each machine must match the "server.X" definition. So, the ZooKeeper instance
(or machine) named "server.1" in the above example, must have a myid file containing the value "1". The
myid file can be any integer between 1 and 255, and must match the server IDs assigned in the zoo.cfg file.
To start the servers, you can simply explicitly reference the configuration files:
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cd <ZOOKEEPER_HOME>
bin/zkServer.sh start zoo.cfg
bin/zkServer.sh start zoo2.cfg
bin/zkServer.sh start zoo3.cfg
Once these servers are running, you can reference them from Solr just as you did before:
bin/solr start -e cloud -z localhost:2181,localhost:2182,localhost:2183 -noprompt
Securing the ZooKeeper connection
You may also want to secure the communication between ZooKeeper and Solr.
To setup ACL protection of znodes, see ZooKeeper Access Control.
For more information on getting the most power from your ZooKeeper installation, check out the ZooKeeper
Administrator’s Guide.
Using ZooKeeper to Manage Configuration Files
With SolrCloud your configuration files are kept in ZooKeeper.
These files are uploaded in either of the following cases:
? When you start a SolrCloud example using the bin/solr script.
? When you create a collection using the bin/solr script.
? Explicitly upload a configuration set to ZooKeeper.
Startup Bootstrap
When you try SolrCloud for the first time using the bin/solr -e cloud , the related configset gets uploaded
to ZooKeeper automatically and is linked with the newly created collection.
The below command would start SolrCloud with the default collection name (gettingstarted) and default
configset (data_driven_schema_configs) uploaded and linked to it.
bin/solr -e cloud -noprompt
You can also explicitly upload a configuration directory when creating a collection using the bin/solr script
with the -d option, such as:
bin/solr create -c mycollection -d data_driven_schema_configs
The create command will upload a copy of the data_driven_schema_configs configuration directory to
ZooKeeper under /configs/mycollection . Refer to the Solr Control Script Reference page for more details
about the create command for creating collections.
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Once a configuration directory has been uploaded to ZooKeeper, you can update them using the Solr
Control Script
?
It’s a good idea to keep these files under version control.
Uploading Configuration Files using bin/solr or SolrJ
In production situations, Config Sets can also be uploaded to ZooKeeper independent of collection creation
using either Solr’s Solr Control Script or the CloudSolrClient.uploadConfig java method.
The below command can be used to upload a new configset using the bin/solr script.
bin/solr zk upconfig -n <name for configset> -d <path to directory with configset>
It is strongly recommended that the configurations be kept in a version control system, Git, SVN or similar.
Managing Your SolrCloud Configuration Files
To update or change your SolrCloud configuration files:
1. Download the latest configuration files from ZooKeeper, using the source control checkout process.
2. Make your changes.
3. Commit your changed file to source control.
4. Push the changes back to ZooKeeper.
5. Reload the collection so that the changes will be in effect.
Preparing ZooKeeper before first cluster start
If you will share the same ZooKeeper instance with other applications you should use a chroot in ZooKeeper.
Please see ZooKeeper chroot for instructions.
There are certain configuration files containing cluster wide configuration. Since some of these are crucial
for the cluster to function properly, you may need to upload such files to ZooKeeper before starting your
Solr cluster for the first time. Examples of such configuration files (not exhaustive) are solr.xml ,
security.json and clusterprops.json .
If you for example would like to keep your solr.xml in ZooKeeper to avoid having to copy it to every node’s
solr_home directory, you can push it to ZooKeeper with the bin/solr utility (Unix example):
bin/solr cp file:local/file/path/to/solr.xml zk:/solr.xml -z localhost:2181
ZooKeeper Access Control
This section describes using ZooKeeper access control lists (ACLs) with Solr. For information about
ZooKeeper ACLs, see the ZooKeeper documentation at http://zookeeper.apache.org/doc/r3.4.10/
zookeeperProgrammers.html#sc_ZooKeeperAccessControl.
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About ZooKeeper ACLs
SolrCloud uses ZooKeeper for shared information and for coordination.
This section describes how to configure Solr to add more restrictive ACLs to the ZooKeeper content it
creates, and how to tell Solr about the credentials required to access the content in ZooKeeper. If you want
to use ACLs in your ZooKeeper nodes, you will have to activate this functionality; by default, Solr behavior is
open-unsafe ACL everywhere and uses no credentials.
Content stored in ZooKeeper is critical to the operation of a SolrCloud cluster. Open access to SolrCloud
content on ZooKeeper could lead to a variety of problems. For example:
? Changing configuration might cause Solr to fail or behave in an unintended way.
? Changing cluster state information into something wrong or inconsistent might very well make a
SolrCloud cluster behave strangely.
? Adding a delete-collection job to be carried out by the Overseer will cause data to be deleted from the
cluster.
You may want to enable ZooKeeper ACLs with Solr if you grant access to your ZooKeeper ensemble to
entities you do not trust, or if you want to reduce risk of bad actions resulting from, e.g.:
? Malware that found its way into your system.
? Other systems using the same ZooKeeper ensemble (a "bad thing" might be done by accident).
You might even want to limit read-access, if you think there is stuff in ZooKeeper that not everyone should
know about. Or you might just in general work on a need-to-know basis.
Protecting ZooKeeper itself could mean many different things. This section is about protecting Solr
content in ZooKeeper. ZooKeeper content basically lives persisted on disk and (partly) in memory of the
ZooKeeper processes. This section is not about protecting ZooKeeper data at storage or ZooKeeper
process levels - that’s for ZooKeeper to deal with.
But this content is also available to "the outside" via the ZooKeeper API. Outside processes can connect to
ZooKeeper and create/update/delete/read content; for example, a Solr node in a SolrCloud cluster wants to
create/update/delete/read, and a SolrJ client wants to read from the cluster. It is the responsibility of the
outside processes that create/update content to setup ACLs on the content. ACLs describe who is allowed to
read, update, delete, create, etc. Each piece of information (znode/content) in ZooKeeper has its own set of
ACLs, and inheritance or sharing is not possible. The default behavior in Solr is to add one ACL on all the
content it creates - one ACL that gives anyone the permission to do anything (in ZooKeeper terms this is
called "the open-unsafe ACL").
How to Enable ACLs
We want to be able to:
1. Control the credentials Solr uses for its ZooKeeper connections. The credentials are used to get
permission to perform operations in ZooKeeper.
2. Control which ACLs Solr will add to znodes (ZooKeeper files/folders) it creates in ZooKeeper.
3. Control it "from the outside", so that you do not have to modify and/or recompile Solr code to turn this
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on.
Solr nodes, clients and tools (e.g. ZkCLI) always use a java class called SolrZkClient to deal with their
ZooKeeper stuff. The implementation of the solution described here is all about changing SolrZkClient . If
you use SolrZkClient in your application, the descriptions below will be true for your application too.
Controlling Credentials
You control which credentials provider will be used by configuring the zkCredentialsProvider property in
solr.xml’s `<solrcloud> section to the name of a class (on the classpath) implementing the
ZkCredentialsProvider interface. server/solr/solr.xml in the Solr distribution defines the
zkCredentialsProvider such that it will take on the value of the same-named zkCredentialsProvider
system property if it is defined (e.g. by uncommenting the SOLR_ZK_CREDS_AND_ACLS environment variable
definition in solr.in.sh/.cmd - see below), or if not, default to the DefaultZkCredentialsProvider
implementation.
Out of the Box Credential Implementations
You can always make you own implementation, but Solr comes with two implementations:
? org.apache.solr.common.cloud.DefaultZkCredentialsProvider : Its getCredentials() returns a list
of length zero, or "no credentials used". This is the default.
? org.apache.solr.common.cloud.VMParamsSingleSetCredentialsDigestZkCredentialsProvider : This
lets you define your credentials using system properties. It supports at most one set of credentials.
? The schema is "digest". The username and password are defined by system properties
zkDigestUsername and zkDigestPassword . This set of credentials will be added to the list of
credentials returned by getCredentials() if both username and password are provided.
? If the one set of credentials above is not added to the list, this implementation will fall back to default
behavior and use the (empty) credentials list from DefaultZkCredentialsProvider .
Controlling ACLs
You control which ACLs will be added by configuring zkACLProvider property in solr.xml’s `<solrcloud>
section to the name of a class (on the classpath) implementing the ZkACLProvider interface.
server/solr/solr.xml in the Solr distribution defines the zkACLProvider such that it will take on the value
of the same-named zkACLProvider system property if it is defined (e.g. by uncommenting the
SOLR_ZK_CREDS_AND_ACLS environment variable definition in solr.in.sh/.cmd - see below), or if not, default
to the DefaultZkACLProvider implementation.
Out of the Box ACL Implementations
You can always make you own implementation, but Solr comes with:
? org.apache.solr.common.cloud.DefaultZkACLProvider : It returns a list of length one for all zNodePath
-s. The single ACL entry in the list is "open-unsafe". This is the default.
? org.apache.solr.common.cloud.VMParamsAllAndReadonlyDigestZkACLProvider : This lets you define
your ACLs using system properties. Its getACLsToAdd() implementation does not use zNodePath for
anything, so all znodes will get the same set of ACLs. It supports adding one or both of these options:
? A user that is allowed to do everything.
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? The permission is ALL (corresponding to all of CREATE , READ , WRITE , DELETE , and ADMIN ), and the
schema is "digest".
? The username and password are defined by system properties zkDigestUsername and
zkDigestPassword , respectively.
? This ACL will not be added to the list of ACLs unless both username and password are provided.
? A user that is only allowed to perform read operations.
? The permission is READ and the schema is digest .
? The username and password are defined by system properties zkDigestReadonlyUsername and
zkDigestReadonlyPassword , respectively.
? This ACL will not be added to the list of ACLs unless both username and password are provided.
? org.apache.solr.common.cloud.SaslZkACLProvider : Requires SASL authentication. Gives all
permissions for the user specified in system property solr.authorization.superuser (default: solr )
when using SASL, and gives read permissions for anyone else. Designed for a setup where
configurations have already been set up and will not be modified, or where configuration changes are
controlled via Solr APIs. This provider will be useful for administration in a kerberos environment. In such
an environment, the administrator wants Solr to authenticate to ZooKeeper using SASL, since this is only
way to authenticate with ZooKeeper via Kerberos.
If none of the above ACLs is added to the list, the (empty) ACL list of DefaultZkACLProvider will be used by
default.
Notice the overlap in system property names with credentials provider
VMParamsSingleSetCredentialsDigestZkCredentialsProvider (described above). This is to let the two
providers collaborate in a nice and perhaps common way: we always protect access to content by limiting to
two users - an admin-user and a readonly-user - AND we always connect with credentials corresponding to
this same admin-user, basically so that we can do anything to the content/znodes we create ourselves.
You can give the readonly credentials to "clients" of your SolrCloud cluster - e.g. to be used by SolrJ clients.
They will be able to read whatever is necessary to run a functioning SolrJ client, but they will not be able to
modify any content in ZooKeeper.
ZooKeeper ACLs in Solr Scripts
There are two scripts that impact ZooKeeper ACLs:
? For *nix systems: bin/solr & server/scripts/cloud-scripts/zkcli.sh
? For Windows systems: bin/solr.cmd & server/scripts/cloud-scripts/zkcli.bat
These Solr scripts can enable use of ZK ACLs by setting the appropriate system properties: uncomment the
following and replace the passwords with ones you choose to enable the above-described VM parameters
ACL and credentials providers in the following files:
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solr.in.sh
# Settings for ZK ACL
#SOLR_ZK_CREDS_AND_ACLS="-
DzkACLProvider=org.apache.solr.common.cloud.VMParamsAllAndReadonlyDigestZkACLProvider \
#
-DzkCredentialsProvider=org.apache.solr.common.cloud.VMParamsSingleSetCredentialsDigestZkCredenti
alsProvider \
# -DzkDigestUsername=admin-user -DzkDigestPassword=CHANGEME-ADMIN-PASSWORD \
# -DzkDigestReadonlyUsername=readonly-user -DzkDigestReadonlyPassword=CHANGEME-READONLY
-PASSWORD"
#SOLR_OPTS="$SOLR_OPTS $SOLR_ZK_CREDS_AND_ACLS"
solr.in.cmd
REM Settings for ZK ACL
REM set SOLR_ZK_CREDS_AND_ACLS=-
DzkACLProvider=org.apache.solr.common.cloud.VMParamsAllAndReadonlyDigestZkACLProvider ^
REM
-DzkCredentialsProvider=org.apache.solr.common.cloud.VMParamsSingleSetCredentialsDigestZkCredenti
alsProvider ^
REM -DzkDigestUsername=admin-user -DzkDigestPassword=CHANGEME-ADMIN-PASSWORD ^
REM -DzkDigestReadonlyUsername=readonly-user -DzkDigestReadonlyPassword=CHANGEME-READONLY
-PASSWORD
REM set SOLR_OPTS=%SOLR_OPTS% %SOLR_ZK_CREDS_AND_ACLS%
zkcli.sh
# Settings for ZK ACL
#SOLR_ZK_CREDS_AND_ACLS="-
DzkACLProvider=org.apache.solr.common.cloud.VMParamsAllAndReadonlyDigestZkACLProvider \
#
-DzkCredentialsProvider=org.apache.solr.common.cloud.VMParamsSingleSetCredentialsDigestZkCredenti
alsProvider \
# -DzkDigestUsername=admin-user -DzkDigestPassword=CHANGEME-ADMIN-PASSWORD \
# -DzkDigestReadonlyUsername=readonly-user -DzkDigestReadonlyPassword=CHANGEME-READONLY
-PASSWORD"
zkcli.bat
REM Settings for ZK ACL
REM set SOLR_ZK_CREDS_AND_ACLS=-
DzkACLProvider=org.apache.solr.common.cloud.VMParamsAllAndReadonlyDigestZkACLProvider ^
REM
-DzkCredentialsProvider=org.apache.solr.common.cloud.VMParamsSingleSetCredentialsDigestZkCredenti
alsProvider ^
REM -DzkDigestUsername=admin-user -DzkDigestPassword=CHANGEME-ADMIN-PASSWORD ^
REM -DzkDigestReadonlyUsername=readonly-user -DzkDigestReadonlyPassword=CHANGEME-READONLY
-PASSWORD
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Changing ACL Schemes
Over the lifetime of operating your Solr cluster, you may decide to move from an unsecured ZooKeeper to a
secured instance. Changing the configured zkACLProvider in solr.xml will ensure that newly created nodes
are secure, but will not protect the already existing data. To modify all existing ACLs, you can use the
updateacls command with Solr’s ZkCLI. First uncomment the SOLR_ZK_CREDS_AND_ACLS environment
variable definition in server/scripts/cloud-scripts/zkcli.sh (or zkcli.bat on Windows) and fill in the
passwords for the admin-user and the readonly-user - see above - then run server/scripts/cloud-
scripts/zkcli.sh -cmd updateacls /zk-path , or on Windows run server\scripts\cloud-
scripts\zkcli.bat cmd updateacls /zk-path .
Changing ACLs in ZK should only be done while your SolrCloud cluster is stopped. Attempting to do so while
Solr is running may result in inconsistent state and some nodes becoming inaccessible.
The VM properties zkACLProvider and zkCredentialsProvider , included in the SOLR_ZK_CREDS_AND_ACLS
environment variable in zkcli.sh/.bat , control the conversion:
? The Credentials Provider must be one that has current admin privileges on the nodes. When omitted, the
process will use no credentials (suitable for an unsecure configuration).
? The ACL Provider will be used to compute the new ACLs. When omitted, the process will set all
permissions to all users, removing any security present.
The uncommented SOLR_ZK_CREDS_AND_ACLS environment variable in zkcli.sh/.bat sets the credentials
and ACL providers to the VMParamsSingleSetCredentialsDigestZkCredentialsProvider and
VMParamsAllAndReadonlyDigestZkACLProvider implementations, described earlier in the page.
Collections API
The Collections API is used to enable you to create, remove, or reload collections, but in the context of
SolrCloud you can also use it to create collections with a specific number of shards and replicas.
CREATE: Create a Collection
/admin/collections?action=CREATE&name=name&numShards=number&replicationFactor=number&maxShar
dsPerNode=number&createNodeSet=nodelist&collection.configName=configname
Input
Query Parameters
Key Type Required Default Description
name string Yes The name of the collection to be created.
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Key Type Required Default Description
router.name string No compositeId The router name that will be used. The
router defines how documents will be
distributed among the shards. Possible
values are implicit or compositeId.
The ‘implicit‘ router does not
automatically route documents to
different shards. Whichever shard you
indicate on the indexing request (or
within each document) will be used as the
destination for those documents.
The ‘compositeId‘ router hashes the value
in the uniqueKey field and looks up that
hash in the collection’s clusterstate to
determine which shard will receive the
document, with the additional ability to
manually direct the routing.
When using the ‘implicit‘ router, the
shards parameter is required. When
using the ‘compositeId‘ router, the
numShards parameter is required.
For more information, see also the section
Document Routing.
numShards integer No empty The number of shards to be created as
part of the collection. This is a required
parameter when using the ‘compositeId‘
router.
shards string No empty A comma separated list of shard names,
e.g., shard-x,shard-y,shard-z. This is a
required parameter when using the
‘implicit‘ router.
replicationFactor integer No 1 The number of replicas to be created for
each shard.
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Key Type Required Default Description
maxShardsPerNode integer No 1 When creating collections, the shards
and/or replicas are spread across all
available (i.e., live) nodes, and two replicas
of the same shard will never be on the
same node. If a node is not live when the
CREATE operation is called, it will not get
any parts of the new collection, which
could lead to too many replicas being
created on a single live node. Defining
maxShardsPerNode sets a limit on the
number of replicas CREATE will spread to
each node. If the entire collection can not
be fit into the live nodes, no collection will
be created at all.
createNodeSet string No Allows defining the nodes to spread the
new collection across. If not provided, the
CREATE operation will create shard-replica
spread across all live Solr nodes. The
format is a comma-separated list of
node_names, such as
localhost:8983_solr,
localhost:8984_solr,
localhost:8985_solr . Alternatively, use
the special value of EMPTY to initially
create no shard-replica within the new
collection and then later use the
ADDREPLICA operation to add shard-
replica when and where required.
createNodeSet.shuffle boolean No true Controls wether or not the shard-replicas
created for this collection will be assigned
to the nodes specified by the
createNodeSet in a sequential manner, or
if the list of nodes should be shuffled
prior to creating individual replicas. A
‘false‘ value makes the results of a
collection creation predictible and gives
more exact control over the location of
the individual shard-replicas, but ‘true‘
can be a better choice for ensuring
replicas are distributed evenly across
nodes.
Ignored if createNodeSet is not also
specified.
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Key Type Required Default Description
collection.configName string No empty Defines the name of the configurations
(which must already be stored in
ZooKeeper) to use for this collection. If
not provided, Solr will default to the
collection name as the configuration
name.
router.field string No empty If this field is specified, the router will look
at the value of the field in an input
document to compute the hash and
identify a shard instead of looking at the
uniqueKey field. If the field specified is
null in the document, the document will
be rejected. Please note that RealTime Get
or retrieval by id would also require the
parameter _route_ (or shard.keys ) to
avoid a distributed search.
property.name=value string No Set core property name to value. See the
section Defining core.properties for
details on supported properties and
values.
autoAddReplicas boolean No false When set to true, enables auto addition of
replicas on shared file systems. See the
section autoAddReplicas Settings for
more details on settings and overrides.
async string No Request ID to track this action which will
be processed asynchronously.
rule string No Replica placement rules. See the section
Rule-based Replica Placement for details.
snitch string No Details of the snitch provider. See the
section Rule-based Replica Placement for
details.
Output
The response will include the status of the request and the new core names. If the status is anything other
than "success", an error message will explain why the request failed.
Examples
Input
http://localhost:8983/solr/admin/collections?action=CREATE&name=newCollection&numShards=2&replica
tionFactor=1
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Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">3764</int>
</lst>
<lst name="success">
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">3450</int>
</lst>
<str name="core">newCollection_shard1_replica1</str>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">3597</int>
</lst>
<str name="core">newCollection_shard2_replica1</str>
</lst>
</lst>
</response>
MODIFYCOLLECTION: Modify Attributes of a Collection
/admin/collections?action=MODIFYCOLLECTION&collection=<collection-name>&<attribute-
name>=<attribute-value>&<another-attribute-name>=<another-value>
It’s possible to edit multiple attributes at a time. Changing these values only updates the z-node on
ZooKeeper, they do not change the topology of the collection. For instance, increasing replicationFactor will
not automatically add more replicas to the collection but will allow more ADDREPLICA commands to succeed.
Query Parameters
Key Type Required Description
collection string Yes The name of the collection to be modified.
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Key Type Required Description
<attribute-name> string Yes Key-value pairs of attribute names and attribute values.
The attributes that can be modified are:
? maxShardsPerNode
? replicationFactor
? autoAddReplicas
? collection.configName
? rule
? snitch
See the CREATE section above for details on these
attributes.
