Flume1.5.0入门：安装、部署、及flume的案例

分手后的思念是犯贱 2022-06-09 01:28 286阅读 0赞

最近在用flume做广告监控用，收集了一些入门资料：

1、http://blog.csdn.net/a2011480169/article/details/51544664   [Flume架构以及应用介绍][Flume]

2、http://www.cnblogs.com/gongxijun/p/5656778.html  flume介绍与原理(一)

3、http://blog.csdn.net/pelick/article/details/18193527  分布式日志收集系统Apache Flume的设计介绍

4、http://www.aboutyun.com/thread-7949-1-1.html  **让你快速认识flume及安装和使用flume1.5传输数据(日志)到hadoop2.2**

5、http://www.aboutyun.com/thread-7924-1-1.html  flume参考文档

6、http://www.aboutyun.com/forum-145-1.html   flume社区主页

**1.什么是flume**  
**2.flume的官方网站在哪里？**  
**3.flume有哪些术语？**  
**4.如何配置flume数据源码？**  
  
![4.gif][]  
  
  
  
  
**　　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：  
  
![015536rufi6pmapcks6vmu.png][]   
  
  
　　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:  
  
![015537b84jaxujvllxj5ac.png][]   
  
  
  
**　　二、flume的官方网站在哪里？**  
　　[http://flume.apache.org/][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][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  
　　　　2)修改 flume-env.sh 配置文件,主要是JAVA\_HOME变量设置

root@m1:/home/hadoop/flume-1.5.0-bin\# cp conf/flume-env.sh.template conf/flume-env.sh

root@m1:/home/hadoop/flume-1.5.0-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=""

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              3)验证是否安装成功

root@m1:/home/hadoop\# /home/hadoop/flume-1.5.0-bin/bin/flume-ng version

Flume 1.5.0

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\#

复制代码　　　　出现上面的信息，表示安装成功了

**　　五、flume的案例**

**经过测试发现下面的启动flume是在conf目录里面启动，由于开始不知道，后面参考别的案例，发现了这个问题，所以看下面的启动，得先看这句话**

**比如：**

**/home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/avro.conf -n a1 -Dflume.root.logger=INFO,console**

**里面的 -c .  这个“ . ” 指的就是/home/hadoop/flume-1.5.0-bin/conf目录，也就是作者都是进入conf目录，启动flume的。**

**一般使用都是cd到flume-1.5.0-bin目录，然后**

**./bin/flume-ng agent -c ./conf \-f ./conf/avro.conf -n a1 -Dflume.root.logger=INFO,console**

否则会提示错误(/usr/local/flume15本人测试路径)：

Warning: JAVA\_HOME is not set!  
\+ exec /usr/bin/java -Xmx20m -Dflume.root.logger=INFO,console -cp '/usr/local/flume15:/usr/local/flume15/lib/\*' -Djava.library.path= org.apache.flume.node.Application -f ./conf/post\_json.conf -n a1  
log4j:WARN No appenders could be found for logger (org.apache.flume.lifecycle.LifecycleSupervisor).  
log4j:WARN Please initialize the log4j system properly.  
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html\#noconfig for more info.

1)案例1：Avro  
　　　　Avro可以发送一个给定的文件给Flume，Avro 源使用AVRO RPC机制。  
　　　　　　a)创建agent配置文件

root@m1:/home/hadoop\#vi /home/hadoop/flume-1.5.0-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 = c1

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　　　　　　b)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/avro.conf -n a1 -Dflume.root.logger=INFO,console

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　　　　　　c)创建指定文件

root@m1:/home/hadoop\# echo "hello world" > /home/hadoop/flume-1.5.0-bin/log.00

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　　　　　　d)使用avro-client发送文件

root@m1:/home/hadoop\# /home/hadoop/flume-1.5.0-bin/bin/flume-ng avro-client -c . -H 127.0.0.1 -p 4141 -F /home/hadoop/flume-1.5.0-bin/log.00

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　　　　　　d)使用avro-client发送文件

root@m1:/home/hadoop\# /home/hadoop/flume-1.5.0-bin/bin/flume-ng avro-client -c . -H 127.0.0.1 -p 4141 -F /

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　　　　　　f)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-1.5.0-bin/conf\# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-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

...

2014-08-10 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

2014-08-10 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.

2014-08-10 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 \}

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　　　　2)案例2：Spool  
　　　　Spool监测配置的目录下新增的文件，并将文件中的数据读取出来。需要注意两点：  
　　　　1) 拷贝到spool目录下的文件不可以再打开编辑。  
　　　　2) spool目录下不可包含相应的子目录  
  
  
　　　　　　a)创建agent配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-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-1.5.0-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 = c1

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　　　　　　b)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/spool.conf -n a1 -Dflume.root.logger=INFO,console

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　　　　　　c)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-1.5.0-bin/logs/spool\_text.log

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　　　　　d)在m1的控制台，可以看到以下相关信息：

14/08/10 11:37:13 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.

14/08/10 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

14/08/10 11:37:14 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.

14/08/10 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 \}

14/08/10 11:37:15 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.

14/08/10 11:37:16 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.

