flume 基本知识

Posted 2020-11-12 lshan

官网指导文档：

http://flume.apache.org/releases/content/1.7.0/FlumeDeveloperGuide.html

 

 

http://flume.apache.org/releases/content/1.9.0/FlumeDeveloperGuide.html

 

 

4.2.1、案例一：监控端口数据

目标：Flume监控一端Console，另一端Console发送消息，使被监控端实时显示。

分步实现：

1) 创建Flume Agent配置文件flume-telnet.conf

# Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1   # Describe/configure the source a1.sources.r1.type = netcat a1.sources.r1.bind = localhost a1.sources.r1.port = 44444   # Describe the sink a1.sinks.k1.type = logger   # Use a channel which buffers events in memory 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) 安装telnet工具

$ sudo rpm -ivh xinetd-2.3.14-40.el6.x86_64.rpm $ sudo rpm -ivh telnet-0.17-48.el6.x86_64.rpm $ sudo rpm -ivh telnet-server-0.17-48.el6.x86_64.rpm

3) 判断44444端口是否被占用

$ netstat -tunlp | grep 44444

4) 先开启flume先听端口

$ bin/flume-ng agent --conf  conf/ --name a1 --conf-file  job/flume-telnet.conf -Dflume.root.logger==INFO,console

Remark: --config     the localtion of config         --name  the  name of  agent

 5) 使用telnet工具向本机的44444端口发送内容

$ telnet localhost 44444

 

 

4.2.2、案例二：实时读取本地文件到HDFS

目标：实时监控hive日志，并上传到HDFS中

分步实现：

1) 拷贝Hadoop相关jar到Flume的lib目录下

$ cp share/hadoop/common/lib/hadoop-auth-2.5.0-cdh5.3.6.jar ./lib/ $ cp share/hadoop/common/lib/commons-configuration-1.6.jar ./lib/ $ cp share/hadoop/mapreduce1/lib/hadoop-hdfs-2.5.0-cdh5.3.6.jar ./lib/ $ cp share/hadoop/common/hadoop-common-2.5.0-cdh5.3.6.jar ./lib/ $ cp ./share/hadoop/hdfs/lib/htrace-core-3.1.0-incubating.jar ./lib/ $ cp ./share/hadoop/hdfs/lib/commons-io-2.4.jar ./lib/

尖叫提示：标红的jar为1.99版本flume必须引用的jar

2) 创建flume-hdfs.conf文件

# Name the components on this agent a2.sources = r2 a2.sinks = k2 a2.channels = c2 # Describe/configure the source a2.sources.r2.type = exec a2.sources.r2.command = tail -F /home/admin/modules/apache-hive-1.2.2-bin/hive.log a2.sources.r2.shell = /bin/bash -c   # Describe the sink a2.sinks.k2.type = hdfs a2.sinks.k2.hdfs.path = hdfs://linux01:8020/flume/%Y%m%d/%H #上传文件的前缀 a2.sinks.k2.hdfs.filePrefix = logs- #是否按照时间滚动文件夹 a2.sinks.k2.hdfs.round = true #多少时间单位创建一个新的文件夹 a2.sinks.k2.hdfs.roundValue = 1 #重新定义时间单位 a2.sinks.k2.hdfs.roundUnit = hour #是否使用本地时间戳 a2.sinks.k2.hdfs.useLocalTimeStamp = true #积攒多少个Event才flush到HDFS一次 a2.sinks.k2.hdfs.batchSize = 1000 #设置文件类型，可支持压缩 a2.sinks.k2.hdfs.fileType = DataStream #多久生成一个新的文件 a2.sinks.k2.hdfs.rollInterval = 600 #设置每个文件的滚动大小 a2.sinks.k2.hdfs.rollSize = 134217700 #文件的滚动与Event数量无关 a2.sinks.k2.hdfs.rollCount = 0 #最小冗余数 a2.sinks.k2.hdfs.minBlockReplicas = 1   # Use a channel which buffers events in memory a2.channels.c2.type = memory a2.channels.c2.capacity = 1000 a2.channels.c2.transactionCapacity = 100   # Bind the source and sink to the channel a2.sources.r2.channels = c2 a2.sinks.k2.channel = c2

 

3) 执行监控配置

$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/flume-hdfs.conf

 

4.2.3、案例三：实时读取目录文件到HDFS

目标：使用flume监听整个目录的文件

分步实现：

1) 创建配置文件flume-dir.conf

a3.sources = r3 a3.sinks = k3 a3.channels = c3   # Describe/configure the source a3.sources.r3.type = spooldir a3.sources.r3.spoolDir = /home/admin/modules/apache-flume-1.7.0-bin/upload a3.sources.r3.fileSuffix = .COMPLETED a3.sources.r3.fileHeader = true #忽略所有以.tmp结尾的文件，不上传 a3.sources.r3.ignorePattern = ([^ ]*\\.tmp)   # Describe the sink a3.sinks.k3.type = hdfs a3.sinks.k3.hdfs.path = hdfs://linux01:8020/flume/upload/%Y%m%d/%H #上传文件的前缀 a3.sinks.k3.hdfs.filePrefix = upload- #是否按照时间滚动文件夹 a3.sinks.k3.hdfs.round = true #多少时间单位创建一个新的文件夹 a3.sinks.k3.hdfs.roundValue = 1 #重新定义时间单位 a3.sinks.k3.hdfs.roundUnit = hour #是否使用本地时间戳 a3.sinks.k3.hdfs.useLocalTimeStamp = true #积攒多少个Event才flush到HDFS一次 a3.sinks.k3.hdfs.batchSize = 100 #设置文件类型，可支持压缩 a3.sinks.k3.hdfs.fileType = DataStream #多久生成一个新的文件 a3.sinks.k3.hdfs.rollInterval = 600 #设置每个文件的滚动大小大概是128M a3.sinks.k3.hdfs.rollSize = 134217700 #文件的滚动与Event数量无关 a3.sinks.k3.hdfs.rollCount = 0 #最小冗余数 a3.sinks.k3.hdfs.minBlockReplicas = 1   # Use a channel which buffers events in memory a3.channels.c3.type = memory a3.channels.c3.capacity = 1000 a3.channels.c3.transactionCapacity = 100   # Bind the source and sink to the channel a3.sources.r3.channels = c3 a3.sinks.k3.channel = c3

