flume是一個分布式、可靠、和高可用的海量日志采集、聚合和傳輸的系統。支持在日志系統中定制各類數據發送方，用於收集數據;同時，Flume提供對數據進行簡單處理，並寫到各種數據接受方(比如文本、HDFS、Hbase等)的能力 。
一、什么是Flume?
flume 作為 cloudera 開發的實時日志收集系統，受到了業界的認可與廣泛應用。Flume 初始的發行版本目前被統稱為 Flume OG（original generation），屬於 cloudera。但隨着 FLume 功能的擴展，Flume OG 代碼工程臃腫、核心組件設計不合理、核心配置不標准等缺點暴露出來，尤其是在 Flume OG 的最后一個發行版本 0.94.0 中，日志傳輸不穩定的現象尤為嚴重，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：重構核心組件、核心配置以及代碼架構，重構后的版本統稱為 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、聚合和傳輸的系統。支持在日志系統中定制各類數據發送方，用於收集數據;同時，Flume提供對數據進行簡單處理，並寫到各種數據接受方(比如文本、HDFS、Hbase等)的能力 。
flume的數據流由事件(Event)貫穿始終。事件是Flume的基本數據單位，它攜帶日志數據(字節數組形式)並且攜帶有頭信息，這些Event由Agent外部的Source生成，當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，它將保存事件直到Sink處理完該事件。Sink負責持久化日志或者把事件推向另一個Source。
flume的可靠性
當節點出現故障時，日志能夠被傳送到其他節點上而不會丟失。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（收到數據agent首先將event寫到磁盤上，當數據傳送成功后，再刪除；如果數據發送失敗，可以重新發送。），Store on failure（這也是scribe采用的策略，當數據接收方crash時，將數據寫到本地，待恢復后，繼續發送），Besteffort（數據發送到接收方后，不會進行確認）。
flume的可恢復性：
還是靠Channel。推薦使用FileChannel，事件持久化在本地文件系統里(性能較差)。
flume的一些核心概念：
Agent使用JVM 運行Flume。每台機器運行一個agent，但是可以在一個agent中包含多個sources和sinks。
Client生產數據，運行在一個獨立的線程。
Source從Client收集數據，傳遞給Channel。
Sink從Channel收集數據，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三大組件構成，如下圖：
值得注意的是，Flume提供了大量內置的Source、Channel和Sink類型。不同類型的Source,Channel和Sink可以自由組合。組合方式基於用戶設置的配置文件，非常靈活。比如：Channel可以把事件暫存在內存里，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可以協同工作，並且支持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變量設置
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
|
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
#
#
# 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
|
1
2
3
4
5
6
7
|
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可以發送一個給定的文件給Flume，Avro 源使用AVRO RPC機制。
a)創建agent配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
|
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
|
1
|
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/avro.conf -n a1 -Dflume.root.logger=INFO,console
|
c)創建指定文件
|
1
|
root@m1:
/home/hadoop
# echo "hello world" > /home/hadoop/flume-1.5.0-bin/log.00
|
d)使用avro-client發送文件
|
1
|
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng avro-client -c . -H m1 -p 4141 -F /home/hadoop/flume-1.5.0-bin/log.00
|
f)在m1的控制台，可以看到以下信息，注意最后一行：
|
1
2
3
4
5
6
7
8
9
10
|
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監測配置的目錄下新增的文件，並將文件中的數據讀取出來。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的子目錄
a)創建agent配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
|
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
|
1
|
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/spool.conf -n a1 -Dflume.root.logger=INFO,console
|
c)追加文件到/home/hadoop/flume-1.5.0-bin/logs目錄
|
1
|
root@m1:
/home/hadoop
# echo "spool test1" > /home/hadoop/flume-1.5.0-bin/logs/spool_text.log
|
d)在m1的控制台，可以看到以下相關信息：
|
1
2
3
4
5
6
7
8
9
10
11
|
/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容
a)創建agent配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
|
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
|
1
|
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/exec_tail.conf -n a1 -Dflume.root.logger=INFO,console
|
c)生成足夠多的內容在文件里
|
1
|
root@m1:
/home/hadoop
# for i in {1..100};do echo "exec tail$i" >> /home/hadoop/flume-1.5.0-bin/log_exec_tail;echo $i;sleep 0.1;done
|
e)在m1的控制台，可以看到以下信息：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
|
-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監聽TCP的端口做為數據源
a)創建agent配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
|
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
|
1
|
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/syslog_tcp.conf -n a1 -Dflume.root.logger=INFO,console
|
c)測試產生syslog
|
1
|
root@m1:
/home/hadoop
# echo "hello idoall.org syslog" | nc localhost 5140
|
d)在m1的控制台，可以看到以下信息：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
|
/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)創建agent配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