RELOAD: Reload a Collection
/admin/collections?action=RELOAD&name=name
The RELOAD action is used when you have changed a configuration in ZooKeeper.
Input
Query Parameters
Key Type Required Description
name string Yes The name of the collection to reload.
async string No Request ID to track this action which will be processed
asynchronously.
Output
The response will include the status of the request and the cores that were reloaded. If the status is
anything other than "success", an error message will explain why the request failed.
Examples
Input
http://localhost:8983/solr/admin/collections?action=RELOAD&name=newCollection
Output
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<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1551</int>
</lst>
<lst name="success">
<lst name="10.0.1.6:8983_solr">
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">761</int>
</lst>
</lst>
<lst name="10.0.1.4:8983_solr">
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1527</int>
</lst>
</lst>
</lst>
</response>
SPLITSHARD: Split a Shard
/admin/collections?action=SPLITSHARD&collection=name&shard=shardID
Splitting a shard will take an existing shard and break it into two pieces which are written to disk as two
(new) shards. The original shard will continue to contain the same data as-is but it will start re-routing
requests to the new shards. The new shards will have as many replicas as the original shard. A soft commit
is automatically issued after splitting a shard so that documents are made visible on sub-shards. An explicit
commit (hard or soft) is not necessary after a split operation because the index is automatically persisted to
disk during the split operation.
This command allows for seamless splitting and requires no downtime. A shard being split will continue to
accept query and indexing requests and will automatically start routing them to the new shards once this
operation is complete. This command can only be used for SolrCloud collections created with numShards
parameter, meaning collections which rely on Solr’s hash-based routing mechanism.
The split is performed by dividing the original shard’s hash range into two equal partitions and dividing up
the documents in the original shard according to the new sub-ranges.
One can also specify an optional ranges parameter to divide the original shard’s hash range into arbitrary
hash range intervals specified in hexadecimal. For example, if the original hash range is 0-1500 then adding
the parameter: ranges=0-1f4,1f5-3e8,3e9-5dc will divide the original shard into three shards with hash range
0-500, 501-1000 and 1001-1500 respectively.
Another optional parameter split.key can be used to split a shard using a route key such that all
documents of the specified route key end up in a single dedicated sub-shard. Providing the ‘shard‘
parameter is not required in this case because the route key is enough to figure out the right shard. A route
key which spans more than one shard is not supported. For example, suppose split.key=A! hashes to the
range 12-15 and belongs to shard ‘shard1‘ with range 0-20 then splitting by this route key would yield three
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sub-shards with ranges 0-11, 12-15 and 16-20. Note that the sub-shard with the hash range of the route key
may also contain documents for other route keys whose hash ranges overlap.
Shard splitting can be a long running process. In order to avoid timeouts, you should run this as an
asynchronous call.
Input
Query Parameters
Key Type Required Description
collection string Yes The name of the collection that includes the shard to
be split.
shard string Yes The name of the shard to be split.
ranges string No A comma-separated list of hash ranges in
hexadecimal, such as ranges=0-1f4,1f5-3e8,3e9-
5dc .
split.key string No The key to use for splitting the index.
property.name=value string No Set core property name to value. See the section
Defining core.properties for details on supported
properties and values.
async string No Request ID to track this action which will be
processed asynchronously
Output
The output will include the status of the request and the new shard names, which will use the original shard
as their basis, adding an underscore and a number. For example, "shard1" will become "shard1_0" and
"shard1_1". If the status is anything other than "success", an error message will explain why the request
failed.
Examples
Input
Split shard1 of the "anotherCollection" collection.
http://localhost:8983/solr/admin/collections?action=SPLITSHARD&collection=anotherCollection&shard
=shard1
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">6120</int>
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</lst>
<lst name="success">
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">3673</int>
</lst>
<str name="core">anotherCollection_shard1_1_replica1</str>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">3681</int>
</lst>
<str name="core">anotherCollection_shard1_0_replica1</str>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">6008</int>
</lst>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">6007</int>
</lst>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">71</int>
</lst>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">0</int>
</lst>
<str name="core">anotherCollection_shard1_1_replica1</str>
<str name="status">EMPTY_BUFFER</str>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">0</int>
</lst>
<str name="core">anotherCollection_shard1_0_replica1</str>
<str name="status">EMPTY_BUFFER</str>
</lst>
</lst>
</response>
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CREATESHARD: Create a Shard
Shards can only created with this API for collections that use the ‘implicit‘ router. Use SPLITSHARD for
collections using the ‘compositeId‘ router. A new shard with a name can be created for an existing ‘implicit‘
collection.
/admin/collections?action=CREATESHARD&shard=shardName&collection=name
Input
Query Parameters
Key Type Required Description
collection string Yes The name of the collection that includes the shard
that will be splitted.
shard string Yes The name of the shard to be created.
createNodeSet string No Allows defining the nodes to spread the new
collection across. If not provided, the CREATE
operation will create shard-replica spread across all
live Solr nodes. The format is a comma-separated list
of node_names, such as localhost:8983_solr,
localhost:8984_solr, localhost:8985_solr .
property.name=value string No Set core property name to value. See the section
Defining core.properties for details on supported
properties and values.
async string No Request ID to track this action which will be
processed asynchronously.
Output
The output will include the status of the request. If the status is anything other than "success", an error
message will explain why the request failed.
Examples
Input
Create ‘shard-z‘ for the "anImplicitCollection" collection.
http://localhost:8983/solr/admin/collections?action=CREATESHARD&collection=anImplicitCollection&s
hard=shard-z
Output
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<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">558</int>
</lst>
</response>
DELETESHARD: Delete a Shard
Deleting a shard will unload all replicas of the shard, remove them from clusterstate.json , and (by
default) delete the instanceDir and dataDir for each replica. It will only remove shards that are inactive, or
which have no range given for custom sharding.
/admin/collections?action=DELETESHARD&shard=shardID&collection=name
Input
Query Parameters
Key Type Required Description
collection string Yes The name of the collection that includes the shard to
be deleted.
shard string Yes The name of the shard to be deleted.
deleteInstanceDir boolean No By default Solr will delete the entire instanceDir of
each replica that is deleted. Set this to false to
prevent the instance directory from being deleted.
deleteDataDir boolean No By default Solr will delete the dataDir of each replica
that is deleted. Set this to false to prevent the data
directory from being deleted.
deleteIndex boolean No By default Solr will delete the index of each replica
that is deleted. Set this to false to prevent the index
directory from being deleted.
async string No Request ID to track this action which will be
processed asynchronously.
Output
The output will include the status of the request. If the status is anything other than "success", an error
message will explain why the request failed.
Examples
Input
Delete ‘shard1‘ of the "anotherCollection" collection.
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http://localhost:8983/solr/admin/collections?action=DELETESHARD&collection=anotherCollection&shar
d=shard1
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">558</int>
</lst>
<lst name="success">
<lst name="10.0.1.4:8983_solr">
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">27</int>
</lst>
</lst>
</lst>
</response>
CREATEALIAS: Create or Modify an Alias for a Collection
The CREATEALIAS action will create a new alias pointing to one or more collections. If an alias by the same
name already exists, this action will replace the existing alias, effectively acting like an atomic "MOVE"
command.
/admin/collections?action=CREATEALIAS&name=name&collections=collectionlist
Input
Query Parameters
Key Type Required Description
name string Yes The alias name to be created.
collections string Yes The list of collections to be aliased, separated by commas.
They must already exist in the cluster.
async string No Request ID to track this action which will be processed
asynchronously.
Output
The output will simply be a responseHeader with details of the time it took to process the request. To
confirm the creation of the alias, you can look in the Solr Admin UI, under the Cloud section and find the
aliases.json file.
Examples
Input
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Create an alias named "testalias" and link it to the collections named "anotherCollection" and
"testCollection".
http://localhost:8983/solr/admin/collections?action=CREATEALIAS&name=testalias&collections=anothe
rCollection,testCollection
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">122</int>
</lst>
</response>
DELETEALIAS: Delete a Collection Alias
/admin/collections?action=DELETEALIAS&name=name
Input
Query Parameters
Key Type Required Description
name string Yes The name of the alias to delete.
async string No Request ID to track this action which will be processed
asynchronously.
Output
The output will simply be a responseHeader with details of the time it took to process the request. To
confirm the removal of the alias, you can look in the Solr Admin UI, under the Cloud section, and find the
aliases.json file.
Examples
Input
Remove the alias named "testalias".
http://localhost:8983/solr/admin/collections?action=DELETEALIAS&name=testalias
Output
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<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">117</int>
</lst>
</response>
LISTALIASES: List of all aliases in the cluster
/admin/collections?action=LISTALIASES
Output
The output will contain a list of aliases with the corresponding collection names.
Examples
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">0</int>
</lst>
<lst name="aliases">
<str name="testalias1">collection1</str>
<str name="testalias2">collection2</str>
</lst>
</response>
DELETE: Delete a Collection
/admin/collections?action=DELETE&name=collection
Input
Query Parameters
Key Type Required Description
name string Yes The name of the collection to delete.
async string No Request ID to track this action which will be processed
asynchronously.
Output
The response will include the status of the request and the cores that were deleted. If the status is anything
other than "success", an error message will explain why the request failed.
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Examples
Input
Delete the collection named "newCollection".
http://localhost:8983/solr/admin/collections?action=DELETE&name=newCollection
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">603</int>
</lst>
<lst name="success">
<lst name="10.0.1.6:8983_solr">
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">19</int>
</lst>
</lst>
<lst name="10.0.1.4:8983_solr">
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">67</int>
</lst>
</lst>
</lst>
</response>
DELETEREPLICA: Delete a Replica
Delete a named replica from the specified collection and shard. If the corresponding core is up and running
the core is unloaded, the entry is removed from the clusterstate, and (by default) delete the instanceDir and
dataDir. If the node/core is down, the entry is taken off the clusterstate and if the core comes up later it is
automatically unregistered.
/admin/collections?action=DELETEREPLICA&collection=collection&shard=shard&replica=replica
Input
Query Parameters
Key Type Required Description
collection string Yes The name of the collection.
shard string Yes The name of the shard that includes the replica to be
removed.
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Key Type Required Description
replica string No The name of the replica to remove. Not required if count
is used instead.
count integer No The number of replicas to remove. If the requested
number exceeds the number of replicas, no replicas will
be deleted. If there is only one replica, it will not be
removed. This parameter is not required if replica is used
instead.
deleteInstanceDir boolean No By default Solr will delete the entire instanceDir of the
replica that is deleted. Set this to false to prevent the
instance directory from being deleted.
deleteDataDir boolean No By default Solr will delete the dataDir of the replica that is
deleted. Set this to false to prevent the data directory
from being deleted.
deleteIndex boolean No By default Solr will delete the index of the replica that is
deleted. Set this to false to prevent the index directory
from being deleted.
onlyIfDown boolean No When set to ‘true‘ will not take any action if the replica is
active. Default ‘false‘
async string No Request ID to track this action which will be processed
asynchronously.
Examples
Input
http://localhost:8983/solr/admin/collections?action=DELETEREPLICA&collection=test2&shard=shard2&r
eplica=core_node3
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">110</int>
</lst>
</response>
ADDREPLICA: Add Replica
Add a replica to a shard in a collection. The node name can be specified if the replica is to be created in a
specific node.
/admin/collections?action=ADDREPLICA&collection=collection&shard=shard&node=nodeName
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Input
Query Parameters
Key Type Required Description
collection string Yes The name of the collection.
shard string Yes* The name of the shard to which replica is to be
added.
If shard is not specified, then route must be.
route string No* If the exact shard name is not known, users may
pass the route value and the system would identify
the name of the shard.
Ignored if the shard param is also specified.
node string No The name of the node where the replica should be
created
instanceDir string No The instanceDir for the core that will be created
dataDir string No The directory in which the core should be created
property.name=value string No Set core property name to value. See Defining
core.properties.
async string No Request ID to track this action which will be
processed asynchronously
Examples
Input
http://localhost:8983/solr/admin/collections?action=ADDREPLICA&collection=test2&shard=shard2&node
=192.167.1.2:8983_solr
Output
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<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">3764</int>
</lst>
<lst name="success">
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">3450</int>
</lst>
<str name="core">test2_shard2_replica4</str>
</lst>
</lst>
</response>
CLUSTERPROP: Cluster Properties
Add, edit or delete a cluster-wide property.
/admin/collections?action=CLUSTERPROP&name=propertyName&val=propertyValue
Input
Query Parameters
Key Type Required Description
name string Yes The name of the property. The supported properties
names are urlScheme and autoAddReplicas and
location . Other names are rejected with an error.
val string Yes The value of the property. If the value is empty or null, the
property is unset.
Output
The response will include the status of the request and the properties that were updated or removed. If the
status is anything other than "0", an error message will explain why the request failed.
Examples
Input
http://localhost:8983/solr/admin/collections?action=CLUSTERPROP&name=urlScheme&val=https
Output
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<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">0</int>
</lst>
</response>
MIGRATE: Migrate Documents to Another Collection
/admin/collections?action=MIGRATE&collection=name&split.key=key1!&target.collection=target_c
ollection&forward.timeout=60
The MIGRATE command is used to migrate all documents having the given routing key to another collection.
The source collection will continue to have the same data as-is but it will start re-routing write requests to
the target collection for the number of seconds specified by the forward.timeout parameter. It is the
responsibility of the user to switch to the target collection for reads and writes after the ‘migrate’ command
completes.
The routing key specified by the split.key parameter may span multiple shards on both the source and the
target collections. The migration is performed shard-by-shard in a single thread. One or more temporary
collections may be created by this command during the ‘migrate’ process but they are cleaned up at the end
automatically.
This is a long running operation and therefore using the async parameter is highly recommended. If the
async parameter is not specified then the operation is synchronous by default and keeping a large read
timeout on the invocation is advised. Even with a large read timeout, the request may still timeout due to
inherent limitations of the Collection APIs but that doesn’t necessarily mean that the operation has failed.
Users should check logs, cluster state, source and target collections before invoking the operation again.
This command works only with collections having the compositeId router. The target collection must not
receive any writes during the time the migrate command is running otherwise some writes may be lost.
Please note that the migrate API does not perform any de-duplication on the documents so if the target
collection contains documents with the same uniqueKey as the documents being migrated then the target
collection will end up with duplicate documents.
Input
Query Parameters
Key Type Required Description
collection string Yes The name of the source collection from which
documents will be split.
target.collection string Yes The name of the target collection to which
documents will be migrated.
split.key string Yes The routing key prefix. For example, if uniqueKey is
a!123, then you would use split.key=a! .
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Key Type Required Description
forward.timeout int No The timeout, in seconds, until which write requests
made to the source collection for the given
split.key will be forwarded to the target shard. The
default is 60 seconds.
property.name=value string No Set core property name to value. See the section
Defining core.properties for details on supported
properties and values.
async string No Request ID to track this action which will be
processed asynchronously.
Output
The response will include the status of the request.
Examples
Input
http://localhost:8983/solr/admin/collections?action=MIGRATE&collection=test1&split.key=a!&target.
collection=test2
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">19014</int>
</lst>
<lst name="success">
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1</int>
</lst>
<str name="core">test2_shard1_0_replica1</str>
<str name="status">BUFFERING</str>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">2479</int>
</lst>
<str name="core">split_shard1_0_temp_shard1_0_shard1_replica1</str>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
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<int name="QTime">1002</int>
</lst>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">21</int>
</lst>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1655</int>
</lst>
<str name="core">split_shard1_0_temp_shard1_0_shard1_replica2</str>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">4006</int>
</lst>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">17</int>
</lst>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1</int>
</lst>
<str name="core">test2_shard1_0_replica1</str>
<str name="status">EMPTY_BUFFER</str>
</lst>
<lst name="192.168.43.52:8983_solr">
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">31</int>
</lst>
</lst>
<lst name="192.168.43.52:8983_solr">
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">31</int>
</lst>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1</int>
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</lst>
<str name="core">test2_shard1_1_replica1</str>
<str name="status">BUFFERING</str>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1742</int>
</lst>
<str name="core">split_shard1_1_temp_shard1_1_shard1_replica1</str>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1002</int>
</lst>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">15</int>
</lst>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1917</int>
</lst>
<str name="core">split_shard1_1_temp_shard1_1_shard1_replica2</str>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">5007</int>
</lst>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">8</int>
</lst>
</lst>
<lst>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1</int>
</lst>
<str name="core">test2_shard1_1_replica1</str>
<str name="status">EMPTY_BUFFER</str>
</lst>
<lst name="192.168.43.52:8983_solr">
<lst name="responseHeader">
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<int name="status">0</int>
<int name="QTime">30</int>
</lst>
</lst>
<lst name="192.168.43.52:8983_solr">
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">30</int>
</lst>
</lst>
</lst>
</response>
ADDROLE: Add a Role
/admin/collections?action=ADDROLE&role=roleName&node=nodeName
Assign a role to a given node in the cluster. The only supported role as of 4.7 is ‘overseer‘. Use this API to
dedicate a particular node as Overseer. Invoke it multiple times to add more nodes. This is useful in large
clusters where an Overseer is likely to get overloaded. If available, one among the list of nodes which are
assigned the ‘overseer‘ role would become the overseer. The system would assign the role to any other
node if none of the designated nodes are up and running.
Input
Query Parameters
Key Type Required Description
role string Yes The name of the role. The only supported role as of now is
overseer.
node string Yes The name of the node. It is possible to assign a role even
before that node is started.
Output
The response will include the status of the request and the properties that were updated or removed. If the
status is anything other than "0", an error message will explain why the request failed.
Examples
Input
http://localhost:8983/solr/admin/collections?action=ADDROLE&role=overseer&node=192.167.1.2:8983_s
olr
Output
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<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">0</int>
</lst>
</response>
REMOVEROLE: Remove Role
Remove an assigned role. This API is used to undo the roles assigned using ADDROLE operation
/admin/collections?action=REMOVEROLE&role=roleName&node=nodeName
Input
Query Parameters
Key Type Required Description
role string Yes The name of the role. The only supported role as of now is
overseer.
node string Yes The name of the node.
Output
The response will include the status of the request and the properties that were updated or removed. If the
status is anything other than "0", an error message will explain why the request failed.
Examples
Input
http://localhost:8983/solr/admin/collections?action=REMOVEROLE&role=overseer&node=192.167.1.2:898
3_solr
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">0</int>
</lst>
</response>
OVERSEERSTATUS: Overseer Status and Statistics
Returns the current status of the overseer, performance statistics of various overseer APIs, and the last 10
failures per operation type.
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/admin/collections?action=OVERSEERSTATUS
Examples
Input:
http://localhost:8983/solr/admin/collections?action=OVERSEERSTATUS&wt=json
{
"responseHeader":{
"status":0,
"QTime":33},
"leader":"127.0.1.1:8983_solr",
"overseer_queue_size":0,
"overseer_work_queue_size":0,
"overseer_collection_queue_size":2,
"overseer_operations":[
"createcollection",{
"requests":2,
"errors":0,
"avgRequestsPerSecond":0.7467088842794136,
"5minRateRequestsPerSecond":7.525069023276674,
"15minRateRequestsPerSecond":10.271274280947182,
"avgTimePerRequest":0.5050685,
"medianRequestTime":0.5050685,
"75thPcRequestTime":0.519016,
"95thPcRequestTime":0.519016,
"99thPcRequestTime":0.519016,
"999thPcRequestTime":0.519016},
"removeshard",{
"..."
}],
"collection_operations":[
"splitshard",{
"requests":1,
"errors":1,
"recent_failures":[{
"request":{
"operation":"splitshard",
"shard":"shard2",
"collection":"example1"},
"response":[
"Operation splitshard caused exception:",
"org.apache.solr.common.SolrException:org.apache.solr.common.SolrException: No shard with the
specified name exists: shard2",
"exception",{
"msg":"No shard with the specified name exists: shard2",
"rspCode":400}]}],
"avgRequestsPerSecond":0.8198143044809885,
"5minRateRequestsPerSecond":8.043840552427673,
"15minRateRequestsPerSecond":10.502079828515368,
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"avgTimePerRequest":2952.7164175,
"medianRequestTime":2952.7164175000003,
"75thPcRequestTime":5904.384052,
"95thPcRequestTime":5904.384052,
"99thPcRequestTime":5904.384052,
"999thPcRequestTime":5904.384052},
"..."
],
"overseer_queue":[
"..."
],
"..."
}
CLUSTERSTATUS: Cluster Status
Fetch the cluster status including collections, shards, replicas, configuration name as well as collection
aliases and cluster properties.
/admin/collections?action=CLUSTERSTATUS
Input
Query Parameters
Key Type Required Description
collection string No The collection name for which information is requested. If
omitted, information on all collections in the cluster will be
returned.
shard string No The shard(s) for which information is requested. Multiple
shard names can be specified as a comma separated list.
route string No This can be used if you need the details of the shard
where a particular document belongs to and you don’t
know which shard it falls under.
Output
The response will include the status of the request and the status of the cluster.
