当前访客身份:游客 [ 登录  | 注册加入尚学堂]
直播

我来了!

拥有积分:3896
尚学堂雄起!!威武。。。

博客分类

笔记中心

课题中心

提问中心

答题中心

解答题中心

Flume1.5.0的安装、部署、简单应用(含伪分布式、与hadoop2.2.0、hbase0.96的案例)

我来了! 发表于 2年前 (2014-11-12 15:06:55)  |  评论(0)  |  阅读次数(486)| 0 人收藏此文章,   我要收藏   
目录:
  一、什么是Flume?
    1)flume的特点
    2)flume的可靠性
    3)flume的可恢复性
    4)flume 的 一些核心概念
  二、flume的官方网站在哪里?
  三、在哪里下载?
  四、如何安装?
  五、flume的案例
    1)案例1:Avro
    2)案例2:Spool
    3)案例3:Exec
    4)案例4:Syslogtcp
    5)案例5:JSONHandler
    6)案例6:Hadoop sink
    7)案例7:File Roll Sink
    8)案例8:Replicating Channel Selector
    9)案例9:Multiplexing Channel Selector
    10)案例10:Flume Sink Processors
    11)案例11:Load balancing Sink Processor
    12)案例12:Hbase sink
 
 
  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)。 
 
  f lume的一些核心概念:
    1. 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。
    2. Client生产数据,运行在一个独立的线程。
    3. Source从Client收集数据,传递给Channel。
    4. Sink从Channel收集数据,运行在一个独立线程。
    5. Channel连接 sources 和 sinks ,这个有点像一个队列。
    6. 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

    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=""




 

    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
Fromsourcewith checksum a01fe726e4380ba0c9f7a7d222db961f
root@m1:/home/hadoop#




    出现上面的信息,表示安装成功了
 
 
  五、flume的案例
    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




       b)启动flume agent a1
?
1
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
       c)创建指定文件
?
1
root@m1:/home/hadoop# echo "hello world" > /home/hadoop/flume-1.5.0-bin/log.00
       d)使用avro-client发送文件
?
1
root@m1:/home/hadoop# /home/hadoop/flume-1.5.0-bin/bin/flume-ng avro-client -c . -H m1 -p 4141 -F /home/hadoop/flume-1.5.0-bin/log.00
       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)forHDFS 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.handleUpstream(NettyServer.java:171)] [id: 0x92464c4f, /192.168.1.50:59850 :> /192.168.1.50:4141] CLOSED
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 }




 
    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




       b)启动flume agent a1
?
1
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
       c)追加文件到/home/hadoop/flume-1.5.0-bin/logs目录
?
1
root@m1:/home/hadoop# echo "spool test1" > /home/hadoop/flume-1.5.0-bin/logs/spool_text.log
       d)在m1的控制台,可以看到以下相关信息:
14/08/1011:37:13 INFOsource.SpoolDirectorySource: Spooling Directory Source runner hasshutdown.
14/08/1011:37:13 INFOsource.SpoolDirectorySource: Spooling Directory Source runner hasshutdown.
14/08/1011:37:14 INFO avro.ReliableSpoolingFileEventReader: Preparing to movefile/home/hadoop/flume-1.5.0-bin/logs/spool_text.log to/home/hadoop/flume-1.5.0-bin/logs/spool_text.log.COMPLETED
14/08/1011:37:14 INFOsource.SpoolDirectorySource: Spooling Directory Source runner hasshutdown.
14/08/1011:37:14 INFOsource.SpoolDirectorySource: Spooling Directory Source runner hasshutdown.
14/08/1011:37:14 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 }
14/08/1011:37:15 INFOsource.SpoolDirectorySource: Spooling Directory Source runner hasshutdown.
14/08/1011:37:15 INFOsource.SpoolDirectorySource: Spooling Directory Source runner hasshutdown.
14/08/1011:37:16 INFOsource.SpoolDirectorySource: Spooling Directory Source runner hasshutdown.
14/08/1011:37:16 INFOsource.SpoolDirectorySource: Spooling Directory Source runner hasshutdown.
14/08/1011:37:17 INFOsource.SpoolDirectorySource: Spooling Directory Source runner hasshutdown.