 

2) 执行测试：执行如下脚本后，请向upload文件夹中添加文件试试

$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/flume-dir.conf

尖叫提示： 在使用Spooling Directory Source时

1) 不要在监控目录中创建并持续修改文件

2) 上传完成的文件会以.COMPLETED结尾

3) 被监控文件夹每600毫秒扫描一次文件变动

4.2.4、案例四：Flume与Flume之间数据传递：单Flume多Channel、Sink， 

 

 

 

目标：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。

分步实现：

1) 创建flume-1.conf，用于监控hive.log文件的变动，同时产生两个channel和两个sink分别输送给flume-2和flume3：

# 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 /home/admin/modules/apache-hive-1.2.2-bin/hive.log a1.sources.r1.shell = /bin/bash -c   # Describe the sink a1.sinks.k1.type = avro a1.sinks.k1.hostname = linux01 a1.sinks.k1.port = 4141   a1.sinks.k2.type = avro a1.sinks.k2.hostname = linux01 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

 

2) 创建flume-2.conf，用于接收flume-1的event，同时产生1个channel和1个sink，将数据输送给hdfs：

# 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 = linux01 a2.sources.r1.port = 4141   # Describe the sink a2.sinks.k1.type = hdfs a2.sinks.k1.hdfs.path = hdfs://linux01:8020/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-3.conf，用于接收flume-1的event，同时产生1个channel和1个sink，将数据输送给本地目录：

# 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 = linux01 a3.sources.r1.port = 4142   # Describe the sink a3.sinks.k1.type = file_roll a3.sinks.k1.sink.directory = /home/admin/Desktop/flume3   # 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

尖叫提示：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) 执行测试：分别开启对应flume-job（依次启动flume-3，flume-2，flume-1），同时产生文件变动并观察结果：

$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group-job1/flume-3.conf $ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group-job1/flume-2.conf $ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group-job1/flume-1.conf

 

 

 

 

4.2.5、案例五：Flume与Flume之间数据传递，多Flume汇总数据到单Flume

 

 

 

目标：flume-1监控文件hive.log，flume-2监控某一个端口的数据流，flume-1与flume-2将数据发送给flume-3，flume3将最终数据写入到HDFS。

分步实现：

1) 创建flume-1.conf，用于监控hive.log文件，同时sink数据到flume-3：

# 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 /home/admin/modules/apache-hive-1.2.2-bin/hive.log a1.sources.r1.shell = /bin/bash -c   # Describe the sink a1.sinks.k1.type = avro a1.sinks.k1.hostname = linux01 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flume-2.conf，用于监控端口44444数据流，同时sink数据到flume-3：

# 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 = linux01 a2.sources.r1.port = 44444   # Describe the sink a2.sinks.k1.type = avro a2.sinks.k1.hostname = linux01 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flume-3.conf，用于接收flume-1与flume-2发送过来的数据流，最终合并后sink到HDFS：

# 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 = linux01 a3.sources.r1.port = 4141   # Describe the sink a3.sinks.k1.type = hdfs a3.sinks.k1.hdfs.path = hdfs://linux01:8020/flume3/%Y%m%d/%H #上传文件的前缀 a3.sinks.k1.hdfs.filePrefix = flume3- #是否按照时间滚动文件夹 a3.sinks.k1.hdfs.round = true #多少时间单位创建一个新的文件夹 a3.sinks.k1.hdfs.roundValue = 1 #重新定义时间单位 a3.sinks.k1.hdfs.roundUnit = hour #是否使用本地时间戳 a3.sinks.k1.hdfs.useLocalTimeStamp = true #积攒多少个Event才flush到HDFS一次 a3.sinks.k1.hdfs.batchSize = 100 #设置文件类型，可支持压缩 a3.sinks.k1.hdfs.fileType = DataStream #多久生成一个新的文件 a3.sinks.k1.hdfs.rollInterval = 600 #设置每个文件的滚动大小大概是128M a3.sinks.k1.hdfs.rollSize = 134217700 #文件的滚动与Event数量无关 a3.sinks.k1.hdfs.rollCount = 0 #最小冗余数 a3.sinks.k1.hdfs.minBlockReplicas = 1   # 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) 执行测试：分别开启对应flume-job（依次启动flume-3，flume-2，flume-1），同时产生文件变动并观察结果：

$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group-job2/flume-3.conf $ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group-job2/flume-2.conf $ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group-job2/flume-1.conf

尖叫提示：测试时记得启动hive产生一些日志，同时使用telnet向44444端口发送内容，如：

$ bin/hive $ telnet linux01 44444