Flume環(huán)境部署和配置詳解及案例大全
更新時間：2014年08月11日 11:26:31 投稿：hebedich
flume是一個分布式、可靠、和高可用的海量日志采集、聚合和傳輸?shù)南到y(tǒng)。支持在日志系統(tǒng)中定制各類數(shù)據(jù)發(fā)送方，用于收集數(shù)據(jù);同時，F(xiàn)lume提供對數(shù)據(jù)進行簡單處理，并寫到各種數(shù)據(jù)接受方(比如文本、HDFS、Hbase等)的能力 。
一、什么是Flume?
flume 作為 cloudera 開發(fā)的實時日志收集系統(tǒng)，受到了業(yè)界的認(rèn)可與廣泛應(yīng)用。Flume 初始的發(fā)行版本目前被統(tǒng)稱為 Flume OG（original generation），屬于 cloudera。但隨著 FLume 功能的擴展，F(xiàn)lume OG 代碼工程臃腫、核心組件設(shè)計不合理、核心配置不標(biāo)準(zhǔn)等缺點暴露出來，尤其是在 Flume OG 的最后一個發(fā)行版本 0.94.0 中，日志傳輸不穩(wěn)定的現(xiàn)象尤為嚴(yán)重，為了解決這些問題，2011 年 10 月 22 號，cloudera 完成了 Flume-728，對 Flume 進行了里程碑式的改動：重構(gòu)核心組件、核心配置以及代碼架構(gòu)，重構(gòu)后的版本統(tǒng)稱為 Flume NG（next generation）；改動的另一原因是將 Flume 納入 apache 旗下，cloudera Flume 改名為 Apache Flume。
flume的特點：
flume是一個分布式、可靠、和高可用的海量日志采集、聚合和傳輸?shù)南到y(tǒng)。支持在日志系統(tǒng)中定制各類數(shù)據(jù)發(fā)送方，用于收集數(shù)據(jù);同時，F(xiàn)lume提供對數(shù)據(jù)進行簡單處理，并寫到各種數(shù)據(jù)接受方(比如文本、HDFS、Hbase等)的能力 。
flume的數(shù)據(jù)流由事件(Event)貫穿始終。事件是Flume的基本數(shù)據(jù)單位，它攜帶日志數(shù)據(jù)(字節(jié)數(shù)組形式)并且攜帶有頭信息，這些Event由Agent外部的Source生成，當(dāng)Source捕獲事件后會進行特定的格式化，然后Source會把事件推入(單個或多個)Channel中。你可以把Channel看作是一個緩沖區(qū)，它將保存事件直到Sink處理完該事件。Sink負(fù)責(zé)持久化日志或者把事件推向另一個Source。
flume的可靠性
當(dāng)節(jié)點出現(xiàn)故障時，日志能夠被傳送到其他節(jié)點上而不會丟失。Flume提供了三種級別的可靠性保障，從強到弱依次分別為：end-to-end（收到數(shù)據(jù)agent首先將event寫到磁盤上，當(dāng)數(shù)據(jù)傳送成功后，再刪除；如果數(shù)據(jù)發(fā)送失敗，可以重新發(fā)送。），Store on failure（這也是scribe采用的策略，當(dāng)數(shù)據(jù)接收方crash時，將數(shù)據(jù)寫到本地，待恢復(fù)后，繼續(xù)發(fā)送），Besteffort（數(shù)據(jù)發(fā)送到接收方后，不會進行確認(rèn)）。
flume的可恢復(fù)性：
還是靠Channel。推薦使用FileChannel，事件持久化在本地文件系統(tǒng)里(性能較差)。
flume的一些核心概念：
Agent使用JVM 運行Flume。每臺機器運行一個agent，但是可以在一個agent中包含多個sources和sinks。
Client生產(chǎn)數(shù)據(jù)，運行在一個獨立的線程。
Source從Client收集數(shù)據(jù)，傳遞給Channel。
Sink從Channel收集數(shù)據(jù)，運行在一個獨立線程。
Channel連接 sources 和 sinks ，這個有點像一個隊列。
Events可以是日志記錄、 avro 對象等。
Flume以agent為最小的獨立運行單位。一個agent就是一個JVM。單agent由Source、Sink和Channel三大組件構(gòu)成，如下圖：
值得注意的是，F(xiàn)lume提供了大量內(nèi)置的Source、Channel和Sink類型。不同類型的Source,Channel和Sink可以自由組合。組合方式基于用戶設(shè)置的配置文件，非常靈活。比如：Channel可以把事件暫存在內(nèi)存里，也可以持久化到本地硬盤上。Sink可以把日志寫入HDFS, HBase，甚至是另外一個Source等等。Flume支持用戶建立多級流，也就是說，多個agent可以協(xié)同工作，并且支持Fan-in、Fan-out、Contextual Routing、Backup Routes，這也正是NB之處。如下圖所示:
二、flume的官方網(wǎng)站在哪里？
http://flume.apache.org/
三、在哪里下載？
http://www.apache.org/dyn/closer.cgi/flume/1.5.0/apache-flume-1.5.0-bin.tar.gz
四、如何安裝？
1)將下載的flume包，解壓到/home/hadoop目錄中，你就已經(jīng)完成了50%：）簡單吧
2)修改 flume-env.sh 配置文件,主要是JAVA_HOME變量設(shè)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)驗證是否安裝成功
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#
出現(xiàn)上面的信息，表示安裝成功了
五、flume的案例
1)案例1：Avro
Avro可以發(fā)送一個給定的文件給Flume，Avro 源使用AVRO RPC機制。
a)創(chuàng)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)啟動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)創(chuàng)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發(fā)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的控制臺，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
...
