# 从http://flume.apache.org/download.html 下载flume ############################################# # 概述：Flume 是Cloudera提供的一个高可用的，高可靠的，分布式的海量日志采集、聚合和传输的软件。 # Flume的核心是把数据从数据源(source)收集过来，送到指定的目的地(sink)。为了保证输送的过程一定 # 成功，在送到目的地(sink)之前，会先缓存数据(channel)，待数据真正到达目的地(sink)后，再删除自 # 己缓存的数据。 ############################################# # 上传到Linux, tar zxvf apache-flume-1.8.0-bin.tar.gz rm -rf apache-flume-1.8.0-bin.tar.gz mv apache-flume-1.8.0-bin/ flume-1.8.0 cd flume-1.8.0/conf/ cp flume-env.sh.template flume-env.sh vim flume-env.sh # 导入正确的JDK路径 export JAVA_HOME=/usr/local/src/jdk1.8.0_161 ######################################## # 从网络端口接收数据，下沉到logger ######################################## 采集配置文件，netcat-logger.conf # Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the sources a1.sources.r1.type = netcat a1.sources.r1.bind = localhost a1.sources.r1.port = 44444 # Describe the sinks 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 ######################################## 采集配置文件 结束 # 启动命令 bin/flume-ng agent --conf conf/ --conf-file conf/netcat-logger.conf --name a1 -Dflume.root.logger=INFO,console # 将出现监听： Created serverSocket:sun.nio.ch.ServerSocketChannelImpl[/127.0.0.1:44444] # 用另一个终端来测试： yum install -y telnet telnet localhost 44444 # 登录成功会显示 Connected to localhost. Escape character is '^]'. hello, world. # 发送一段文字。 看启动监听的终端有没有收到。 # 监听端：2018-05-27 20:33:29,974 (SinkRunner-PollingRunner-DefaultSinkProcessor) [INFO - org.apache.flume.sink.LoggerSink.process(LoggerSink.java:95)] Event: { headers:{} body: 68 65 6C 6C 6F 2C 77 6F 72 6C 64 2E 0D hello,world.. } ########################################## # 采集目录到HDFS上。# 启动好HDFS， ################################## spooldir-hdfs.cnf 文件： #Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the sources # 注意不能往监控目录中重复放置同名文件，一旦重名，服务将出错并停止。 a1.sources.r1.type = spooldir a1.sources.r1.spoolDir = /root/logs a1.sources.r1.fileHeader = true # Describe the sinks a1.sinks.k1.type = hdfs a1.sinks.k1.channel = c1 a1.sinks.k1.hdfs.path = /flume/events/%y-%m-%d/%H%M/ a1.sinks.k1.hdfs.filePrefix = events- a1.sinks.k1.hdfs.round = true a1.sinks.k1.hdfs.roundValue = 10 a1.sinks.k1.hdfs.roundUnit = minute a1.sinks.k1.hdfs.rollInterval = 3 a1.sinks.k1.hdfs.rollSize = 20 a1.sinks.k1.hdfs.rollCount = 5 a1.sinks.k1.hdfs.batchSize = 1 a1.sinks.k1.hdfs.useLocalTimeStamp = true # 生成的文件类型，默认是Sequencefile, 可用DataStream ，则为普通文本 a1.sinks.k1.hdfs.fileType = DataStream # 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 ################################## # 启动命令 如果/root/logs中已有文件，则会被立刻采集到HDFS bin/flume-ng agent -c conf/ -f conf/spooldir-hdfs.cnf -n a1 -Dflume.root.logger=INFO,console # 成功后：2018-05-27 22:08:02,505 (lifecycleSupervisor-1-0) [INFO - org.apache.flume.instrumentation.MonitoredCounterGroup.start(MonitoredCounterGroup.java:95)] Component type: SOURCE, name: r1 started # 在/root/logs/下创建一个文件，监听端会显示：Writer callback called. # HDFS上则得到文件：/flume/events/18-05-27/2210/events-.1527430208616 # 注意 spooldir 不能往源目录/root/logs/中重复放置同名文件，一旦重名，服务将出错并停止工作。 ########################################## ### 增量采集内容变化的文件到HDFS ########################################## tail-hdfs.cnf 文件 #Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the sources # 注意不能往监控目录中重复放置同名文件，一旦重名，服务将出错并停止。 a1.sources.r1.type = exec a1.sources.r1.command = tail -F /root/logs/test.log a1.sources.r1.channels = c1 # Describe the sinks a1.sinks.k1.type = hdfs a1.sinks.k1.channel = c1 a1.sinks.k1.hdfs.path = /flume/tailout/%y-%m-%d/%H%M/ a1.sinks.k1.hdfs.filePrefix = events- a1.sinks.k1.hdfs.round = true a1.sinks.k1.hdfs.roundValue = 10 a1.sinks.k1.hdfs.roundUnit = minute a1.sinks.k1.hdfs.rollInterval = 3 a1.sinks.k1.hdfs.rollSize = 20 a1.sinks.k1.hdfs.rollCount = 5 a1.sinks.k1.hdfs.batchSize = 1 a1.sinks.k1.hdfs.useLocalTimeStamp = true # 生成的文件类型，默认是Sequencefile, 可用DataStream ，则为普通文本 a1.sinks.k1.hdfs.fileType = DataStream # 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 ########################################## # 启动命令 如果/root/logs中已有文件，则会被立刻采集到HDFS bin/flume-ng agent -c conf -f conf/tail-hdfs.cnf -n a1 -Dflume.root.logger=INFO,console # 模拟数据不断写入. while true; do date >>/root/logs/test.log;sleep 1.5;done ########################################## #Load balance 负载均衡 ########################################## # 使用三台机器，设置二级flume, 前面一台采集，使用轮询方式发往后面的二台，后二台再收集前一台发来的数据，下沉到目标。 scp -r flume-1.8.0/ slave2:/usr/local/src/ scp -r flume-1.8.0/ slave3:/usr/local/src/ # 使用slave1在最前，slave2 , slave3在其后的方式。 ################# 第一级slave1 配置文件：exec-avro.cnf #agent1 name agent1.channels = c1 agent1.sources = r1 agent1.sinks = k1 k2 # set group agent1.sinkgroups = g1 # set channel agent1.channels.c1.type = memory agent1.channels.c1.capacity = 1000 agent1.channels.c1.transactionCapacity = 100 agent1.sources.r1.channels = c1 agent1.sources.r1.type = exec agent1.sources.r1.command = tail -F /root/logs/123.log # set sink1 agent1.sinks.k1.channel = c1 agent1.sinks.k1.type = avro agent1.sinks.k1.hostname = slave2 agent1.sinks.k1.port = 52020 # set sink2 agent1.sinks.k2.channel = c1 agent1.sinks.k2.type = avro agent1.sinks.k2.hostname = slave3 agent1.sinks.k2.port = 52020 # set sink group agent1.sinkgroups.g1.sinks = k1 k2 # set failover agent1.sinkgroups.g1.processor.type = load_balance agent1.sinkgroups.g1.processor.backoff = true agent1.sinkgroups.g1.processor.selector = round_robin agent1.sinkgroups.g1.processor.selector.maxTimeOut = 10000 ############# end ############## ################# 第二级slave2 配置文件：avro-logger.cnf # Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the sources a1.sources.r1.type = avro a1.sources.r1.channels = c1 a1.sources.r1.bind = slave2 a1.sources.r1.port = 52020 # Describe the sinks 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 ############# slave2 end ############## ################# 第二级slave3 配置文件：avro-logger.cnf 唯一的改变是slave3 # Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the sources a1.sources.r1.type = avro a1.sources.r1.channels = c1 a1.sources.r1.bind = slave3 a1.sources.r1.port = 52020 # Describe the sinks 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 ############# slave3 end ############## ## 先启动第二级的slave2, slave3 bin/flume-ng agent -c conf -f conf/avro-logger.cnf -n a1 -Dflume.root.logger=INFO,console ## 再启动一级的slave1 bin/flume-ng agent -c conf -f conf/exec-avro.cnf -n agent1 -Dflume.root.logger=INFO,console # 启动成功后，第二级终端会出现类似：CONNECTED: /192.168.112.11:56404 # 而后续终止第一级时，第二级会出现类似： /192.168.112.11:56404 disconnected. # 模拟数据写入. 