 
    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




       b)启动flume agent a1
?
1
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
       c)生成足够多的内容在文件里
?
1
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;done
       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      exectailtest}
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      exectailtest}
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                  exectail1 }
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                  exectail2 }
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 33                  exectail3 }
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                  exectail4 }
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 35                  exectail5 }
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 36                  exectail6 }
....
....
....
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               exectail96 }
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 37               exectail97 }
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               exectail98 }
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 39               exectail99 }
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 31 30 30            exectail100 }




 
    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




       b)启动flume agent a1
?
1
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
       c)测试产生syslog
?
1
root@m1:/home/hadoop# echo "hello idoall.org syslog" | nc localhost 5140
       d)在m1的控制台,可以看到以下信息:
14/08/1011:41:45 INFO node.PollingPropertiesFileConfigurationProvider: Reloading configurationfile:/home/hadoop/flume-1.5.0-bin/conf/syslog_tcp.conf
14/08/1011:41:45 INFO conf.FlumeConfiguration: Added sinks: k1 Agent: a1
14/08/1011:41:45 INFO conf.FlumeConfiguration: Processing:k1
14/08/1011:41:45 INFO conf.FlumeConfiguration: Processing:k1
14/08/1011:41:45 INFO conf.FlumeConfiguration: Post-validation flume configuration contains configurationforagents: [a1]
14/08/1011:41:45 INFO node.AbstractConfigurationProvider: Creating channels
14/08/1011:41:45 INFO channel.DefaultChannelFactory: Creating instance of channel c1typememory
14/08/1011:41:45 INFO node.AbstractConfigurationProvider: Created channel c1
14/08/1011:41:45 INFOsource.DefaultSourceFactory: Creating instance ofsourcer1,typesyslogtcp
14/08/1011:41:45 INFO sink.DefaultSinkFactory: Creating instance of sink: k1,type: logger
14/08/1011:41:45 INFO node.AbstractConfigurationProvider: Channel c1 connected to [r1, k1]
14/08/1011:41:45 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}} }
14/08/1011:41:45 INFO node.Application: Starting Channel c1
14/08/1011:41:45 INFO instrumentation.MonitoredCounterGroup: Monitored counter groupfortype: CHANNEL, name: c1: Successfully registered new MBean.
14/08/1011:41:45 INFO instrumentation.MonitoredCounterGroup: Componenttype: CHANNEL, name: c1 started
14/08/1011:41:45 INFO node.Application: Starting Sink k1
14/08/1011:41:45 INFO node.Application: Starting Source r1
14/08/1011:41:45 INFOsource.SyslogTcpSource: Syslog TCP Source starting...
14/08/1011:42:15 WARNsource.SyslogUtils: Event created from Invalid Syslog data.
14/08/1011:42:15 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 }




