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flume是一个分布式、可靠、和高可用的海量日志采集、聚合和传输的系统。支持在日志系统中定制各类数据发送方,用于收集数据;同时,Flume提供对数据进行简单处理,并写到各种数据接受方(比如文本、HDFS、Hbase等)的能力 。
一、什么是Flume? flume 作为 cloudera 开发的实时日志收集系统,受到了业界的认可与广泛应用。Flume 初始的发行版本目前被统称为 Flume OG(original generation),属于 cloudera。但随着 FLume 功能的扩展,Flume OG 代码工程臃肿、核心组件设计不合理、核心配置不标准等缺点暴露出来,尤其是在 Flume OG 的最后一个发行版本 0.94.0 中,日志传输不稳定的现象尤为严重,为了解决这些问题,2011 年 10 月 22 号,cloudera 完成了 Flume-728,对 Flume 进行了里程碑式的改动:重构核心组件、核心配置以及代码架构,重构后的版本统称为 Flume NG(next generation);改动的另一原因是将 Flume 纳入 apache 旗下,cloudera Flume 改名为 Apache Flume。 flume的特点: flume是一个分布式、可靠、和高可用的海量日志采集、聚合和传输的系统。支持在日志系统中定制各类数据发送方,用于收集数据;同时,Flume提供对数据进行简单处理,并写到各种数据接受方(比如文本、HDFS、Hbase等)的能力 。 flume的数据流由事件(Event)贯穿始终。事件是Flume的基本数据单位,它携带日志数据(字节数组形式)并且携带有头信息,这些Event由Agent外部的Source生成,当Source捕获事件后会进行特定的格式化,然后Source会把事件推入(单个或多个)Channel中。你可以把Channel看作是一个缓冲区,它将保存事件直到Sink处理完该事件。Sink负责持久化日志或者把事件推向另一个Source。 flume的可靠性 当节点出现故障时,日志能够被传送到其他节点上而不会丢失。Flume提供了三种级别的可靠性保障,从强到弱依次分别为:end-to-end(收到数据agent首先将event写到磁盘上,当数据传送成功后,再删除;如果数据发送失败,可以重新发送。),Store on failure(这也是scribe采用的策略,当数据接收方crash时,将数据写到本地,待恢复后,继续发送),Besteffort(数据发送到接收方后,不会进行确认)。 flume的可恢复性: 还是靠Channel。推荐使用FileChannel,事件持久化在本地文件系统里(性能较差)。 flume的一些核心概念:Agent使用JVM 运行Flume。每台机器运行一个agent,但是可以在一个agent中包含多个sources和sinks。Client生产数据,运行在一个独立的线程。Source从Client收集数据,传递给Channel。Sink从Channel收集数据,运行在一个独立线程。Channel连接 sources 和 sinks ,这个有点像一个队列。Events可以是日志记录、 avro 对象等。 Flume以agent为最小的独立运行单位。一个agent就是一个JVM。单agent由Source、Sink和Channel三大组件构成,如下图:
值得注意的是,Flume提供了大量内置的Source、Channel和Sink类型。不同类型的Source,Channel和Sink可以自由组合。组合方式基于用户设置的配置文件,非常灵活。比如:Channel可以把事件暂存在内存里,也可以持久化到本地硬盘上。Sink可以把日志写入HDFS, HBase,甚至是另外一个Source等等。Flume支持用户建立多级流,也就是说,多个agent可以协同工作,并且支持Fan-in、Fan-out、Contextual Routing、Backup Routes,这也正是NB之处。如下图所示:
二、flume的官方网站在哪里? http://flume.apache.org/
三、在哪里下载?