-08-10 10:43:25,112 (New I/O worker #1) [INFO - org.apache.avro.ipc.NettyServer$NettyServerAvroHandler.handleUpstream(NettyServer.java:171)] [id: 0x92464c4f, /192.168.1.50:59850 :> /192.168.1.50:4141] UNBOUND
-08-10 10:43:25,112 (New I/O worker #1) [INFO - org.apache.avro.ipc.NettyServer$NettyServerAvroHandler.handleUpstream(NettyServer.java:171)] [id: 0x92464c4f, /192.168.1.50:59850 :> /192.168.1.50:4141] CLOSED
-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.
-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監(jiān)測配置的目錄下新增的文件，并將文件中的數(shù)據(jù)讀取出來。需要注意兩點：
1) 拷貝到spool目錄下的文件不可以再打開編輯。
2) spool目錄下不可包含相應(yīng)的子目錄
a)創(chuàng)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)啟動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目錄
root@m1:/home/hadoop# echo "spool test1" > /home/hadoop/flume-1.5.0-bin/logs/spool_text.log
d)在m1的控制臺，可以看到以下相關(guān)信息：
/08/10 11:37:13 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.
/08/10 11:37:13 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.
/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
/08/10 11:37:14 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.
/08/10 11:37:14 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.
/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 }
/08/10 11:37:15 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.
/08/10 11:37:15 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.
/08/10 11:37:16 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.
/08/10 11:37:16 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.
/08/10 11:37:17 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.
3)案例3：Exec
EXEC執(zhí)行一個給定的命令獲得輸出的源,如果要使用tail命令，必選使得file足夠大才能看到輸出內(nèi)容
a)創(chuàng)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)啟動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)生成足夠多的內(nèi)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的控制臺，可以看到以下信息：
-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 }
-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 }
-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 }
-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 }
-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 }
-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 }
-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 }
-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 }
....
....
....
-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 }
-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 }
-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 }
-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 }
-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監(jiān)聽TCP的端口做為數(shù)據(jù)源
a)創(chuàng)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)啟動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)測試產(chǎn)生syslog
root@m1:/home/hadoop# echo "hello idoall.org syslog" | nc localhost 5140
d)在m1的控制臺，可以看到以下信息：
/08/10 11:41:45 INFO node.PollingPropertiesFileConfigurationProvider: Reloading configuration file:/home/hadoop/flume-1.5.0-bin/conf/syslog_tcp.conf
/08/10 11:41:45 INFO conf.FlumeConfiguration: Added sinks: k1 Agent: a1
/08/10 11:41:45 INFO conf.FlumeConfiguration: Processing:k1
/08/10 11:41:45 INFO conf.FlumeConfiguration: Processing:k1
/08/10 11:41:45 INFO conf.FlumeConfiguration: Post-validation flume configuration contains configuration for agents: [a1]
/08/10 11:41:45 INFO node.AbstractConfigurationProvider: Creating channels
/08/10 11:41:45 INFO channel.DefaultChannelFactory: Creating instance of channel c1 type memory
/08/10 11:41:45 INFO node.AbstractConfigurationProvider: Created channel c1
/08/10 11:41:45 INFO source.DefaultSourceFactory: Creating instance of source r1, type syslogtcp
/08/10 11:41:45 INFO sink.DefaultSinkFactory: Creating instance of sink: k1, type: logger
/08/10 11:41:45 INFO node.AbstractConfigurationProvider: Channel c1 connected to [r1, k1]
/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}} }
/08/10 11:41:45 INFO node.Application: Starting Channel c1
/08/10 11:41:45 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: CHANNEL, name: c1: Successfully registered new MBean.
