狂甩酷拽吊炸天
分类： LINUX
2019-07-10 10:45:17
flume的特点：
flume是一个分布式、可靠、和高可用的海量日志采集、聚合和传输的系统。支持在日志系统中定制各类数据发送方，用于收集数据;同时，Flume提供对数据进行简单处理，并写到各种数据接受方(比如文本、HDFS、Hbase等)的能力 。
flume的数据流由事件(Event)贯穿始终。事件是Flume的基本数据单位，它携带日志数据(字节数组形式)并且携带有头信息，这些Event由Agent外部的Source生成，当Source捕获事件后会进行特定的格式化，然后Source会把事件推入(单个或多个)Channel中。你可以把Channel看作是一个缓冲区，它将保存事件直到Sink处理完该事件。Sink负责持久化日志或者把事件推向另一个Source。
flume的可靠性 ：
当节点出现故障时，日志能够被传送到其他节点上而不会丢失。Flume提供了三种级别的可靠性保障，从强到弱依次分别为：end-to-end（收到数据agent首先将event写到磁盘上，当数据传送成功后，再删除；如果数据发送失败，可以重新发送。），Store on failure（这也是scribe采用的策略，当数据接收方crash时，将数据写到本地，待恢复后，继续发送），Besteffort（数据发送到接收方后，不会进行确认）。
flume的可恢复性：
还是靠Channel。推荐使用FileChannel，事件持久化在本地文件系统里(性能较差)。
flume的一些核心概念：
Agent使用JVM 运行Flume。每台机器运行一个agent，但是可以在一个agent中包含多个sources和sinks。
Client生产数据，运行在一个独立的线程。
Source从Client收集数据，传递给Channel。
Sink从Channel收集数据，运行在一个独立线程。
Channel连接 sources 和 sinks ，这个有点像一个队列。
Events可以是日志记录、 avro 对象等。
Flume以agent为最小的独立运行单位。一个agent就是一个JVM。单agent由Source、Sink和Channel三大组件构成，如下图：
值得注意的是，Flume提供了大量内置的Source、Channel和Sink类型。不同类型的Source,Channel和Sink可以自由组合。组合方式基于用户设置的配置文件，非常灵活。比如：Channel可以把事件暂存在内存里，也可以持久化到本地硬盘上。Sink可以把日志写入HDFS, HBase，甚至是另外一个Source等等。Flume支持用户建立多级流，也就是说，多个agent可以协同工作，并且支持Fan-in、Fan-out、Contextual Routing、Backup Routes，这也正是NB之处。如下图所示:
二、如何安装？
1.下载安装包
2.配置环境变量
3.修改配置文件(案例给出)
4.启动服务(案例给出)
5.验证 flume-ng -version
三、flume的案例
案例1：Avro 可以发送一个给定的文件给Flume，Avro 源使用AVRO RPC机制
(a)创建agent配置文件
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vi /home/hadoop/flume-1.5.0-bin/conf/avro.conf
a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type= avro a1.sources.r1.channels = c1 a1.sources.r1.bind = 0.0.0.0 a1.sources.r1.port = 4141 # Describe the sink a1.sinks.k1.type= logger # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1 |
(b)启动服务 flume agent a1
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flume-ng agent -c .-f /home/hadoop/flume-1.5.0-bin/conf/avro.conf -n a1 -Dflume.root.logger=INFO,console |
(c)创建指定文件
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echo "hello world" > /home/hadoop/flume-1.5.0-bin/log.00 |
(d)使用avro-client发送文件
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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： hello world
案例2：Spool 监测配置的目录下新增的文件，并将文件中的数据读取出来。需要注意两点：
1) 拷贝到spool目录下的文件不可以再打开编辑。
2) spool目录下不可包含相应的子目录
(a)创建agent配置文件
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vi /home/hadoop/flume-1.5.0-bin/conf/spool.conf
a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type= spooldir a1.sources.r1.channels = c1 a1.sources.r1.spoolDir = /home/hadoop/flume-1.5.0-bin/logs a1.sources.r1.fileHeader = true # Describe the sink a1.sinks.k1.type= logger # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1 |
(b)启动服务flume agent a1
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flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/spool.conf -n a1 -Dflume.root.logger=INFO,console |
(c)追加文件到/home/hadoop/flume-1.5.0-bin/logs目录
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echo "spool test1" > /home/hadoop/flume-1.5.0-bin/logs/spool_text.log |
(d)在m1的控制台，可以看到以下相关信息：
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 }
案例3：Exec 执行一个给定的命令获得输出的源,如果要使用tail命令，必选使得file足够大才能看到输出内容
(a)创建agent配置文件
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vi /home/hadoop/flume-1.5.0-bin/conf/exec_tail.conf
a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type= exec a1.sources.r1.channels = c1 a1.sources.r1.command= tail-F /home/hadoop/flume-1.5.0-bin/log_exec_tail # Describe the sink a1.sinks.k1.type= logger # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1 |
(b)启动服务flume agent a1
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flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/exec_tail.conf -n a1 -Dflume.root.logger=INFO,console |
(c)生成足够多的内容在文件里
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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：
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Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 20 74 65 73 74 exec tail test } Event: { headers:{} body: 65 78 65 63 20 74 61 69 6C 20 74 65 73 74 exec tail test } |
案例4：Syslogtcp 监听TCP的端口做为数据源