http://www.apache.org/dyn/closer.cgi/flume/1.5.0/apache-flume-1.5.0-bin.tar.gz
四、如何安装?1)将下载的flume包,解压到/home/hadoop目录中,你就已经完成了50%:)简单吧
tar -zxvf apache-flume-1.6.0-bin -C /home/hadoop
2)修改 flume-env.sh 配置文件,主要是JAVA_HOME变量设置(可根据自己的java_home进行配置)
root@m1:/home/hadoop/flume-1.6.0-bin# cp conf/flume-env.sh.template conf/flume-env.sh root@m1:/home/hadoop/flume-1.6.0-bin# vi conf/flume-env.sh # 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. # If this file is placed at FLUME_CONF_DIR/flume-env.sh, it will be sourced # during Flume startup. # Enviroment variables can be set here. JAVA_HOME=/usr/lib/jvm/java-7-oracle # Give Flume more memory and pre-allocate, enable remote monitoring via JMX #JAVA_OPTS="-Xms100m -Xmx200m -Dcom.sun.management.jmxremote" # Note that the Flume conf directory is always included in the classpath. #FLUME_CLASSPATH=""
3)验证是否安装成功
root@m1:/home/hadoop# /home/hadoop/flume-1.6.0-bin/bin/flume-ng version Flume 1.6.0 Source code repository: https://git-wip-us.apache.org/repos/asf/flume.git Revision: 8633220df808c4cd0c13d1cf0320454a94f1ea97 Compiled by hshreedharan on Wed May 7 14:49:18 PDT 2014 From source with checksum a01fe726e4380ba0c9f7a7d222db961f root@m1:/home/hadoop#出现上面的信息,表示安装成功了
root@m1:/home/hadoop#vi /home/hadoop/flume-1.6.0-bin/conf/avro.conf a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = avro a1.sources.r1.channels = c1 a1.sources.r1.bind = 0.0.0.0 a1.sources.r1.port = 4141 # Describe the sink a1.sinks.k1.type = logger # Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1b)启动flume agent a1
root@m1:/home/hadoop# /home/hadoop/flume-1.6.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.6.0-bin/conf/avro.conf -n a1 -Dflume.root.logger=INFO,consolec)创建指定文件
root@m1:/home/hadoop# echo "hello world" > /home/hadoop/flume-1.6.0-bin/log.00d)使用avro-client发送文件
root@m1:/home/hadoop# /home/hadoop/flume-1.6.0-bin/bin/flume-ng avro-client -c . -H m1 -p 4141 -F /home/hadoop/flume-1.6.0-bin/log.00f)在m1的控制台,可以看到以下信息,注意最后一行:
root@m1:/home/hadoop/flume-1.6.0-bin/conf# /home/hadoop/flume-1.6.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.6.0-bin/conf/avro.conf -n a1 -Dflume.root.logger=INFO,console Info: Sourcing environment configuration script /home/hadoop/flume-1.5.0-bin/conf/flume-env.sh Info: Including Hadoop libraries found via (/home/hadoop/hadoop-2.2.0/bin/hadoop) for HDFS access Info: Excluding /home/hadoop/hadoop-2.2.0/share/hadoop/common/lib/slf4j-api-1.7.5.jar from classpath Info: Excluding /home/hadoop/hadoop-2.2.0/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar from classpath ... -08-10 10:43:25,112 (New I/O worker #1) [INFO - org.apache.avro.ipc.NettyServer$NettyServerAvroHandler.handleUpstream(NettyServer.java:171)] [id: 0x92464c4f, /192.168.1.50:59850 :> /192.168.1.50:4141] UNBOUND -08-10 10:43:25,112 (New I/O worker #1) [INFO - org.apache.avro.ipc.NettyServer$NettyServerAvroHandler.handleUpstream(NettyServer.java:171)] [id: 0x92464c4f, /192.168.1.50:59850 :> /192.168.1.50:4141] CLOSED -08-10 10:43:25,112 (New I/O worker #1) [INFO - org.apache.avro.ipc.NettyServer$NettyServerAvroHandler.channelClosed(NettyServer.java:209)] Connection to /192.168.1.50:59850 disconnected. -08-10 10:43:26,718 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 68 65 6C 6C 6F 20 77 6F 72 6C 64 hello world }
root@m1:/home/hadoop# vi /home/hadoop/flume-1.6.0-bin/conf/spool.conf a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = spooldir a1.sources.r1.channels = c1 a1.sources.r1.spoolDir = /home/hadoop/flume-1.6.0-bin/logs a1.sources.r1.fileHeader = true # Describe the sink a1.sinks.k1.type = logger # Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1b)启动flume agent a1
root@m1:/home/hadoop# /home/hadoop/flume-1.6.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.6.0-bin/conf/spool.conf -n a1 -Dflume.root.logger=INFO,consolec)追加文件到/home/hadoop/flume-1.5.0-bin/logs目录