Examples
Input
http://localhost:8983/solr/admin/collections?action=clusterstatus&wt=json
Output
{
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"responseHeader":{
"status":0,
"QTime":333},
"cluster":{
"collections":{
"collection1":{
"shards":{
"shard1":{
"range":"80000000-ffffffff",
"state":"active",
"replicas":{
"core_node1":{
"state":"active",
"core":"collection1",
"node_name":"127.0.1.1:8983_solr",
"base_url":"http://127.0.1.1:8983/solr",
"leader":"true"},
"core_node3":{
"state":"active",
"core":"collection1",
"node_name":"127.0.1.1:8900_solr",
"base_url":"http://127.0.1.1:8900/solr"}}},
"shard2":{
"range":"0-7fffffff",
"state":"active",
"replicas":{
"core_node2":{
"state":"active",
"core":"collection1",
"node_name":"127.0.1.1:7574_solr",
"base_url":"http://127.0.1.1:7574/solr",
"leader":"true"},
"core_node4":{
"state":"active",
"core":"collection1",
"node_name":"127.0.1.1:7500_solr",
"base_url":"http://127.0.1.1:7500/solr"}}}},
"maxShardsPerNode":"1",
"router":{"name":"compositeId"},
"replicationFactor":"1",
"znodeVersion": 11,
"autoCreated":"true",
"configName" : "my_config",
"aliases":["both_collections"]
},
"collection2":{
"..."
}
},
"aliases":{ "both_collections":"collection1,collection2" },
"roles":{
"overseer":[
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"127.0.1.1:8983_solr",
"127.0.1.1:7574_solr"]
},
"live_nodes":[
"127.0.1.1:7574_solr",
"127.0.1.1:7500_solr",
"127.0.1.1:8983_solr",
"127.0.1.1:8900_solr"]
}
}
REQUESTSTATUS: Request Status of an Async Call
Request the status and response of an already submitted Asynchronous Collection API (below) call. This call
is also used to clear up the stored statuses.
/admin/collections?action=REQUESTSTATUS&requestid=request-id
Input
Query Parameters
Key Type Required Description
requestid string Yes The user defined request-id for the request. This can be
used to track the status of the submitted asynchronous
task.
Examples
Input: Valid Request Status
http://localhost:8983/solr/admin/collections?action=REQUESTSTATUS&requestid=1000
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1</int>
</lst>
<lst name="status">
<str name="state">completed</str>
<str name="msg">found 1000 in completed tasks</str>
</lst>
</response>
Input: Invalid RequestId
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http://localhost:8983/solr/admin/collections?action=REQUESTSTATUS&requestid=1004
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1</int>
</lst>
<lst name="status">
<str name="state">notfound</str>
<str name="msg">Did not find taskid [1004] in any tasks queue</str>
</lst>
</response>
DELETESTATUS: Delete Status
Delete the stored response of an already failed or completed Asynchronous Collection API call.
/admin/collections?action=DELETESTATUS&requestid=request-id
Input
Query Parameters
Key Type Required Description
requestid string No The request-id of the async call we need to clear the
stored response for.
flush boolean No Set to true to clear all stored completed and failed async
request responses.
Examples
Input: Valid Request Status
http://localhost:8983/solr/admin/collections?action=DELETESTATUS&requestid=foo
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1</int>
</lst>
<str name="status">successfully removed stored response for [foo]</str>
</response>
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Input: Invalid RequestId
http://localhost:8983/solr/admin/collections?action=DELETESTATUS&requestid=bar
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1</int>
</lst>
<str name="status">[bar] not found in stored responses</str>
</response>
Input: Clear all the stored statuses
http://localhost:8983/solr/admin/collections?action=DELETESTATUS&flush=true
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1</int>
</lst>
<str name="status"> successfully cleared stored collection api responses </str>
</response>
LIST: List Collections
Fetch the names of the collections in the cluster.
/admin/collections?action=LIST
Example
Input
http://localhost:8983/solr/admin/collections?action=LIST&wt=json
Output
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{
"responseHeader":{
"status":0,
"QTime":2011},
"collections":["collection1",
"example1",
"example2"]}
ADDREPLICAPROP: Add Replica Property
Assign an arbitrary property to a particular replica and give it the value specified. If the property already
exists, it will be overwritten with the new value.
/admin/collections?action=ADDREPLICAPROP&collection=collectionName&shard=shardName&replica=r
eplicaName&property=propertyName&property.value=value
Input
Query Parameters
Key Type Required Description
collection string Yes The name of the collection this replica belongs to.
shard string Yes The name of the shard the replica belongs to.
replica string Yes The replica, e.g. core_node1.
property string Yes The property to add. Note: this will have the literal
‘property.‘ prepended to distinguish it from system-
maintained properties. So these two forms are equivalent:
property=special
and
property=property.special
property.value string Yes The value to assign to the property.
shardUnique Boolean No default: false. If true, then setting this property in one
replica will remove the property from all other replicas in
that shard.
There is one pre-defined property preferredLeader for
which shardUnique is forced to ‘true‘ and an error
returned if shardUnique is explicitly set to ‘false‘.
PreferredLeader is a boolean property, any value
assigned that is not equal (case insensitive) to ‘true‘ will
be interpreted as ‘false‘ for preferredLeader .
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Output
The response will include the status of the request. If the status is anything other than "0", an error
message will explain why the request failed.
Examples
Input
This command would set the preferredLeader ( property.preferredLeader ) to true on core_node1, and
remove that property from any other replica in the shard.
http://localhost:8983/solr/admin/collections?action=ADDREPLICAPROP&shard=shard1&collection=collec
tion1&replica=core_node1&property=preferredLeader&property.value=true
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">46</int>
</lst>
</response>
Input
This pair of commands will set the "testprop" ( property.testprop ) to ‘value1‘ and ‘value2‘ respectively for
two nodes in the same shard.
http://localhost:8983/solr/admin/collections?action=ADDREPLICAPROP&shard=shard1&collection=collec
tion1&replica=core_node1&property=testprop&property.value=value1
http://localhost:8983/solr/admin/collections?action=ADDREPLICAPROP&shard=shard1&collection=collec
tion1&replica=core_node3&property=property.testprop&property.value=value2
Input
This pair of commands would result in core_node_3 having the testprop ( property.testprop ) value set
because the second command specifies shardUnique=true , which would cause the property to be removed
from core_node_1.
http://localhost:8983/solr/admin/collections?action=ADDREPLICAPROP&shard=shard1&collection=collec
tion1&replica=core_node1&property=testprop&property.value=value1
http://localhost:8983/solr/admin/collections?action=ADDREPLICAPROP&shard=shard1&collection=collec
tion1&replica=core_node3&property=testprop&property.value=value2&shardUnique=true
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DELETEREPLICAPROP: Delete Replica Property
Deletes an arbitrary property from a particular replica.
/admin/collections?action=DELETEREPLICAPROP&collection=collectionName&shard=shardName&replic
a=replicaName&property=propertyName
Input
Query Parameters
Key Type Required Description
collection string Yes The name of the collection this replica belongs to
shard string Yes The name of the shard the replica belongs to.
replica string Yes The replica, e.g. core_node1.
property string Yes The property to add. Note: this will have the literal
‘property.‘ prepended to distinguish it from system-
maintained properties. So these two forms are equivalent:
property=special
and
property=property.special
Output
The response will include the status of the request. If the status is anything other than "0", an error
message will explain why the request failed.
Examples
Input
This command would delete the preferredLeader ( property.preferredLeader ) from core_node1.
http://localhost:8983/solr/admin/collections?action=DELETEREPLICAPROP&shard=shard1&collection=col
lection1&replica=core_node1&property=preferredLeader
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">9</int>
</lst>
</response>
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BALANCESHARDUNIQUE: Balance a Property Across Nodes
/admin/collections?action=BALANCESHARDUNIQUE&collection=collectionName&property=propertyName
Insures that a particular property is distributed evenly amongst the physical nodes that make up a
collection. If the property already exists on a replica, every effort is made to leave it there. If the property is
not on any replica on a shard, one is chosen and the property is added.
Input
Query Parameters
Key Type Required Description
collection string Yes The name of the collection to balance the property in.
property string Yes The property to balance. The literal "property." is
prepended to this property if not specified explicitly.
onlyactivenodes boolean No Defaults to true. Normally, the property is instantiated on
active nodes only. If this parameter is specified as "false",
then inactive nodes are also included for distribution.
shardUnique boolean No Something of a safety valve. There is one pre-defined
property (preferredLeader) that defaults this value to
"true". For all other properties that are balanced, this
must be set to "true" or an error message is returned.
Output
The response will include the status of the request. If the status is anything other than "0", an error
message will explain why the request failed.
Examples
Input
Either of these commands would put the "preferredLeader" property on one replica in every shard in the
"collection1" collection.
http://localhost:8983/solr/admin/collections?action=BALANCESHARDUNIQUE&collection=collection1&pro
perty=preferredLeader
http://localhost:8983/solr/admin/collections?action=BALANCESHARDUNIQUE&collection=collection1&pro
perty=property.preferredLeader
Output
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<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">9</int>
</lst>
</response>
Examining the clusterstate after issuing this call should show exactly one replica in each shard that has this
property.
REBALANCELEADERS: Rebalance Leaders
Reassign leaders in a collection according to the preferredLeader property across active nodes.
/admin/collections?action=REBALANCELEADERS&collection=collectionName
Assigns leaders in a collection according to the preferredLeader property on active nodes. This command
should be run after the preferredLeader property has been assigned via the BALANCESHARDUNIQUE or
ADDREPLICAPROP commands. NOTE: it is not required that all shards in a collection have a preferredLeader
property. Rebalancing will only attempt to reassign leadership to those replicas that have the
preferredLeader property set to "true" and are not currently the shard leader and are currently active.
Input
Query Parameters
Key Type Required Description
collection string Yes The name of the collection to rebalance preferredLeaders
on.
maxAtOnce string No The maximum number of reassignments to have queue
up at once. Values ?0 are use the default value
Integer.MAX_VALUE. When this number is reached, the
process waits for one or more leaders to be successfully
assigned before adding more to the queue.
maxWaitSeconds string No Defaults to 60. This is the timeout value when waiting for
leaders to be reassigned. NOTE: if maxAtOnce is less than
the number of reassignments that will take place, this is
the maximum interval that any single wait for at least one
reassignment. For example, if 10 reassignments are to
take place and maxAtOnce is 1 and maxWaitSeconds is 60,
the upper bound on the time that the command may wait
is 10 minutes.
Output
The response will include the status of the request. If the status is anything other than "0", an error
message will explain why the request failed.
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Examples
Input
Either of these commands would cause all the active replicas that had the "preferredLeader" property set
and were not already the preferred leader to become leaders.
http://localhost:8983/solr/admin/collections?action=REBALANCELEADERS&collection=collection1
http://localhost:8983/solr/admin/collections?action=REBALANCELEADERS&collection=collection1&maxAt
Once=5&maxWaitSeconds=30
Output
In this example, two replicas in the "alreadyLeaders" section already had the leader assigned to the same
node as the preferredLeader property so no action was taken. The replica in the "inactivePreferreds" section
had the preferredLeader property set but the node was down and no action was taken. The three nodes in
the "successes" section were made leaders because they had the preferredLeader property set but were not
leaders and they were active.
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<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">123</int>
</lst>
<lst name="alreadyLeaders">
<lst name="core_node1">
<str name="status">success</str>
<str name="msg">Already leader</str>
<str name="nodeName">192.168.1.167:7400_solr</str>
</lst>
<lst name="core_node17">
<str name="status">success</str>
<str name="msg">Already leader</str>
<str name="nodeName">192.168.1.167:7600_solr</str>
</lst>
</lst>
<lst name="inactivePreferreds">
<lst name="core_node4">
<str name="status">skipped</str>
<str name="msg">Node is a referredLeader, but it‘s inactive. Skipping</str>
<str name="nodeName">192.168.1.167:7500_solr</str>
</lst>
</lst>
<lst name="successes">
<lst name="_collection1_shard3_replica1">
<str name="status">success</str>
<str name="msg">
Assigned ‘Collection: ‘collection1‘, Shard: ‘shard3‘, Core:
‘collection1_shard3_replica1‘, BaseUrl:
‘http://192.168.1.167:8983/solr‘‘ to be leader
</str>
</lst>
<lst name="_collection1_shard5_replica3">
<str name="status">success</str>
<str name="msg">
Assigned ‘Collection: ‘collection1‘, Shard: ‘shard5‘, Core:
‘collection1_shard5_replica3‘, BaseUrl:
‘http://192.168.1.167:7200/solr‘‘ to be leader
</str>
</lst>
<lst name="_collection1_shard4_replica2">
<str name="status">success</str>
<str name="msg">
Assigned ‘Collection: ‘collection1‘, Shard: ‘shard4‘, Core:
‘collection1_shard4_replica2‘, BaseUrl:
‘http://192.168.1.167:7300/solr‘‘ to be leader
</str>
</lst>
</lst>
</response>
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Examining the clusterstate after issuing this call should show that every live node that has the
"preferredLeader" property should also have the "leader" property set to true.
FORCELEADER: Force Shard Leader
In the unlikely event of a shard losing its leader, this command can be invoked to force the election of a new
leader
/admin/collections?action=FORCELEADER&collection=<collectionName>&shard=<shardName>
Input
Query Parameters
Key Type Required Description
collection string Yes The name of the collection
shard string Yes The name of the shard
?
This is an expert level command, and should be invoked only when regular leader election
is not working. This may potentially lead to loss of data in the event that the new leader
doesn’t have certain updates, possibly recent ones, which were acknowledged by the old
leader before going down.
MIGRATESTATEFORMAT: Migrate Cluster State
A expert level utility API to move a collection from shared clusterstate.json zookeeper node (created with
stateFormat=1 , the default in all Solr releases prior to 5.0) to the per-collection state.json stored in
ZooKeeper (created with stateFormat=2 , the current default) seamlessly without any application down-time.
/admin/collections?action=MIGRATESTATEFORMAT&collection=<collection_name>
Key Type Required Description
collection string Yes The name of the collection to be migrated from
clusterstate.json to its own state.json zookeeper
node
async string No Request ID to track this action which will be processed
asynchronously.
This API is useful in migrating any collections created prior to Solr 5.0 to the more scalable cluster state
format now used by default. If a collection was created in any Solr 5.x version or higher, then executing this
command is not necessary.
BACKUP: Backup Collection
Backup Solr collections and it’s associated configurations to a shared filesystem - for example a Network File
System
/admin/collections?action=BACKUP&name=myBackupName&collection=myCollectionName&location=/pat
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h/to/my/shared/drive
The backup command will backup Solr indexes and configurations for a specified collection. The backup
command takes one copy from each shard for the indexes. For configurations it backs up the configSet that
was associated with the collection and metadata.
Input
Query Parameters
Key Type Required Description
collection string Yes The name of the collection that needs to be backed up
location string No The location on the shared drive for the backup command
to write to. Alternately it can be set as a cluster property
async string No Request ID to track this action which will be processed
asynchronously
repository string No The name of the repository to be used for the backup. If
no repository is specified then the local filesystem
repository will be used automatically.
RESTORE: Restore Collection
Restores Solr indexes and associated configurations.
/admin/collections?action=RESTORE&name=myBackupName&location=/path/to/my/shared/drive&collec
tion=myRestoredCollectionName
The restore operation will create a collection with the specified name in the collection parameter. You
cannot restore into the same collection the backup was taken from and the target collection should not be
present at the time the API is called as Solr will create it for you.
The collection created will be of the same number of shards and replicas as the original collection,
preserving routing information, etc. Optionally, you can override some parameters documented below.
While restoring, if a configSet with the same name exists in ZooKeeper then Solr will reuse that, or else it will
upload the backed up configSet in ZooKeeper and use that.
You can use the collection alias API to make sure client’s don’t need to change the endpoint to query or
index against the newly restored collection.
Input
Query Parameters
Key Type Required Description
collection string Yes The collection where the indexes will be restored into.
location string No The location on the shared drive for the restore command
to read from. Alternately it can be set as a cluster
property.
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Key Type Required Description
async string No Request ID to track this action which will be processed
asynchronously.
repository string No The name of the repository to be used for the backup. If
no repository is specified then the local filesystem
repository will be used automatically.
Additionally, there are several parameters that can be overridden:
Override Parameters
Key Type Required Description
collection.configName String No Defines the name of the configurations to use for
this collection. These must already be stored in
ZooKeeper. If not provided, Solr will default to the
collection name as the configuration name.
replicationFactor Integer No The number of replicas to be created for each shard.
maxShardsPerNode Integer No When creating collections, the shards and/or replicas
are spread across all available (i.e., live) nodes, and
two replicas of the same shard will never be on the
same node. If a node is not live when the CREATE
operation is called, it will not get any parts of the
new collection, which could lead to too many replicas
being created on a single live node. Defining
maxShardsPerNode sets a limit on the number of
replicas CREATE will spread to each node. If the
entire collection can not be fit into the live nodes, no
collection will be created at all.
autoAddReplicas Boolean No When set to true, enables auto addition of replicas
on shared file systems. See the section Automatically
Add Replicas in SolrCloud for more details on
settings and overrides.
property.name=value String No Set core property name to value. See the section
Defining core.properties for details on supported
properties and values.
DELETENODE: Delete Replicas in a Node
Deletes all replicas of all collections in that node. Please note that the node itself will remain as a live node
after this operation.
/admin/collections?action=DELETENODE&node=nodeName
Input
Query Parameters
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Key Type Required Description
node string Yes The node to be cleaned up
async string No Request ID to track this action which will be processed
asynchronously.
REPLACENODE: Move All Replicas in a Node to Another
This command recreates replicas in the source node to the target node. After each replica is copied, the
replicas in the source node are deleted.
/admin/collections?action=REPLACENODE&source=source-node&target=target-node
Input
Query Parameters
Key Type Required Description
source string Yes The source node from which the replicas need to be
copied from
target string Yes The target node
parallel boolean No default=false. if this flag is set to true, all replicas are
created inseparatee threads. Keep in mind that this can
lead to very high network and disk I/O if the replicas have
very large indices.
async string No Request ID to track this action which will be processed
asynchronously.
?
This operation does not hold necessary locks on the replicas that belong to on the source
node. So don’t perform other collection operations in this period.
MOVEREPLICA: Move a Replica to a New node
This command move a replica from a node to a new node, in case of shared-file systems the dataDir will be
reused.
/admin/collections?action=MOVEREPLICA&collection=collection&shard=shard&replica=replica&node
=nodeName&toNode=nodeName
Input
Query Parameters
Key Type Required Description
collection string Yes The name of the collection.
shard string Yes The name of shard that replica belong to.
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Key Type Required Description
replica string Yes The name of the replica.
node string Yes The name of the node that contains the replica.
toNode string Yes The name of the destination node.
async string No Request ID to track this action which will be processed
asynchronously.
Asynchronous Calls
Since some collection API calls can be long running tasks e.g. Shard Split, you can optionally have the calls
run asynchronously. Specifying async=<request-id> enables you to make an asynchronous call, the status
of which can be requested using the REQUESTSTATUS call at any time.
As of now, REQUESTSTATUS does not automatically clean up the tracking data structures, meaning the
status of completed or failed tasks stays stored in ZooKeeper unless cleared manually. DELETESTATUS can
be used to clear the stored statuses. However, there is a limit of 10,000 on the number of async call
responses stored in a cluster.
Example
Input
http://localhost:8983/solr/admin/collections?action=SPLITSHARD&collection=collection1&shard=shard
1&async=1000
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">99</int>
</lst>
<str name="requestid">1000</str>
</response>
Parameter Reference
Cluster Parameters
numShards
Defaults to 1 The number of shards to hash documents to. There must be
one leader per shard and each leader can have N replicas.
SolrCloud Instance Parameters
These are set in solr.xml , but by default the host and hostContext parameters are set up to also work with
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system properties.
host
Defaults to the first
local host address
found
If the wrong host address is found automatically, you can
override the host address with this parameter.
hostPort
Defaults to the port
specified via
bin/solr -p
<port> , or 8983 if
not specified.
The port that Solr is running on. This value is only used when
-DzkRun is specified without a value (see below), to calculate the
default port on which embedded ZooKeeper will run. In the
solr.xml shipped with Solr, the hostPort system property is
not referenced, and so is ignored. If you want to run Solr on a
non-default port, use bin/solr -p <port> rather than
specifying -DhostPort .
hostContext
Defaults to solr The context path for the Solr web application.
SolrCloud Instance ZooKeeper Parameters
zkRun
Defaults to
localhost:<hostPo
rt+1000>
Causes Solr to run an embedded version of ZooKeeper. Set to
the address of ZooKeeper on this node; this allows us to know
who you are in the list of addresses in the zkHost connect
string. Use -DzkRun (with no value) to get the default value.
zkHost
No default The host address for ZooKeeper. Usually this is a comma-
separated list of addresses to each node in your ZooKeeper
ensemble.
zkClientTimeout
Defaults to 15000 The time a client is allowed to not talk to ZooKeeper before its
session expires.
zkRun and zkHost are set up using system properties. zkClientTimeout is set up in solr.xml by default, but
can also be set using a system property.