(a)创建agent配置文件
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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 |
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(b)启动flume agent a1
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flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/syslog_tcp.conf -n a1 -Dflume.root.logger=INFO,console |
(c)测试产生syslog
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echo "hello idoall.org syslog" | nc localhost 5140 |
(d)在m1的控制台，可以看到以下信息：
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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 } |
案例5：JSONHandler
(a)创建agent配置文件
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vi /home/hadoop/flume-1.5.0-bin/conf/post_json.conf
a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type= org.apache.flume.source.http.HTTPSource a1.sources.r1.port = 8888 a1.sources.r1.channels = c1 # Describe the sink a1.sinks.k1.type= logger # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1 |
(b)启动flume agent a1
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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
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curl -X POST -d '[{ "headers" :{"a" : "a1","b" : "b1"},"body" : "idoall.org_body"}]' |
(d)在m1的控制台，可以看到以下信息：
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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 } |
案例6：Hadoop sink
(a)创建agent配置文件
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vi /home/hadoop/flume-1.5.0-bin/conf/hdfs_sink.conf
a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type= syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.host = localhost a1.sources.r1.channels = c1 # Describe the sink a1.sinks.k1.type= hdfs a1.sinks.k1.channel = c1 a1.sinks.k1.hdfs.path = hdfs://m1:9000/user/flume/syslogtcp a1.sinks.k1.hdfs.filePrefix = Syslog a1.sinks.k1.hdfs.round = true a1.sinks.k1.hdfs.roundValue = 10 a1.sinks.k1.hdfs.roundUnit = minute # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1 |
(b)启动flume agent a1
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flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/hdfs_sink.conf -n a1 -Dflume.root.logger=INFO,console |
(c)测试产生syslog
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echo "hello idoall flume -> hadoop testing one" | nc localhost 5140 |
(d) 在m1上再打开一个窗口，去hadoop上检查文件是否生成
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hadoop fs -ls /user/flume/syslogtcp hadoop fs -cat /user/flume/syslogtcp/Syslog.1407644509504 |
案例7：File Roll Sink
(a)创建agent配置文件
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vi /home/hadoop/flume-1.5.0-bin/conf/file_roll.conf
a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type= syslogtcp a1.sources.r1.port = 5555 a1.sources.r1.host = localhost a1.sources.r1.channels = c1 # Describe the sink a1.sinks.k1.type= file_roll a1.sinks.k1.sink.directory = /home/hadoop/flume-1.5.0-bin/logs # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1 |
(b)启动flume agent a1
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flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/file_roll.conf -n a1 -Dflume.root.logger=INFO,console |
(c)测试产生log
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echo "hello idoall.org syslog" | nc localhost 5555 echo "hello idoall.org syslog 2" | nc localhost 5555 |
(d)查看/home/hadoop/flume-1.5.0-bin/logs下是否生成文件,默认每30秒生成一个新文件
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ll /home/hadoop/flume-1.5.0-bin/logs cat /home/hadoop/flume-1.5.0-bin/logs/1407646164782-1 cat /home/hadoop/flume-1.5.0-bin/logs/1407646164782-2 hello idoall.org syslog hello idoall.org syslog 2 |
案例8：Replicating Channel Selector Flume支持Fan out流从一个源到多个通道。有两种模式的Fan out，分别是复制和复用。在复制的情况下，流的事件被发送到所有的配置通道。在复用的情况下，事件被发送到可用的渠道中的一个子集。Fan out流需要指定源和Fan out通道的规则。这次我们需要用到m1,m2两台机器
(a)在m1创建replicating_Channel_Selector配置文件
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vi /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector.conf
