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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。
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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？
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。
|