 
    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




       b)启动flume agent a1
?
1
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
       c)生成JSON 格式的POST request
?
1
root@m1:/home/hadoop# curl -X POST -d '[{ "headers" :{"a" : "a1","b" : "b1"},"body" : "idoall.org_body"}]' http://localhost:8888
       d)在m1的控制台,可以看到以下信息:
?
1
2
3
4
5
6
7
8
9
10
11
14/08/1011:49:59 INFO node.Application: Starting Channel c1
14/08/1011:49:59 INFO instrumentation.MonitoredCounterGroup: Monitored counter groupfortype: CHANNEL, name: c1: Successfully registered new MBean.
14/08/1011:49:59 INFO instrumentation.MonitoredCounterGroup: Componenttype: CHANNEL, name: c1 started
14/08/1011:49:59 INFO node.Application: Starting Sink k1
14/08/1011:49:59 INFO node.Application: Starting Source r1
14/08/1011:49:59 INFO mortbay.log: Logging to org.slf4j.impl.Log4jLoggerAdapter(org.mortbay.log) via org.mortbay.log.Slf4jLog
14/08/1011:49:59 INFO mortbay.log: jetty-6.1.26
14/08/1011:50:00 INFO mortbay.log: Started SelectChannelConnector@0.0.0.0:8888
14/08/1011:50:00 INFO instrumentation.MonitoredCounterGroup: Monitored counter groupfortype: SOURCE, name: r1: Successfully registered new MBean.
14/08/1011:50:00 INFO instrumentation.MonitoredCounterGroup: Componenttype: SOURCE, name: r1 started
14/08/1012:14:32 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
     其中关于hadoop2.2.0部分的安装部署,请参考文章《 ubuntu12.04+hadoop2.2.0+zookeeper3.4.5+hbase0.96.2+hive0.13.1分布式环境部署
       a)创建agent配置文件
?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
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
       b)启动flume agent a1
?
1
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
       c)测试产生syslog
?
1
root@m1:/home/hadoop# echo "hello idoall flume -> hadoop testing one" | nc localhost 5140
       d)在m1的控制台,可以看到以下信息:
?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
14/08/1012:20:39 INFO instrumentation.MonitoredCounterGroup: Monitored counter groupfortype: CHANNEL, name: c1: Successfully registered new MBean.
14/08/1012:20:39 INFO instrumentation.MonitoredCounterGroup: Componenttype: CHANNEL, name: c1 started
14/08/1012:20:39 INFO node.Application: Starting Sink k1
14/08/1012:20:39 INFO node.Application: Starting Source r1
14/08/1012:20:39 INFO instrumentation.MonitoredCounterGroup: Monitored counter groupfortype: SINK, name: k1: Successfully registered new MBean.
14/08/1012:20:39 INFO instrumentation.MonitoredCounterGroup: Componenttype: SINK, name: k1 started
14/08/1012:20:39 INFOsource.SyslogTcpSource: Syslog TCP Source starting...
14/08/1012:21:46 WARNsource.SyslogUtils: Event created from Invalid Syslog data.
14/08/1012:21:49 INFO hdfs.HDFSSequenceFile: writeFormat = Writable, UseRawLocalFileSystem =false
14/08/1012:21:49 INFO hdfs.BucketWriter: Creating hdfs://m1:9000/user/flume/syslogtcp//Syslog.1407644509504.tmp
14/08/1012:22:20 INFO hdfs.BucketWriter: Closing hdfs://m1:9000/user/flume/syslogtcp//Syslog.1407644509504.tmp
14/08/1012:22:20 INFO hdfs.BucketWriter: Close tries incremented
14/08/1012:22:20 INFO hdfs.BucketWriter: Renaming hdfs://m1:9000/user/flume/syslogtcp/Syslog.1407644509504.tmp to hdfs://m1:9000/user/flume/syslogtcp/Syslog.1407644509504
14/08/1012:22:20 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
?
1
2
3
4
5
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
 
    7)案例7:File Roll Sink
       a)创建agent配置文件
?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
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
       b)启动flume agent a1
?
1
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
       c)测试产生log
?
1
2
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
       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
?
1
2
3
4
5
6
7
8
9
10
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
 