14/08/10 11:37:17 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.

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　　　　3)案例3：Exec  
　　　　EXEC执行一个给定的命令获得输出的源,如果要使用tail命令，必选使得file足够大才能看到输出内容  
  
　　　　　　a)创建agent配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-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 = c1

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　　　　　　b)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/exec\_tail.conf -n a1 -Dflume.root.logger=INFO,console

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　　　　　　c)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\_

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　　　　　　e)在m1的控制台，可以看到以下信息：

2014-08-10 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 \}

2014-08-10 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 \}

2014-08-10 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 \}

2014-08-10 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 \}

2014-08-10 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 \}

....

2014-08-10 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 \}

2014-08-10 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 \}

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　　　　4)案例4：Syslogtcp  
　　　　Syslogtcp监听TCP的端口做为数据源  
  
　　　　　　a)创建agent配置文件

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 = c1

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　　　　　　b)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,console

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　　　　　　c)测试产生syslog

root@m1:/home/hadoop\# echo "hello idoall.org syslog" | nc localhost 5140

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　　　　　　d)在m1的控制台，可以看到以下信息：

14/08/10 11:41:45 INFO node.PollingPropertiesFileConfigurationProvider: Reloading configuration file:/home/hadoop/flume-1.5.0-bin/conf/syslog\_tcp.conf

14/08/10 11:41:45 INFO conf.FlumeConfiguration: Added sinks: k1 Agent: a1

14/08/10 11:41:45 INFO conf.FlumeConfiguration: Processing:k1

14/08/10 11:41:45 INFO conf.FlumeConfiguration: Post-validation flume configuration contains configuration for agents: \[a1\]

14/08/10 11:41:45 INFO node.AbstractConfigurationProvider: Creating channels

14/08/10 11:41:45 INFO channel.DefaultChannelFactory: Creating instance of channel c1 type memory

14/08/10 11:41:45 INFO node.AbstractConfigurationProvider: Created channel c1

14/08/10 11:41:45 INFO source.DefaultSourceFactory: Creating instance of source r1, type syslogtcp

14/08/10 11:41:45 INFO sink.DefaultSinkFactory: Creating instance of sink: k1, type: logger

14/08/10 11:41:45 INFO node.AbstractConfigurationProvider: Channel c1 connected to \[r1, k1\]

14/08/10 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\}\} \}

14/08/10 11:41:45 INFO node.Application: Starting Channel c1

14/08/10 11:41:45 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: CHANNEL, name: c1: Successfully registered new MBean.

14/08/10 11:41:45 INFO instrumentation.MonitoredCounterGroup: Component type: CHANNEL, name: c1 started

14/08/10 11:41:45 INFO node.Application: Starting Sink k1

14/08/10 11:41:45 INFO node.Application: Starting Source r1

14/08/10 11:41:45 INFO source.SyslogTcpSource: Syslog TCP Source starting...

14/08/10 11:42:15 WARN source.SyslogUtils: Event created from Invalid Syslog data.

14/08/10 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 \}

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　　　　5)案例5：JSONHandler  
　　　　　　a)创建agent配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-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 = c1

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　　　　　　b)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/post\_json.conf -n a1 -Dflume.root.logger=INFO,console

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　　　　　　c)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:8888

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　　　　　　d)在m1的控制台，可以看到以下信息：

14/08/10 11:49:59 INFO node.Application: Starting Channel c1

14/08/10 11:49:59 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: CHANNEL, name: c1: Successfully registered new MBean.

14/08/10 11:49:59 INFO instrumentation.MonitoredCounterGroup: Component type: CHANNEL, name: c1 started

14/08/10 11:49:59 INFO node.Application: Starting Sink k1

14/08/10 11:49:59 INFO node.Application: Starting Source r1

14/08/10 11:49:59 INFO mortbay.log: Logging to org.slf4j.impl.Log4jLoggerAdapter(org.mortbay.log) via org.mortbay.log.Slf4jLog

14/08/10 11:49:59 INFO mortbay.log: jetty-6.1.26

14/08/10 11:50:00 INFO mortbay.log: Started SelectChannelConnector@0.0.0.0:8888

14/08/10 11:50:00 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: SOURCE, name: r1: Successfully registered new MBean.

14/08/10 11:50:00 INFO instrumentation.MonitoredCounterGroup: Component type: SOURCE, name: r1 started

14/08/10 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 \}

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　　　　6)案例6：Hadoop sink  
　　　　　　a)创建agent配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-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 = c1

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　　　　　　b)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/hdfs\_sink.conf -n a1 -Dflume.root.logger=INFO,console

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　　　　　　c)测试产生syslog

root@m1:/home/hadoop\# echo "hello idoall flume -> hadoop testing one" | nc localhost 5140

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　　　　　　d)在m1的控制台，可以看到以下信息：

14/08/10 12:20:39 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: CHANNEL, name: c1: Successfully registered new MBean.

14/08/10 12:20:39 INFO instrumentation.MonitoredCounterGroup: Component type: CHANNEL, name: c1 started

14/08/10 12:20:39 INFO node.Application: Starting Sink k1

14/08/10 12:20:39 INFO node.Application: Starting Source r1

14/08/10 12:20:39 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: SINK, name: k1: Successfully registered new MBean.