/08/10 11:41:45 INFO instrumentation.MonitoredCounterGroup: Component type: CHANNEL, name: c1 started
/08/10 11:41:45 INFO node.Application: Starting Sink k1
/08/10 11:41:45 INFO node.Application: Starting Source r1
/08/10 11:41:45 INFO source.SyslogTcpSource: Syslog TCP Source starting...
/08/10 11:42:15 WARN source.SyslogUtils: Event created from Invalid Syslog data.
/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)創(chuàng)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)啟動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的控制臺，可以看到以下信息：
/
08/10 11:49:59 INFO node.Application: Starting Channel c1
/08/10 11:49:59 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: CHANNEL, name: c1: Successfully registered new MBean.
/08/10 11:49:59 INFO instrumentation.MonitoredCounterGroup: Component type: CHANNEL, name: c1 started
/08/10 11:49:59 INFO node.Application: Starting Sink k1
/08/10 11:49:59 INFO node.Application: Starting Source r1
/08/10 11:49:59 INFO mortbay.log: Logging to org.slf4j.impl.Log4jLoggerAdapter(org.mortbay.log) via org.mortbay.log.Slf4jLog
/08/10 11:49:59 INFO mortbay.log: jetty-6.1.26
/08/10 11:50:00 INFO mortbay.log: Started SelectChannelConnector@0.0.0.0:8888
/08/10 11:50:00 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: SOURCE, name: r1: Successfully registered new MBean.
/08/10 11:50:00 INFO instrumentation.MonitoredCounterGroup: Component type: SOURCE, name: r1 started
/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
其中關(guān)于hadoop2.2.0部分的安裝部署，請參考文章《ubuntu12.04+hadoop2.2.0+zookeeper3.4.5+hbase0.96.2+hive0.13.1分布式環(huán)境部署》
a)創(chuàng)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)啟動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)測試產(chǎn)生syslog
root@m1:/home/hadoop# echo "hello idoall flume -> hadoop testing one" | nc localhost 5140
d)在m1的控制臺，可以看到以下信息：
/08/10 12:20:39 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: CHANNEL, name: c1: Successfully registered new MBean. /08/10 12:20:39 INFO instrumentation.MonitoredCounterGroup: Component type: CHANNEL, name: c1 started /08/10 12:20:39 INFO node.Application: Starting Sink k1 /08/10 12:20:39 INFO node.Application: Starting Source r1 /08/10 12:20:39 INFO instrumentation.MonitoredCounterGroup: Monitored counter group for type: SINK, name: k1: Successfully registered new MBean. /08/10 12:20:39 INFO instrumentation.MonitoredCounterGroup: Component type: SINK, name: k1 started /08/10 12:20:39 INFO source.SyslogTcpSource: Syslog TCP Source starting... /08/10 12:21:46 WARN source.SyslogUtils: Event created from Invalid Syslog data. /08/10 12:21:49 INFO hdfs.HDFSSequenceFile: writeFormat = Writable, UseRawLocalFileSystem = false /08/10 12:21:49 INFO hdfs.BucketWriter: Creating hdfs://m1:9000/user/flume/syslogtcp//Syslog.1407644509504.tmp /08/10 12:22:20 INFO hdfs.BucketWriter: Closing hdfs://m1:9000/user/flume/syslogtcp//Syslog.1407644509504.tmp /08/10 12:22:20 INFO hdfs.BucketWriter: Close tries incremented /08/10 12:22:20 INFO hdfs.BucketWriter: Renaming hdfs://m1:9000/user/flume/syslogtcp/Syslog.1407644509504.tmp to hdfs://m1:9000/user/flume/syslogtcp/Syslog.1407644509504 /08/10 12:22:20 INFO hdfs.HDFSEventSink: Writer callback called.
