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HADOOP：建立单节点集群
目的
前置条件
支持的平台
需要的软件
安装软件
下载
准备开始建立hadoop集群
单机操作
伪分布式操作
配置
设置ssh免密登陆
扩展
单节点中YARN
完全分布式
This document describes how to set up and configure a single-node Hadoop installation so that you can quickly perform simple operations using Hadoop MapReduce and the Hadoop Distributed File System (HDFS).
目的
该文档描述了如何安装和配置一个单节点的Hadoop,以便于你可以快速的使用MapReduce和HDFS执行简单的操作。
前置条件
GNU/Linux is supported as a development and production platform. Hadoop has been demonstrated on GNU/Linux clusters with 2000 nodes.
Windows is also a supported platform but the followings steps are for Linux only. To set up Hadoop on Windows, see wiki page.
支持的平台
开发和生产环境支持GUN/linux环境。Hadoop在GUN/LINUX平台下证实可以创建2000个节点。
windows平台也是支持的，但是如下的操作只是针对linux平台的，在windows上安装hadoop,请参考 wiki page.
Required software for Linux include:
Java? must be installed. Recommended Java versions are described at HadoopJavaVersions.
ssh must be installed and sshd must be running to use the Hadoop scripts that manage remote Hadoop daemons if the optional start and stop scripts are to be used. Additionally, it is recommmended that pdsh also be installed for better ssh resource management.
需要的软件
Java是必须的，需求的Java版本请查看HadoopJavaVersions.
ssh是必须的，sshd必须使用hadoop脚本运行，如果使用开启或关闭脚本来管理远程机器上的hadoop进程。此外，为了更好的管理ssh资源pdsh也是需要安装的。
If your cluster doesn’t have the requisite software you will need to install it.
For example on Ubuntu Linux:
$ sudo apt-get install ssh $ sudo apt-get install pdsh
安装软件
如果你的集群没有必要的软件，你需要去安装它。
例如在Ubuntu linux系统上：
sudo apt-get install ssh
sudo apt-get install pdsh
To get a Hadoop distribution, download a recent stable release from one of the Apache Download Mirrors.
下载：
为了获取hadoop
Unpack the downloaded Hadoop distribution. In the distribution, edit the file etc/hadoop/hadoop-env.sh to define some parameters as follows:
# set to the root of your Java installation export JAVA_HOME=/usr/java/latest
Try the following command:
$ bin/hadoop
This will display the usage documentation for the hadoop script.
Now you are ready to start your Hadoop cluster in one of the three supported modes:
By default, Hadoop is configured to run in a non-distributed mode, as a single Java process. This is useful for debugging.
The following example copies the unpacked conf directory to use as input and then finds and displays every match of the given regular expression. Output is written to the given output directory.
$ mkdir input $ cp etc/hadoop/*.xml input $ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.0.0-alpha4.jar grep input output ‘dfs[a-z.]+‘ $ cat output/*
Hadoop can also be run on a single-node in a pseudo-distributed mode where each Hadoop daemon runs in a separate Java process.
Use the following:
etc/hadoop/core-site.xml:
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://localhost:9000</value>
</property>
</configuration>
etc/hadoop/hdfs-site.xml:
<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
</configuration>
Now check that you can ssh to the localhost without a passphrase:
$ ssh localhost
If you cannot ssh to localhost without a passphrase, execute the following commands:
$ ssh-keygen -t rsa -P ‘‘ -f ~/.ssh/id_rsa $ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys $ chmod 0600 ~/.ssh/authorized_keys
The following instructions are to run a MapReduce job locally. If you want to execute a job on YARN, see YARN on Single Node.
Format the filesystem:
$ bin/hdfs namenode -format
Start NameNode daemon and DataNode daemon:
$ sbin/start-dfs.sh
The hadoop daemon log output is written to the $HADOOP_LOG_DIR directory (defaults to $HADOOP_HOME/logs).
Browse the web interface for the NameNode; by default it is available at:
Make the HDFS directories required to execute MapReduce jobs:
$ bin/hdfs dfs -mkdir /user $ bin/hdfs dfs -mkdir /user/<username>
Copy the input files into the distributed filesystem:
$ bin/hdfs dfs -mkdir input $ bin/hdfs dfs -put etc/hadoop/*.xml input
Run some of the examples provided:
$ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.0.0-alpha4.jar grep input output ‘dfs[a-z.]+‘
Examine the output files: Copy the output files from the distributed filesystem to the local filesystem and examine them:
$ bin/hdfs dfs -get output output $ cat output/*
or
View the output files on the distributed filesystem:
$ bin/hdfs dfs -cat output/*
When you’re done, stop the daemons with:
$ sbin/stop-dfs.sh
You can run a MapReduce job on YARN in a pseudo-distributed mode by setting a few parameters and running ResourceManager daemon and NodeManager daemon in addition.
The following instructions assume that 1. ~ 4. steps of the above instructions are already executed.
Configure parameters as follows:
etc/hadoop/mapred-site.xml:
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
</configuration>
etc/hadoop/yarn-site.xml:
<configuration>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
<property>
<name>yarn.nodemanager.env-whitelist</name>
<value>JAVA_HOME,HADOOP_COMMON_HOME,HADOOP_HDFS_HOME,HADOOP_CONF_DIR,CLASSPATH_PREPEND_DISTCACHE,HADOOP_YARN_HOME,HADOOP_MAPRED_HOME</value>
</property>
</configuration>
Start ResourceManager daemon and NodeManager daemon:
$ sbin/start-yarn.sh
Browse the web interface for the ResourceManager; by default it is available at:
Run a MapReduce job.
When you’re done, stop the daemons with:
$ sbin/stop-yarn.sh
标签：link 创建 简单 开启 data demo running lin with
原文地址：http://www.cnblogs.com/dream-to-pku/p/7298423.html