SolrCloud Core Parameters
shard
Defaults to being
automatically
assigned based on
numShards
Specifies which shard this core acts as a replica of.
shard can be specified in the core.properties for each core.
Additional cloud related parameters are discussed in Format of solr.xml
Command Line Utilities
A ZooKeeper Command Line Interface (CLI) script is available to allow you to interact directly with Solr
configuration files stored in ZooKeeper.
While Solr’s Administration UI includes pages dedicated to the state of your SolrCloud cluster, it does not
allow you to download or modify related configuration files.
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?
See the section Cloud Screens for more information about using the Admin UI screens.
The ZooKeeper CLI scripts found in server/scripts/cloud-scripts let you upload configuration
information to ZooKeeper, in the same ways shown in the examples in Parameter Reference. It also provides
a few other commands that let you link collection sets to collections, make ZooKeeper paths or clear them,
and download configurations from ZooKeeper to the local filesystem.
Many of the functions provided by the zkCli.sh script are also provided by the Solr Control Script, which may
be more familiar as the start script ZooKeeper maintenance commands are very similar to Unix commands.
?
Solr’s zkcli.sh vs ZooKeeper’s zkCli.sh
The zkcli.sh provided by Solr is not the same as the zkCli.sh included in ZooKeeper
distributions.
ZooKeeper’s zkCli.sh provides a completely general, application-agnostic shell for
manipulating data in ZooKeeper. Solr’s zkcli.sh – discussed in this section – is specific to
Solr, and has command line arguments specific to dealing with Solr data in ZooKeeper.
Using Solr’s ZooKeeper CLI
Use the help option to get a list of available commands from the script itself, as in
./server/scripts/cloud-scrips/zkcli.sh help .
Both zkcli.sh (for Unix environments) and zkcli.bat (for Windows environments) support the following
command line options:
-cmd <arg>
The CLI Command to be executed. This parameter is mandatory. The following commands are
supported:
? bootstrap
? upconfig
? downconfig
? linkconfig
? makepath
? get and getfile
? put and putfile
? clear
? list
? ls
? clusterprop
-z or -zkhost <locations>
ZooKeeper host address. This parameter is mandatory for all CLI commands.
-c or -collection <name>
For linkconfig : name of the collection.
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-d or -confdir <path>
For upconfig : a directory of configuration files. For downconfig: the destination of files pulled from
ZooKeeper
-h or -help
Display help text.
-n or -confname <arg>
For upconfig , linkconfig , downconfig : name of the configuration set.
-r or -runzk <port>
Run ZooKeeper internally by passing the Solr run port; only for clusters on one machine.
-s or -solrhome <path>
For bootstrap or when using -runzk : the mandatory solrhome location.
-name <name>
For clusterprop : the mandatory cluster property name.
-val <value>
For clusterprop : the cluster property value. If not specified, null will be used as value.
?
The short form parameter options may be specified with a single dash (eg: -c
mycollection ).
The long form parameter options may be specified using either a single dash (eg:
-collection mycollection ) or a double dash (eg: --collection mycollection )
ZooKeeper CLI Examples
Below are some examples of using the zkcli.sh CLI which assume you have already started the SolrCloud
example ( bin/solr -e cloud -noprompt )
If you are on Windows machine, simply replace zkcli.sh with zkcli.bat in these examples.
Upload a configuration directory
./server/scripts/cloud-scripts/zkcli.sh -zkhost 127.0.0.1:9983 -cmd upconfig -confname
my_new_config -confdir server/solr/configsets/basic_configs/conf
Bootstrap ZooKeeper from existing SOLR_HOME
./server/scripts/cloud-scripts/zkcli.sh -zkhost 127.0.0.1:2181 -cmd bootstrap -solrhome
/var/solr/data
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?
Bootstrap with chroot
Using the boostrap command with a zookeeper chroot in the -zkhost parameter, e.g.
-zkhost 127.0.0.1:2181/solr , will automatically create the chroot path before uploading
the configs.
Put arbitrary data into a new ZooKeeper file
./server/scripts/cloud-scripts/zkcli.sh -zkhost 127.0.0.1:9983 -cmd put /my_zk_file.txt ‘some
data‘
Put a local file into a new ZooKeeper file
./server/scripts/cloud-scripts/zkcli.sh -zkhost 127.0.0.1:9983 -cmd putfile /my_zk_file.txt
/tmp/my_local_file.txt
Link a collection to a configuration set
./server/scripts/cloud-scripts/zkcli.sh -zkhost 127.0.0.1:9983 -cmd linkconfig -collection
gettingstarted -confname my_new_config
Create a new ZooKeeper path
This can be useful to create a chroot path in ZooKeeper before first cluster start.
./server/scripts/cloud-scripts/zkcli.sh -zkhost 127.0.0.1:2181 -cmd makepath /solr
Set a cluster property
This command will add or modify a single cluster property in clusterprops.json . Use this command instead
of the usual getfile → edit → putfile cycle.
Unlike the CLUSTERPROP command on the Collections API, this command does not require a running Solr
cluster.
./server/scripts/cloud-scripts/zkcli.sh -zkhost 127.0.0.1:2181 -cmd clusterprop -name urlScheme
-val https
SolrCloud with Legacy Configuration Files
If you are migrating from a non-SolrCloud environment to SolrCloud, this information may be helpful.
All of the required configuration is already set up in the sample configurations shipped with Solr. You only
need to add the following if you are migrating old configuration files. Do not remove these files and
parameters from a new Solr instance if you intend to use Solr in SolrCloud mode.
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These properties exist in 3 files: schema.xml , solrconfig.xml , and solr.xml .
1. In schema.xml , you must have a _version_ field defined:
<field name="_version_" type="long" indexed="true" stored="true" multiValued="false"/>
2. In solrconfig.xml , you must have an UpdateLog defined. This should be defined in the updateHandler
section.
<updateHandler>
...
<updateLog>
<str name="dir">${solr.data.dir:}</str>
</updateLog>
...
</updateHandler>
3. The DistributedUpdateProcessor is part of the default update chain and is automatically injected into any
of your custom update chains, so you don’t actually need to make any changes for this capability.
However, should you wish to add it explicitly, you can still add it to the solrconfig.xml file as part of an
updateRequestProcessorChain . For example:
<updateRequestProcessorChain name="sample">
<processor class="solr.LogUpdateProcessorFactory" />
<processor class="solr.DistributedUpdateProcessorFactory"/>
<processor class="my.package.UpdateFactory"/>
<processor class="solr.RunUpdateProcessorFactory" />
</updateRequestProcessorChain>
If you do not want the DistributedUpdateProcessFactory auto-injected into your chain (for example, if
you want to use SolrCloud functionality, but you want to distribute updates yourself) then specify the
NoOpDistributingUpdateProcessorFactory update processor factory in your chain:
<updateRequestProcessorChain name="sample">
<processor class="solr.LogUpdateProcessorFactory" />
<processor class="solr.NoOpDistributingUpdateProcessorFactory"/>
<processor class="my.package.MyDistributedUpdateFactory"/>
<processor class="solr.RunUpdateProcessorFactory" />
</updateRequestProcessorChain>
In the update process, Solr skips updating processors that have already been run on other nodes.
ConfigSets API
The ConfigSets API enables you to create, delete, and otherwise manage ConfigSets.
To use a ConfigSet created with this API as the configuration for a collection, use the Collections API.
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This API can only be used with Solr running in SolrCloud mode. If you are not running Solr in SolrCloud
mode but would still like to use shared configurations, please see the section Config Sets.
API Entry Points
The base URL for all API calls is http://<hostname>:<port>/solr .
? /admin/configs?action=CREATE : create a ConfigSet, based on an existing ConfigSet
? /admin/configs?action=DELETE : delete a ConfigSet
? /admin/configs?action=LIST : list all ConfigSets
? /admin/configs?action=UPLOAD : upload a ConfigSet
Create a ConfigSet
/admin/configs?action=CREATE&name=name&baseConfigSet=baseConfigSet
Create a ConfigSet, based on an existing ConfigSet.
Input
Key Type Required Default Description
name String Yes ConfigSet to be created
baseConfigSet String Yes ConfigSet to copy as a base
configSetProp.name=valu
e
String No ConfigSet property from base to override
Output
Output Content
The output will include the status of the request. If the status is anything other than "success", an error
message will explain why the request failed.
Examples
Input
Create a ConfigSet named ‘myConfigSet‘ based on a ‘predefinedTemplate‘ ConfigSet, overriding the
immutable property to false.
http://localhost:8983/solr/admin/configs?action=CREATE&name=myConfigSet&baseConfigSet=predefinedT
emplate&configSetProp.immutable=false
Output
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<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">323</int>
</lst>
</response>
Delete a ConfigSet
/admin/configs?action=DELETE&name=name
Delete a ConfigSet
Input
Query Parameters
Key Type Required Default Description
name String Yes ConfigSet to be deleted
Output
Output Content
The output will include the status of the request. If the status is anything other than "success", an error
message will explain why the request failed.
Examples
Input
Delete ConfigSet ‘myConfigSet‘
http://localhost:8983/solr/admin/configs?action=DELETE&name=myConfigSet
Output
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">170</int>
</lst>
</response>
List ConfigSets
/admin/configs?action=LIST
Fetch the names of the ConfigSets in the cluster.
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Examples
Input
http://localhost:8983/solr/admin/configs?action=LIST&wt=json
Output
{
"responseHeader":{
"status":0,
"QTime":203},
"configSets":["myConfigSet1",
"myConfig2"]}
Upload a ConfigSet
/admin/configs?action=UPLOAD&name=name
Upload a ConfigSet, sent in as a zipped file. Please note that a ConfigSet is uploaded in a "trusted" mode if
authentication is enabled and this upload operation is performed as an authenticated request. Without
authentication, a ConfigSet is uploaded in an "untrusted" mode. Upon creation of a collection using an
"untrusted" ConfigSet, the following functionality would not work:
? RunExecutableListener does not initialize, if specified in the ConfigSet.
? DataImportHandler’s ScriptTransformer does not initialize, if specified in the ConfigSet.
? XSLT transformer (tr parameter) cannot be used at request processing time.
? StatelessScriptUpdateProcessor does not initialize, if specified in the ConfigSet.
Input
Key Type Required Default Description
name String Yes ConfigSet to be created
The body of the request should contain a zipped config set.
Output
Output Content
The output will include the status of the request. If the status is anything other than "success", an error
message will explain why the request failed.
Examples
Create a ConfigSet named ‘myConfigSet‘ based on a ‘predefinedTemplate‘ ConfigSet, overriding the
immutable property to false.
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curl -X POST -d @myconfigset.zip
http://localhost:8983/solr/admin/configs?action=CREATE&name=myConfigSet --header "Content-
Type:text/xml"
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Rule-based Replica Placement
When Solr needs to assign nodes to collections, it can either automatically assign them randomly or the user
can specify a set of nodes where it should create the replicas.
With very large clusters, it is hard to specify exact node names and it still does not give you fine grained
control over how nodes are chosen for a shard. The user should be in complete control of where the nodes
are allocated for each collection, shard and replica. This helps to optimally allocate hardware resources
across the cluster.
Rule-based replica assignment allows the creation of rules to determine the placement of replicas in the
cluster. In the future, this feature will help to automatically add or remove replicas when systems go down,
or when higher throughput is required. This enables a more hands-off approach to administration of the
cluster.
This feature is used in the following instances:
? Collection creation
? Shard creation
? Replica creation
? Shard splitting
Common Use Cases
There are several situations where this functionality may be used. A few of the rules that could be
implemented are listed below:
? Don’t assign more than 1 replica of this collection to a host.
? Assign all replicas to nodes with more than 100GB of free disk space or, assign replicas where there is
more disk space.
? Do not assign any replica on a given host because I want to run an overseer there.
? Assign only one replica of a shard in a rack.
? Assign replica in nodes hosting less than 5 cores.
? Assign replicas in nodes hosting the least number of cores.
Rule Conditions
A rule is a set of conditions that a node must satisfy before a replica core can be created there.
There are three possible conditions.
? shard: this is the name of a shard or a wild card (* means for all shards). If shard is not specified, then
the rule applies to the entire collection.
? replica: this can be a number or a wild-card (* means any number zero to infinity).
? tag: this is an attribute of a node in the cluster that can be used in a rule, e.g., “freedisk”, “cores”, “rack”,
“dc”, etc. The tag name can be a custom string. If creating a custom tag, a snitch is responsible for
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providing tags and values. The section Snitches below describes how to add a custom tag, and defines
six pre-defined tags (cores, freedisk, host, port, node, and sysprop).
Rule Operators
A condition can have one of the following operators to set the parameters for the rule.
? equals (no operator required): tag:x means tag value must be equal to ‘x’
? greater than (>): tag:>x means tag value greater than ‘x’. x must be a number
? less than (<): tag:<x means tag value less than ‘x’. x must be a number
? not equal (!): tag:!x means tag value MUST NOT be equal to ‘x’. The equals check is performed on
String value
Fuzzy Operator (~)
This can be used as a suffix to any condition. This would first try to satisfy the rule strictly. If Solr can’t find
enough nodes to match the criterion, it tries to find the next best match which may not satisfy the criterion.
For example, if we have a rule such as, freedisk:>200~ , Solr will try to assign replicas of this collection on
nodes with more than 200GB of free disk space. If that is not possible, the node which has the most free disk
space will be chosen instead.
Choosing Among Equals
The nodes are sorted first and the rules are used to sort them. This ensures that even if many nodes match
the rules, the best nodes are picked up for node assignment. For example, if there is a rule such as
freedisk:>20 , nodes are sorted first on disk space descending and the node with the most disk space is
picked up first. Or, if the rule is cores:<5 , nodes are sorted with number of cores ascending and the node
with the least number of cores is picked up first.
Rules for new shards
The rules are persisted along with collection state. So, when a new replica is created, the system will assign
replicas satisfying the rules. When a new shard is created as a result of using the Collection API’s
CREATESHARD command, ensure that you have created rules specific for that shard name. Rules can be
altered using the MODIFYCOLLECTION command. However, it is not required to do so if the rules do not
specify explicit shard names. For example, a rule such as shard:shard1,replica:*,ip_3:168: , will not
apply to any new shard created. But, if your rule is replica:*,ip_3:168 , then it will apply to any new shard
created.
The same is applicable to shard splitting. Shard splitting is treated exactly the same way as shard creation.
Even though shard1_1 and shard1_2 may be created from shard1 , the rules treat them as distinct, unrelated
shards.
Snitches
Tag values come from a plugin called Snitch. If there is a tag named ‘rack’ in a rule, there must be Snitch
which provides the value for ‘rack’ for each node in the cluster. A snitch implements the Snitch interface.
Solr, by default, provides a default snitch which provides the following tags:
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? cores: Number of cores in the node
? freedisk: Disk space available in the node
? host: host name of the node
? port: port of the node
? node: node name
? role : The role of the node. The only supported role is ‘overseer‘
? ip_1, ip_2, ip_3, ip_4: These are ip fragments for each node. For example, in a host with ip 192.168.1.2 ,
ip_1 = 2 , ip_2 =1 , ip_3 = 168 and` ip_4 = 192`
? sysprop.{PROPERTY_NAME}: These are values available from system properties. sysprop.key means a
value that is passed to the node as -Dkey=keyValue during the node startup. It is possible to use rules
like sysprop.key:expectedVal,shard:*
How Snitches are Configured
It is possible to use one or more snitches for a set of rules. If the rules only need tags from default snitch it
need not be explicitly configured. For example:
snitch=class:fqn.ClassName,key1:val1,key2:val2,key3:val3
How Tag Values are Collected
1. Identify the set of tags in the rules
2. Create instances of Snitches specified. The default snitch is always created.
3. Ask each Snitch if it can provide values for the any of the tags. If even one tag does not have a snitch, the
assignment fails.
4. After identifying the Snitches, they provide the tag values for each node in the cluster.
5. If the value for a tag is not obtained for a given node, it cannot participate in the assignment.
Examples
Keep less than 2 replicas (at most 1 replica) of this collection on any node
For this rule, we define the replica condition with operators for "less than 2", and use a pre-defined tag
named node to define nodes with any name.
replica:<2,node:*
// this is equivalent to replica:<2,node:*,shard:**. We can omit shard:** because ** is the
default value of shard
For a given shard, keep less than 2 replicas on any node
For this rule, we use the shard condition to define any shard , the replica condition with operators for "less
than 2", and finally a pre-defined tag named node to define nodes with any name.
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shard:*,replica:<2,node:*
Assign all replicas in shard1 to rack 730
This rule limits the shard condition to ‘shard1‘, but any number of replicas. We’re also referencing a custom
tag named rack . Before defining this rule, we will need to configure a custom Snitch which provides values
for the tag rack .
shard:shard1,replica:*,rack:730
In this case, the default value of replica is * (or, all replicas). So, it can be omitted and the rule can be
reduced to:
shard:shard1,rack:730
Create replicas in nodes with less than 5 cores only
This rule uses the replica condition to define any number of replicas, but adds a pre-defined tag named
core and uses operators for "less than 5".
replica:*,cores:<5
Again, we can simplify this to use the default value for replica , like so:
cores:<5
Do not create any replicas in host 192.45.67.3
This rule uses only the pre-defined tag host to define an IP address where replicas should not be placed.
host:!192.45.67.3
Defining Rules
Rules are specified per collection during collection creation as request parameters. It is possible to specify
multiple ‘rule’ and ‘snitch’ params as in this example:
snitch=class:EC2Snitch&rule=shard:*,replica:1,dc:dc1&rule=shard:*,replica:<2,dc:dc3
These rules are persisted in clusterstate.json in ZooKeeper and are available throughout the lifetime of
the collection. This enables the system to perform any future node allocation without direct user interaction.
The rules added during collection creation can be modified later using the MODIFYCOLLECTION API.
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Cross Data Center Replication (CDCR)
Cross Data Center Replication (CDCR) allows you to create multiple SolrCloud data centers and keep them in
sync in case they are needed at a future time.
The SolrCloud architecture is not particularly well suited for situations where a single SolrCloud cluster
consists of nodes in separated data clusters connected by an expensive pipe. The root problem is that
SolrCloud is designed to support Near Real Time Searching by immediately forwarding updates between
nodes in the cluster on a per-shard basis. "CDCR" features exist to help mitigate the risk of an entire data
center outage.
What is CDCR?
CDCR supports replicating data from one data center to multiple data centers. The initial version of the
solution supports an active-passive scenario where data updates are replicated from a Source data center to
one or more target data centers.
The target data center(s) will not propagate updates such as adds, updates, or deletes to the source data
center and updates should not be sent to any of the target data center(s).
Source and target data centers can serve search queries when CDCR is operating. The target data centers
will have slightly stale views of the corpus due to propagation delays, but this is minimal (perhaps a few
seconds).
Data changes on the source data center are replicated to the target data center only after they are persisted
to disk. The data changes can be replicated in near real-time (with a small delay) or could be scheduled to be
sent in intervals to the target data center. This solution pre-supposes that the source and target data centers
begin with the same documents indexed. Of course the indexes may be empty to start.
Each shard leader in the source data center will be responsible for replicating its updates to the
corresponding leader in the target data center. When receiving updates from the source data center, shard
leaders in the target data center will replicate the changes to their own replicas.
This replication model is designed to tolerate some degradation in connectivity, accommodate limited
bandwidth, and support batch updates to optimize communication.
Replication supports both a new empty index and pre-built indexes. In the scenario where the replication is
set up on a pre-built index, CDCR will ensure consistency of the replication of the updates, but cannot ensure
consistency on the full index. Therefore any index created before CDCR was set up will have to be replicated
by other means (described in the section Initial Startup) so source and target indexes are fully consistent.
The active-passive nature of the initial implementation implies a "push" model from the source collection to
the target collection. Therefore, the source configuration must be able to "see" the ZooKeeper ensemble in
the target cluster. The ZooKeeper ensemble is provided configured in the Source’s solrconfig.xml file.
CDCR is configured to replicate from collections in the source cluster to collections in the target cluster on a
collection-by-collection basis. Since CDCR is configured in solrconfig.xml (on both source and target
clusters), the settings can be tailored for the needs of each collection.
CDCR can be configured to replicate from one collection to a second collection within the same cluster. That is
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a specialized scenario not covered in this document.
CDCR Glossary
Terms used in this document include:
Node
A JVM instance running Solr; a server.
Cluster
A set of Solr nodes managed as a single unit by a ZooKeeper ensemble, hosting one or more Collections.
Data Center
A group of networked servers hosting a Solr cluster. In this document, the terms Cluster and Data Center
are interchangeable as we assume that each Solr cluster is hosted in a different group of networked
servers.
Shard
A sub-index of a single logical collection. This may be spread across multiple nodes of the cluster. Each
shard can have as many replicas as needed.
Leader
Each shard has one node identified as its leader. All the writes for documents belonging to a shard are
routed through the leader.