    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




      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




      c)在m1上将2个配置文件复制到m2上一份
?
1
2
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<br>
      d)打开4个窗口,在m1和m2上同时启动两个flume agent
?
1
2
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
      e)然后在m1或m2的任意一台机器上,测试产生syslog
?
1
root@m1:/home/hadoop# echo "hello idoall.org syslog" | nc localhost 5140
      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:
?
1
2
3
4
5
6
7
8
14/08/1014:08:18 INFO ipc.NettyServer: Connection to/192.168.1.51:46844 disconnected.
14/08/1014:08:52 INFO ipc.NettyServer: [id: 0x90f8fe1f,/192.168.1.50:35873 =>/192.168.1.50:5555] OPEN
14/08/1014:08:52 INFO ipc.NettyServer: [id: 0x90f8fe1f,/192.168.1.50:35873 =>/192.168.1.50:5555] BOUND:/192.168.1.50:5555
14/08/1014:08:52 INFO ipc.NettyServer: [id: 0x90f8fe1f,/192.168.1.50:35873 =>/192.168.1.50:5555] CONNECTED:/192.168.1.50:35873
14/08/1014:08:59 INFO ipc.NettyServer: [id: 0xd6318635,/192.168.1.51:46858 =>/192.168.1.50:5555] OPEN
14/08/1014:08:59 INFO ipc.NettyServer: [id: 0xd6318635,/192.168.1.51:46858 =>/192.168.1.50:5555] BOUND:/192.168.1.50:5555
14/08/1014:08:59 INFO ipc.NettyServer: [id: 0xd6318635,/192.168.1.51:46858 =>/192.168.1.50:5555] CONNECTED:/192.168.1.51:46858
14/08/1014:09:20 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 }
 
    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




      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




      c)将2个配置文件复制到m2上一份
?
1
2
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
      d)打开4个窗口,在m1和m2上同时启动两个flume agent
?
1
2
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
      e)然后在m1或m2的任意一台机器上,测试产生syslog
?
1
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
      f)在m1的sink窗口,可以看到以下信息:
?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
14/08/1014:32:21 INFO node.Application: Starting Sink k1
14/08/1014:32:21 INFO node.Application: Starting Source r1
14/08/1014:32:21 INFOsource.AvroSource: Starting Avrosourcer1: { bindAddress: 0.0.0.0, port: 5555 }...
14/08/1014:32:21 INFO instrumentation.MonitoredCounterGroup: Monitored counter groupfortype: SOURCE, name: r1: Successfully registered new MBean.
14/08/1014:32:21 INFO instrumentation.MonitoredCounterGroup: Componenttype: SOURCE, name: r1 started
14/08/1014:32:21 INFOsource.AvroSource: Avrosourcer1 started.
14/08/1014:32:36 INFO ipc.NettyServer: [id: 0xcf00eea6,/192.168.1.50:35916 =>/192.168.1.50:5555] OPEN
14/08/1014:32:36 INFO ipc.NettyServer: [id: 0xcf00eea6,/192.168.1.50:35916 =>/192.168.1.50:5555] BOUND:/192.168.1.50:5555
14/08/1014:32:36 INFO ipc.NettyServer: [id: 0xcf00eea6,/192.168.1.50:35916 =>/192.168.1.50:5555] CONNECTED:/192.168.1.50:35916
14/08/1014:32:44 INFO ipc.NettyServer: [id: 0x432f5468,/192.168.1.51:46945 =>/192.168.1.50:5555] OPEN
14/08/1014:32:44 INFO ipc.NettyServer: [id: 0x432f5468,/192.168.1.51:46945 =>/192.168.1.50:5555] BOUND:/192.168.1.50:5555
14/08/1014:32:44 INFO ipc.NettyServer: [id: 0x432f5468,/192.168.1.51:46945 =>/192.168.1.50:5555] CONNECTED:/192.168.1.51:46945
14/08/1014:34:11 INFO sink.LoggerSink: Event: { headers:{type=baidu} body: 69 64 6F 61 6C 6C 5F 54 45 53 54 31             idoall_TEST1 }
14/08/1014:34:57 INFO sink.LoggerSink: Event: { headers:{type=qq} body: 69 64 6F 61 6C 6C 5F 54 45 53 54 33             idoall_TEST3 }
      g) 在m2的sink窗口,可以看到以下信息:
?
1
2
3
4
5
6
7
8
9
10
11
12
13
14/08/1014:32:27 INFO node.Application: Starting Sink k1
14/08/1014:32:27 INFO node.Application: Starting Source r1
14/08/1014:32:27 INFOsource.AvroSource: Starting Avrosourcer1: { bindAddress: 0.0.0.0, port: 5555 }...
14/08/1014:32:27 INFO instrumentation.MonitoredCounterGroup: Monitored counter groupfortype: SOURCE, name: r1: Successfully registered new MBean.
14/08/1014:32:27 INFO instrumentation.MonitoredCounterGroup: Componenttype: SOURCE, name: r1 started
14/08/1014:32:27 INFOsource.AvroSource: Avrosourcer1 started.
14/08/1014:32:36 INFO ipc.NettyServer: [id: 0x7c2f0aec,/192.168.1.50:38104 =>/192.168.1.51:5555] OPEN
14/08/1014:32:36 INFO ipc.NettyServer: [id: 0x7c2f0aec,/192.168.1.50:38104 =>/192.168.1.51:5555] BOUND:/192.168.1.51:5555
14/08/1014:32:36 INFO ipc.NettyServer: [id: 0x7c2f0aec,/192.168.1.50:38104 =>/192.168.1.51:5555] CONNECTED:/192.168.1.50:38104
14/08/1014:32:44 INFO ipc.NettyServer: [id: 0x3d36f553,/192.168.1.51:48599 =>/192.168.1.51:5555] OPEN
14/08/1014:32:44 INFO ipc.NettyServer: [id: 0x3d36f553,/192.168.1.51:48599 =>/192.168.1.51:5555] BOUND:/192.168.1.51:5555
14/08/1014:32:44 INFO ipc.NettyServer: [id: 0x3d36f553,/192.168.1.51:48599 =>/192.168.1.51:5555] CONNECTED:/192.168.1.51:48599
14/08/1014:34:33 INFO sink.LoggerSink: Event: { headers:{type=ali} body: 69 64 6F 61 6C 6C 5F 54 45 53 54 32             idoall_TEST2 }
     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