14/08/10 12:20:39 INFO instrumentation.MonitoredCounterGroup: Component type: SINK, name: k1 started

14/08/10 12:20:39 INFO source.SyslogTcpSource: Syslog TCP Source starting...

14/08/10 12:21:46 WARN source.SyslogUtils: Event created from Invalid Syslog data.

14/08/10 12:21:49 INFO hdfs.HDFSSequenceFile: writeFormat = Writable, UseRawLocalFileSystem = false

14/08/10 12:21:49 INFO hdfs.BucketWriter: Creating hdfs://m1:9000/user/flume/syslogtcp//Syslog.1407644509504.tmp

14/08/10 12:22:20 INFO hdfs.BucketWriter: Closing hdfs://m1:9000/user/flume/syslogtcp//Syslog.1407644509504.tmp

14/08/10 12:22:20 INFO hdfs.BucketWriter: Close tries incremented

14/08/10 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

14/08/10 12:22:20 INFO hdfs.HDFSEventSink: Writer callback called.

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　　　　　　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 one

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　　　　7)案例7：File Roll Sink  
　　　　　　a)创建agent配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-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 = c1

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　　　　　　b)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/file\_roll.conf -n a1 -Dflume.root.logger=INFO,console

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　　　　　　c)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 5555

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　　　　　　d)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-1.5.0-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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　　　　8)案例8：Replicating Channel Selector  
　　　　Flume支持Fan out流从一个源到多个通道。有两种模式的Fan out，分别是复制和复用。在复制的情况下，流的事件被发送到所有的配置通道。在复用的情况下，事件被发送到可用的渠道中的一个子集。Fan out流需要指定源和Fan out通道的规则。  
  
　　　　这次我们需要用到m1,m2两台机器  
  
　　　　　　a)在m1创建replicating\_Channel\_Selector配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-bin/conf/replicating\_Channel\_Selector.conf

a1.sources = r1

a1.sinks = k1 k2

a1.channels = c1 c2

\# Describe/configure the source

a1.sources.r1.type = syslogtcp

a1.sources.r1.port = 5140

a1.sources.r1.host = localhost

a1.sources.r1.channels = c1 c2

a1.sources.r1.selector.type = replicating

\# Describe the sink

a1.sinks.k1.type = avro

a1.sinks.k1.channel = c1

a1.sinks.k1.hostname = m1

a1.sinks.k1.port = 5555

a1.sinks.k2.type = avro

a1.sinks.k2.channel = c2

a1.sinks.k2.hostname = m2

a1.sinks.k2.port = 5555

\# Use a channel which buffers events in memory

a1.channels.c1.type = memory

a1.channels.c1.capacity = 1000

a1.channels.c1.transactionCapacity = 100

a1.channels.c2.type = memory

a1.channels.c2.capacity = 1000

a1.channels.c2.transactionCapacity = 100

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　　　　　　b)在m1创建replicating\_Channel\_Selector\_avro配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-bin/conf/replicating\_Channel\_Selector\_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 = 5555

\# 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 = c1

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　　　　　　c)在m1上将2个配置文件复制到m2上一份

root@m1:/home/hadoop/flume-1.5.0-bin\# scp -r /home/hadoop/flume-1.5.0-bin/conf/replicating\_Channel\_Selector.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/replicating\_Channel\_Selector.conf

root@m1:/home/hadoop/flume-1.5.0-bin\# scp -r /home/hadoop/flume-1.5.0-bin/conf/replicating\_Channel\_Selector\_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/replicating\_Channel\_Selector\_avro.conf

复制代码　　　　　　d)打开4个窗口，在m1和m2上同时启动两个flume agent

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/replicating\_Channel\_Selector\_avro.conf -n a1 -Dflume.root.logger=INFO,console

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/replicating\_Channel\_Selector.conf -n a1 -Dflume.root.logger=INFO,console

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　　　　　　e)然后在m1或m2的任意一台机器上，测试产生syslog

root@m1:/home/hadoop\# echo "hello idoall.org syslog" | nc localhost 5140

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　　　　　　f)在m1和m2的sink窗口，分别可以看到以下信息,这说明信息得到了同步：

14/08/10 14:08:18 INFO ipc.NettyServer: Connection to /192.168.1.51:46844 disconnected.