root@m1:/home/hadoop# echo "spool test1" > /home/hadoop/flume-1.6.0-bin/logs/spool_text.logd)在m1的控制台,可以看到以下相关信息:
/08/10 11:37:13 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown. /08/10 11:37:13 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown. /08/10 11:37:14 INFO avro.ReliableSpoolingFileEventReader: Preparing to move file /home/hadoop/flume-1.5.0-bin/logs/spool_text.log to /home/hadoop/flume-1.5.0-bin/logs/spool_text.log.COMPLETED /08/10 11:37:14 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown. /08/10 11:37:14 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown. /08/10 11:37:14 INFO sink.LoggerSink: Event: { headers:{file=/home/hadoop/flume-1.5.0-bin/logs/spool_text.log} body: 73 70 6F 6F 6C 20 74 65 73 74 31 spool test1 } /08/10 11:37:15 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown. /08/10 11:37:15 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown. /08/10 11:37:16 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown. /08/10 11:37:16 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown. /08/10 11:37:17 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.3)案例3:Exec
root@m1:/home/hadoop# vi /home/hadoop/flume-1.6.0-bin/conf/exec_tail.conf a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = exec a1.sources.r1.channels = c1 a1.sources.r1.command = tail -F /home/hadoop/flume-1.5.0-bin/log_exec_tail # Describe the sink a1.sinks.k1.type = logger # Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1b)启动flume agent a1
root@m1:/home/hadoop# /home/hadoop/flume-1.6.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.6.0-bin/conf/exec_tail.conf -n a1 -Dflume.root.logger=INFO,consolec)生成足够多的内容在文件里
root@m1:/home/hadoop# for i in {1..100};do echo "exec tail$i" >> /home/hadoop/flume-1.5.0-bin/log_exec_tail;echo $i;sleep 0.1;donee)在m1的控制台,可以看到以下信息:
-08-10 10:59:25,513 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 20 74 65 73 74 exec tail test } -08-10 10:59:34,535 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 20 74 65 73 74 exec tail test } -08-10 11:01:40,557 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 31 exec tail1 } -08-10 11:01:41,180 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 32 exec tail2 } -08-10 11:01:41,180 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 33 exec tail3 } -08-10 11:01:41,181 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 34 exec tail4 } -08-10 11:01:41,181 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 35 exec tail5 } -08-10 11:01:41,181 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 36 exec tail6 } .... .... .... -08-10 11:01:51,550 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 39 36 exec tail96 } -08-10 11:01:51,550 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 39 37 exec tail97 } -08-10 11:01:51,551 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 39 38 exec tail98 } -08-10 11:01:51,551 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 39 39 exec tail99 } -08-10 11:01:51,551 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 31 30 30 exec tail100 }4)案例4:Syslogtcp
root@m1:/home/hadoop# vi /home/hadoop/flume-1.5.0-bin/conf/syslog_tcp.conf a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.host = localhost a1.sources.r1.channels = c1 # Describe the sink a1.sinks.k1.type = logger # Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1b)启动flume agent a1
root@m1:/home/hadoop# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/syslog_tcp.conf -n a1 -Dflume.root.logger=INFO,consolec)测试产生syslog
root@m1:/home/hadoop# echo "hello idoall.org syslog" | nc localhost 5140d)在m1的控制台,可以看到以下信息:
/08/10 11:41:45 INFO node.PollingPropertiesFileConfigurationProvider: Reloading configuration file:/home/hadoop/flume-1.5.0-bin/conf/syslog_tcp.conf /08/10 11:41:45 INFO conf.FlumeConfiguration: Added sinks: k1 Agent: a1 /08/10 11:41:45 INFO conf.FlumeConfiguration: Processing:k1 /08/10 11:41:45 INFO conf.FlumeConfiguration: Processing:k1 /08/10 11:41:45 INFO conf.FlumeConfiguration: Post-validation flume configuration contains configuration for agents: [a1] /08/10 11:41:45 INFO node.AbstractConfigurationProvider: Creating channels /08/10 11:41:45 INFO channel.DefaultChannelFactory: Creating instance of channel c1 type memory /08/10 11:41:45 INFO node.AbstractConfigurationProvider: Created channel c1 /08/10 11:41:45 INFO source.DefaultSourceFactory: Creating instance of source r1, type syslogtcp /08/10 11:41:45 INFO sink.DefaultSinkFactory: Creating instance of sink: k1, type: logger /08/10 11:41:45 INFO node.AbstractConfigurationProvider: Channel c1 connected to [r1, k1] /08/10 11:41:45 INFO node.Application: Starting new configuration:{ sourceRunners:{r1=EventDrivenSourceRunner: { source:org.apache.flume.source.SyslogTcpSource{name:r1,state:IDLE} }} sinkRunners:{k1=SinkRunner: { policy:org.apache.flume.sink.DefaultSinkProcessor@6538b14 counterGroup:{ name:null counters:{} } }} channels:{c1=org.apache.flume.channel.MemoryChannel{name: c1}} } /08/10 11:41:45 INFO node.Application: Starting Channel c1 /08/10 11:41:45 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: CHANNEL, name: c1: Successfully registered new MBean. /08/10 11:41:45 INFO instrumentation.MonitoredCounterGroup: Component type: CHANNEL, name: c1 started /08/10 11:41:45 INFO node.Application: Starting Sink k1 /08/10 11:41:45 INFO node.Application: Starting Source r1 /08/10 11:41:45 INFO source.SyslogTcpSource: Syslog TCP Source starting... /08/10 11:42:15 WARN source.SyslogUtils: Event created from Invalid Syslog data. /08/10 11:42:15 INFO sink.LoggerSink: Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 68 65 6C 6C 6F 20 69 64 6F 61 6C 6C 2E 6F 72 67 hello idoall.org }
root@m1:/home/hadoop# vi /home/hadoop/flume-1.6.0-bin/conf/post_json.conf a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = org.apache.flume.source.http.HTTPSource a1.sources.r1.port = 8888 a1.sources.r1.channels = c1 # Describe the sink a1.sinks.k1.type = logger # Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1b)启动flume agent a1
root@m1:/home/hadoop# /home/hadoop/flume-1.6.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.6.0-bin/conf/post_json.conf -n a1 -Dflume.root.logger=INFO,consolec)生成JSON 格式的POST request
root@m1:/home/hadoop# curl -X POST -d '[{ "headers" :{"a" : "a1","b" : "b1"},"body" : "idoall.org_body"}]' http://localhost:8888d)在m1的控制台,可以看到以下信息:
08/10 11:49:59 INFO node.Application: Starting Channel c1 /08/10 11:49:59 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: CHANNEL, name: c1: Successfully registered new MBean. /08/10 11:49:59 INFO instrumentation.MonitoredCounterGroup: Component type: CHANNEL, name: c1 started /08/10 11:49:59 INFO node.Application: Starting Sink k1 /08/10 11:49:59 INFO node.Application: Starting Source r1 /08/10 11:49:59 INFO mortbay.log: Logging to org.slf4j.impl.Log4jLoggerAdapter(org.mortbay.log) via org.mortbay.log.Slf4jLog /08/10 11:49:59 INFO mortbay.log: jetty-6.1.26 /08/10 11:50:00 INFO mortbay.log: Started SelectChannelConnector@0.0.0.0:8888 /08/10 11:50:00 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: SOURCE, name: r1: Successfully registered new MBean. /08/10 11:50:00 INFO instrumentation.MonitoredCounterGroup: Component type: SOURCE, name: r1 started /08/10 12:14:32 INFO sink.LoggerSink: Event: { headers:{b=b1, a=a1} body: 69 64 6F 61 6C 6C 2E 6F 72 67 5F 62 6F 64 79 idoall.org_body }6)案例6:Hadoop sink