      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
      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
      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
      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
      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:
 
14/08/1015:02:46 INFO ipc.NettyServer: Connection to/192.168.1.51:48692 disconnected.
14/08/1015:03:12 INFO ipc.NettyServer: [id: 0x09a14036,/192.168.1.51:48704 =>/192.168.1.51:5555] OPEN
14/08/1015:03:12 INFO ipc.NettyServer: [id: 0x09a14036,/192.168.1.51:48704 =>/192.168.1.51:5555] BOUND:/192.168.1.51:5555
14/08/1015:03:12 INFO ipc.NettyServer: [id: 0x09a14036,/192.168.1.51:48704 =>/192.168.1.51:5555] CONNECTED:/192.168.1.51:48704
14/08/1015:03:26 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 }
      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
      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 :
 
14/08/1015:02:46 INFO ipc.NettyServer: Connection to/192.168.1.51:47036 disconnected.
14/08/1015:03:12 INFO ipc.NettyServer: [id: 0xbcf79851,/192.168.1.51:47048 =>/192.168.1.50:5555] OPEN
14/08/1015:03:12 INFO ipc.NettyServer: [id: 0xbcf79851,/192.168.1.51:47048 =>/192.168.1.50:5555] BOUND:/192.168.1.50:5555
14/08/1015:03:12 INFO ipc.NettyServer: [id: 0xbcf79851,/192.168.1.51:47048 =>/192.168.1.50:5555] CONNECTED:/192.168.1.51:47048
14/08/1015:07:56 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 }
14/08/1015:07:56 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 }
      i)我们再在m2的sink窗口中,启动sink:
?
1
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
      j)输入两批测试数据:
?
1
root@m1:/home/hadoop# echo "idoall.org test3 failover" | nc localhost 5140 && echo "idoall.org test4 failover" | nc localhost 5140
     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:
 