14/08/10 14:08:52 INFO ipc.NettyServer: \[id: 0x90f8fe1f, /192.168.1.50:35873 => /192.168.1.50:5555\] OPEN

14/08/10 14:08:52 INFO ipc.NettyServer: \[id: 0x90f8fe1f, /192.168.1.50:35873 => /192.168.1.50:5555\] BOUND: /192.168.1.50:5555

14/08/10 14:08:52 INFO ipc.NettyServer: \[id: 0x90f8fe1f, /192.168.1.50:35873 => /192.168.1.50:5555\] CONNECTED: /192.168.1.50:35873

14/08/10 14:08:59 INFO ipc.NettyServer: \[id: 0xd6318635, /192.168.1.51:46858 => /192.168.1.50:5555\] OPEN

14/08/10 14:08:59 INFO ipc.NettyServer: \[id: 0xd6318635, /192.168.1.51:46858 => /192.168.1.50:5555\] BOUND: /192.168.1.50:5555

14/08/10 14:08:59 INFO ipc.NettyServer: \[id: 0xd6318635, /192.168.1.51:46858 => /192.168.1.50:5555\] CONNECTED: /192.168.1.51:46858

14/08/10 14:09:20 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 \}

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　　　　9)案例9：Multiplexing Channel Selector  
　　　　　　a)在m1创建Multiplexing\_Channel\_Selector配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-bin/conf/Multiplexing\_Channel\_Selector.conf

a1.sources = r1

a1.sinks = k1 k2

a1.channels = c1 c2

\# Describe/configure the source

a1.sources.r1.type = org.apache.flume.source.http.HTTPSource

a1.sources.r1.port = 5140

a1.sources.r1.channels = c1 c2

a1.sources.r1.selector.type = multiplexing

a1.sources.r1.selector.header = type

\#映射允许每个值通道可以重叠。默认值可以包含任意数量的通道。

a1.sources.r1.selector.mapping.baidu = c1

a1.sources.r1.selector.mapping.ali = c2

a1.sources.r1.selector.default = c1

\# Describe the sink

a1.sinks.k1.type = avro

a1.sinks.k1.channel = c1

a1.sinks.k1.hostname = m1

a1.sinks.k1.port = 5555

a1.sinks.k2.type = avro

a1.sinks.k2.channel = c2

a1.sinks.k2.hostname = m2

a1.sinks.k2.port = 5555

\# Use a channel which buffers events in memory

a1.channels.c1.type = memory

a1.channels.c1.capacity = 1000

a1.channels.c1.transactionCapacity = 100

a1.channels.c2.type = memory

a1.channels.c2.capacity = 1000

a1.channels.c2.transactionCapacity = 100

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　　　　　　b)在m1创建Multiplexing\_Channel\_Selector\_avro配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-bin/conf/Multiplexing\_Channel\_Selector\_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 = 5555

\# 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 = c1

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　　　　　　c)将2个配置文件复制到m2上一份

root@m1:/home/hadoop/flume-1.5.0-bin\# scp -r /home/hadoop/flume-1.5.0-bin/conf/Multiplexing\_Channel\_Selector.conf  root@m2:/home/hadoop/flume-1.5.0-bin/conf/Multiplexing\_Channel\_Selector.conf

root@m1:/home/hadoop/flume-1.5.0-bin\# scp -r /home/hadoop/flume-1.5.0-bin/conf/Multiplexing\_Channel\_Selector\_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Multiplexing\_Channel\_Selector\_avro.conf

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　　　　　　d)打开4个窗口，在m1和m2上同时启动两个flume agent

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/Multiplexing\_Channel\_Selector\_avro.conf -n a1 -Dflume.root.logger=INFO,console

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/Multiplexing\_Channel\_Selector.conf -n a1 -Dflume.root.logger=INFO,console

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　　　　　　e)然后在m1或m2的任意一台机器上，测试产生syslog

root@m1:/home/hadoop\# curl -X POST -d '\[\{ "headers" :\{"type" : "baidu"\},"body" : "idoall\_TEST1"\}\]' http://localhost:5140 && curl -X POST -d '\[\{ "headers" :\{"type" : "ali"\},"body" : "idoall\_TEST2"\}\]' http://localhost:5140 && curl -X POST -d '\[\{ "headers" :\{"type" : "qq"\},"body" : "idoall\_TEST3"\}\]' http://localhost:5140

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　　　　　f)在m1的sink窗口，可以看到以下信息：

14/08/10 14:32:21 INFO node.Application: Starting Sink k1

14/08/10 14:32:21 INFO node.Application: Starting Source r1

14/08/10 14:32:21 INFO source.AvroSource: Starting Avro source r1: \{ bindAddress: 0.0.0.0, port: 5555 \}...

14/08/10 14:32:21 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: SOURCE, name: r1: Successfully registered new MBean.

14/08/10 14:32:21 INFO instrumentation.MonitoredCounterGroup: Component type: SOURCE, name: r1 started

14/08/10 14:32:21 INFO source.AvroSource: Avro source r1 started.

14/08/10 14:32:36 INFO ipc.NettyServer: \[id: 0xcf00eea6, /192.168.1.50:35916 => /192.168.1.50:5555\] OPEN

14/08/10 14:32:36 INFO ipc.NettyServer: \[id: 0xcf00eea6, /192.168.1.50:35916 => /192.168.1.50:5555\] BOUND: /192.168.1.50:5555

14/08/10 14:32:36 INFO ipc.NettyServer: \[id: 0xcf00eea6, /192.168.1.50:35916 => /192.168.1.50:5555\] CONNECTED: /192.168.1.50:35916

14/08/10 14:32:44 INFO ipc.NettyServer: \[id: 0x432f5468, /192.168.1.51:46945 => /192.168.1.50:5555\] OPEN

