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