root@m1:/home/hadoop# vi /home/hadoop/flume-1.6.0-bin/conf/hdfs_sink.conf a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.host = localhost a1.sources.r1.channels = c1 # Describe the sink a1.sinks.k1.type = hdfs a1.sinks.k1.channel = c1 a1.sinks.k1.hdfs.path = hdfs://m1:9000/user/flume/syslogtcp a1.sinks.k1.hdfs.filePrefix = Syslog a1.sinks.k1.hdfs.round = true a1.sinks.k1.hdfs.roundValue = 10 a1.sinks.k1.hdfs.roundUnit = minute # Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1b)启动flume agent a1
root@m1:/home/hadoop# /home/hadoop/flume-1.6.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.6.0-bin/conf/hdfs_sink.conf -n a1 -Dflume.root.logger=INFO,consolec)测试产生syslog
root@m1:/home/hadoop# echo "hello idoall flume -> hadoop testing one" | nc localhost 5140d)在m1的控制台,可以看到以下信息:
/08/10 12:20:39 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: CHANNEL, name: c1: Successfully registered new MBean. /08/10 12:20:39 INFO instrumentation.MonitoredCounterGroup: Component type: CHANNEL, name: c1 started /08/10 12:20:39 INFO node.Application: Starting Sink k1 /08/10 12:20:39 INFO node.Application: Starting Source r1 /08/10 12:20:39 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: SINK, name: k1: Successfully registered new MBean. /08/10 12:20:39 INFO instrumentation.MonitoredCounterGroup: Component type: SINK, name: k1 started /08/10 12:20:39 INFO source.SyslogTcpSource: Syslog TCP Source starting... /08/10 12:21:46 WARN source.SyslogUtils: Event created from Invalid Syslog data. /08/10 12:21:49 INFO hdfs.HDFSSequenceFile: writeFormat = Writable, UseRawLocalFileSystem = false /08/10 12:21:49 INFO hdfs.BucketWriter: Creating hdfs://m1:9000/user/flume/syslogtcp//Syslog.1407644509504.tmp /08/10 12:22:20 INFO hdfs.BucketWriter: Closing hdfs://m1:9000/user/flume/syslogtcp//Syslog.1407644509504.tmp /08/10 12:22:20 INFO hdfs.BucketWriter: Close tries incremented /08/10 12:22:20 INFO hdfs.BucketWriter: Renaming hdfs://m1:9000/user/flume/syslogtcp/Syslog.1407644509504.tmp to hdfs://m1:9000/user/flume/syslogtcp/Syslog.1407644509504 /08/10 12:22:20 INFO hdfs.HDFSEventSink: Writer callback called.e)在m1上再打开一个窗口,去hadoop上检查文件是否生成
root@m1:/home/hadoop# /home/hadoop/hadoop-2.2.0/bin/hadoop fs -ls /user/flume/syslogtcp Found 1 items -rw-r--r-- 3 root supergroup 155 2014-08-10 12:22 /user/flume/syslogtcp/Syslog.1407644509504 root@m1:/home/hadoop# /home/hadoop/hadoop-2.2.0/bin/hadoop fs -cat /user/flume/syslogtcp/Syslog.1407644509504 SEQ!org.apache.hadoop.io.LongWritable"org.apache.hadoop.io.BytesWritable^;>Gv$hello idoall flume -> hadoop testing one7)案例7:File Roll Sink
root@m1:/home/hadoop# vi /home/hadoop/flume-1.6.0-bin/conf/file_roll.conf a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = syslogtcp a1.sources.r1.port = 5555 a1.sources.r1.host = localhost a1.sources.r1.channels = c1 # Describe the sink a1.sinks.k1.type = file_roll a1.sinks.k1.sink.directory = /home/hadoop/flume-1.5.0-bin/logs # Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1b)启动flume agent a1
root@m1:/home/hadoop# /home/hadoop/flume-1.6.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.6.0-bin/conf/file_roll.conf -n a1 -Dflume.root.logger=INFO,consolec)测试产生log
root@m1:/home/hadoop# echo "hello idoall.org syslog" | nc localhost 5555 root@m1:/home/hadoop# echo "hello idoall.org syslog 2" | nc localhost 5555d)查看/home/hadoop/flume-1.5.0-bin/logs下是否生成文件,默认每30秒生成一个新文件
root@m1:/home/hadoop# ll /home/hadoop/flume-1.6.0-bin/logs 总用量 272 drwxr-xr-x 3 root root 4096 Aug 10 12:50 ./ drwxr-xr-x 9 root root 4096 Aug 10 10:59 ../ -rw-r--r-- 1 root root 50 Aug 10 12:49 1407646164782-1 -rw-r--r-- 1 root root 0 Aug 10 12:49 1407646164782-2 -rw-r--r-- 1 root root 0 Aug 10 12:50 1407646164782-3 root@m1:/home/hadoop# cat /home/hadoop/flume-1.5.0-bin/logs/1407646164782-1 /home/hadoop/flume-1.5.0-bin/logs/1407646164782-2 hello idoall.org syslog hello idoall.org syslog 2
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原文地址:http://blog.csdn.net/yjl33/article/details/51980312