a1.sources = r1 a1.sinks = k1 k2 a1.channels = c1 c2 # Describe/configure the source a1.sources.r1.type= syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.host = localhost a1.sources.r1.channels = c1 c2 a1.sources.r1.selector.type= replicating # Describe the sink a1.sinks.k1.type= avro a1.sinks.k1.channel = c1 a1.sinks.k1.hostname= m1 a1.sinks.k1.port = 5555 a1.sinks.k2.type= avro a1.sinks.k2.channel = c2 a1.sinks.k2.hostname= m2 a1.sinks.k2.port = 5555 # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 a1.channels.c2.type= memory a1.channels.c2.capacity = 1000 a1.channels.c2.transactionCapacity = 100 |
(b)在m1创建replicating_Channel_Selector_avro配置文件
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vi /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector_avro.conf
a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type= avro a1.sources.r1.channels = c1 a1.sources.r1.bind = 0.0.0.0 a1.sources.r1.port = 5555 # Describe the sink a1.sinks.k1.type= logger # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1 |
(c)在m1上将2个配置文件复制到m2上一份
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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 scp -r /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector_avro.conf |
(d)打开4个窗口，在m1和m2上同时启动两个flume agent
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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 flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/replicating_Channel_Selector.conf -n a1 -Dflume.root.logger=INFO,console |
(e)然后在m1或m2的任意一台机器上，测试产生syslog
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echo "hello idoall.org syslog" | nc localhost 5140 |
(f)在m1和m2的sink窗口，分别可以看到以下信息,这说明信息得到了同步：
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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 } |
案例9：Multiplexing Channel Selector
(a)在m1创建Multiplexing_Channel_Selector配置文件
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vi /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector.conf
a1.sources = r1 a1.sinks = k1 k2 a1.channels = c1 c2 # Describe/configure the source a1.sources.r1.type= org.apache.flume.source.http.HTTPSource a1.sources.r1.port = 5140 a1.sources.r1.channels = c1 c2 a1.sources.r1.selector.type= multiplexing a1.sources.r1.selector.header = type #映射允许每个值通道可以重叠。默认值可以包含任意数量的通道。 a1.sources.r1.selector.mapping.baidu = c1 a1.sources.r1.selector.mapping.ali = c2 a1.sources.r1.selector.default = c1 # Describe the sink a1.sinks.k1.type= avro a1.sinks.k1.channel = c1 a1.sinks.k1.hostname= m1 a1.sinks.k1.port = 5555 a1.sinks.k2.type= avro a1.sinks.k2.channel = c2 a1.sinks.k2.hostname= m2 a1.sinks.k2.port = 5555 # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 a1.channels.c2.type= memory a1.channels.c2.capacity = 1000 a1.channels.c2.transactionCapacity = 100 |
(b)在m1创建Multiplexing_Channel_Selector_avro配置文件
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vi /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector_avro.conf a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type= avro a1.sources.r1.channels = c1 a1.sources.r1.bind = 0.0.0.0 a1.sources.r1.port = 5555 # Describe the sink a1.sinks.k1.type= logger # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1 |
(c)将2个配置文件复制到m2上一份
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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 scp -r /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector_avro.conf |
(d)打开4个窗口，在m1和m2上同时启动两个flume agent
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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
flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Multiplexing_Channel_Selector.conf -n a1 -Dflume.root.logger=INFO,console |
(e)然后在m1或m2的任意一台机器上，测试产生syslog
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curl -X POST -d '[{ "headers" :{"type" : "baidu"},"body" : "idoall_TEST1"}]' && curl -X POST -d '[{ "headers" :{"type" : "ali"},"body" : "idoall_TEST2"}]' && curl -X POST -d '[{ "headers" :{"type" : "qq"},"body" : "idoall_TEST3"}]' |
(f)在m1的sink窗口，可以看到以下信息：
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Event: { headers:{type=baidu} body: 69 64 6F 61 6C 6C 5F 54 45 53 54 31} Event: { headers:{type=qq} body: 69 64 6F 61 6C 6C 5F 54 45 53 54 33} |