Replica
A copy of a shard for use in failover or load balancing. Replicas comprising a shard can either be leaders
or non-leaders.
Follower
A convenience term for a replica that is not the leader of a shard.
Collection
Multiple documents that make up one logical index. A cluster can have multiple collections.
Updates Log
An append-only log of write operations maintained by each node.
CDCR Architecture
Here is a picture of the data flow.
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CDCR Data Flow
Updates and deletes are first written to the Source cluster, then forwarded to the Target cluster. The data
flow sequence is:
1. A shard leader receives a new data update that is processed by its update processor chain.
2. The data update is first applied to the local index.
3. Upon successful application of the data update on the local index, the data update is added to the
Updates Log queue.
4. After the data update is persisted to disk, the data update is sent to the replicas within the data center.
5. After Step 4 is successful, CDCR reads the data update from the Updates Log and pushes it to the
corresponding collection in the target data center. This is necessary in order to ensure consistency
between the Source and target data centers.
6. The leader on the target data center writes the data locally and forwards it to all its followers.
Steps 1, 2, 3 and 4 are performed synchronously by SolrCloud; Step 5 is performed asynchronously by a
background thread. Given that CDCR replication is performed asynchronously, it becomes possible to push
batch updates in order to minimize network communication overhead. Also, if CDCR is unable to push the
update at a given time, for example, due to a degradation in connectivity, it can retry later without any
impact on the source data center.
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One implication of the architecture is that the leaders in the source cluster must be able to "see" the leaders
in the target cluster. Since leaders may change, this effectively means that all nodes in the source cluster
must be able to "see" all Solr nodes in the target cluster so firewalls, ACL rules, etc. must be configured with
care.
The current design works most robustly if both the Source and target clusters have the same number of
shards. There is no requirement that the shards in the Source and target collection have the same number
of replicas.
Having different numbers of shards on the Source and target cluster is possible, but is also an "expert"
configuration as that option imposes certain constraints and is not recommended. Most of the scenarios
where having differing numbers of shards are contemplated are better accomplished by hosting multiple
shards on each target Solr instance.
Major Components of CDCR
There are a number of key features and components in CDCR’s architecture:
CDCR Configuration
In order to configure CDCR, the Source data center requires the host address of the ZooKeeper cluster
associated with the target data center. The ZooKeeper host address is the only information needed by CDCR
to instantiate the communication with the target Solr cluster. The CDCR configuration file on the source
cluster will therefore contain a list of ZooKeeper hosts. The CDCR configuration file might also contain
secondary/optional configuration, such as the number of CDC Replicator threads, batch updates related
settings, etc.
CDCR Initialization
CDCR supports incremental updates to either new or existing collections. CDCR may not be able to keep up
with very high volume updates, especially if there are significant communications latencies due to a slow
"pipe" between the data centers. Some scenarios:
? There is an initial bulk load of a corpus followed by lower volume incremental updates. In this case, one
can do the initial bulk load and then enable CDCR. See the section Initial Startup for more information.
? The index is being built up from scratch, without a significant initial bulk load. CDCR can be set up on
empty collections and keep them synchronized from the start.
? The index is always being updated at a volume too high for CDCR to keep up. This is especially possible in
situations where the connection between the Source and target data centers is poor. This scenario is
unsuitable for CDCR in its current form.
Inter-Data Center Communication
Communication between data centers will be achieved through HTTP and the Solr REST API using the SolrJ
client. The SolrJ client will be instantiated with the ZooKeeper host of the target data center. SolrJ will
manage the shard leader discovery process.
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Updates Tracking & Pushing
CDCR replicates data updates from the source to the target data center by leveraging the Updates Log.
A background thread regularly checks the Updates Log for new entries, and then forwards them to the
target data center. The thread therefore needs to keep a checkpoint in the form of a pointer to the last
update successfully processed in the Updates Log. Upon acknowledgement from the target data center that
updates have been successfully processed, the Updates Log pointer is updated to reflect the current
checkpoint.
This pointer must be synchronized across all the replicas. In the case where the leader goes down and a new
leader is elected, the new leader will be able to resume replication from the last update by using this
synchronized pointer. The strategy to synchronize such a pointer across replicas will be explained next.
If for some reason, the target data center is offline or fails to process the updates, the thread will
periodically try to contact the target data center and push the updates.
Synchronization of Update Checkpoints
A reliable synchronization of the update checkpoints between the shard leader and shard replicas is critical
to avoid introducing inconsistency between the Source and target data centers. Another important
requirement is that the synchronization must be performed with minimal network traffic to maximize
scalability.
In order to achieve this, the strategy is to:
? Uniquely identify each update operation. This unique identifier will serve as pointer.
? Rely on two storages: an ephemeral storage on the Source shard leader, and a persistent storage on the
target cluster.
The shard leader in the source cluster will be in charge of generating a unique identifier for each update
operation, and will keep a copy of the identifier of the last processed updates in memory. The identifier will
be sent to the target cluster as part of the update request. On the target data center side, the shard leader
will receive the update request, store it along with the unique identifier in the Updates Log, and replicate it
to the other shards.
SolrCloud already provides a unique identifier for each update operation, i.e., a “version” number. This
version number is generated using a time-based lmport clock which is incremented for each update
operation sent. This provides an “happened-before” ordering of the update operations that will be
leveraged in (1) the initialization of the update checkpoint on the source cluster, and in (2) the maintenance
strategy of the Updates Log.
The persistent storage on the target cluster is used only during the election of a new shard leader on the
Source cluster. If a shard leader goes down on the source cluster and a new leader is elected, the new leader
will contact the target cluster to retrieve the last update checkpoint and instantiate its ephemeral pointer.
On such a request, the target cluster will retrieve the latest identifier received across all the shards, and send
it back to the source cluster. To retrieve the latest identifier, every shard leader will look up the identifier of
the first entry in its Update Logs and send it back to a coordinator. The coordinator will have to select the
highest among them.
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This strategy does not require any additional network traffic and ensures reliable pointer synchronization.
Consistency is principally achieved by leveraging SolrCloud. The update workflow of SolrCloud ensures that
every update is applied to the leader but also to any of the replicas. If the leader goes down, a new leader is
elected. During the leader election, a synchronization is performed between the new leader and the other
replicas. As a result, this ensures that the new leader has a consistent Update Logs with the previous leader.
Having a consistent Updates Log means that:
? On the source cluster, the update checkpoint can be reused by the new leader.
? On the target cluster, the update checkpoint will be consistent between the previous and new leader.
This ensures the correctness of the update checkpoint sent by a newly elected leader from the target
cluster.
Maintenance of Updates Log
The CDCR replication logic requires modification to the maintenance logic of the Updates Log on the source
data center. Initially, the Updates Log acts as a fixed size queue, limited to 100 update entries. In the CDCR
scenario, the Update Logs must act as a queue of variable size as they need to keep track of all the updates
up through the last processed update by the target data center. Entries in the Update Logs are removed
only when all pointers (one pointer per target data center) are after them.
If the communication with one of the target data center is slow, the Updates Log on the source data center
can grow to a substantial size. In such a scenario, it is necessary for the Updates Log to be able to efficiently
find a given update operation given its identifier. Given that its identifier is an incremental number, it is
possible to implement an efficient search strategy. Each transaction log file contains as part of its filename
the version number of the first element. This is used to quickly traverse all the transaction log files and find
the transaction log file containing one specific version number.
Monitoring
CDCR provides the following monitoring capabilities over the replication operations:
? Monitoring of the outgoing and incoming replications, with information such as the Source and target
nodes, their status, etc.
? Statistics about the replication, with information such as operations (add/delete) per second, number of
documents in the queue, etc.
Information about the lifecycle and statistics will be provided on a per-shard basis by the CDC Replicator
thread. The CDCR API can then aggregate this information an a collection level.
CDC Replicator
The CDC Replicator is a background thread that is responsible for replicating updates from a Source data
center to one or more target data centers. It is responsible in providing monitoring information on a per-
shard basis. As there can be a large number of collections and shards in a cluster, we will use a fixed-size
pool of CDC Replicator threads that will be shared across shards.
Limitations
The current design of CDCR has some limitations. CDCR will continue to evolve over time and many of these
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limitations will be addressed. Among them are:
? CDCR is unlikely to be satisfactory for bulk-load situations where the update rate is high, especially if the
bandwidth between the Source and target clusters is restricted. In this scenario, the initial bulk load
should be performed, the Source and target data centers synchronized and CDCR be utilized for
incremental updates.
? CDCR is currently only active-passive; data is pushed from the Source cluster to the target cluster. There
is active work being done in this area in the 6x code line to remove this limitation.
? CDCR works most robustly with the same number of shards in the Source and target collection. The
shards in the two collections may have different numbers of replicas.
Configuration
The source and target configurations differ in the case of the data centers being in separate clusters.
"Cluster" here means separate ZooKeeper ensembles controlling disjoint Solr instances. Whether these data
centers are physically separated or not is immaterial for this discussion.
Source Configuration
Here is a sample of a source configuration file, a section in solrconfig.xml . The presence of the <replica>
section causes CDCR to use this cluster as the Source and should not be present in the target collections in
the cluster-to-cluster case. Details about each setting are after the two examples:
<requestHandler name="/cdcr" class="solr.CdcrRequestHandler">
<lst name="replica">
<str name="zkHost">10.240.18.211:2181</str>
<str name="source">collection1</str>
<str name="target">collection1</str>
</lst>
<lst name="replicator">
<str name="threadPoolSize">8</str>
<str name="schedule">1000</str>
<str name="batchSize">128</str>
</lst>
<lst name="updateLogSynchronizer">
<str name="schedule">1000</str>
</lst>
</requestHandler>
<!-- Modify the <updateLog> section of your existing <updateHandler>
in your config as below -->
<updateHandler class="solr.DirectUpdateHandler2">
<updateLog class="solr.CdcrUpdateLog">
<str name="dir">${solr.ulog.dir:}</str>
<!--Any parameters from the original <updateLog> section -->
</updateLog>
</updateHandler>
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Target Configuration
Here is a typical target configuration.
Target instance must configure an update processor chain that is specific to CDCR. The update processor
chain must include the CdcrUpdateProcessorFactory. The task of this processor is to ensure that the
version numbers attached to update requests coming from a CDCR source SolrCloud are reused and not
overwritten by the target. A properly configured Target configuration looks similar to this.
<requestHandler name="/cdcr" class="solr.CdcrRequestHandler">
<lst name="buffer">
<str name="defaultState">disabled</str>
</lst>
</requestHandler>
<requestHandler name="/update" class="solr.UpdateRequestHandler">
<lst name="defaults">
<str name="update.chain">cdcr-processor-chain</str>
</lst>
</requestHandler>
<updateRequestProcessorChain name="cdcr-processor-chain">
<processor class="solr.CdcrUpdateProcessorFactory"/>
<processor class="solr.RunUpdateProcessorFactory"/>
</updateRequestProcessorChain>
<!-- Modify the <updateLog> section of your existing <updateHandler> in your
config as below -->
<updateHandler class="solr.DirectUpdateHandler2">
<updateLog class="solr.CdcrUpdateLog">
<str name="dir">${solr.ulog.dir:}</str>
<!--Any parameters from the original <updateLog> section -->
</updateLog>
</updateHandler>
Configuration Details
The configuration details, defaults and options are as follows:
The Replica Element
CDCR can be configured to forward update requests to one or more replicas. A replica is defined with a
“replica” list as follows:
Parameter Required Default Description
zkHost Yes none The host address for ZooKeeper of the target SolrCloud.
Usually this is a comma-separated list of addresses to
each node in the target ZooKeeper ensemble.
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Parameter Required Default Description
Source Yes none The name of the collection on the Source SolrCloud to be
replicated.
Target Yes none The name of the collection on the target SolrCloud to
which updates will be forwarded.
The Replicator Element
The CDC Replicator is the component in charge of forwarding updates to the replicas. The replicator will
monitor the update logs of the Source collection and will forward any new updates to the target collection.
The replicator uses a fixed thread pool to forward updates to multiple replicas in parallel. If more than one
replica is configured, one thread will forward a batch of updates from one replica at a time in a round-robin
fashion. The replicator can be configured with a “replicator” list as follows:
Parameter Required Default Description
threadPoolSize No 2 The number of threads to use for forwarding updates.
One thread per replica is recommended.
schedule No 10 The delay in milliseconds for the monitoring the update
log(s).
batchSize No 128 The number of updates to send in one batch. The optimal
size depends on the size of the documents. Large batches
of large documents can increase your memory usage
significantly.
The updateLogSynchronizer Element
Expert: Non-leader nodes need to synchronize their update logs with their leader node from time to time in
order to clean deprecated transaction log files. By default, such a synchronization process is performed
every minute. The schedule of the synchronization can be modified with a “updateLogSynchronizer” list as
follows:
Parameter Required Default Description
schedule No 60000 The delay in milliseconds for synchronizing the updates
log.
The Buffer Element
CDCR is configured by default to buffer any new incoming updates. When buffering updates, the updates
log will store all the updates indefinitely. Replicas do not need to buffer updates, and it is recommended to
disable buffer on the target SolrCloud. The buffer can be disabled at startup with a “buffer” list and the
parameter “defaultState” as follows:
Parameter Required Default Description
defaultState No enabled The state of the buffer at startup.
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CDCR API
The CDCR API is used to control and monitor the replication process. Control actions are performed at a
collection level, i.e., by using the following base URL for API calls:
http://localhost:8983/solr/<collection> .
Monitor actions are performed at a core level, i.e., by using the following base URL for API calls:
http://localhost:8983/solr/<collection> .
Currently, none of the CDCR API calls have parameters.
API Entry Points (Control)
? <collection>/cdcr?action=STATUS : Returns the current state of CDCR.
? <collection>/cdcr?action=START : Starts CDCR replication
? <collection>/cdcr?action=STOP : Stops CDCR replication.
? <collection>/cdcr?action=ENABLEBUFFER : Enables the buffering of updates.
? <collection>/cdcr?action=DISABLEBUFFER : Disables the buffering of updates.
API Entry Points (Monitoring)
? core/cdcr?action=QUEUES : Fetches statistics about the queue for each replica and about the update
logs.
? core/cdcr?action=OPS : Fetches statistics about the replication performance (operations per second) for
each replica.
? core/cdcr?action=ERRORS : Fetches statistics and other information about replication errors for each
replica.
Control Commands
STATUS
/collection/cdcr?action=STATUS
Input
Query Parameters: There are no parameters to this command.
Output
Output Content
The current state of the CDCR, which includes the state of the replication process and the state of the buffer.
Examples
Input
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http://host:8983/solr/<collection_name>/cdcr?action=STATUS
Output
{
"responseHeader": {
"status": 0,
"QTime": 0
},
"status": {
"process": "stopped",
"buffer": "enabled"
}
}
ENABLEBUFFER
/collection/cdcr?action=ENABLEBUFFER
Input
Query Parameters: There are no parameters to this command.
Output
Output Content
The status of the process and an indication of whether the buffer is enabled
Examples
Input
http://host:8983/solr/<collection_name>/cdcr?action=ENABLEBUFFER
Output
{
"responseHeader": {
"status": 0,
"QTime": 0
},
"status": {
"process": "started",
"buffer": "enabled"
}
}
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DISABLEBUFFER
/collection/cdcr?action=DISABLEBUFFER
Input
Query Parameters: There are no parameters to this command
Output
Output Content: The status of CDCR and an indication that the buffer is disabled.
Examples
Input
http://host:8983/solr/<collection_name>/cdcr?action=DISABLEBUFFER
Output
{
"responseHeader": {
"status": 0,
"QTime": 0
},
"status": {
"process": "started",
"buffer": "disabled"
}
}
START
/collection/cdcr?action=START
Input
Query Parameters: There are no parameters for this action
Output
Output Content: Confirmation that CDCR is started and the status of buffering
Examples
Input
http://host:8983/solr/<collection_name>/cdcr?action=START
Output
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{
"responseHeader": {
"status": 0,
"QTime": 0
},
"status": {
"process": "started",
"buffer": "enabled"
}
}
STOP
/collection/cdcr?action=STOP
Input
Query Parameters: There are no parameters for this command.
Output
Output Content: The status of CDCR, including the confirmation that CDCR is stopped
Examples
Input
http://host:8983/solr/<collection_name>/cdcr?action=STOP
Output
{
"responseHeader": {
"status": 0,
"QTime": 0
},
"status": {
"process": "stopped",
"buffer": "enabled"
}
}
Monitoring commands
QUEUES
/core/cdcr?action=QUEUES
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Input
Query Parameters: There are no parameters for this command
Output
Output Content
The output is composed of a list “queues” which contains a list of (ZooKeeper) target hosts, themselves
containing a list of target collections. For each collection, the current size of the queue and the timestamp of
the last update operation successfully processed is provided. The timestamp of the update operation is the
original timestamp, i.e., the time this operation was processed on the Source SolrCloud. This allows an
estimate the latency of the replication process.
The “queues” object also contains information about the updates log, such as the size (in bytes) of the
updates log on disk (“tlogTotalSize”), the number of transaction log files (“tlogTotalCount”) and the status
of the updates log synchronizer (“updateLogSynchronizer”).
Examples
Input
http://host:8983/solr/<replica_name>/cdcr?action=QUEUES
Output
{
"responseHeader":{
"status": 0,
"QTime": 1
},
"queues":{
"127.0.0.1: 40342/solr":{
"Target_collection":{
"queueSize": 104,
"lastTimestamp": "2014-12-02T10:32:15.879Z"
}
}
},
"tlogTotalSize":3817,
"tlogTotalCount":1,
"updateLogSynchronizer": "stopped"
}
OPS
/core/cdcr?action=OPS
Input
Query Parameters: There are no parameters for this command.
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Output
Output Content: The output is composed of a list “operationsPerSecond” which contains a list of
(ZooKeeper) target hosts, themselves containing a list of target collections. For each collection, the average
number of processed operations per second since the start of the replication process is provided. The
operations are further broken down into two groups: add and delete operations.
Examples
Input
http://host:8983/solr/<collection_name>/cdcr?action=OPS
Output
{
"responseHeader":{
"status":0,
"QTime":1
},
"operationsPerSecond":{
"127.0.0.1: 59661/solr":{
"Target_collection":{
"all": 297.102944952749052,
"adds": 297.102944952749052,
"deletes": 0.0
}
}
}
}
ERRORS
/core/cdcr?action=ERRORS
Input
Query Parameters: There are no parameters for this command.
Output
Output Content: The output is composed of a list “errors” which contains a list of (ZooKeeper) target hosts,
themselves containing a list of target collections. For each collection, information about errors encountered
during the replication is provided, such as the number of consecutive errors encountered by the replicator
thread, the number of bad requests or internal errors since the start of the replication process, and a list of
the last errors encountered ordered by timestamp.
Examples
Input
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http://host:8983/solr/<collection_name>/cdcr?action=ERRORS
Output
{
"responseHeader":{
"status":0,
"QTime":2
},
"errors": {
"127.0.0.1: 36872/solr":{
"Target_collection":{
"consecutiveErrors":3,
"bad_request":0,
"internal":3,
"last":{
"2014-12-02T11:04:42.523Z":"internal",
"2014-12-02T11:04:39.223Z":"internal",
"2014-12-02T11:04:38.22Z":"internal"
}
}
}
}
}
Initial Startup
This is a general approach for initializing CDCR in a production environment based upon an approach taken
by the initial working installation of CDCR and generously contributed to illustrate a "real world" scenario.
? Customer uses the CDCR approach to keep a remote disaster-recovery instance available for production
backup. This is an active-passive solution.
? Customer has 26 clouds with 200 million assets per cloud (15GB indexes). Total document count is over
4.8 billion.
? Source and target clouds were synched in 2-3 hour maintenance windows to establish the base index
for the targets.
As usual, it is good to start small. Sync a single cloud and monitor for a period of time before doing the
others. You may need to adjust your settings several times before finding the right balance.
? Before starting, stop or pause the indexers. This is best done during a small maintenance window.
? Stop the SolrCloud instances at the Source
? Include the CDCR request handler configuration in solrconfig.xml as in the below example.
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<requestHandler name="/cdcr" class="solr.CdcrRequestHandler">
<lst name="replica">
<str name="zkHost">${TargetZk}</str>
<str name="Source">${SourceCollection}</str>
<str name="Target">${TargetCollection}</str>
</lst>
<lst name="replicator">
<str name="threadPoolSize">8</str>
<str name="schedule">10</str>
<str name="batchSize">2000</str>
</lst>
<lst name="updateLogSynchronizer">
<str name="schedule">1000</str>
</lst>
</requestHandler>
<updateRequestProcessorChain name="cdcr-processor-chain">
<processor class="solr.CdcrUpdateProcessorFactory" />
<processor class="solr.RunUpdateProcessorFactory" />
</updateRequestProcessorChain>
? Upload the modified solrconfig.xml to ZooKeeper on both Source and Target
? Sync the index directories from the Source collection to target collection across to the corresponding
shard nodes. rsync works well for this.
