Flume—(4)单数据源多出口

1）案例需求

　　使用Flume-1监控文件变动，Flume-1将变动内容传递给Flume-2，Flume-2负责存储到HDFS。同时Flume-1将变动内容传底给Flume-3，Flume-3负责传递给Local FileSystem。

2）需求分析

 

3）实现步骤

1.准备工作

在/opt/module/flume-1.9.0/job目录下创建group1文件夹

[ck@hadoop102 job]$ mkdir group1
[ck@hadoop102 job]$ cd group1/

在/opt/module/datas/目录下创建flume3文件夹

[ck@hadoop102 datas]$ mkdir flume3

2.创建flume-file-flume.conf

配置1个接收日志文件的source和两个Channel、两个sink，分别输送给flume-flume-hdfs 和flume-flume-dir。

创建配置文件并打开

[ck@hadoop102 group1]$ touch flume-file-flume.conf
[ck@hadoop102 group1]$ vim flume-file-flume.conf

添加如下内容：

#Name the components on this agent

a1.sources = r1
a1.sinks = k1 k2
a1.channels = c1 c2

#将数据流复制给所有Channel
a1.sources.r1.selector.type = replicating

#Describe/configure the source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /opt/module/hive/logs/hive.log
a1.sources.r1.shell = /bin/bash -c

#Describe the sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = hadoop102
a1.sinks.k1.port = 4141
a1.sinks.k2.type = avro
a1.sinks.k2.hostname = hadoop102
a1.sinks.k2.port = 4142

#Describe the channel
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

#Bind the Source and sink to the channel
a1.sources.r1.channels = c1 c2
a1.sinks.k1.channel = c1
a1.sinks.k2.channel = c2

 注：Avro是hadoop创始人Doug Cutting创建的一种语言无关的数据序列化和RPC框架。

 注：RPC（Remote Procedure Call）已远程过程调用，他是一种通过网络从远程计算机程序上请求服务，而不需要了解底层网络技术的协议。

3.创建flume-flume-hdfs.conf

配置上级Flume输出的Source，输出到HDFS的Sink。

创建配置文件并打开

[ck@hadoop102 group1]$ touch flume-flume-hdfs.conf
[ck@hadoop102 group1]$ vim flume-flume-hdfs.conf

添加如下内容：

#Name the components on this agent
a2.sources = r1
a2.sinks = k1
a2.channels = c1

#Describe/configure the source
a2.sources.r1.type = avro
a2.sources.r1.bind = hadoop102
a2.sources.r1.port = 4141

#Describe the sink
a2.sinks.k1.type = hdfs
a2.sinks.k1.hdfs.path = hdfs://hadoop102:9000/flume-1.9.0/flume2/%Y%m%d/%H
a2.sinks.k1.hdfs.filePrefix = flume2-
a2.sinks.k1.hdfs.round = true
a2.sinks.k1.hdfs.roundValue = 1
a2.sinks.k1.hdfs.roundUnit = hour
a2.sinks.k1.hdfs.useLocalTimeStamp = true
a2.sinks.k1.hdfs.batchSize = 100
a2.sinks.k1.hdfs.fileType = DataStream
a2.sinks.k1.hdfs.rollInterval = 600
a2.sinks.k1.hdfs.rollSize = 134217700
a2.sinks.k1.hdfs.rollCount = 0
a2.sinks.k1.hdfs.minBlockReplicas = 1

#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

4.创建flume-flume-dir.conf

配置上级Flume输出的Source，输出是本地目录的Sink。

创建配置文件并打开

 [ck@hadoop102 group1]$ touch flume-flume-dir.conf
 [ck@hadoop102 group1]$ vim flume-flume-dir.conf

添加如下内容

#Name the components on this agent
a3.sources = r1
a3.sinks = k1
a3.channels = c2

#Describe/configure the source
a3.sources.r1.type = avro
a3.sources.r1.bind = hadoop102
a3.sources.r1.port = 4142

#Describe the sink
a3.sinks.k1.type = file_roll
a3.sinks.k1.sink.directory = /opt/module/datas/flume3

#Use a channel which buffers events in memory
a3.channels.c2.type = memory
a3.channels.c2.capacity = 1000
a3.channels.c2.transactionCapacity = 100

#Bind the Source and sink to the channel
a3.sources.r1.channels = c2
a3.sinks.k1.channel = c2

提示：输出的本地目录必须是已经存在的目录，如果该目录不存在，并不会创建新的目录。

5.执行配置文件

分别开启对应配置文件：flume-flume-dir，flume-flume-hdfs，flume-file-flume。

[ck@hadoop102 flume]$ bin/flume-ng agent -–name a3 -–conf conf/ -–conf-file job/group1/flume-flume-dir.conf 
[ck@hadoop102 flume]$ bin/flume-ng agent -–name a2 -–conf conf/ -–conf-file job/group1/flume-flume-hdfs.conf 
[ck@hadoop102 flume]$ bin/flume-ng agent -–name a1 -–conf conf/ –-conf-file job/group1/flume-file-flume.conf

6.启动Hadoop和Hive

[ck@hadoop102 hadoop-2.7.2]$ sbin/start-dfs.sh
[ck@hadoop103 hadoop-2.7.2]$ sbin/start-yarn.sh

[ck@hadoop102 hive]$ bin/hive
hive (default)>

7. 检查HDFS上的数据

 

8. 检查/opt/module/datas/flume3目录中的数据

[ck@hadoop102 flume3]$ ll
总用量 8
-rw-rw-r–. 1 ck ck 5942 5月  22 00:09 1526918887550-3