(g)在m2的sink窗口，可以看到以下信息：
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Event: { headers:{type=ali} body: 69 64 6F 61 6C 6C 5F 54 45 53 54 32} |
可以看到，根据header中不同的条件分布到不同的channel上
案例10：Flume Sink Processors failover的机器是一直发送给其中一个sink，当这个sink不可用的时候，自动发送到下一个sink。
(a)在m1创建Flume_Sink_Processors配置文件
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vi /home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors.conf
a1.sources = r1 a1.sinks = k1 k2 a1.channels = c1 c2 #这个是配置failover的关键，需要有一个sink group a1.sinkgroups = g1 a1.sinkgroups.g1.sinks = k1 k2 #处理的类型是failover a1.sinkgroups.g1.processor.type= failover #优先级，数字越大优先级越高，每个sink的优先级必须不相同 a1.sinkgroups.g1.processor.priority.k1 = 5 a1.sinkgroups.g1.processor.priority.k2 = 10 #设置为10秒，当然可以根据你的实际状况更改成更快或者很慢 a1.sinkgroups.g1.processor.maxpenalty = 10000 # Describe/configure the source a1.sources.r1.type= syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.channels = c1 c2 a1.sources.r1.selector.type= replicating # Describe the sink a1.sinks.k1.type= avro a1.sinks.k1.channel = c1 a1.sinks.k1.hostname= m1 a1.sinks.k1.port = 5555 a1.sinks.k2.type= avro a1.sinks.k2.channel = c2 a1.sinks.k2.hostname= m2 a1.sinks.k2.port = 5555 # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 a1.channels.c2.type= memory a1.channels.c2.capacity = 1000 a1.channels.c2.transactionCapacity = 100 |
(b)在m1创建Flume_Sink_Processors_avro配置文件
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vi /home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors_avro.conf
a1.sources = r1 a1.sinks = k1 a1.channels = c # Describe/configure the source a1.sources.r1.type= avro a1.sources.r1.channels = c1 a1.sources.r1.bind = 0.0.0.0 a1.sources.r1.port = 5555 # Describe the sink a1.sinks.k1.type= logger # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1 |
(c)将2个配置文件复制到m2上一份
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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
scp -r /home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors_avro.conf |
(d)打开4个窗口，在m1和m2上同时启动两个flume agent
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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 flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors.conf -n a1 -Dflume.root.logger=INFO,console |
(e)然后在m1或m2的任意一台机器上，测试产生log
|
echo "idoall.org test1 failover" | nc localhost 5140 |
(f)因为m2的优先级高，所以在m2的sink窗口，可以看到以下信息，而m1没有：
|
Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 31 idoall.org test1 } |
(g)这时我们停止掉m2机器上的sink(ctrl+c)，再次输出测试数据：
|
echo "idoall.org test2 failover" | nc localhost 5140 |
(h)可以在m1的sink窗口，看到读取到了刚才发送的两条测试数据：
|
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 } Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 32 idoall.org test2 } |
(i)我们再在m2的sink窗口中，启动sink：
|
flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Flume_Sink_Processors_avro.conf -n a1 -Dflume.root.logger=INFO,console |
(j)输入两批测试数据：
|
echo "idoall.org test3 failover" | nc localhost 5140 && echo "idoall.org test4 failover" | nc localhost 5140 |
(k)在m2的sink窗口,我们可以看到以下信息,因为优先级的关系,log消息会再次落到m2上：
|
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 } 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 } |
案例11：Load balancing Sink Processor load balance type和failover不同的地方是,load balance有两个配置,一个是轮询,一个是随机。两种情况下如果被选择的sink不可用,就会自动尝试发送到下一个可用的sink上面。
(a)在m1创建Load_balancing_Sink_Processors配置文件
|
vi /home/hadoop/flume-1.5.0-bin/conf/Load_balancing_Sink_Processors.conf
a1.sources = r1 a1.sinks = k1 k2 a1.channels = c1 #这个是配置Load balancing的关键，需要有一个sink group a1.sinkgroups = g1 a1.sinkgroups.g1.sinks = k1 k2 a1.sinkgroups.g1.processor.type= load_balance a1.sinkgroups.g1.processor.backoff = true a1.sinkgroups.g1.processor.selector = round_robin # Describe/configure the source a1.sources.r1.type= syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.channels = c1 # Describe the sink a1.sinks.k1.type= avro a1.sinks.k1.channel = c1 a1.sinks.k1.hostname= m1 a1.sinks.k1.port = 5555 a1.sinks.k2.type= avro a1.sinks.k2.channel = c1 a1.sinks.k2.hostname= m2 a1.sinks.k2.port = 5555 # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 |