|
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
|
1
|
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/post_json.conf -n a1 -Dflume.root.logger=INFO,console
|
c)生成JSON 格式的POST request
|
1
|
root@m1:
/home/hadoop
# curl -X POST -d '[{ "headers" :{"a" : "a1","b" : "b1"},"body" : "idoall.org_body"}]' http://localhost:8888
|
d)在m1的控制台，可以看到以下信息：
/
|
1
2
3
4
5
6
7
8
9
10
11
|
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
其中關於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分布式環境部署》
a)創建agent配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
|
root@m1:
/home/hadoop
# vi /home/hadoop/flume-1.5.0-bin/conf/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
|
1
|
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/hdfs_sink.conf -n a1 -Dflume.root.logger=INFO,console
|
c)測試產生syslog
|
1
|
root@m1:
/home/hadoop
# echo "hello idoall flume -> hadoop testing one" | nc localhost 5140
|
d)在m1的控制台，可以看到以下信息：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
|
/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
|
1
2
3
4
5
|
root@m1:
/home/hadoop
# /home/hadoop/hadoop-2.2.0/bin/hadoop fs -ls /user/flume/syslogtcp
Found 1 items
-rw-r--r-- 3 root supergroup 155 2014-08-10 12:22
/user/flume/syslogtcp/Syslog
.1407644509504
root@m1:
/home/hadoop
# /home/hadoop/hadoop-2.2.0/bin/hadoop fs -cat /user/flume/syslogtcp/Syslog.1407644509504
SEQ!org.apache.hadoop.io.LongWritable"org.apache.hadoop.io.BytesWritable^;>Gv$hello idoall flume -> hadoop testing one
|
7)案例7：File Roll Sink
a)創建agent配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
|
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
|
1
|
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/file_roll.conf -n a1 -Dflume.root.logger=INFO,console
|
c)測試產生log
|
1
2
|
root@m1:
/home/hadoop
# echo "hello idoall.org syslog" | nc localhost 5555
root@m1:
/home/hadoop
# echo "hello idoall.org syslog 2" | nc localhost 5555
|
d)查看/home/hadoop/flume-1.5.0-bin/logs下是否生成文件,默認每30秒生成一個新文件
|
1
2
3
4
5
6
7
8
9
10
|
root@m1:/home/hadoop# ll /home/hadoop/flume-1.5.0-bin/logs
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，分別是復制和復用。在復制的情況下，流的事件被發送到所有的配置通道。在復用的情況下，事件被發送到可用的渠道中的一個子集。Fan out流需要指定源和Fan out通道的規則。
這次我們需要用到m1,m2兩台機器
a)在m1創建replicating_Channel_Selector配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
|
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創建replicating_Channel_Selector_avro配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
|
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個配置文件復制到m2上一份
|
1
2
|
root@m1:
/home/hadoop/flume-1
.5.0-bin
# scp -r /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector.conf
root@m1:
/home/hadoop/flume-1
.5.0-bin
# scp -r /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector_avro.conf<br>
|
d)打開4個窗口，在m1和m2上同時啟動兩個flume agent
|
1
2
|
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector_avro.conf -n a1 -Dflume.root.logger=INFO,console
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector.conf -n a1 -Dflume.root.logger=INFO,console
|
e)然后在m1或m2的任意一台機器上，測試產生syslog
|
1
|
root@m1:
/home/hadoop
# echo "hello idoall.org syslog" | nc localhost 5140
|
f)在m1和m2的sink窗口，分別可以看到以下信息,這說明信息得到了同步：
|
1
2
3
4
5
6
7
8
|
/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創建Multiplexing_Channel_Selector配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
|
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。默認值可以包含任意數量的通道。
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創建Multiplexing_Channel_Selector_avro配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
|
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個配置文件復制到m2上一份
|
1
2
|
root@m1:
/home/hadoop/flume-1
.5.0-bin
# scp -r /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector.conf
root@m1:
/home/hadoop/flume-1
.5.0-bin
# scp -r /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector_avro.conf
|
d)打開4個窗口，在m1和m2上同時啟動兩個flume agent
|
1
2
|
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector_avro.conf -n a1 -Dflume.root.logger=INFO,console