14/08/10 14:32:44 INFO ipc.NettyServer: \[id: 0x432f5468, /192.168.1.51:46945 => /192.168.1.50:5555\] BOUND: /192.168.1.50:5555

14/08/10 14:32:44 INFO ipc.NettyServer: \[id: 0x432f5468, /192.168.1.51:46945 => /192.168.1.50:5555\] CONNECTED: /192.168.1.51:46945

14/08/10 14:34:11 INFO sink.LoggerSink: Event: \{ headers:\{type=baidu\} body: 69 64 6F 61 6C 6C 5F 54 45 53 54 31             idoall\_TEST1 \}

14/08/10 14:34:57 INFO sink.LoggerSink: Event: \{ headers:\{type=qq\} body: 69 64 6F 61 6C 6C 5F 54 45 53 54 33             idoall\_TEST3 \}

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　　　　　g)在m2的sink窗口，可以看到以下信息：

14/08/10 14:32:27 INFO node.Application: Starting Sink k1

14/08/10 14:32:27 INFO node.Application: Starting Source r1

14/08/10 14:32:27 INFO source.AvroSource: Starting Avro source r1: \{ bindAddress: 0.0.0.0, port: 5555 \}...

14/08/10 14:32:27 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: SOURCE, name: r1: Successfully registered new MBean.

14/08/10 14:32:27 INFO instrumentation.MonitoredCounterGroup: Component type: SOURCE, name: r1 started

14/08/10 14:32:27 INFO source.AvroSource: Avro source r1 started.

14/08/10 14:32:36 INFO ipc.NettyServer: \[id: 0x7c2f0aec, /192.168.1.50:38104 => /192.168.1.51:5555\] OPEN

14/08/10 14:32:36 INFO ipc.NettyServer: \[id: 0x7c2f0aec, /192.168.1.50:38104 => /192.168.1.51:5555\] BOUND: /192.168.1.51:5555

14/08/10 14:32:36 INFO ipc.NettyServer: \[id: 0x7c2f0aec, /192.168.1.50:38104 => /192.168.1.51:5555\] CONNECTED: /192.168.1.50:38104

14/08/10 14:32:44 INFO ipc.NettyServer: \[id: 0x3d36f553, /192.168.1.51:48599 => /192.168.1.51:5555\] OPEN

14/08/10 14:32:44 INFO ipc.NettyServer: \[id: 0x3d36f553, /192.168.1.51:48599 => /192.168.1.51:5555\] BOUND: /192.168.1.51:5555

14/08/10 14:32:44 INFO ipc.NettyServer: \[id: 0x3d36f553, /192.168.1.51:48599 => /192.168.1.51:5555\] CONNECTED: /192.168.1.51:48599

14/08/10 14:34:33 INFO sink.LoggerSink: Event: \{ headers:\{type=ali\} body: 69 64 6F 61 6C 6C 5F 54 45 53 54 32             idoall\_TEST2 \}

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　　　　可以看到，根据header中不同的条件分布到不同的channel上  
  
　　　　10)案例10：Flume Sink Processors  
　　　　failover的机器是一直发送给其中一个sink，当这个sink不可用的时候，自动发送到下一个sink。  
  
　　　　　　a)在m1创建Flume\_Sink\_Processors配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-bin/conf/Flume\_Sink\_Processors.conf

a1.sources = r1

a1.sinks = k1 k2

a1.channels = c1 c2

\#这个是配置failover的关键，需要有一个sink group

a1.sinkgroups = g1

a1.sinkgroups.g1.sinks = k1 k2

\#处理的类型是failover

a1.sinkgroups.g1.processor.type = failover

\#优先级，数字越大优先级越高，每个sink的优先级必须不相同

a1.sinkgroups.g1.processor.priority.k1 = 5

a1.sinkgroups.g1.processor.priority.k2 = 10

\#设置为10秒，当然可以根据你的实际状况更改成更快或者很慢

a1.sinkgroups.g1.processor.maxpenalty = 10000

\# Describe/configure the source

a1.sources.r1.type = syslogtcp

a1.sources.r1.port = 5140

a1.sources.r1.channels = c1 c2

a1.sources.r1.selector.type = replicating

\# Describe the sink

a1.sinks.k1.type = avro

a1.sinks.k1.channel = c1

a1.sinks.k1.hostname = m1

a1.sinks.k1.port = 5555

a1.sinks.k2.type = avro

a1.sinks.k2.channel = c2

a1.sinks.k2.hostname = m2

a1.sinks.k2.port = 5555

\# Use a channel which buffers events in memory

a1.channels.c1.type = memory

a1.channels.c1.capacity = 1000

a1.channels.c1.transactionCapacity = 100

a1.channels.c2.type = memory

a1.channels.c2.capacity = 1000

a1.channels.c2.transactionCapacity = 100

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　　　　　　b)在m1创建Flume\_Sink\_Processors\_avro配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-bin/conf/Flume\_Sink\_Processors\_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 = 5555