(b)在m1创建Load_balancing_Sink_Processors_avro配置文件
|
vi /home/hadoop/flume-1.5.0-bin/conf/Load_balancing_Sink_Processors_avro.conf
a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type= avro a1.sources.r1.channels = c1 a1.sources.r1.bind = 0.0.0.0 a1.sources.r1.port = 5555 # Describe the sink a1.sinks.k1.type= logger # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1 |
(c)将2个配置文件复制到m2上一份
|
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
scp -r /home/hadoop/flume-1.5.0-bin/conf/Load_balancing_Sink_Processors_avro.conf root@m2:/home/hadoop/flume-1.5.0-bin/conf/Load_balancing_Sink_Processors_avro.conf |
(d)打开4个窗口，在m1和m2上同时启动两个flume agent
|
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 flume-ng agent -c . -f /home/hadoop/flume-1.5.0-bin/conf/Load_balancing_Sink_Processors.conf -n a1 -Dflume.root.logger=INFO,console |
(e)然后在m1或m2的任意一台机器上，测试产生log，一行一行输入，输入太快，容易落到一台机器上
|
echo "idoall.org test1" | nc localhost 5140 echo "idoall.org test2" | nc localhost 5140 echo "idoall.org test3" | nc localhost 5140 echo "idoall.org test4" | nc localhost 5140 |
(f)在m1的sink窗口，可以看到以下信息：
|
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 } 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：
|
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 } Event: { headers:{Severity=0, flume.syslog.status=Invalid, Facility=0} body: 69 64 6F 61 6C 6C 2E 6F 72 67 20 74 65 73 74 33 idoall.org test3 } |
说明轮询模式起到了作用。
案例12：Hbase sink
(a)在测试之前，请先将hbase启动
(b)然后将以下文件复制到flume中：
|
cp/home/hadoop/hbase-0.96.2-hadoop2/lib/protobuf-java-2.5.0.jar /home/hadoop/flume-1.5.0-bin/lib cp/home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-client-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib cp/home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-common-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib cp/home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-protocol-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib cp/home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-server-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib cp/home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-hadoop2-compat-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib cp/home/hadoop/hbase-0.96.2-hadoop2/lib/hbase-hadoop-compat-0.96.2-hadoop2.jar /home/hadoop/flume-1.5.0-bin/lib cp/home/hadoop/hbase-0.96.2-hadoop2/lib/htrace-core-2.04.jar /home/hadoop/flume-1.5.0-bin/lib |
(c)确保test_idoall_org表在hbase中已经存在。
(d)在m1创建hbase_simple配置文件
|
vi /home/hadoop/flume-1.5.0-bin/conf/hbase_simple.conf
a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type= syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.host = localhost a1.sources.r1.channels = c1 # Describe the sink a1.sinks.k1.type= logger a1.sinks.k1.type= hbase a1.sinks.k1.table = test_idoall_org a1.sinks.k1.columnFamily = name a1.sinks.k1.column = idoall a1.sinks.k1.serializer = org.apache.flume.sink.hbase.RegexHbaseEventSerializer a1.sinks.k1.channel = memoryChannel # Use a channel which buffers events in memory a1.channels.c1.type= memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1 |
(e)启动flume agent
|
flume-ngagent -c . –f /home/hadoop/flume-1.5.0-bin/conf/hbase_simple.conf -n a1 -Dflume.root.logger=INFO,console |
(f)测试产生syslog
|
echo "hello idoall.org from flume" | nc localhost 5140 |
(g)这时登录到hbase中，可以发现新数据已经插入
|
hbase shell
hbase(main):001:0> list TABLE hbase2hive_idoall hive2hbase_idoall test_idoall_org
=> ["hbase2hive_idoall","hive2hbase_idoall","test_idoall_org"]
hbase(main):002:0> scan "test_idoall_org"
hbase(main):004:0> quit |
| 名称 | 含义 | 注意点 |
|---|---|---|
| avro | avro协议的数据源 |
|
| exec | unix命令 | 可以命令监控文件 tail -F |
| spooldir | 监控一个文件夹 |
不能含有子文件夹，不监控windows文件夹 处理完文件不能再写数据到文件 文件名不能冲突 |
| TAILDIR | 既可以监控文件也可以监控文件夹 | 支持断点续传功能， 重点使用这个 |
| netcat | 监听某个端口 |
|
| kafka | 监控卡夫卡数据 |
|
| 名称 | 含义 | 注意点 |
|---|---|---|
| kafka | 写到kafka中 |
|
| HDFS | 将数据写到HDFS中 |
|
| logger | 输出到控制台 |
|
| avro | avro协议 | 配合avro source使用 |
| 名称 | 含义 | 注意点 |
|---|---|---|
| memory | 存在内存中 |
|
| kafka | 将数据存到kafka中 |
|
| file | 存在本地磁盘文件中 |