e)在m1上再打開一個窗口，去hadoop上檢查文件是否生成
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)創(chuàng)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)啟動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)測試產(chǎn)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,默認(rèn)每30秒生成一個新文件
root@m1:/home/hadoop# ll /home/hadoop/flume-1.5.0-bin/logs 總用量 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流從一個源到多個通道。有兩種模式的Fan out，分別是復(fù)制和復(fù)用。在復(fù)制的情況下，流的事件被發(fā)送到所有的配置通道。在復(fù)用的情況下，事件被發(fā)送到可用的渠道中的一個子集。Fan out流需要指定源和Fan out通道的規(guī)則。
這次我們需要用到m1,m2兩臺機器
a)在m1創(chuàng)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創(chuàng)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上將2個配置文件復(fù)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<br>
d)打開4個窗口，在m1和m2上同時啟動兩個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的任意一臺機器上，測試產(chǎn)生syslog
root@m1:/home/hadoop# echo "hello idoall.org syslog" | nc localhost 5140
f)在m1和m2的sink窗口，分別可以看到以下信息,這說明信息得到了同步：
/08/10 14:08:18 INFO ipc.NettyServer: Connection to /192.168.1.51:46844 disconnected.
/08/10 14:08:52 INFO ipc.NettyServer: [id: 0x90f8fe1f, /192.168.1.50:35873 => /192.168.1.50:5555] OPEN
/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
/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
/08/10 14:08:59 INFO ipc.NettyServer: [id: 0xd6318635, /192.168.1.51:46858 => /192.168.1.50:5555] OPEN
/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
/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
/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創(chuàng)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 #映射允許每個值通道可以重疊。默認(rèn)值可以包含任意數(shù)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創(chuàng)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)將2個配置文件復(fù)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)打開4個窗口，在m1和m2上同時啟動兩個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的任意一臺機器上，測試產(chǎn)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：
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，根據(jù)header中不同的條件分布到不同的channel上
10)案例10：Flume Sink Processors
failover的機器是一直發(fā)送給其中一個sink，當(dāng)這個sink不可用的時候，自動發(fā)送到下一個sink。
a)在m1創(chuàng)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 #這個是配置failover的關(guān)鍵，需要有一個sink group a1.sinkgroups = g1 a1.sinkgroups.g1.sinks = k1 k2 #處理的類型是failover a1.sinkgroups.g1.processor.type = failover #優(yōu)先級，數(shù)字越大優(yōu)先級越高，每個sink的優(yōu)先級必須不相同 a1.sinkgroups.g1.processor.priority.k1 = 5 a1.sinkgroups.g1.processor.priority.k2 = 10 #設(shè)置為10秒，當(dāng)然可以根據(jù)你的實際狀況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創(chuàng)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)將2個配置文件復(fù)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)打開4個窗口，在m1和m2上同時啟動兩個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的任意一臺機器上，測試產(chǎn)生log
root@m1:/home/hadoop# echo "idoall.org test1 failover" | nc localhost 5140
f)因為m2的優(yōu)先級高，所以在m2的sink窗口，可以看到以下信息，而m1沒有：
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)這時我們停止掉m2機器上的sink(ctrl+c)，再次輸出測試數(shù)據(jù)：
root@m1:/home/hadoop# echo "idoall.org test2 failover" | nc localhost 5140
h)可以在m1的sink窗口，看到讀取到了剛才發(fā)送的兩條測試數(shù)據(jù)：
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)我們再在m2的sink窗口中，啟動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)輸入兩批測試數(shù)據(jù)：
