前言

本节内容我们主要介绍一下Flume数据采集过程中，如何把多个数据采集点的数据聚合到一个地方供分析使用。我们使用hadoop101服务器采集nc数据，hadoop102采集文件数据，将hadoop101和hadoop102服务器采集的数据聚合到hadoop103服务器输出到控制台。其整体架构如下：

正文

①在hadoop101服务器的/opt/module/apache-flume-1.9.0/job/group1目录下创建job-nc-flume-avro.conf配置文件，用于监控nc发送的数据，通过avro sink传输到avro source

- job-nc-flume-avro.conf配置文件

# 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/apache-flume-1.9.0/a.log
a1.sources.r1.shell = /bin/bash -c
# Describe the sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = hadoop103
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

②在hadoop102服务器的/opt/module/apache-flume-1.9.0/job/group1目录下创建job-file-flume-avro.conf配置文件，用于监控目录/opt/module/apache-flume-1.9.0/a.log的数据，通过avro sink传输到avro source 

- job-file-flume-avro.conf配置文件

# 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/apache-flume-1.9.0/a.log
a1.sources.r1.shell = /bin/bash -c
# Describe the sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = hadoop103
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

③在hadoop103服务器的/opt/module/apache-flume-1.9.0/job/group1目录下创建job-avro-flume-console.conf配置文件，用户将avro source聚合的数据输出到控制台

- job-avro-flume-console.conf配置文件

# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source
a1.sources.r1.type = avro
a1.sources.r1.bind = hadoop103
a1.sources.r1.port = 4141
# Describe the sink
# Describe the sink
a1.sinks.k1.type = logger
# 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 在hadoop103启动job-avro-flume-console.conf任务

- 命令：

bin/flume-ng agent -c conf/ -n a1 -f job/group1/job-avro-flume-console.conf -Dflume.root.logger=INFO,console

⑤在hadoop101启动job-nc-flume-avro.conf任务

- 命令：

bin/flume-ng agent -c conf/ -n a1 -f job/group1/job-nc-flume-avro.conf -Dflume.root.logger=INFO,console

⑥在hadoop102启动job-file-flume-avro.conf任务

- 命令：

bin/flume-ng agent -c conf/ -n a1 -f job/group1/job-file-flume-avro.conf -Dflume.root.logger=INFO,console

⑦使用nc工具向hadoop101发送数据

- nc发送数据

- hadoop103接收到数据

⑧在hadoop102的a.log中写入数据

- 写入数据

-  hadoop103接收到数据

结语

flume数据聚合就是为了将具有相同属性的数据聚合到一起，便于管理、分析、统计等。至此，关于Flume数据采集之采集数据聚合案例实战到这里就结束了，我们下期见。。。。。。