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)。
flume的一些核心概念：
-
- Agent使用JVM 运行Flume。每台机器运行一个agent，但是可以在一个agent中包含多个sources和sinks。
- Client生产数据，运行在一个独立的线程。
- Source从Client收集数据，传递给Channel。
- Sink从Channel收集数据，运行在一个独立线程。
- Channel连接 sources 和 sinks ，这个有点像一个队列。
- Events可以是日志记录、 avro 对象等。
Flume以agent为最小的独立运行单位。一个agent就是一个JVM。单agent由Source、Sink和Channel三大组件构成，如下图：
值得注意的是，Flume提供了大量内置的Source、Channel和Sink类型。不同类型的Source,Channel和Sink可以*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
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
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
f)在m1的控制台，可以看到以下信息，注意最后一行：
root@m1:
/home/hadoop/flume-1
.5.0-bin
/conf
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/avro.conf -n a1 -Dflume.root.logger=INFO,console
Info: Sourcing environment configuration script
/home/hadoop/flume-1
.5.0-bin
/conf/flume-env
.sh
Info: Including Hadoop libraries found via (
/home/hadoop/hadoop-2
.2.0
/bin/hadoop
)
for
HDFS access
Info: Excluding
/home/hadoop/hadoop-2
.2.0
/share/hadoop/common/lib/slf4j-api-1
.7.5.jar from classpath
Info: Excluding
/home/hadoop/hadoop-2
.2.0
/share/hadoop/common/lib/slf4j-log4j12-1
.7.5.jar from classpath
...
2014-08-10 10:43:25,112 (New I
/O
worker
#1) [INFO - org.apache.avro.ipc.NettyServer$NettyServerAvroHandler.handleUpstream(NettyServer.java:171)] [id: 0x92464c4f, /192.168.1.50:59850 :> /192.168.1.50:4141] UNBOUND
2014-08-10 10:43:25,112 (New I
/O
worker
#1) [INFO - org.apache.avro.ipc.NettyServer$NettyServerAvroHandler.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
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
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_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
exec
tail
test
}
2014-08-10 10:59:34,535 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 20 74 65 73 74
exec
tail
test
}
2014-08-10 11:01:40,557 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 31
exec
tail1 }
2014-08-10 11:01:41,180 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 32
exec
tail2 }
2014-08-10 11:01:41,180 (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-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:70)] Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 39 37
exec
tail97 }
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
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
d)在m1的控制台，可以看到以下信息：
14
/08/10
11:41:45 INFO node.PollingPropertiesFileConfigurationProvider: Reloading configuration
file
:
/home/hadoop/flume-1
.5.0-bin
/conf/syslog_tcp
.conf
14
/08/10
11:41:45 INFO conf.FlumeConfiguration: Added sinks: k1 Agent: a1
14
/08/10
11:41:45 INFO conf.FlumeConfiguration: Processing:k1
14
/08/10
11:41:45 INFO conf.FlumeConfiguration: Processing:k1
14
/08/10
11:41:45 INFO conf.FlumeConfiguration: Post-validation flume configuration contains configuration
for
agents: [a1]
14
/08/10
11:41:45 INFO node.AbstractConfigurationProvider: Creating channels
14
/08/10
11:41:45 INFO channel.DefaultChannelFactory: Creating instance of channel c1
type
memory
14
/08/10
11:41:45 INFO node.AbstractConfigurationProvider: Created channel c1
14
/08/10
11:41:45 INFO
source
.DefaultSourceFactory: Creating instance of
source
r1,
type
syslogtcp
14
/08/10
11:41:45 INFO sink.DefaultSinkFactory: Creating instance of sink: k1,
type
: logger
14
/08/10
11:41:45 INFO node.AbstractConfigurationProvider: Channel c1 connected to [r1, k1]
14
/08/10
11:41:45 INFO node.Application: Starting new configuration:{ sourceRunners:{r1=EventDrivenSourceRunner: {
source
:org.apache.flume.
source
.SyslogTcpSource{name:r1,state:IDLE} }} sinkRunners:{k1=SinkRunner: { policy:org.apache.flume.sink.DefaultSinkProcessor@6538b14 counterGroup:{ name:null counters:{} } }} channels:{c1=org.apache.flume.channel.MemoryChannel{name: c1}} }
14
/08/10
11:41:45 INFO node.Application: Starting Channel c1
14
/08/10
11:41:45 INFO instrumentation.MonitoredCounterGroup: Monitored counter group
for
type
: CHANNEL, name: c1: Successfully registered new MBean.
14
/08/10
11:41:45 INFO instrumentation.MonitoredCounterGroup: Component
type
: CHANNEL, name: c1 started
14
/08/10
11:41:45 INFO node.Application: Starting Sink k1
14
/08/10
11:41:45 INFO node.Application: Starting Source r1
14
/08/10
11:41:45 INFO
source
.SyslogTcpSource: Syslog TCP Source starting...
14
/08/10
11:42:15 WARN
source
.SyslogUtils: Event created from Invalid Syslog data.
14
/08/10
11:42:15 INFO sink.LoggerSink: Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 68 65 6C 6C 6F 20 69 64 6F 61 6C 6C 2E 6F 72 67 hello idoall.org }
5)案例5：JSONHandler
a)创建agent配置文件
b)启动flume agent a1
c)生成JSON 格式的POST request
d)在m1的控制台，可以看到以下信息：
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
b)启动flume agent a1
c)测试产生syslog
d)在m1的控制台，可以看到以下信息：
e)在m1上再打开一个窗口，去hadoop上检查文件是否生成
7)案例7：File Roll Sink
a)创建agent配置文件
b)启动flume agent a1
c)测试产生log
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
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
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
d)打开4个窗口，在m1和m2上同时启动两个flume agent
e)然后在m1或m2的任意一台机器上，测试产生syslog
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：
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
c)将2个配置文件复制到m2上一份
d)打开4个窗口，在m1和m2上同时启动两个flume agent
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：
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：
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
b)在m1创建
Flume_Sink_Processors_avro
配置文件
c)将2个配置文件复制到m2上一份
d)打开4个窗口，在m1和m2上同时启动两个flume agent
e)然后在m1或m2的任意一台机器上，测试产生log
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：
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
：
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：
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
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
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
f)在m1的sink窗口，可以看到以下信息：
g)
在m2的sink窗口，可以看到以下信息：
说明轮询模式起到了作用。
12)案例12：Hbase sink
b)然后将以下文件复制到flume中：
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 }
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
e)启动flume agent
f)测试产生syslog
g)这时登录到hbase中，可以发现新数据已经插入
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。