2)执行监控配置
[root@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/flume-file-hdfs.conf3)开启hadoop和hive并操作hive产生日志
[root@hadoop102 hadoop-2.7.2]$ sbin/start-dfs.sh [root@hadoop102 hadoop-2.7.2]$ sbin/start-yarn.sh [root@hadoop102 hive]$ bin/hive 单数据源多出口案例 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将变动内容传递给flume-3，flume-3负责输出到local filesystem。 2）实现步骤： 在job目录下创建group1文件夹 [root@hadoop102 job]$ cd group1/ 在/opt/module/datas/目录下创建flume3文件夹 [root@hadoop102 datas]$ mkdir flume3 1．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 [root@hadoop102 group1]$ touch flume-file-flume.conf [root@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 # 将数据流复制给多个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是由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）—远程过程调用，它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。 2．创建flume-flume-hdfs.conf 配置上级flume输出的source，输出是到hdfs的sink。 创建配置文件并打开 [root@hadoop102 group1]$ touch flume-flume-hdfs.conf [root@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://hadoop101:9000/flume2/%Y%m%d/%H #上传文件的前缀 a2.sinks.k1.hdfs.filePrefix = flume2- #是否按照时间滚动文件夹 a2.sinks.k1.hdfs.round = true #多少时间单位创建一个新的文件夹 a2.sinks.k1.hdfs.roundValue = 1 #重新定义时间单位 a2.sinks.k1.hdfs.roundUnit = hour #是否使用本地时间戳 a2.sinks.k1.hdfs.useLocalTimeStamp = true #积攒多少个Event才flush到HDFS一次 a2.sinks.k1.hdfs.batchSize = 100 #设置文件类型，可支持压缩 a2.sinks.k1.hdfs.fileType = DataStream #多久生成一个新的文件 a2.sinks.k1.hdfs.rollInterval = 600 #设置每个文件的滚动大小大概是128M a2.sinks.k1.hdfs.rollSize = 134217700 #文件的滚动与Event数量无关 a2.sinks.k1.hdfs.rollCount = 0 #最小冗余数 a2.sinks.k1.hdfs.minBlockReplicas = 1 # Describe the channel 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 3．创建flume-flume-dir.conf 配置上级flume输出的source，输出是到本地目录的sink。 创建配置文件并打开 [root@hadoop102 group1]$ touch flume-flume-dir.conf [root@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 # Describe the channel 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。 4．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。 [root@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group1/flume-flume-dir.conf [root@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group1/flume-flume-hdfs.conf [root@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group1/flume-file-flume.conf 5．启动hadoop和hive [root@hadoop102 hadoop-2.7.2]$ sbin/start-dfs.sh [root@hadoop102 hadoop-2.7.2]$ sbin/start-yarn.sh [root@hadoop102 hive]$ bin/hive hive (default)> 6．检查HDFS上数据 7检查/opt/module/datas/flume3目录中数据 [root@hadoop102 flume3]$ ll 总用量 8 -rw-rw-r--. 1 root root 5942 5月 22 00:09 1526918887550-3 多数据源汇总案例 1）案例需求： hadoop102上的flume-1监控文件hive.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hadoop102上的flume-3，flume-3将最终数据打印到控制台 0．准备工作 分发flume [root@hadoop102 module]$ scp flume 在hadoop102、hadoop103以及hadoop104的/opt/module/flume/job目录下创建一个group2文件夹 [root@hadoop102 job]$ mkdir group2 [root@hadoop102 job]$ mkdir group2 [root@hadoop103 job]$ mkdir group2 1．创建flume1.conf 配置source用于监控hive.log文件，配置sink输出数据到下一级flume。 在hadoop102上创建配置文件并打开 [root@hadoop102 group2]$ touch flume-file.conf [root@hadoop102 group2]$ vim flume-file.conf 添加如下内容 # 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/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 # 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．创建flume2.conf 配置source监控端口44444数据流，配置sink数据到下一级flume： 在hadoop104上创建配置文件并打开 [root@hadoop103 group2]$ touch flume2.conf [root@hadoop103 group2]$ vim flume2.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 = netcat a2.sources.r1.bind = hadoop102 a2.sources.r1.port = 44444 # Describe the sink a2.sinks.k1.type = avro a2.sinks.k1.hostname = hadoop102 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 3．创建flume3.conf 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。 在hadoop102上创建配置文件并打开 [root@hadoop102 group2]$ touch flume3.conf [root@hadoop102 group2]$ vim flume3.conf 添加如下内容 # 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 = hadoop102 a3.sources.r1.port = 4141 # Describe the sink # 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 4．执行配置文件 分别开启对应配置文件：flume3.conf，flume2.conf，flume1.conf。 [root@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group2/flume3.conf -Dflume.root.logger=INFO,console [root@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group2/flume2.conf [root@hadoop103 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group3/flume-file.conf 5．在hadoop102上向/opt/module目录下的group.log追加内容 [root@hadoop102 module]$ echo 'hello' > group.log 6．在hadoop103上向44444端口发送数据 [root@hadoop103 flume]$ telnet hadoop104 44444 7. 检查数据