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，我們可以看到以下信息，因為優(yōu)先級的關(guān)系，log消息會再次落到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有兩個配置，一個是輪詢，一個是隨機。兩種情況下如果被選擇的sink不可用，就會自動嘗試發(fā)送到下一個可用的sink上面。
a)在m1創(chuàng)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 #這個是配置Load balancing的關(guān)鍵，需要有一個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創(chuàng)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)將2個配置文件復(fù)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)打開4個窗口，在m1和m2上同時啟動兩個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的任意一臺機器上，測試產(chǎn)生log，一行一行輸入，輸入太快，容易落到一臺機器上
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 }
說明輪詢模式起到了作用。
12)案例12：Hbase sink
a)在測試之前，請先參考《ubuntu12.04+hadoop2.2.0+zookeeper3.4.5+hbase0.96.2+hive0.13.1分布式環(huán)境部署》將hbase啟動
b)然后將以下文件復(fù)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)確保test_idoall_org表在hbase中已經(jīng)存在，test_idoall_org表的格式以及字段請參考《ubuntu12.04+hadoop2.2.0+zookeeper3.4.5+hbase0.96.2+hive0.13.1分布式環(huán)境部署》中關(guān)于hbase部分的建表代碼。
d)在m1創(chuàng)建hbase_simple配置文件
root@m1:/home/hadoop# vi /home/hadoop/flume-1.5.0-bin/conf/hbase_simple.conf a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.host = localhost a1.sources.r1.channels = c1 # Describe the sink a1.sinks.k1.type = logger a1.sinks.k1.type = hbase a1.sinks.k1.table = test_idoall_org a1.sinks.k1.columnFamily = name a1.sinks.k1.column = idoall a1.sinks.k1.serializer = org.apache.flume.sink.hbase.RegexHbaseEventSerializer a1.sinks.k1.channel = memoryChannel # Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1
e)啟動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)測試產(chǎn)生syslog
root@m1:/home/hadoop# echo "hello idoall.org from flume" | nc localhost 5140
g)這時登錄到hbase中，可以發(fā)現(xiàn)新數(shù)據(jù)已經(jīng)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
經(jīng)過這么多flume的例子測試，如果你全部做完后，會發(fā)現(xiàn)flume的功能真的很強大，可以進行各種搭配來完成你想要的工作，俗話說師傅領(lǐng)進門，修行在個人，如何能夠結(jié)合你的產(chǎn)品業(yè)務(wù)，將flume更好的應(yīng)用起來，快去動手實踐吧。
這篇文章做為一個筆記，希望能夠?qū)側(cè)腴T的同學(xué)起到幫助作用。
相關(guān)文章
最完的htaccess文件用法收集,這篇文章確實很詳細(xì)，需要的朋友可以參考下2012-05-05
這篇文章主要介紹了關(guān)于SSH服務(wù),具有很好的參考價值,希望對大家有所幫助,如有錯誤或未考慮完全的地方,望不吝賜教2024-04-04
Apache No space left on device的解決辦法
[Fri Aug 15 10:54:31 2008] [emerg] (28)No space left on device: Couldn't create accept lockdf一下發(fā)現(xiàn)不是磁盤空間的問題。Google了一下就找到了解決方案，原來是系統(tǒng)的信號量(?)不夠用了。2008-08-08
Ubuntu設(shè)置靜態(tài)IP地址的方法步驟(親測有效)
在某些場景下,我們可能需要將一臺電腦的IP地址設(shè)置成靜態(tài)的,例如在局域網(wǎng)內(nèi)部署一個服務(wù)器,我們可能不希望他通過DHCP獲取動態(tài)地址,而造成無法訪問,設(shè)置靜態(tài)IP在window系統(tǒng)下比較容易,但在Linux系統(tǒng)下稍微麻煩一點,本文給大家介紹了Ubuntu設(shè)置靜態(tài)IP地址的方法步驟2024-10-10
在CentOS7系統(tǒng)上編譯安裝MySQL 5.7.13步驟詳解
本篇文章主要介紹了在CentOS7系統(tǒng)上編譯安裝MySQL 5.7.13步驟詳解，具有一定的參考價值，有興趣的可以了解一下。2017-01-01
正確維護配置Apache服務(wù)器的方法 保護系統(tǒng)安全
Apache是目前最流行的Web服務(wù)器端軟件之一，它可以運行在幾乎所有被廣泛使用的計算機平臺上。2009-09-09
這篇文章主要給大家介紹了關(guān)于Linux中top命令輸出的相關(guān)資料，文中通過示例代碼介紹的非常詳細(xì)，對大家學(xué)習(xí)或者使用Linux系統(tǒng)具有一定的參考學(xué)習(xí)價值，需要的朋友們下面來一起學(xué)習(xí)學(xué)習(xí)吧2019-07-07
Ubuntu20.04桌面安裝及root權(quán)限開通和ssh安裝詳解
這篇文章主要介紹了Ubuntu20.04桌面安裝及root權(quán)限開通和ssh安裝詳解，文中通過示例代碼介紹的非常詳細(xì)，對大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價值，需要的朋友們下面隨著小編來一起學(xué)習(xí)學(xué)習(xí)吧2020-08-08
這篇文章主要為大家詳細(xì)介紹了linux搭建SVN服務(wù)器的思路，具有一定的參考價值，感興趣的小伙伴們可以參考一下2017-03-03
這篇文章主要介紹了Apache服務(wù)的主要目錄和配置文件詳解,需要的朋友可以參考下2017-05-05