Flume聚合

案例需求：

hadoop102 上的 Flume-1 监控文件/opt/module/group.log，

hadoop103 上的 Flume-2 监控某一个端口的数据流，

Flume-1 与 Flume-2 将数据发送给 hadoop104 上的 Flume-3，Flume-3 将最终数据打印到控制台。

1. 在module目录下分发 Flume

   xsync flume

   

2. 在/opt/module/flume/job下创建group3文件夹

   mkdir group3/

   

3. 配置 Source 用于监控 hive.log 文件，配置 Sink 输出数据到下一级 Flume。

   vim flume1-logger-flume.conf

   

   # Name the components on this agent
   a1.sources = r1
   a1.sinks = k1
   a1.channels = c1
   
   # Describe/configure the source
   a1.sources.r1.type = exec
   a1.sources.r1.command = tail -F /opt/module/group.log
   a1.sources.r1.shell = /bin/bash -c
   
   # Describe the sink
   a1.sinks.k1.type = avro
   a1.sinks.k1.hostname = hadoop104
   a1.sinks.k1.port = 4141
   
   # Describe the channel
   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

4. 配置 Source 监控端口 44444 数据流，配置 Sink 数据到下一级 Flume

   vim flume2-netcat-flume.conf

   

   # Name the components on this agent
   a2.sources = r1
   a2.sinks = k1
   a2.channels = c1
   
   # Describe/configure the source
   a2.sources.r1.type = netcat
   a2.sources.r1.bind = hadoop103
   a2.sources.r1.port = 44444
   
   # Describe the sink
   a2.sinks.k1.type = avro
   a2.sinks.k1.hostname = hadoop104
   a2.sinks.k1.port = 4141
   
   # Use a channel which buffers events in memory
   a2.channels.c1.type = memory
   a2.channels.c1.capacity = 1000
   a2.channels.c1.transactionCapacity = 100
   
   # Bind the source and sink to the channel
   a2.sources.r1.channels = c1
   a2.sinks.k1.channel = c1

    
5. 配置 source 用于接收 flume1 与 flume2 发送过来的数据流，最终合并后 sink 到控制台。

   vim flume3-flume-logger.conf

   

   # Name the components on this agent
   a3.sources = r1
   a3.sinks = k1
   a3.channels = c1
   
   # Describe/configure the source
   a3.sources.r1.type = avro
   a3.sources.r1.bind = hadoop104
   a3.sources.r1.port = 4141
   
   # Describe the sink
   a3.sinks.k1.type = logger
   
   # Describe the channel
   a3.channels.c1.type = memory
   a3.channels.c1.capacity = 1000
   a3.channels.c1.transactionCapacity = 100
   
   # Bind the source and sink to the channel
   a3.sources.r1.channels = c1
   a3.sinks.k1.channel = c1

6. 分group3文件夹

   xsync group3

   

7. 分别开启对应配置文件：flume3-flume-logger.conf，flume2-netcat-flume.conf，flume1-logger-flume.conf。在hadoop104上

   bin/flume-ng agent -n a3 -c conf/ -f job/group3/flume3-flume-logger.conf -Dflume.root.logger=INFO,console

   

   在hadoop103上

   bin/flume-ng agent -n a2 -c conf/ -f job/group3/flume2-netcat-flume.conf

   

   在hadoop102上

   bin/flume-ng agent -n a1 -c conf/ -f job/group3/flume1-logger-flume.conf

   

8. 在/opt/module 下创建 group.log

   touch group.log

   

   可以在hadoop104上看到与hadoop102,hadoop103连接成功。

9. 测试：在hadoop103上打开端口

   nc hadoop103 44444

   

   

   查看hadoop104上的信息

   在hadoop102上往group.log里写东西

   echo flume >> group.log

   

   查看hadoop104上的信息

10. 这就实现了跨服务器的数据聚集