会看到仅第二级有采集动作，第一级不作显示。 while true; do date >>/root/logs/123.log;sleep 1;done ############################################# # Failover 容错 # 同一时间后端只有一台机器工作. ############################################# # 还是使用三台机器，设置二级flume, 前面一台采集，发往后面的某一台，优先级最高的收集前一台发来的数据； # 如果这台机器挂了，另一台自动替补 scp -r flume-1.8.0/ slave2:/usr/local/src/ scp -r flume-1.8.0/ slave3:/usr/local/src/ # 使用slave1在最前，slave2 , slave3在其后的方式。 ################# 第一级slave1 配置文件：exec-avro.cnf #agent1 name agent1.channels = c1 agent1.sources = r1 agent1.sinks = k1 k2 # set group agent1.sinkgroups = g1 # set channel agent1.channels.c1.type = memory agent1.channels.c1.capacity = 1000 agent1.channels.c1.transactionCapacity = 100 agent1.sources.r1.channels = c1 agent1.sources.r1.type = exec agent1.sources.r1.command = tail -F /root/logs/456.log # set sink1 agent1.sinks.k1.channel = c1 agent1.sinks.k1.type = avro agent1.sinks.k1.hostname = slave2 agent1.sinks.k1.port = 52020 # set sink2 agent1.sinks.k2.channel = c1 agent1.sinks.k2.type = avro agent1.sinks.k2.hostname = slave3 agent1.sinks.k2.port = 52020 # set sink group agent1.sinkgroups.g1.sinks = k1 k2 # set failover agent1.sinkgroups.g1.processor.type = failover agent1.sinkgroups.g1.processor.priority.k1 = 10 agent1.sinkgroups.g1.processor.priority.k2 = 1 agent1.sinkgroups.g1.processor.maxpenalty = 10000 ############# end ############## ################# 第二级slave2 配置文件：avro-logger.cnf # Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the sources a1.sources.r1.type = avro a1.sources.r1.channels = c1 a1.sources.r1.bind = slave2 a1.sources.r1.port = 52020 # Describe the sinks 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 ############# slave2 end ############## ################# 第二级slave3 配置文件：avro-logger.cnf 唯一的改变是slave3 # Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the sources a1.sources.r1.type = avro a1.sources.r1.channels = c1 a1.sources.r1.bind = slave3 a1.sources.r1.port = 52020 # Describe the sinks 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 ############# slave3 end ############## ## 先启动第二级的slave3, slave2 bin/flume-ng agent -c conf -f conf/avro-logger.cnf -n a1 -Dflume.root.logger=INFO,console ## 再启动一级的slave1 bin/flume-ng agent -c conf -f conf/exec-avro.cnf -n agent1 -Dflume.root.logger=INFO,console # 启动成功后，第二级终端会出现类似：CONNECTED: /192.168.112.11:56404 # 而后续终止第一级时，第二级会出现类似： /192.168.112.11:56404 disconnected. # 模拟数据写入. 会看到仅第二级slave2有采集动作，第一级不作显示。slave3待命。 while true; do date >>/root/logs/123.log;sleep 1;done # 一旦slave2终止，则slave3自动顶上，继续接收。
更新一个练习：