\# 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 = c1

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　　　　　　c)将2个配置文件复制到m2上一份

root@m1:/home/hadoop/flume-1.5.0-bin\# scp -r /home/hadoop/flume-1.5.0-bin/conf/Flume\_Sink\_Processors.conf  root@m2:/home/hadoop/flume-1.5.0-bin/conf/Flume\_Sink\_Processors.conf

root@m1:/home/hadoop/flume-1.5.0-bin\# scp -r /home/hadoop/flume-1.5.0-bin/conf/Flume\_Sink\_Processors\_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Flume\_Sink\_Processors\_avro.conf

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　　　　　　d)打开4个窗口，在m1和m2上同时启动两个flume agent

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/Flume\_Sink\_Processors\_avro.conf -n a1 -Dflume.root.logger=INFO,console

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/Flume\_Sink\_Processors.conf -n a1 -Dflume.root.logger=INFO,console

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　　　　　　e)然后在m1或m2的任意一台机器上，测试产生log

root@m1:/home/hadoop\# echo "idoall.org test1 failover" | nc localhost 5140

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　　　　　　f)因为m2的优先级高，所以在m2的sink窗口，可以看到以下信息，而m1没有：

14/08/10 15:02:46 INFO ipc.NettyServer: Connection to /192.168.1.51:48692 disconnected.

14/08/10 15:03:12 INFO ipc.NettyServer: \[id: 0x09a14036, /192.168.1.51:48704 => /192.168.1.51:5555\] OPEN

14/08/10 15:03:12 INFO ipc.NettyServer: \[id: 0x09a14036, /192.168.1.51:48704 => /192.168.1.51:5555\] BOUND: /192.168.1.51:5555

14/08/10 15:03:12 INFO ipc.NettyServer: \[id: 0x09a14036, /192.168.1.51:48704 => /192.168.1.51:5555\] CONNECTED: /192.168.1.51:48704

14/08/10 15:03:26 INFO sink.LoggerSink: Event: \{ headers:\{Severity=0, flume.syslog.status=Invalid, Facility=0\} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 31 idoall.org test1 \}

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　　　　　　g)这时我们停止掉m2机器上的sink(ctrl+c)，再次输出测试数据：

root@m1:/home/hadoop\# echo "idoall.org test2 failover" | nc localhost 5140

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　　　　　　h)可以在m1的sink窗口，看到读取到了刚才发送的两条测试数据：

14/08/10 15:02:46 INFO ipc.NettyServer: Connection to /192.168.1.51:47036 disconnected.

14/08/10 15:03:12 INFO ipc.NettyServer: \[id: 0xbcf79851, /192.168.1.51:47048 => /192.168.1.50:5555\] OPEN

14/08/10 15:03:12 INFO ipc.NettyServer: \[id: 0xbcf79851, /192.168.1.51:47048 => /192.168.1.50:5555\] BOUND: /192.168.1.50:5555

14/08/10 15:03:12 INFO ipc.NettyServer: \[id: 0xbcf79851, /192.168.1.51:47048 => /192.168.1.50:5555\] CONNECTED: /192.168.1.51:47048

14/08/10 15:07:56 INFO sink.LoggerSink: Event: \{ headers:\{Severity=0, flume.syslog.status=Invalid, Facility=0\} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 31 idoall.org test1 \}

14/08/10 15:07:56 INFO sink.LoggerSink: Event: \{ headers:\{Severity=0, flume.syslog.status=Invalid, Facility=0\} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 32 idoall.org test2 \}

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　　　　　　i)我们再在m2的sink窗口中，启动sink：

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/Flume\_Sink\_Processors\_avro.conf -n a1 -Dflume.root.logger=INFO,console

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　　　　　　j)输入两批测试数据：

root@m1:/home/hadoop\# echo "idoall.org test3 failover" | nc localhost 5140 && echo "idoall.org test4 failover" | nc localhost 5140

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　　　　　k)在m2的sink窗口，我们可以看到以下信息，因为优先级的关系，log消息会再次落到m2上：

14/08/10 15:09:47 INFO node.Application: Starting Sink k1

14/08/10 15:09:47 INFO node.Application: Starting Source r1

14/08/10 15:09:47 INFO source.AvroSource: Starting Avro source r1: \{ bindAddress: 0.0.0.0, port: 5555 \}...

14/08/10 15:09:47 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: SOURCE, name: r1: Successfully registered new MBean.

14/08/10 15:09:47 INFO instrumentation.MonitoredCounterGroup: Component type: SOURCE, name: r1 started

14/08/10 15:09:47 INFO source.AvroSource: Avro source r1 started.