For example, if there are 2 shards on collection1 with 2 replicas for each shard, copy the corresponding
index directories from
shard1replica1Source to shard1replica1Target
shard1replica2Source to shard1replica2Target
shard2replica1Source to shard2replica1Target
shard2replica2Source to shard2replica2Target
? Start the ZooKeeper on the Target (DR) side
? Start the SolrCloud on the Target (DR) side
? Start the ZooKeeper on the Source side
? Start the SolrCloud on the Source side. As a general rule, the Target (DR) side of the SolrCloud should be
started before the Source side.
? Activate the CDCR on Source instance using the CDCR API:
http://host:port/solr/collection_name/cdcr?action=START
http://host:port/solr/<collection_name>/cdcr?action=START
? There is no need to run the /cdcr?action=START command on the Target
? Disable the buffer on the Target
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http://host:port/solr/collection_name/cdcr?action=DISABLEBUFFER
? Renable indexing
Monitoring
1. Network and disk space monitoring are essential. Ensure that the system has plenty of available storage
to queue up changes if there is a disconnect between the Source and Target. A network outage between
the two data centers can cause your disk usage to grow.
a. Tip: Set a monitor for your disks to send alerts when the disk gets over a certain percentage (e.g.,
70%)
b. Tip: Run a test. With moderate indexing, how long can the system queue changes before you run out
of disk space?
2. Create a simple way to check the counts between the Source and the Target.
a. Keep in mind that if indexing is running, the Source and Target may not match document for
document. Set an alert to fire if the difference is greater than some percentage of the overall cloud
size.
ZooKeeper Settings
With CDCR, the target ZooKeepers will have connections from the Target clouds and the Source clouds. You
may need to increase the maxClientCnxns setting in zoo.cfg .
## set numbers of connection to 200 from client
## is maxClientCnxns=0 that means no limit
maxClientCnxns=800
Upgrading and Patching Production
When rolling in upgrades to your indexer or application, you should shutdown the Source (production) and
the Target (DR). Depending on your setup, you may want to pause/stop indexing. Deploy the release or
patch and renable indexing. Then start the Target (DR).
? There is no need to reissue the DISABLEBUFFERS or START commands. These are persisted.
? After starting the Target, run a simple test. Add a test document to each of the Source clouds. Then
check for it on the Target.
#send to the Source
curl http://<Source>/solr/cloud1/update -H ‘Content-type:application/json‘ -d ‘[{"SKU":"ABC"}]‘
#check the Target
curl "http://<Target>:8983/solr/<collection_name>/select?q=SKU:ABC&wt=json&indent=true"
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Limitations
? Running CDCR with the indexes on HDFS is not currently supported, see: Solr CDCR over HDFS.
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Legacy Scaling and Distribution
This section describes how to set up distribution and replication in Solr. It is considered "legacy" behavior,
since while it is still supported in Solr, the SolrCloud functionality described in the previous chapter is where
the current development is headed. However, if you don’t need all that SolrCloud delivers, search
distribution and index replication may be sufficient.
This section covers the following topics:
Introduction to Scaling and Distribution: Conceptual information about distribution and replication in Solr.
Distributed Search with Index Sharding: Detailed information about implementing distributed searching in
Solr.
Index Replication: Detailed information about replicating your Solr indexes.
Combining Distribution and Replication: Detailed information about replicating shards in a distributed index.
Merging Indexes: Information about combining separate indexes in Solr.
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Introduction to Scaling and Distribution
Both Lucene and Solr were designed to scale to support large implementations with minimal custom coding.
This section covers:
? distributing an index across multiple servers
? replicating an index on multiple servers
? merging indexes
If you need full scale distribution of indexes and queries, as well as replication, load balancing and failover,
you may want to use SolrCloud. Full details on configuring and using SolrCloud is available in the section
SolrCloud.
What Problem Does Distribution Solve?
If searches are taking too long or the index is approaching the physical limitations of its machine, you
should consider distributing the index across two or more Solr servers.
To distribute an index, you divide the index into partitions called shards, each of which runs on a separate
machine. Solr then partitions searches into sub-searches, which run on the individual shards, reporting
results collectively.
The architectural details underlying index sharding are invisible to end users, who simply experience faster
performance on queries against very large indexes.
What Problem Does Replication Solve?
Replicating an index is useful when:
? You have a large search volume which one machine cannot handle, so you need to distribute searches
across multiple read-only copies of the index.
? There is a high volume/high rate of indexing which consumes machine resources and reduces search
performance on the indexing machine, so you need to separate indexing and searching.
? You want to make a backup of the index (see Making and Restoring Backups).
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Distributed Search with Index Sharding
When using traditional index sharding, you will need to consider how to query your documents.
It is highly recommended that you use SolrCloud when needing to scale up or scale out. The setup described
below is legacy and was used prior to the existence of SolrCloud. SolrCloud provides for a truly distributed
set of features with support for things like automatic routing, leader election, optimistic concurrency and
other sanity checks that are expected out of a distributed system.
Everything on this page is specific to legacy setup of distributed search. Users trying out SolrCloud should
not follow any of the steps or information below.
Update reorders (i.e., replica A may see update X then Y, and replica B may see update Y then X).
deleteByQuery also handles reorders the same way, to ensure replicas are consistent. All replicas of a shard
are consistent, even if the updates arrive in a different order on different replicas.
Distributing Documents across Shards
When not using SolrCloud, it is up to you to get all your documents indexed on each shard of your server
farm. Solr supports distributed indexing (routing) in its true form only in the SolrCloud mode.
In the legacy distributed mode, Solr does not calculate universal term/doc frequencies. For most large-scale
implementations, it is not likely to matter that Solr calculates TF/IDF at the shard level. However, if your
collection is heavily skewed in its distribution across servers, you may find misleading relevancy results in
your searches. In general, it is probably best to randomly distribute documents to your shards.
Executing Distributed Searches with the shards Parameter
If a query request includes the shards parameter, the Solr server distributes the request across all the
shards listed as arguments to the parameter. The shards parameter uses this syntax:
host:port/base_url,host:port/base_url*
For example, the shards parameter below causes the search to be distributed across two Solr servers: solr1
and solr2, both of which are running on port 8983:
http://localhost:8983/solr/core1/select?shards=solr1:8983/solr/core1,solr2:8983/solr/core1&i
ndent=true&q=ipod+solr
Rather than require users to include the shards parameter explicitly, it is usually preferred to configure this
parameter as a default in the RequestHandler section of solrconfig.xml .
?
Do not add the shards parameter to the standard request handler; doing so may cause
search queries may enter an infinite loop. Instead, define a new request handler that uses
the shards parameter, and pass distributed search requests to that handler.
With Legacy mode, only query requests are distributed. This includes requests to the SearchHandler (or any
handler extending from org.apache.solr.handler.component.SearchHandler ) using standard
components that support distributed search.
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As in SolrCloud mode, when shards.info=true , distributed responses will include information about the
shard (where each shard represents a logically different index or physical location)
The following components support distributed search:
? The Query component, which returns documents matching a query
? The Facet component, which processes facet.query and facet.field requests where facets are sorted by
count (the default).
? The Highlighting component, which enables Solr to include "highlighted" matches in field values.
? The Stats component, which returns simple statistics for numeric fields within the DocSet.
? The Debug component, which helps with debugging.
Limitations to Distributed Search
Distributed searching in Solr has the following limitations:
? Each document indexed must have a unique key.
? If Solr discovers duplicate document IDs, Solr selects the first document and discards subsequent ones.
? The index for distributed searching may become momentarily out of sync if a commit happens between
the first and second phase of the distributed search. This might cause a situation where a document that
once matched a query and was subsequently changed may no longer match the query but will still be
retrieved. This situation is expected to be quite rare, however, and is only possible for a single query
request.
? The number of shards is limited by number of characters allowed for GET method’s URI; most Web
servers generally support at least 4000 characters, but many servers limit URI length to reduce their
vulnerability to Denial of Service (DoS) attacks.
? Shard information can be returned with each document in a distributed search by including fl=id,
[shard] in the search request. This returns the shard URL.
? In a distributed search, the data directory from the core descriptor overrides any data directory in
solrconfig.xml.
? Update commands may be sent to any server with distributed indexing configured correctly. Document
adds and deletes are forwarded to the appropriate server/shard based on a hash of the unique
document id. commit commands and deleteByQuery commands are sent to every server in shards .
Formerly a limitation was that TF/IDF relevancy computations only used shard-local statistics. This is still the
case by default. If your data isn’t randomly distributed, or if you want more exact statistics, then remember
to configure the ExactStatsCache.
Avoiding Distributed Deadlock
Like in SolrCloud mode, inter-shard requests could lead to a distributed deadlock. It can be avoided by
following the instructions in the section Distributed Requests.
Testing Index Sharding on Two Local Servers
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For simple functional testing, it’s easiest to just set up two local Solr servers on different ports. (In a
production environment, of course, these servers would be deployed on separate machines.)
1. Make two Solr home directories and copy solr.xml into the new directories:
mkdir example/nodes
mkdir example/nodes/node1
# Copy solr.xml into this solr.home
cp server/solr/solr.xml example/nodes/node1/.
# Repeat the above steps for the second node
mkdir example/nodes/node2
cp server/solr/solr.xml example/nodes/node2/.
2. Start the two Solr instances
# Start first node on port 8983
bin/solr start -s example/nodes/node1 -p 8983
# Start second node on port 8984
bin/solr start -s example/nodes/node2 -p 8984
3. Create a core on both the nodes with the sample_techproducts_configs.
bin/solr create_core -c core1 -p 8983 -d sample_techproducts_configs
# Create a core on the Solr node running on port 8984
bin/solr create_core -c core1 -p 8984 -d sample_techproducts_configs
4. In a third window, index an example document to each of the server:
bin/post -c core1 example/exampledocs/monitor.xml -port 8983
bin/post -c core1 example/exampledocs/monitor2.xml -port 8984
5. Search on the node on port 8983:
curl http://localhost:8983/solr/core1/select?q=*:*&wt=xml&indent=true
This should bring back one document.
Search on the node on port 8984:
curl http://localhost:8984/solr/core1/select?q=*:*&wt=xml&indent=true
This should also bring back a single document.
Now do a distributed search across both servers with your browser or curl. In the example below, an
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extra parameter ‘fl‘ is passed to restrict the returned fields to id and name.
curl
http://localhost:8983/solr/core1/select?q=*:*&indent=true&shards=localhost:8983/solr/core1,loc
alhost:8984/solr/core1&fl=id,name
This should contain both the documents as shown below:
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">8</int>
<lst name="params">
<str name="q">*:*</str>
<str name="shards">localhost:8983/solr/core1,localhost:8984/solr/core1</str>
<str name="indent">true</str>
<str name="fl">id,name</str>
<str name="wt">xml</str>
</lst>
</lst>
<result name="response" numFound="2" start="0" maxScore="1.0">
<doc>
<str name="id">3007WFP</str>
<str name="name">Dell Widescreen UltraSharp 3007WFP</str>
</doc>
<doc>
<str name="id">VA902B</str>
<str name="name">ViewSonic VA902B - flat panel display - TFT - 19"</str>
</doc>
</result>
</response>
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Index Replication
Index Replication distributes complete copies of a master index to one or more slave servers. The master
server continues to manage updates to the index. All querying is handled by the slaves. This division of labor
enables Solr to scale to provide adequate responsiveness to queries against large search volumes.
The figure below shows a Solr configuration using index replication. The master server’s index is replicated
on the slaves.
A Solr index can be replicated across multiple slave servers, which then process requests.
Index Replication in Solr
Solr includes a Java implementation of index replication that works over HTTP:
? The configuration affecting replication is controlled by a single file, solrconfig.xml
? Supports the replication of configuration files as well as index files
? Works across platforms with same configuration
? No reliance on OS-dependent file system features (eg: hard links)
? Tightly integrated with Solr; an admin page offers fine-grained control of each aspect of replication
? The Java-based replication feature is implemented as a request handler. Configuring replication is
therefore similar to any normal request handler.
?
Replication In SolrCloud
Although there is no explicit concept of "master/slave" nodes in a SolrCloud cluster, the
ReplicationHandler discussed on this page is still used by SolrCloud as needed to support
"shard recovery" – but this is done in a peer to peer manner.
When using SolrCloud, the ReplicationHandler must be available via the /replication
path. Solr does this implicitly unless overridden explicitly in your solrconfig.xml , but if you
wish to override the default behavior, make certain that you do not explicitly set any of the
"master" or "slave" configuration options mentioned below, or they will interfere with
normal SolrCloud operation.
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Replication Terminology
The table below defines the key terms associated with Solr replication.
Term Definition
Index A Lucene index is a directory of files. These files make up the searchable
and returnable data of a Solr Core.
Distribution The copying of an index from the master server to all slaves. The
distribution process takes advantage of Lucene’s index file structure.
Inserts and Deletes As inserts and deletes occur in the index, the directory remains
unchanged. Documents are always inserted into newly created files.
Documents that are deleted are not removed from the files. They are
flagged in the file, deletable, and are not removed from the files until the
index is optimized.
Master and Slave A Solr replication master is a single node which receives all updates initially
and keeps everything organized. Solr replication slave nodes receive no
updates directly, instead all changes (such as inserts, updates, deletes,
etc.) are made against the single master node. Changes made on the
master are distributed to all the slave nodes which service all query
requests from the clients.
Update An update is a single change request against a single Solr instance. It may
be a request to delete a document, add a new document, change a
document, delete all documents matching a query, etc. Updates are
handled synchronously within an individual Solr instance.
Optimization A process that compacts the index and merges segments in order to
improve query performance. Optimization should only be run on the
master nodes. An optimized index may give query performance gains
compared to an index that has become fragmented over a period of time
with many updates. Distributing an optimized index requires a much
longer time than the distribution of new segments to an un-optimized
index.
Segments A self contained subset of an index consisting of some documents and
data structures related to the inverted index of terms in those documents.
mergeFactor A parameter that controls the number of segments in an index. For
example, when mergeFactor is set to 3, Solr will fill one segment with
documents until the limit maxBufferedDocs is met, then it will start a new
segment. When the number of segments specified by mergeFactor is
reached (in this example, 3) then Solr will merge all the segments into a
single index file, then begin writing new documents to a new segment.
Snapshot A directory containing hard links to the data files of an index. Snapshots
are distributed from the master nodes when the slaves pull them, "smart
copying" any segments the slave node does not have in snapshot directory
that contains the hard links to the most recent index data files.
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Configuring the ReplicationHandler
In addition to ReplicationHandler configuration options specific to the master/slave roles, there are a few
special configuration options that are generally supported (even when using SolrCloud).
? maxNumberOfBackups an integer value dictating the maximum number of backups this node will keep on
disk as it receives backup commands.
? Similar to most other request handlers in Solr you may configure a set of defaults, invariants, and/or
appends parameters corresponding with any request parameters supported by the ReplicationHandler
when processing commands.
Configuring the Replication RequestHandler on a Master Server
Before running a replication, you should set the following parameters on initialization of the handler:
Name Description
replicateAfter String specifying action after which replication should occur. Valid values
are commit, optimize, or startup. There can be multiple values for this
parameter. If you use "startup", you need to have a "commit" and/or
"optimize" entry also if you want to trigger replication on future commits
or optimizes.
backupAfter String specifying action after which a backup should occur. Valid values are
commit, optimize, or startup. There can be multiple values for this
parameter. It is not required for replication, it just makes a backup.
maxNumberOfBackups Integer specifying how many backups to keep. This can be used to delete
all but the most recent N backups.
confFiles The configuration files to replicate, separated by a comma.
commitReserveDuration If your commits are very frequent and your network is slow, you can tweak
this parameter to increase the amount of time taken to download 5Mb
from the master to a slave. The default is 10 seconds.
The example below shows a possible ‘master‘ configuration for the ReplicationHandler , including a fixed
number of backups and an invariant setting for the maxWriteMBPerSec request parameter to prevent slaves
from saturating its network interface
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<requestHandler name="/replication" class="solr.ReplicationHandler">
<lst name="master">
<str name="replicateAfter">optimize</str>
<str name="backupAfter">optimize</str>
<str name="confFiles">schema.xml,stopwords.txt,elevate.xml</str>
<str name="commitReserveDuration">00:00:10</str>
</lst>
<int name="maxNumberOfBackups">2</int>
<lst name="invariants">
<str name="maxWriteMBPerSec">16</str>
</lst>
</requestHandler>
Replicating solrconfig.xml
In the configuration file on the master server, include a line like the following:
<str name="confFiles">solrconfig_slave.xml:solrconfig.xml,x.xml,y.xml</str>
This ensures that the local configuration solrconfig_slave.xml will be saved as solrconfig.xml on the
slave. All other files will be saved with their original names.
On the master server, the file name of the slave configuration file can be anything, as long as the name is
correctly identified in the confFiles string; then it will be saved as whatever file name appears after the
colon ‘:‘.
Configuring the Replication RequestHandler on a Slave Server
The code below shows how to configure a ReplicationHandler on a slave.
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<requestHandler name="/replication" class="solr.ReplicationHandler">
<lst name="slave">
<!-- fully qualified url for the replication handler of master. It is
possible to pass on this as a request param for the fetchindex command -->
<str name="masterUrl">http://remote_host:port/solr/core_name/replication</str>
<!-- Interval in which the slave should poll master. Format is HH:mm:ss .
If this is absent slave does not poll automatically.
But a fetchindex can be triggered from the admin or the http API -->
<str name="pollInterval">00:00:20</str>
<!-- THE FOLLOWING PARAMETERS ARE USUALLY NOT REQUIRED-->
<!-- To use compression while transferring the index files. The possible
values are internal|external. If the value is ‘external‘ make sure
that your master Solr has the settings to honor the accept-encoding header.
See here for details: http://wiki.apache.org/solr/SolrHttpCompression
If it is ‘internal‘ everything will be taken care of automatically.
USE THIS ONLY IF YOUR BANDWIDTH IS LOW.
THIS CAN ACTUALLY SLOWDOWN REPLICATION IN A LAN -->
<str name="compression">internal</str>
<!-- The following values are used when the slave connects to the master to
download the index files. Default values implicitly set as 5000ms and
10000ms respectively. The user DOES NOT need to specify these unless the
bandwidth is extremely low or if there is an extremely high latency -->
<str name="httpConnTimeout">5000</str>
<str name="httpReadTimeout">10000</str>
<!-- If HTTP Basic authentication is enabled on the master, then the slave
can be configured with the following -->
<str name="httpBasicAuthUser">username</str>
<str name="httpBasicAuthPassword">password</str>
</lst>
</requestHandler>
Setting Up a Repeater with the ReplicationHandler
A master may be able to serve only so many slaves without affecting performance. Some organizations have
deployed slave servers across multiple data centers. If each slave downloads the index from a remote data
center, the resulting download may consume too much network bandwidth. To avoid performance
degradation in cases like this, you can configure one or more slaves as repeaters. A repeater is simply a
node that acts as both a master and a slave.
? To configure a server as a repeater, the definition of the Replication requestHandler in the
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solrconfig.xml file must include file lists of use for both masters and slaves.
? Be sure to set the replicateAfter parameter to commit, even if replicateAfter is set to optimize on
the main master. This is because on a repeater (or any slave), a commit is called only after the index is
downloaded. The optimize command is never called on slaves.
? Optionally, one can configure the repeater to fetch compressed files from the master through the
compression parameter to reduce the index download time.
Here is an example of a ReplicationHandler configuration for a repeater:
<requestHandler name="/replication" class="solr.ReplicationHandler">
<lst name="master">
<str name="replicateAfter">commit</str>
<str name="confFiles">schema.xml,stopwords.txt,synonyms.txt</str>
</lst>
<lst name="slave">
<str name="masterUrl">http://master.solr.company.com:8983/solr/core_name/replication</str>
<str name="pollInterval">00:00:60</str>
</lst>
</requestHandler>
Commit and Optimize Operations
When a commit or optimize operation is performed on the master, the RequestHandler reads the list of file
names which are associated with each commit point. This relies on the replicateAfter parameter in the
configuration to decide which types of events should trigger replication.
Setting on the Master Description
commit Triggers replication whenever a commit is performed on the master index.
optimize Triggers replication whenever the master index is optimized.
startup Triggers replication whenever the master index starts up.
The replicateAfter parameter can accept multiple arguments. For example:
<str name="replicateAfter">startup</str>
<str name="replicateAfter">commit</str>
<str name="replicateAfter">optimize</str>
Slave Replication
The master is totally unaware of the slaves.
The slave continuously keeps polling the master (depending on the pollInterval parameter) to check the
current index version of the master. If the slave finds out that the master has a newer version of the index it
initiates a replication process. The steps are as follows:
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? The slave issues a filelist command to get the list of the files. This command returns the names of the
files as well as some metadata (for example, size, a lastmodified timestamp, an alias if any).