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector.conf -n a1 -Dflume.root.logger=INFO,console
|
e)然后在m1或m2的任意一台機器上，測試產生syslog
|
1
|
root@m1:
/home/hadoop
# curl -X POST -d '[{ "headers" :{"type" : "baidu"},"body" : "idoall_TEST1"}]' http://localhost:5140 && curl -X POST -d '[{ "headers" :{"type" : "ali"},"body" : "idoall_TEST2"}]' http://localhost:5140 && curl -X POST -d '[{ "headers" :{"type" : "qq"},"body" : "idoall_TEST3"}]' http://localhost:5140
|
f)在m1的sink窗口，可以看到以下信息：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
|
14/08/10 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：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
|
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的機器是一直發送給其中一個sink，當這個sink不可用的時候，自動發送到下一個sink。
a)在m1創建Flume_Sink_Processors配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
|
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
#優先級，數字越大優先級越高，每個sink的優先級必須不相同
a1.sinkgroups.g1.processor.priority.k1 = 5
a1.sinkgroups.g1.processor.priority.k2 = 10
#設置為10秒，當然可以根據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創建Flume_Sink_Processors_avro配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
|
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個配置文件復制到m2上一份
|
1
2
|
root@m1:
/home/hadoop/flume-1
.5.0-bin
# scp -r /home/hadoop/flume-1.5.0-bin/conf/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
|
1
2
|
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/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的任意一台機器上，測試產生log
|
1
|
root@m1:
/home/hadoop
# echo "idoall.org test1 failover" | nc localhost 5140
|
f)因為m2的優先級高，所以在m2的sink窗口，可以看到以下信息，而m1沒有：
|
1
2
3
4
5
|
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)，再次輸出測試數據：
|
1
|
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送的兩條測試數據：
|
1
2
3
4
5
6
|
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：
|
1
|
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors_avro.conf -n a1 -Dflume.root.logger=INFO,console
|
j)輸入兩批測試數據：
|
1
|
root@m1:
/home/hadoop
# echo "idoall.org test3 failover" | nc localhost 5140 && echo "idoall.org test4 failover" | nc localhost 5140
|
k)在m2的sink窗口，我們可以看到以下信息，因為優先級的關系，log消息會再次落到m2上：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
|
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，就會自動嘗試發送到下一個可用的sink上面。
a)在m1創建Load_balancing_Sink_Processors配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
|
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創建Load_balancing_Sink_Processors_avro配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
|
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個配置文件復制到m2上一份
|
1
2
|
root@m1:
/home/hadoop/flume-1
.5.0-bin
# scp -r /home/hadoop/flume-1.5.0-bin/conf/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
|
1
2
|
root@m1:
/home/hadoop
# /home/hadoop/flume-1.5.0-bin/bin/flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/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的任意一台機器上，測試產生log，一行一行輸入，輸入太快，容易落到一台機器上
|
1
2
3
4
|
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：
|
1
2
|
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：
|
1
2
|
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分布式環境部署》將hbase啟動
b)然后將以下文件復制到flume中：
|
1
2
3
4
5
6
7
8
|
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分布式環境部署》中關於hbase部分的建表代碼。
d)在m1創建hbase_simple配置文件
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
|
root@m1:
/home/hadoop
# vi /home/hadoop/flume-1.5.0-bin/conf/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
|
1
|
/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)測試產生syslog
|
1
|
root@m1:
/home/hadoop
# echo "hello idoall.org from flume" | nc localhost 5140
|
g)這時登錄到hbase中，可以發現新數據已經插入
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
|
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
|
經過這么多flume的例子測試，如果你全部做完后，會發現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，俗話說師傅領進門，修行在個人，如何能夠結合你的產品業務，將flume更好的應用起來，快去動手實踐吧。
這篇文章做為一個筆記，希望能夠對剛入門的同學起到幫助作用。