14/08/1015:09:47 INFO node.Application: Starting Sink k1
14/08/1015:09:47 INFO node.Application: Starting Source r1
14/08/1015:09:47 INFOsource.AvroSource: Starting Avrosourcer1: { bindAddress: 0.0.0.0, port: 5555 }...
14/08/1015:09:47 INFO instrumentation.MonitoredCounterGroup: Monitored counter groupfortype: SOURCE, name: r1: Successfully registered new MBean.
14/08/1015:09:47 INFO instrumentation.MonitoredCounterGroup: Componenttype: SOURCE, name: r1 started
14/08/1015:09:47 INFOsource.AvroSource: Avrosourcer1 started.
14/08/1015:09:54 INFO ipc.NettyServer: [id: 0x96615732,/192.168.1.51:48741 =>/192.168.1.51:5555] OPEN
14/08/1015:09:54 INFO ipc.NettyServer: [id: 0x96615732,/192.168.1.51:48741 =>/192.168.1.51:5555] BOUND:/192.168.1.51:5555
14/08/1015:09:54 INFO ipc.NettyServer: [id: 0x96615732,/192.168.1.51:48741 =>/192.168.1.51:5555] CONNECTED:/192.168.1.51:48741
14/08/1015:09:57 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 }
14/08/1015:10:43 INFO ipc.NettyServer: [id: 0x12621f9a,/192.168.1.50:38166 =>/192.168.1.51:5555] OPEN
14/08/1015:10:43 INFO ipc.NettyServer: [id: 0x12621f9a,/192.168.1.50:38166 =>/192.168.1.51:5555] BOUND:/192.168.1.51:5555
14/08/1015:10:43 INFO ipc.NettyServer: [id: 0x12621f9a,/192.168.1.50:38166 =>/192.168.1.51:5555] CONNECTED:/192.168.1.50:38166
14/08/1015:10:43 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 }
14/08/1015:10:43 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 }
 
    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
      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
      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
      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
      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
      f)在m1的sink窗口,可以看到以下信息:
 
14/08/1015:35:29 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 }
14/08/1015:35:33 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 }
      g) 在m2的sink窗口,可以看到以下信息:
 
14/08/1015:35:27 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 }
14/08/1015:35:29 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 }
     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 》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
      c)确保test_idoall_org表在hbase中已经存在, test_idoall_org表的格式以及字段请参考 《 ubuntu12.04+hadoop2.2.0+zookeeper3.4.5+hbase0.96.2+hive0.13.1分布式环境部署 》 中关于hbase部分的建表代码。
 
      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
      e)启动flume agent
 
/home/hadoop/flume-1.5.0-bin/bin/flume-ngagent -c . -f/home/hadoop/flume-1.5.0-bin/conf/hbase_simple.conf -n a1 -Dflume.root.logger=INFO,console
      f)测试产生syslog
 
root@m1:/home/hadoop# echo "hello idoall.org from flume" | nc localhost 5140
      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>'forlist 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 bindingin[jar:file:/home/hadoop/hbase-0.96.2-hadoop2/lib/slf4j-log4j12-1.6.4.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found bindingin[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)in2.6880 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)in0.0550 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)in0.0200 seconds
 
hbase(main):004:0> quit
    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。
 
    这篇文章做为一个笔记,希望能够对刚入门的同学起到帮助作用。




 
分享到:0
关注微信,跟着我们扩展技术视野。每天推送IT新技术文章,每周聚焦一门新技术。微信二维码如下:
微信公众账号:尚学堂(微信号:bjsxt-java)
北京总部地址:北京市海淀区西三旗桥东建材城西路85号神州科技园B座三层尚学堂 咨询电话:400-009-1906 010-56233821
Copyright 2007-2015 北京尚学堂科技有限公司 京ICP备13018289号-1 京公网安备11010802015183