14/08/10 15:09:54 INFO ipc.NettyServer: \[id: 0x96615732, /192.168.1.51:48741 => /192.168.1.51:5555\] OPEN

14/08/10 15:09:54 INFO ipc.NettyServer: \[id: 0x96615732, /192.168.1.51:48741 => /192.168.1.51:5555\] BOUND: /192.168.1.51:5555

14/08/10 15:09:54 INFO ipc.NettyServer: \[id: 0x96615732, /192.168.1.51:48741 => /192.168.1.51:5555\] CONNECTED: /192.168.1.51:48741

14/08/10 15:09:57 INFO sink.LoggerSink: Event: \{ headers:\{Severity=0, flume.syslog.status=Invalid, Facility=0\} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 32 idoall.org test2 \}

14/08/10 15:10:43 INFO ipc.NettyServer: \[id: 0x12621f9a, /192.168.1.50:38166 => /192.168.1.51:5555\] OPEN

14/08/10 15:10:43 INFO ipc.NettyServer: \[id: 0x12621f9a, /192.168.1.50:38166 => /192.168.1.51:5555\] BOUND: /192.168.1.51:5555

14/08/10 15:10:43 INFO ipc.NettyServer: \[id: 0x12621f9a, /192.168.1.50:38166 => /192.168.1.51:5555\] CONNECTED: /192.168.1.50:38166

14/08/10 15:10:43 INFO sink.LoggerSink: Event: \{ headers:\{Severity=0, flume.syslog.status=Invalid, Facility=0\} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 33 idoall.org test3 \}

14/08/10 15:10:43 INFO sink.LoggerSink: Event: \{ headers:\{Severity=0, flume.syslog.status=Invalid, Facility=0\} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 34 idoall.org test4 \}

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　　　　11)案例11：Load balancing Sink Processor  
　　　　load balance type和failover不同的地方是，load balance有两个配置，一个是轮询，一个是随机。两种情况下如果被选择的sink不可用，就会自动尝试发送到下一个可用的sink上面。  
  
　　　　　　a)在m1创建Load\_balancing\_Sink\_Processors配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-bin/conf/Load\_balancing\_Sink\_Processors.conf

a1.sources = r1

a1.sinks = k1 k2

a1.channels = c1

\#这个是配置Load balancing的关键，需要有一个sink group

a1.sinkgroups = g1

a1.sinkgroups.g1.sinks = k1 k2

a1.sinkgroups.g1.processor.type = load\_balance

a1.sinkgroups.g1.processor.backoff = true

a1.sinkgroups.g1.processor.selector = round\_robin

\# Describe/configure the source

a1.sources.r1.type = syslogtcp

a1.sources.r1.port = 5140

a1.sources.r1.channels = c1

\# Describe the sink

a1.sinks.k1.type = avro

a1.sinks.k1.channel = c1

a1.sinks.k1.hostname = m1

a1.sinks.k1.port = 5555

a1.sinks.k2.type = avro

a1.sinks.k2.channel = c1

a1.sinks.k2.hostname = m2

a1.sinks.k2.port = 5555

\# Use a channel which buffers events in memory

a1.channels.c1.type = memory

a1.channels.c1.capacity = 1000

a1.channels.c1.transactionCapacity = 100

复制代码  
  
  
　　　　　　b)在m1创建Load\_balancing\_Sink\_Processors\_avro配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-bin/conf/Load\_balancing\_Sink\_Processors\_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 = 5555

\# 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 = c1

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　　　　　　c)将2个配置文件复制到m2上一份

root@m1:/home/hadoop/flume-1.5.0-bin\# scp -r /home/hadoop/flume-1.5.0-bin/conf/Load\_balancing\_Sink\_Processors.conf  root@m2:/home/hadoop/flume-1.5.0-bin/conf/Load\_balancing\_Sink\_Processors.conf

root@m1:/home/hadoop/flume-1.5.0-bin\# scp -r /home/hadoop/flume-1.5.0-bin/conf/Load\_balancing\_Sink\_Processors\_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Load\_balancing\_Sink\_Processors\_avro.conf

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　　　　　　d)打开4个窗口，在m1和m2上同时启动两个flume agent

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/Load\_balancing\_Sink\_Processors\_avro.conf -n a1 -Dflume.root.logger=INFO,console

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/Load\_balancing\_Sink\_Processors.conf -n a1 -Dflume.root.logger=INFO,console

复制代码  
　　　　　　e)然后在m1或m2的任意一台机器上，测试产生log，一行一行输入，输入太快，容易落到一台机器上

root@m1:/home/hadoop\# echo "idoall.org test1" | nc localhost 5140

root@m1:/home/hadoop\# echo "idoall.org test2" | nc localhost 5140

root@m1:/home/hadoop\# echo "idoall.org test3" | nc localhost 5140

root@m1:/home/hadoop\# echo "idoall.org test4" | nc localhost 5140

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　　　　　　f)在m1的sink窗口，可以看到以下信息：

14/08/10 15:35:29 INFO sink.LoggerSink: Event: \{ headers:\{Severity=0, flume.syslog.status=Invalid, Facility=0\} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 32 idoall.org test2 \}

14/08/10 15:35:33 INFO sink.LoggerSink: Event: \{ headers:\{Severity=0, flume.syslog.status=Invalid, Facility=0\} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 34 idoall.org test4 \}

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　　　　　　g)在m2的sink窗口，可以看到以下信息：

14/08/10 15:35:27 INFO sink.LoggerSink: Event: \{ headers:\{Severity=0, flume.syslog.status=Invalid, Facility=0\} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 31 idoall.org test1 \}

14/08/10 15:35:29 INFO sink.LoggerSink: Event: \{ headers:\{Severity=0, flume.syslog.status=Invalid, Facility=0\} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 33 idoall.org test3 \}