? The slave checks with its own index if it has any of those files in the local index. It then runs the
filecontent command to download the missing files. This uses a custom format (akin to the HTTP
chunked encoding) to download the full content or a part of each file. If the connection breaks in
between, the download resumes from the point it failed. At any point, the slave tries 5 times before
giving up a replication altogether.
? The files are downloaded into a temp directory, so that if either the slave or the master crashes during
the download process, no files will be corrupted. Instead, the current replication will simply abort.
? After the download completes, all the new files are moved to the live index directory and the file’s
timestamp is same as its counterpart on the master.
? A commit command is issued on the slave by the Slave’s ReplicationHandler and the new index is loaded.
Replicating Configuration Files
To replicate configuration files, list them using using the confFiles parameter. Only files found in the conf
directory of the master’s Solr instance will be replicated.
Solr replicates configuration files only when the index itself is replicated. That means even if a configuration
file is changed on the master, that file will be replicated only after there is a new commit/optimize on
master’s index.
Unlike the index files, where the timestamp is good enough to figure out if they are identical, configuration
files are compared against their checksum. The schema.xml files (on master and slave) are judged to be
identical if their checksums are identical.
As a precaution when replicating configuration files, Solr copies configuration files to a temporary directory
before moving them into their ultimate location in the conf directory. The old configuration files are then
renamed and kept in the same conf/ directory. The ReplicationHandler does not automatically clean up
these old files.
If a replication involved downloading of at least one configuration file, the ReplicationHandler issues a core-
reload command instead of a commit command.
Resolving Corruption Issues on Slave Servers
If documents are added to the slave, then the slave is no longer in sync with its master. However, the slave
will not undertake any action to put itself in sync, until the master has new index data.
When a commit operation takes place on the master, the index version of the master becomes different
from that of the slave. The slave then fetches the list of files and finds that some of the files present on the
master are also present in the local index but with different sizes and timestamps. This means that the
master and slave have incompatible indexes.
To correct this problem, the slave then copies all the index files from master to a new index directory and
asks the core to load the fresh index from the new directory.
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HTTP API Commands for the ReplicationHandler
You can use the HTTP commands below to control the ReplicationHandler’s operations.
Command Description
http://master_host:port/solr/core_name/replication?c
ommand=enablereplication
Enables replication on the master for all its slaves.
http://master_host:port/solr/core_name/replication?c
ommand=disablereplication
Disables replication on the master for all its slaves.
http://host:port/solr/core_name/replication?comman
d=indexversion
Returns the version of the latest replicatable index
on the specified master or slave.
http://slave_host:port/solr/core_name/replication?co
mmand=fetchindex
Forces the specified slave to fetch a copy of the index
from its master. If you like, you can pass an extra
attribute such as masterUrl or compression (or any
other parameter which is specified in the <lst
name="slave"> tag) to do a one time replication
from a master. This obviates the need for hard-
coding the master in the slave.
http://slave_host:port/solr/core_name/replication?co
mmand=abortfetch
Aborts copying an index from a master to the
specified slave.
http://slave_host:port/solr/core_name/replication?co
mmand=enablepoll
Enables the specified slave to poll for changes on the
master.
http://slave_host:port/solr/core_name/replication?co
mmand=disablepoll
Disables the specified slave from polling for changes
on the master.
http://slave_host:port/solr/core_name/replication?co
mmand=details
Retrieves configuration details and current status.
http://host:port/solr/core_name/replication?comman
d=filelist&generation=<_generation-number_>
Retrieves a list of Lucene files present in the specified
host’s index. You can discover the generation
number of the index by running the indexversion
command.
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Command Description
http://master_host:port/solr/core_name/replication?c
ommand=backup
Creates a backup on master if there are committed
index data in the server; otherwise, does nothing.
This command is useful for making periodic
backups.
supported request parameters:
? numberToKeep: request parameter can be used
with the backup command unless the
maxNumberOfBackups initialization parameter has
been specified on the handler – in which case
maxNumberOfBackups is always used and
attempts to use the numberToKeep request
parameter will cause an error.
? name : (optional) Backup name . The snapshot will
be created in a directory called snapshot.<name>
within the data directory of the core . By default
the name is generated using date in
yyyyMMddHHmmssSSS format. If location
parameter is passed , that would be used instead
of the data directory
? location : Backup location
http://master_host:port /solr/
core_name/replication?command=deletebackup
Delete any backup created using the backup
command .
Request parameters:
? name: The name of the snapshot . A snapshot
with the name snapshot.<name> must exist .If
not, an error is thrown
? location: Location where the snapshot is created
Distribution and Optimization
Optimizing an index is not something most users should generally worry about - but in particular users
should be aware of the impacts of optimizing an index when using the ReplicationHandler .
The time required to optimize a master index can vary dramatically. A small index may be optimized in
minutes. A very large index may take hours. The variables include the size of the index and the speed of the
hardware.
Distributing a newly optimized index may take only a few minutes or up to an hour or more, again
depending on the size of the index and the performance capabilities of network connections and disks.
During optimization the machine is under load and does not process queries very well. Given a schedule of
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updates being driven a few times an hour to the slaves, we cannot run an optimize with every committed
snapshot.
Copying an optimized index means that the entire index will need to be transferred during the next
snappull. This is a large expense, but not nearly as huge as running the optimize everywhere. Consider this
example: on a three-slave one-master configuration, distributing a newly-optimized index takes
approximately 80 seconds total. Rolling the change across a tier would require approximately ten minutes
per machine (or machine group). If this optimize were rolled across the query tier, and if each slave node
being optimized were disabled and not receiving queries, a rollout would take at least twenty minutes and
potentially as long as an hour and a half. Additionally, the files would need to be synchronized so that the
following the optimize, snappull would not think that the independently optimized files were different in any
way. This would also leave the door open to independent corruption of indexes instead of each being a
perfect copy of the master.
Optimizing on the master allows for a straight-forward optimization operation. No query slaves need to be
taken out of service. The optimized index can be distributed in the background as queries are being
normally serviced. The optimization can occur at any time convenient to the application providing index
updates.
While optimizing may have some benefits in some situations, a rapidly changing index will not retain those
benefits for long, and since optimization is an intensive process, it may be better to consider other options,
such as lowering the merge factor (discussed in the section on Index Configuration).
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Combining Distribution and Replication
When your index is too large for a single machine and you have a query volume that single shards cannot
keep up with, it’s time to replicate each shard in your distributed search setup.
The idea is to combine distributed search with replication. As shown in the figure below, a combined
distributed-replication configuration features a master server for each shard and then 1-n slaves that are
replicated from the master. As in a standard replicated configuration, the master server handles updates
and optimizations without adversely affecting query handling performance.
Query requests should be load balanced across each of the shard slaves. This gives you both increased
query handling capacity and fail-over backup if a server goes down.
A Solr configuration combining both replication and master-slave distribution.
None of the master shards in this configuration know about each other. You index to each master, the index
is replicated to each slave, and then searches are distributed across the slaves, using one slave from each
master/slave shard.
For high availability you can use a load balancer to set up a virtual IP for each shard’s set of slaves. If you are
new to load balancing, HAProxy (http://haproxy.1wt.eu/) is a good open source software load-balancer. If a
slave server goes down, a good load-balancer will detect the failure using some technique (generally a
heartbeat system), and forward all requests to the remaining live slaves that served with the failed slave. A
single virtual IP should then be set up so that requests can hit a single IP, and get load balanced to each of
the virtual IPs for the search slaves.
With this configuration you will have a fully load balanced, search-side fault-tolerant system (Solr does not
yet support fault-tolerant indexing). Incoming searches will be handed off to one of the functioning slaves,
then the slave will distribute the search request across a slave for each of the shards in your configuration.
The slave will issue a request to each of the virtual IPs for each shard, and the load balancer will choose one
of the available slaves. Finally, the results will be combined into a single results set and returned. If any of
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the slaves go down, they will be taken out of rotation and the remaining slaves will be used. If a shard
master goes down, searches can still be served from the slaves until you have corrected the problem and
put the master back into production.
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Merging Indexes
If you need to combine indexes from two different projects or from multiple servers previously used in a
distributed configuration, you can use either the IndexMergeTool included in lucene-misc or the
CoreAdminHandler .
To merge indexes, they must meet these requirements:
? The two indexes must be compatible: their schemas should include the same fields and they should
analyze fields the same way.
? The indexes must not include duplicate data.
Optimally, the two indexes should be built using the same schema.
Using IndexMergeTool
To merge the indexes, do the following:
1. Make sure that both indexes you want to merge are closed.
2. Issue this command:
java -cp $SOLR/server/solr-webapp/webapp/WEB-INF/lib/lucene-core-
VERSION.jar:$SOLR/server/solr-webapp/webapp/WEB-INF/lib/lucene-misc-VERSION.jar
org/apache/lucene/misc/IndexMergeTool /path/to/newindex /path/to/old/index1
/path/to/old/index2
This will create a new index at /path/to/newindex that contains both index1 and index2.
3. Copy this new directory to the location of your application’s solr index (move the old one aside first, of
course) and start Solr.
Using CoreAdmin
The MERGEINDEXES command of the CoreAdminHandler can be used to merge indexes into a new core –
either from one or more arbitrary indexDir directories or by merging from one or more existing srcCore
core names.
See the CoreAdminHandler section for details.
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Client APIs
This section discusses the available client APIs for Solr. It covers the following topics:
Introduction to Client APIs: A conceptual overview of Solr client APIs.
Choosing an Output Format: Information about choosing a response format in Solr.
Using JavaScript: Explains why a client API is not needed for JavaScript responses.
Using Python: Information about Python and JSON responses.
Client API Lineup: A list of all Solr Client APIs, with links.
Using SolrJ: Detailed information about SolrJ, an API for working with Java applications.
Using Solr From Ruby: Detailed information about using Solr with Ruby applications.
MBean Request Handler: Describes the MBean request handler for programmatic access to Solr server
statistics and information.
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Introduction to Client APIs
At its heart, Solr is a Web application, but because it is built on open protocols, any type of client application
can use Solr.
HTTP is the fundamental protocol used between client applications and Solr. The client makes a request and
Solr does some work and provides a response. Clients use requests to ask Solr to do things like perform
queries or index documents.
Client applications can reach Solr by creating HTTP requests and parsing the HTTP responses. Client APIs
encapsulate much of the work of sending requests and parsing responses, which makes it much easier to
write client applications.
Clients use Solr’s five fundamental operations to work with Solr. The operations are query, index, delete,
commit, and optimize.
Queries are executed by creating a URL that contains all the query parameters. Solr examines the request
URL, performs the query, and returns the results. The other operations are similar, although in certain cases
the HTTP request is a POST operation and contains information beyond whatever is included in the request
URL. An index operation, for example, may contain a document in the body of the request.
Solr also features an EmbeddedSolrServer that offers a Java API without requiring an HTTP connection. For
details, see Using SolrJ.
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Choosing an Output Format
Many programming environments are able to send HTTP requests and retrieve responses. Parsing the
responses is a slightly more thorny problem. Fortunately, Solr makes it easy to choose an output format that
will be easy to handle on the client side.
Specify a response format using the wt parameter in a query. The available response formats are
documented in Response Writers.
Most client APIs hide this detail for you, so for many types of client applications, you won’t ever have to
specify a wt parameter. In JavaScript, however, the interface to Solr is a little closer to the metal, so you will
need to add this parameter yourself.
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Client API Lineup
The Solr Wiki contains a list of client APIs at http://wiki.apache.org/solr/IntegratingSolr.
Here is the list of client APIs, current at this writing (November 2011):
Name Environment URL
SolRuby Ruby https://github.com/rsolr/rsolr
DelSolr Ruby https://github.com/avvo/delsolr
acts_as_solr Rails http://acts-as-solr.rubyforge.org/, http://rubyforge.org/
projects/background-solr/
Flare Rails http://wiki.apache.org/solr/Flare
SolPHP PHP http://wiki.apache.org/solr/SolPHP
SolrJ Java http://wiki.apache.org/solr/SolJava
Python API Python http://wiki.apache.org/solr/SolPython
PySolr Python http://code.google.com/p/pysolr/
SolPerl Perl http://wiki.apache.org/solr/SolPerl
Solr.pm Perl http://search.cpan.org/~garafola/Solr-0.03/lib/Solr.pm
SolrForrest Forrest/Cocoon http://wiki.apache.org/solr/SolrForrest
SolrSharp C# http://www.codeplex.com/solrsharp
SolColdfusion ColdFusion http://solcoldfusion.riaforge.org/
SolrNet .NET https://github.com/mausch/SolrNet
AJAX Solr AJAX http://github.com/evolvingweb/ajax-solr/wiki
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Using JavaScript
Using Solr from JavaScript clients is so straightforward that it deserves a special mention. In fact, it is so
straightforward that there is no client API. You don’t need to install any packages or configure anything.
HTTP requests can be sent to Solr using the standard XMLHttpRequest mechanism.
Out of the box, Solr can send JavaScript Object Notation (JSON) responses, which are easily interpreted in
JavaScript. Just add wt=json to the request URL to have responses sent as JSON.
For more information and an excellent example, read the SolJSON page on the Solr Wiki:
http://wiki.apache.org/solr/SolJSON
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Using Python
Solr includes an output format specifically for Python, but JSON output is a little more robust.
Simple Python
Making a query is a simple matter. First, tell Python you will need to make HTTP connections.
from urllib2 import *
Now open a connection to the server and get a response. The wt query parameter tells Solr to return results
in a format that Python can understand.
connection = urlopen(‘http://localhost:8983/solr/collection_name/select?q=cheese&wt=python‘)
response = eval(connection.read())
Now interpreting the response is just a matter of pulling out the information that you need.
print response[‘response‘][‘numFound‘], "documents found."
# Print the name of each document.
for document in response[‘response‘][‘docs‘]:
print " Name =", document[‘name‘]
Python with JSON
JSON is a more robust response format, but you will need to add a Python package in order to use it. At a
command line, install the simplejson package like this:
sudo easy_install simplejson
Once that is done, making a query is nearly the same as before. However, notice that the wt query
parameter is now json, and the response is now digested by simplejson.load() .
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from urllib2 import *
import simplejson
connection = urlopen(‘http://localhost:8983/solr/collection_name/select?q=cheese&wt=json‘)
response = simplejson.load(connection)
print response[‘response‘][‘numFound‘], "documents found."
# Print the name of each document.
for document in response[‘response‘][‘docs‘]:
print " Name =", document[‘name‘]
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Using SolrJ
SolrJ is an API that makes it easy for Java applications to talk to Solr. SolrJ hides a lot of the details of
connecting to Solr and allows your application to interact with Solr with simple high-level methods.
The center of SolrJ is the org.apache.solr.client.solrj package, which contains just five main classes.
Begin by creating a SolrClient , which represents the Solr instance you want to use. Then send
SolrRequests or SolrQuerys and get back SolrResponses.
SolrClient is abstract, so to connect to a remote Solr instance, you’ll actually create an instance of either
HttpSolrClient , or CloudSolrClient . Both communicate with Solr via HTTP, the difference is that
HttpSolrClient is configured using an explicit Solr URL, while CloudSolrClient is configured using the
zkHost String for a SolrCloud cluster.
Single node Solr client
String urlString = "http://localhost:8983/solr/techproducts";
SolrClient solr = new HttpSolrClient.Builder(urlString).build();
SolrCloud client
// Using a ZK Host String
String zkHostString = "zkServerA:2181,zkServerB:2181,zkServerC:2181/solr";
SolrClient solr = new CloudSolrClient.Builder().withZkHost(zkHostString).build();
// Using already running Solr nodes
SolrClient solr = new CloudSolrClient.Builder().withSolrUrl("http://localhost:8983/solr").build(
);
Once you have a SolrClient , you can use it by calling methods like query() , add() , and commit() .
Building and Running SolrJ Applications
The SolrJ API is included with Solr, so you do not have to download or install anything else. However, in order
to build and run applications that use SolrJ, you have to add some libraries to the classpath.
At build time, the examples presented with this section require solr-solrj-x.y.z.jar to be in the
classpath.
At run time, the examples in this section require the libraries found in the ‘dist/solrj-lib‘ directory.
The Ant script bundled with this sections‘ examples includes the libraries as appropriate when building and
running.
You can sidestep a lot of the messing around with the JAR files by using Maven instead of Ant. All you will
need to do to include SolrJ in your application is to put the following dependency in the project’s pom.xml :
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<dependency>
<groupId>org.apache.solr</groupId>
<artifactId>solr-solrj</artifactId>
<version>x.y.z</version>
</dependency>
If you are worried about the SolrJ libraries expanding the size of your client application, you can use a code
obfuscator like ProGuard to remove APIs that you are not using.
Setting XMLResponseParser
SolrJ uses a binary format, rather than XML, as its default response format. If you are trying to mix Solr and
SolrJ versions where one is version 1.x and the other is 3.x or later, then you MUST use the XML response
parser. The binary format changed in 3.x, and the two javabin versions are entirely incompatible. The
following code will make this change:
solr.setParser(new XMLResponseParser());
Performing Queries
Use query() to have Solr search for results. You have to pass a SolrQuery object that describes the query,
and you will get back a QueryResponse (from the org.apache.solr.client.solrj.response package).
SolrQuery has methods that make it easy to add parameters to choose a request handler and send
parameters to it. Here is a very simple example that uses the default request handler and sets the query
string:
SolrQuery query = new SolrQuery();
query.setQuery(mQueryString);
To choose a different request handler, there is a specific method available in SolrJ version 4.0 and later:
query.setRequestHandler("/spellCheckCompRH");
You can also set arbitrary parameters on the query object. The first two code lines below are equivalent to
each other, and the third shows how to use an arbitrary parameter q to set the query string:
query.set("fl", "category,title,price");
query.setFields("category", "title", "price");
query.set("q", "category:books");
Once you have your SolrQuery set up, submit it with query() :
QueryResponse response = solr.query(query);
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The client makes a network connection and sends the query. Solr processes the query, and the response is
sent and parsed into a QueryResponse .
The QueryResponse is a collection of documents that satisfy the query parameters. You can retrieve the
documents directly with getResults() and you can call other methods to find out information about
highlighting or facets.
SolrDocumentList list = response.getResults();
Indexing Documents
Other operations are just as simple. To index (add) a document, all you need to do is create a
SolrInputDocument and pass it along to the SolrClient’s `add() method. This example assumes that the
SolrClient object called ‘solr‘ is already created based on the examples shown earlier.
SolrInputDocument document = new SolrInputDocument();
document.addField("id", "552199");
document.addField("name", "Gouda cheese wheel");
document.addField("price", "49.99");
UpdateResponse response = solr.add(document);
// Remember to commit your changes!
solr.commit();
Uploading Content in XML or Binary Formats
SolrJ lets you upload content in binary format instead of the default XML format. Use the following code to
upload using binary format, which is the same format SolrJ uses to fetch results. If you are trying to mix Solr
and SolrJ versions where one is version 1.x and the other is 3.x or later, then you MUST stick with the XML
request writer. The binary format changed in 3.x, and the two javabin versions are entirely incompatible.
solr.setRequestWriter(new BinaryRequestWriter());
Using the ConcurrentUpdateSolrClient
When implementing java applications that will be bulk loading a lot of documents at once,
ConcurrentUpdateSolrClient is an alternative to consider instead of using HttpSolrClient . The
ConcurrentUpdateSolrClient buffers all added documents and writes them into open HTTP connections.
This class is thread safe. Although any SolrClient request can be made with this implementation, it is only
recommended to use the ConcurrentUpdateSolrClient for /update requests.
EmbeddedSolrServer
The EmbeddedSolrServer class provides an implementation of the SolrClient client API talking directly to
an micro-instance of Solr running directly in your Java application. This embedded approach is not
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recommended in most cases and fairly limited in the set of features it supports – in particular it can not be
used with SolrCloud or Index Replication. EmbeddedSolrServer exists primarily to help facilitate testing.
For information on how to use EmbeddedSolrServer please review the SolrJ JUnit tests in the
org.apache.solr.client.solrj.embedded package of the Solr source release.