################################################################ # 案例： # A、B两台日志服务器实时生产日志，主要类型为access.log, nginx.log, web.log # 要求：把A、B中的三种日志采集汇总到C机器上，然后收集到HDFS # 且HDFS中要求按类别存放到不同的目录 ################################################################ ### 现将slave1 slave2 slave3 分别对应A B C ### A & B 配置文件 exec_source_avro_sink.conf 基本上一样，仅hostname不一样 # Name the components on this agent a1.sources = r1 r2 r3 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = exec a1.sources.r1.command = tail -F /root/logs1/access.log a1.sources.r1.interceptors = i1 a1.sources.r1.interceptors.i1.type = static a1.sources.r1.interceptors.i1.key = type a1.sources.r1.interceptors.i1.value = access a1.sources.r2.type = exec a1.sources.r2.command = tail -F /root/logs1/nginx.log a1.sources.r2.interceptors = i2 a1.sources.r2.interceptors.i2.type = static a1.sources.r2.interceptors.i2.key = type a1.sources.r2.interceptors.i2.value = nginx a1.sources.r3.type = exec a1.sources.r3.command = tail -F /root/logs1/web.log a1.sources.r3.interceptors = i3 a1.sources.r3.interceptors.i3.type = static a1.sources.r3.interceptors.i3.key = type a1.sources.r3.interceptors.i3.value = web # Describe the sink 发送到下一级主机 a1.sinks.k1.type = avro a1.sinks.k1.hostname = slave3 a1.sinks.k1.port = 41414 # Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 2000000 a1.channels.c1.transactionCapacity = 100000 # Bind the sourceand sink to the channel a1.sources.r1.channels = c1 a1.sources.r2.channels = c1 a1.sources.r3.channels = c1 a1.sinks.k1.channel = c1 ### end ### ### C 配置文件 avro_source_hdfs_sink.conf # 定义agent名, source channel sink的名称 a1.sources = r1 a1.sinks = k1 a1.channels = c1 # 定义source a1.sources.r1.type = avro a1.sources.r1.bind = slave3 a1.sources.r1.port = 41414 # 添加时间拦截器 a1.sources.r1.interceptors = i1 a1.sources.r1.interceptors.i1.type = org.apache.flume.interceptor.TimestampInterceptor$Builder # 定义channels a1.channels.c1.type = memory a1.channels.c1.capacity = 20000 a1.channels.c1.transactionCapacity = 10000 # 定义sink a1.sinks.k1.type = hdfs a1.sinks.k1.hdfs.path = hdfs://master:9000/source/logs/%{type}/%Y%m%d a1.sinks.k1.hdfs.filePrefix = events a1.sinks.k1.hdfs.fileType = DataStream a1.sinks.k1.hdfs.writeFormat = Text # 时间类型 # a1.sinks.k1.hdfs.useLocalTimeStamp = true # 生成的文件不按条数生成 a1.sinks.k1.hdfs.rollCount = 0 # 生成的文件不按时间生成 a1.sinks.k1.hdfs.rollInterval = 30 # 生成的文件按大小生成 a1.sinks.k1.hdfs.rollSize = 10485760 # 批量写入HDFS的个数 a1.sinks.k1.hdfs.batchSize = 20 # flume操作hdfs的线程数(包括新建，写入等) a1.sinks.k1.hdfs.threadsPoolSize = 10 # 操作hdfs超时时间 a1.sinks.k1.hdfs.callTimeout = 30000 # 组装source channel sink a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1 ### end ### ## 先启动第二级的slave2 bin/flume-ng agent -c conf -f conf/avro_source_hdfs_sink.conf -name a1 -Dflume.root.logger=DEBUG,console ## 再启动一级的slave1 bin/flume-ng agent -c conf -f conf/exec_source_avro_sink.conf -name a1 -Dflume.root.logger=DEBUG,console # 启动成功后，slave2会出现类似：CONNECTED: /192.168.112.11:56404 # 模拟数据写入. while true; do echo "access.. `date` " >>/root/logs1/access.log;sleep 1;done while true; do echo "nginx.. `date` " >>/root/logs1/nginx.log;sleep 1;done while true; do echo "web.. `date` " >>/root/logs1/web.log;sleep 1;done # 查看hdfs上采集成功。
今天的练习完成，成功了。