复制代码  
  
  
　　　　说明轮询模式起到了作用。  
  
　　　　12)案例12：Hbase sink  
  
  
　　　　　　a)在测试之前，请先参考《[ubuntu12.04+hadoop2.2.0+zookeeper3.4.5+hbase0.96.2+hive0.13.1分布式环境部署][ubuntu12.04_hadoop2.2.0_zookeeper3.4.5_hbase0.96.2_hive0.13.1]》将hbase启动  
  
　　　　　　b)然后将以下文件复制到flume中：

cp /home/hadoop/hbase-0.96.2-hadoop2/lib/protobuf-java-2.5.0.jar /home/hadoop/flume-1.5.0-bin/lib

cp /home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-client-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib

cp /home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-common-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib

cp /home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-protocol-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib

cp /home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-server-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib

cp /home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-hadoop2-compat-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib

cp /home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-hadoop-compat-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib@@@

cp /home/hadoop/hbase-0.96.2-hadoop2/lib/htrace-core-2.04.jar /home/hadoop/flume-1.5.0-bin/lib

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　　　　　　c)确保test\_idoall\_org表在hbase中已经存在  
  
　　　　　　d)在m1创建hbase\_simple配置文件

root@m1:/home/hadoop\# vi /home/hadoop/flume-1.5.0-bin/conf/hbase\_simple.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

a1.sinks.k1.type = hbase

a1.sinks.k1.table = test\_idoall\_org

a1.sinks.k1.columnFamily = name

a1.sinks.k1.column = idoall

a1.sinks.k1.serializer =  org.apache.flume.sink.hbase.RegexHbaseEventSerializer

a1.sinks.k1.channel = memoryChannel

\# 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 = c1

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　　　　　　e)启动flume agent

/home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/hbase\_simple.conf -n a1 -Dflume.root.logger=INFO,console

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　　　　　　f)测试产生syslog

root@m1:/home/hadoop\# echo "hello idoall.org from flume" | nc localhost 5140

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　　　　　　g)这时登录到hbase中，可以发现新数据已经插入

root@m1:/home/hadoop\# /home/hadoop/hbase-0.96.2-hadoop2/bin/hbase shell

2014-08-10 16:09:48,984 INFO  \[main\] Configuration.deprecation: hadoop.native.lib is deprecated. Instead, use io.native.lib.available

HBase Shell; enter 'help<RETURN>' for list of supported commands.

Type "exit<RETURN>" to leave the HBase Shell

Version 0.96.2-hadoop2, r1581096, Mon Mar 24 16:03:18 PDT 2014

hbase(main):001:0> list

TABLE

SLF4J: Class path contains multiple SLF4J bindings.

SLF4J: Found binding in \[jar:file:/home/hadoop/hbase-0.96.2-hadoop2/lib/slf4j-log4j12-1.6.4.jar!/org/slf4j/impl/StaticLoggerBinder.class\]

SLF4J: Found binding in \[jar:file:/home/hadoop/hadoop-2.2.0/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class\]

SLF4J: See http://www.slf4j.org/codes.html\#multiple\_bindings for an explanation.

hbase2hive\_idoall

hive2hbase\_idoall

test\_idoall\_org

3 row(s) in 2.6880 seconds

=> \["hbase2hive\_idoall", "hive2hbase\_idoall", "test\_idoall\_org"\]

hbase(main):002:0> scan "test\_idoall\_org"

ROW                                                    COLUMN+CELL

10086                                                 column=name:idoall, timestamp=1406424831473, value=idoallvalue

1 row(s) in 0.0550 seconds

hbase(main):003:0> scan "test\_idoall\_org"

ROW                                                    COLUMN+CELL

10086                                                 column=name:idoall, timestamp=1406424831473, value=idoallvalue

1407658495588-XbQCOZrKK8-0                            column=name:payload, timestamp=1407658498203, value=hello idoall.org from flume

2 row(s) in 0.0200 seconds

hbase(main):004:0> quit

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经过这么多flume的例子测试，如果你全部做完后，会发现flume的功能真的很强大，可以进行各种搭配来完成你想要的工作，俗话说师傅领进门，修行在个人，如何能够结合你的产品业务，将flume更好的应用起来，快去动手实践吧。

[Flume]: http://blog.csdn.net/a2011480169/article/details/51544664
[4.gif]: /images/20220609/694a413718d148e884b1119f0f43b094.png
[015536rufi6pmapcks6vmu.png]: /images/20220609/fc3f9ab24de04ae1955a34d9dc473ddd.png
[015537b84jaxujvllxj5ac.png]: /images/20220609/170fb42588854c8ea6c597e4da4512ea.png
[http_flume.apache.org]: http://flume.apache.org/
[http_www.apache.org_dyn_closer.cgi_flume_1.5.0_apache-flume-1.5.0-bin.tar.gz]: http://www.apache.org/dyn/closer.cgi/flume/1.5.0/apache-flume-1.5.0-bin.tar.gz
[ubuntu12.04_hadoop2.2.0_zookeeper3.4.5_hbase0.96.2_hive0.13.1]: http://idoall.org/home.php?mod=space&uid=1&do=blog&id=542