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Using Solr From Ruby
Solr has an optional Ruby response format that extends the JSON output to allow the response to be safely
eval’d by Ruby’s interpreter
This Ruby response format differs from JSON in the following ways:
? Ruby’s single quoted strings are used to prevent possible string exploits
? \ and ‘ are the only two characters escaped…
? unicode escapes not used… data is written as raw UTF-8
? nil used for null
? => used as the key/value separator in maps
Here’s an example Ruby response from Solr, for a request like
http://localhost:8983/solr/techproducts/select?q=iPod&wt=ruby&indent=on (with Solr launching
using bin/solr start -e techproducts ):
{
‘responseHeader‘=>{
‘status‘=>0,
‘QTime‘=>0,
‘params‘=>{
‘q‘=>‘iPod‘,
‘indent‘=>‘on‘,
‘wt‘=>‘ruby‘}},
‘response‘=>{‘numFound‘=>3,‘start‘=>0,‘docs‘=>[
{
‘id‘=>‘IW-02‘,
‘name‘=>‘iPod & iPod Mini USB 2.0 Cable‘,
‘manu‘=>‘Belkin‘,
‘manu_id_s‘=>‘belkin‘,
‘cat‘=>[‘electronics‘,
‘connector‘],
‘features‘=>[‘car power adapter for iPod, white‘],
‘weight‘=>2.0,
‘price‘=>11.5,
‘price_c‘=>‘11.50,USD‘,
‘popularity‘=>1,
‘inStock‘=>false,
‘store‘=>‘37.7752,-122.4232‘,
‘manufacturedate_dt‘=>‘2006-02-14T23:55:59Z‘,
‘_version_‘=>1491038048794705920},
{
‘id‘=>‘F8V7067-APL-KIT‘,
‘name‘=>‘Belkin Mobile Power Cord for iPod w/ Dock‘,
‘manu‘=>‘Belkin‘,
‘manu_id_s‘=>‘belkin‘,
‘cat‘=>[‘electronics‘,
‘connector‘],
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‘features‘=>[‘car power adapter, white‘],
‘weight‘=>4.0,
‘price‘=>19.95,
‘price_c‘=>‘19.95,USD‘,
‘popularity‘=>1,
‘inStock‘=>false,
‘store‘=>‘45.18014,-93.87741‘,
‘manufacturedate_dt‘=>‘2005-08-01T16:30:25Z‘,
‘_version_‘=>1491038048792608768},
{
‘id‘=>‘MA147LL/A‘,
‘name‘=>‘Apple 60 GB iPod with Video Playback Black‘,
‘manu‘=>‘Apple Computer Inc.‘,
‘manu_id_s‘=>‘apple‘,
‘cat‘=>[‘electronics‘,
‘music‘],
‘features‘=>[‘iTunes, Podcasts, Audiobooks‘,
‘Stores up to 15,000 songs, 25,000 photos, or 150 hours of video‘,
‘2.5-inch, 320x240 color TFT LCD display with LED backlight‘,
‘Up to 20 hours of battery life‘,
‘Plays AAC, MP3, WAV, AIFF, Audible, Apple Lossless, H.264 video‘,
‘Notes, Calendar, Phone book, Hold button, Date display, Photo wallet, Built-in games,
JPEG photo playback, Upgradeable firmware, USB 2.0 compatibility, Playback speed control,
Rechargeable capability, Battery level indication‘],
‘includes‘=>‘earbud headphones, USB cable‘,
‘weight‘=>5.5,
‘price‘=>399.0,
‘price_c‘=>‘399.00,USD‘,
‘popularity‘=>10,
‘inStock‘=>true,
‘store‘=>‘37.7752,-100.0232‘,
‘manufacturedate_dt‘=>‘2005-10-12T08:00:00Z‘,
‘_version_‘=>1491038048799948800}]
}}
Here is a simple example of how one may query Solr using the Ruby response format:
require ‘net/http‘
h = Net::HTTP.new(‘localhost‘, 8983)
http_response = h.get(‘/solr/techproducts/select?q=iPod&wt=ruby‘)
rsp = eval(http_response.body)
puts ‘number of matches = ‘ + rsp[‘response‘][‘numFound‘].to_s
#print out the name field for each returned document
rsp[‘response‘][‘docs‘].each { |doc| puts ‘name field = ‘ + doc[‘name‘] }
For simple interactions with Solr, this may be all you need! If you are building complex interactions with Solr,
then consider the libraries mentioned at https://wiki.apache.org/solr/Ruby%20Response%20Format
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Major Changes from Solr 5 to Solr 6
There are some major changes in Solr 6 to consider before starting to migrate your configurations and
indexes. There are many hundreds of changes, so a thorough review of the Upgrading Solr section as well as
the CHANGES.txt file in your Solr instance will help you plan your migration to Solr 6. This section attempts
to highlight some of the major changes you should be aware of.
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Highlights of New Features in Solr 6
Some of the major improvements in Solr 6 include:
Streaming Expressions
Introduced in Solr 5, Streaming Expressions allow querying Solr and getting results as a stream of data,
sorted and aggregated as requested.
Several new expression types have been added in Solr 6:
? Parallel expressions using a MapReduce-like shuffling for faster throughput of high-cardinality fields.
? Daemon expressions to support continuous push or pull streaming.
? Advanced parallel relational algebra like distributed joins, intersections, unions and complements.
? Publish/Subscribe messaging.
? JDBC connections to pull data from other systems and join with documents in the Solr index.
Parallel SQL Interface
Built on streaming expressions, new in Solr 6 is a Parallel SQL interface to be able to send SQL queries to
Solr. SQL statements are compiled to streaming expressions on the fly, providing the full range of
aggregations available to streaming expression requests. A JDBC driver is included, which allows using SQL
clients and database visualization tools to query your Solr index and import data to other systems.
Cross Data Center Replication
Replication across data centers is now possible with Cross Data Center Replication. Using an active-passive
model, a SolrCloud cluster can be replicated to another data center, and monitored with a new API.
Graph Query Parser
A new graph query parser makes it possible to to graph traversal queries of Directed (Cyclic) Graphs
modelled using Solr documents.
DocValues
Most non-text field types in the Solr sample configsets now default to using DocValues.
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Java 8 Required
The minimum supported version of Java for Solr 6 (and the SolrJ client libraries) is now Java 8.
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Index Format Changes
Solr 6 has no support for reading Lucene/Solr 4.x and earlier indexes. Be sure to run the Lucene
IndexUpgrader included with Solr 5.5 if you might still have old 4x formatted segments in your index.
Alternatively: fully optimize your index with Solr 5.5 to make sure it consists only of one up-to-date index
segment.
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Managed Schema is now the Default
Solr’s default behavior when a solrconfig.xml does not explicitly define a <schemaFactory/> is now
dependent on the luceneMatchVersion specified in that solrconfig.xml . When luceneMatchVersion <
6.0 , ClassicIndexSchemaFactory will continue to be used for back compatibility, otherwise an instance of
ManagedIndexSchemaFactory will be used.
The most notable impacts of this change are:
? Existing solrconfig.xml files that are modified to use luceneMatchVersion >= 6.0 , but do not have an
explicitly configured ClassicIndexSchemaFactory , will have their schema.xml file automatically
upgraded to a managed-schema file.
? Schema modifications via the Schema API will now be enabled by default.
Please review the Schema Factory Definition in SolrConfig section for more details.
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Default Similarity Changes
Solr’s default behavior when a Schema does not explicitly define a global <similarity/> is now dependent
on the luceneMatchVersion specified in the solrconfig.xml . When luceneMatchVersion < 6.0 , an
instance of ClassicSimilarityFactory will be used, otherwise an instance of SchemaSimlarityFactory will
be used. Most notably this change means that users can take advantage of per Field Type similarity
declarations, with out needing to also explicitly declare a global usage of SchemaSimlarityFactory .
Regardless of whether it is explicitly declared, or used as an implicit global default,
SchemaSimlarityFactory’s implicit behavior when a Field Types do not declare an explicit
`<similarity /> has also been changed to depend on the the luceneMatchVersion . When
luceneMatchVersion < 6.0 , an instance of ClassicSimilarity will be used, otherwise an instance of
BM25Simlarity will be used. A defaultSimFromFieldType init option may be specified on the
SchemaSimilarityFactory declaration to change this behavior. Please review the SchemaSimlarityFactory
javadocs for more details
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Replica & Shard Delete Command Changes
DELETESHARD and DELETEREPLICA now default to deleting the instance directory, data directory, and index
directory for any replica they delete. Please review the Collection API documentation for details on new
request parameters to prevent this behavior if you wish to keep all data on disk when using these
commands
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facet.date.* Parameters Removed
The facet.date parameter (and associated facet.date.* parameters) that were deprecated in Solr 3.x have
been removed completely. If you have not yet switched to using the equivalent facet.range functionality
you must do so now before upgrading.
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Upgrading a Solr Cluster
This page covers how to upgrade an existing Solr cluster that was installed using the service installation
scripts.
?
The steps outlined on this page assume you use the default service name of “ solr ”. If you
use an alternate service name or Solr installation directory, some of the paths and
commands mentioned below will have to be modified accordingly.
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Planning Your Upgrade
Here is a checklist of things you need to prepare before starting the upgrade process:
1. Examine the Upgrading Solr page to determine if any behavior changes in the new version of Solr will
affect your installation.
2. If not using replication (ie: collections with replicationFactor > 1), then you should make a backup of each
collection. If all of your collections use replication, then you don’t technically need to make a backup
since you will be upgrading and verifying each node individually.
3. Determine which Solr node is currently hosting the Overseer leader process in SolrCloud, as you should
upgrade this node last. To determine the Overseer, use the Overseer Status API, see: Collections API.
4. Plan to perform your upgrade during a system maintenance window if possible. You’ll be doing a rolling
restart of your cluster (each node, one-by-one), but we still recommend doing the upgrade when system
usage is minimal.
5. Verify the cluster is currently healthy and all replicas are active, as you should not perform an upgrade
on a degraded cluster.
6. Re-build and test all custom server-side components against the new Solr JAR files.
7. Determine the values of the following variables that are used by the Solr Control Scripts:
? ZK_HOST : The ZooKeeper connection string your current SolrCloud nodes use to connect to
ZooKeeper; this value will be the same for all nodes in the cluster.
? SOLR_HOST : The hostname each Solr node used to register with ZooKeeper when joining the
SolrCloud cluster; this value will be used to set the host Java system property when starting the new
Solr process.
? SOLR_PORT : The port each Solr node is listening on, such as 8983.
? SOLR_HOME : The absolute path to the Solr home directory for each Solr node; this directory must
contain a solr.xml file. This value will be passed to the new Solr process using the solr.solr.home
system property, see: Solr Cores and solr.xml.
If you are upgrading from an installation of Solr 5.x or later, these values can typically be found in
either /var/solr/solr.in.sh or /etc/default/solr.in.sh .
You should now be ready to upgrade your cluster. Please verify this process in a test / staging cluster before
doing it in production.
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Upgrade Process
The approach we recommend is to perform the upgrade of each Solr node, one-by-one. In other words, you
will need to stop a node, upgrade it to the new version of Solr, and restart it before moving on to the next
node. This means that for a short period of time, there will be a mix of "Old Solr" and "New Solr" nodes
running in your cluster. We also assume that you will point the new Solr node to your existing Solr home
directory where the Lucene index files are managed for each collection on the node. This means that you
won’t need to move any index files around to perform the upgrade.
Step 1: Stop Solr
Begin by stopping the Solr node you want to upgrade. After stopping the node, if using a replication, (ie:
collections with replicationFactor > 1) verify that all leaders hosted on the downed node have successfully
migrated to other replicas; you can do this by visiting the Cloud panel in the Solr Admin UI. If not using
replication, then any collections with shards hosted on the downed node will be temporarily off-line.
Step 2: Install Solr as a Service
Please follow the instructions to install Solr as a Service on Linux documented at Taking Solr to Production.
Use the -n parameter to avoid automatic start of Solr by the installer script. You need to update the
/etc/default/solr.in.sh include file in the next step to complete the upgrade process.
?
If you have a /var/solr/solr.in.sh file for your existing Solr install, running the
install_solr_service.sh script will move this file to its new location:
/etc/default/solr.in.sh (see SOLR-8101 for more details)
Step 3: Set Environment Variable Overrides
Open /etc/default/solr.in.sh with a text editor and verify that the following variables are set correctly,
or add them bottom of the include file as needed:
ZK_HOST=SOLR_HOST=SOLR_PORT=SOLR_HOME=
Make sure the user you plan to own the Solr process is the owner of the SOLR_HOME directory. For instance, if
you plan to run Solr as the "solr" user and SOLR_HOME is /var/solr/data , then you would do: sudo chown -R
solr: /var/solr/data
Step 4: Start Solr
You are now ready to start the upgraded Solr node by doing: sudo service solr start . The upgraded
instance will join the existing cluster because you’re using the same SOLR_HOME , SOLR_PORT , and SOLR_HOST
settings used by the old Solr node; thus, the new server will look like the old node to the running cluster. Be
sure to look in /var/solr/logs/solr.log for errors during startup.
Step 5: Run Healthcheck
You should run the Solr healthcheck command for all collections that are hosted on the upgraded node
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before proceeding to upgrade the next node in your cluster. For instance, if the newly upgraded node hosts
a replica for the MyDocuments collection, then you can run the following command (replace ZK_HOST with
the ZooKeeper connection string):
/opt/solr/bin/solr healthcheck -c MyDocuments -z ZK_HOST
Look for any problems reported about any of the replicas for the collection.
Lastly, repeat Steps 1-5 for all nodes in your cluster.
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IndexUpgrader Tool
The Lucene distribution includes a tool that upgrades an index from previous Lucene versions to the current
file format.
The tool can be used from command line, or it can be instantiated and executed in Java.
In a Solr distribution, the Lucene files are located in ./server/solr-webapp/webapp/WEB-INF/lib . You will
need to include the lucene-core-<version>.jar and lucene-backwards-codecs-<version>.jar on the
classpath when running the tool.
java -cp lucene-core-6.0.0.jar:lucene-backward-codecs-6.0.0.jar
org.apache.lucene.index.IndexUpgrader [-delete-prior-commits] [-verbose] /path/to/index
This tool keeps only the last commit in an index. For this reason, if the incoming index has more than one
commit, the tool refuses to run by default. Specify -delete-prior-commits to override this, allowing the tool
to delete all but the last commit.
Upgrading large indexes may take a long time. As a rule of thumb, the upgrade processes about 1 GB per
minute.
?
This tool may reorder documents if the index was partially upgraded before execution (e.g.,
documents were added). If your application relies on monotonicity of document IDs (which
means that the order in which the documents were added to the index is preserved), do a
full forceMerge instead.
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Further Assistance
There is a very active user community around Solr and Lucene. The solr-user mailing list, and #solr IRC
channel are both great resources for asking questions.
To view the mailing list archives, subscribe to the list, or join the IRC channel, please see
https://lucene.apache.org/solr/community.html.
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Solr Glossary
These are common terms used with Solr.
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Solr Terms
Where possible, terms are linked to relevant parts of the Solr Reference Guide for more information.
Jump to a letter:
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
A
Atomic updates
An approach to updating only one or more fields of a document, instead of reindexing the entire
document.
B
Boolean operators
These control the inclusion or exclusion of keywords in a query by using operators such as AND, OR, and
NOT.
C
Cluster
In Solr, a cluster is a set of Solr nodes operating in coordination with each other via ZooKeeper, and
managed as a unit. A cluster may contain many collections. See also SolrCloud.
Collection
In Solr, one or more Documents grouped together in a single logical index using a single configuration
and Schema.
In SolrCloud a collection may be divided up into multiple logical shards, which may in turn be distributed
across many nodes, or in a Single node Solr installation, a collection may be a single Core.
Commit
To make document changes permanent in the index. In the case of added documents, they would be
searchable after a commit.
Core
An individual Solr instance (represents a logical index). Multiple cores can run on a single node. See also
SolrCloud.
Core reload
To re-initialize a Solr core after changes to schema.xml , solrconfig.xml or other configuration files.
D
Distributed search
Distributed search is one where queries are processed across more than one Shard.
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Document
A group of fields and their values. Documents are the basic unit of data in a collection. Documents are
assigned to shards using standard hashing, or by specifically assigning a shard within the document ID.
Documents are versioned after each write operation.
E
Ensemble
A ZooKeeper term to indicate multiple ZooKeeper instances running simultaneously and in coordination
with each other for fault tolerance.
F
Facet
The arrangement of search results into categories based on indexed terms.
Field
The content to be indexed/searched along with metadata defining how the content should be processed
by Solr.
I
Inverse document frequency (IDF)
A measure of the general importance of a term. It is calculated as the number of total Documents divided
by the number of Documents that a particular word occurs in the collection. See http://en.wikipedia.org/
wiki/Tf-idf and the Lucene TFIDFSimilarity javadocs for more info on TF-IDF based scoring and Lucene
scoring in particular. See also Term frequency.
Inverted index
A way of creating a searchable index that lists every word and the documents that contain those words,
similar to an index in the back of a book which lists words and the pages on which they can be found.
When performing keyword searches, this method is considered more efficient than the alternative, which
would be to create a list of documents paired with every word used in each document. Since users search
using terms they expect to be in documents, finding the term before the document saves processing
resources and time.
L
Leader
A single Replica for each Shard that takes charge of coordinating index updates (document additions or
deletions) to other replicas in the same shard. This is a transient responsibility assigned to a node via an
election, if the current Shard Leader goes down, a new node will automatically be elected to take its place.
See also SolrCloud.
M
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Metadata
Literally, data about data. Metadata is information about a document, such as its title, author, or location.
N
Natural language query
A search that is entered as a user would normally speak or write, as in, "What is aspirin?"
Node
A JVM instance running Solr. Also known as a Solr server.
O
Optimistic concurrency
Also known as "optimistic locking", this is an approach that allows for updates to documents currently in
the index while retaining locking or version control.
Overseer
A single node in SolrCloud that is responsible for processing and coordinating actions involving the entire
cluster. It keeps track of the state of existing nodes, collections, shards, and replicas, and assigns new
replicas to nodes. This is a transient responsibility assigned to a node via an election, if the current
Overseer goes down, a new node will be automatically elected to take its place. See also SolrCloud.
Q
Query parser
A query parser processes the terms entered by a user.
R
Recall
The ability of a search engine to retrieve all of the possible matches to a user’s query.
Relevance
The appropriateness of a document to the search conducted by the user.
Replica
A Core that acts as a physical copy of a Shard in a SolrCloud Collection.
Replication
A method of copying a master index from one server to one or more "slave" or "child" servers.
RequestHandler
Logic and configuration parameters that tell Solr how to handle incoming "requests", whether the
requests are to return search results, to index documents, or to handle other custom situations.
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S
SearchComponent
Logic and configuration parameters used by request handlers to process query requests. Examples of
search components include faceting, highlighting, and "more like this" functionality.
Shard
In SolrCloud, a logical partition of a single Collection. Every shard consists of at least one physical Replica,
but there may be multiple Replicas distributed across multiple Nodes for fault tolerance. See also
SolrCloud.
SolrCloud
Umbrella term for a suite of functionality in Solr which allows managing a Cluster of Solr Nodes for
scalability, fault tolerance, and high availability.
Solr Schema (managed-schema or schema.xml)
The Solr index Schema defines the fields to be indexed and the type for the field (text, integers, etc.) By
default schema data can be "managed" at run time using the Schema API and is typically kept in a file
named managed-schema which Solr modifies as needed, but a collection may be configured to use a static
Schema, which is only loaded on startup from a human edited configuration file - typically named
schema.xml . See Schema Factory Definition in SolrConfig for details.
SolrConfig (solrconfig.xml)
The Apache Solr configuration file. Defines indexing options, RequestHandlers, highlighting,
spellchecking and various other configurations. The file, solrconfig.xml is located in the Solr home conf
directory.
Spell Check
The ability to suggest alternative spellings of search terms to a user, as a check against spelling errors
causing few or zero results.
Stopwords
Generally, words that have little meaning to a user’s search but which may have been entered as part of a
natural language query. Stopwords are generally very small pronouns, conjunctions and prepositions
(such as, "the", "with", or "and")
Suggester
Functionality in Solr that provides the ability to suggest possible query terms to users as they type.
Synonyms
Synonyms generally are terms which are near to each other in meaning and may substitute for one
another. In a search engine implementation, synonyms may be abbreviations as well as words, or terms
that are not consistently hyphenated. Examples of synonyms in this context would be "Inc." and
"Incorporated" or "iPod" and "i-pod".
T
Term frequency
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The number of times a word occurs in a given document. See http://en.wikipedia.org/wiki/Tf-idf and the
Lucene TFIDFSimilarity javadocs for more info on TF-IDF based scoring and Lucene scoring in particular.
See also Inverse document frequency (IDF).
Transaction log
An append-only log of write operations maintained by each Replica. This log is required with SolrCloud
implementations and is created and managed automatically by Solr.
W
Wildcard
A wildcard allows a substitution of one or more letters of a word to account for possible variations in
spelling or tenses.
Z
ZooKeeper
Also known as Apache ZooKeeper. The system used by SolrCloud to keep track of configuration files and
node names for a cluster. A ZooKeeper cluster is used as the central configuration store for the cluster, a
coordinator for operations requiring distributed synchronization, and the system of record for cluster
topology. See also SolrCloud.
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Errata
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Errata For This Documentation
Any mistakes found in this documentation after its release will be listed on the on-line version of this page:
https://lucene.apache.org/solr/guide/6.6/errata.html
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Errata For Past Versions of This Documentation
Any known mistakes in past releases of this documentation will be noted below.
v2 API Blob store api path: The 6.5 guide listed the Blob store api path as /v2/blob , but the correct path is
/v2/c/.system/blob .
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Apache Solr Reference Guide For Solr 6.6
标签：max-age .config boot stdout bridged other signed documents reader
原文地址：http://www.cnblogs.com/yun-wei/p/7567233.html
