Flume1.5.0安装、部署及案例

2018-01-19 10:36:00来源:http://www.uml.org.cn/bigdata/201801181.asp作者:火龙果软件工程人点击

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本文来自于about云开发,文章主要从安装、部署、及flume的案例,运用代码进行详细介绍,希望对大家有帮助。



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:


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


From source with 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


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


复制代码


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


c)创建指定文件


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


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


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 m1 -p 4141 -F /


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 a1Dflume.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$NettyServer


AvroHandler.


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$NettyServerAvr


oHandler


.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$NettyServerA


vroHandler.


channelClosed(NettyServer.java:209)] Connection to /192.


168.1.50:59850 disconnected.


2014-08-10 10:43:26,718 (SinkRunner-PollingRunner-Defaul


tSinkProcessor) [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


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


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:


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


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 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 11:37:15 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 11:37:16 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.


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


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


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


root@m1:/home/hadoop# for i in {1..100};do echo "exec tail$i" >> /home/hadoop/flume-1.5.0-bin/log_


e)在m1的控制台,可以看到以下信息:


2014-08-10 10:59:25,513 (SinkRunner-Polling


Runner-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-Polling


Runner-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-Polling


Runner-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.proc


ess(LoggerSink.


java:70)] Event: { headers:{} body:


65 78 65 63 20


74 61 69 6C 32 exec tail2 }


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 exec tail3 }


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 11:01:41,181 (SinkRunner-


PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process


(LoggerSink.


java:70)] Event: { headers:{} body:


65 78 65 63 20


74 61 69 6C 35 exec tail5 }


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 exec tail6 }


....


....


....


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 11:01:51,550 (SinkRunner-Polling


Runner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process


(LoggerSink.


java:70)] Event: { headers:{} body:


65 78 65 63 20


74 61 69 6C 39 37 exec tail97 }


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 }


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 exec tail99 }


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 exec tail100 }


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


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


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:


14/08/10 11:41:45 INFO node.PollingProperties


FileConfiguration


Provider:


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:


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.AbstractConfigura


tionProvider:


Creating channels


14/08/10 11:41:45 INFO channel.DefaultChanne


lFactory:


Creating instance of channel c1 type memory


14/08/10 11:41:45 INFO node.AbstractConfigur


ationProvider:


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.AbstractConfigurat


ionProvider:


Channel c1 connected to [r1, k1]


14/08/10 11:41:45 INFO node.Application: Starting new configuration:{ sourceRunners:{r1=EventDrivenSour


ceRunner: { source:org.apache.flume.source.


SyslogTcpSource{name:r1,


state:IDLE} }} sinkRunners:{k1=SinkRunner: { policy:org.apache.flume.sink.DefaultSinkProce


ssor@6538b14 counterGroup:{ name:null counters:{} } }} channels:{c1=org.apache.flume.channel.Memory


Channel{name:


c1}} }


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


Channel c1


14/08/10 11:41:45 INFO instrumentation.Monitored


CounterGroup: Monitored counter group for type:


CHANNEL, name: c1:


Successfully registered new MBean.


14/08/10 11:41:45 INFO instrumentation.Monitored


CounterGroup: 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 }


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


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


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:


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 }


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


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


c)测试产生syslog


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:


14/08/10 12:20:39 INFO instrumentation.


Monitored


CounterGroup: Monitored counter group for


type: CHANNEL, name: c1: Successfully


registered new MBean.


14/08/10 12:20:39 INFO instrumentation.


Monitored


CounterGroup: 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.


Monitored


CounterGroup: Monitored counter group


for type:


SINK, name: k1: Successfully registered


new MBean.


14/08/10 12:20:39 INFO instrumentation.


Monitored


CounterGroup: 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.


1407644


509504.tmp


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


Closing hdfs://m1:9000/user/flume/syslogtcp


//Syslog.14076445


09504.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.


1407644509


504.tmp to hdfs://m1:9000/user/flume/syslogtcp


/Syslog.


1407644509504


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


callback called.


e)在m1上再打开一个窗口,去hadoop上检查文件是否生成


root@m1:/home/hadoop# /home/hadoop/hadoop-2.2.0


/bin/hadoop fs -ls /user/flume/syslogtcp


Found 1 items


-rw-r--r-- 3 root supergroup 155 2014-08-10 12:22 /user/flume/syslogtcp/Syslog.1407644509504


root@m1:/home/hadoop# /home/hadoop/hadoop-2.2.0


/bin/hadoop fs -cat /user/flume/syslogtcp/Syslog.


1407644509504


SEQ!org.apache.hadoop.io.LongWritable"org.apache.


hadoop.io.BytesWritable^;>Gv$hello idoall flume


-> hadoop testing one


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


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


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


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


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


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


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


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


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 }


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


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


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


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:/192.168.1.50:5555] OPEN


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 }


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 }


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:


14/08/10 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 }


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:


14/08/10 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 }


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


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


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 }


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


Hello World!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:


14/08/10 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 }


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 }


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


d)在m1创建hbase_simple配置文件


Hello World!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-ng agent -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>' 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


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。


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