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flume如何安装并整合kafka
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flume
Flume agent之间的通信（参考图书）
flume内置了专门的RPC sink-source对来处理agent之间的数据传输。 source是负责接收数据到Flume Agent的组件。包括Avro Source、Thrift source 、HTTP Source、Spooling Directory Source、Syslog Source、Exec Source、JMS Source等。 channel是位于source和sink之间的缓冲区，是保证数据不丢失的关键。 sink从Channel中读取事件，每一个sink只能从一个Channel钟读取事件，必须给每一个sink配置Channel，否则会从agent中移除。
安装flume
下载安装
cd /data/
wget http://mirrors.hust.edu.cn/apache/flume/1.8.0/apache-flume-1.8.0-bin.tar.gz
tar axf apache-flume-1.8.0-bin.tar.gz
cd apache-flume-1.8.0-bin
修改环境变量
vim /etc/profile
#FLUSM
export FLUME_HOME=/data/apache-flume-1.8.0-bin
export PATH=$PATH:${FLUME_HOME}/bin
export HADOOP_HOME=/data/hadoopsource /etc/profile
修改配置文件
cd ${FLUME_HOME}/conf/
cp flume-env.sh.template flume-env.sh
修改 flume-env.sh
export JAVA_HOME=/usr/local/jdk export JAVA_OPTS="-Xms100m -Xmx2000m -Dcom.sun.management.jmxremote" export HADOOP_HOME=/data/hadoop
验证安装
flume-ng version
使用flume
单节点agent传输信息
cd ${FLUME_HOME}/conf/
添加配置文件
vim avro.conf
#Name the components on this agent agent.sources = avroSrc agent.channels = avroChannel #Describe/configure the source agent.sources.avroSrc.type = netcat agent.sources.avroSrc.bind = localhost agent.sources.avroSrc.port = 62000 #Describe the sink agent.sinks.avroSink.type = logger #Use a channel that buffers events in memory agent.channels.avroChannel.type = memory agent.channels.avroChannel.capacity = 1000 agent.channels.avroChannel.transactionCapacity = 100 #Bind the source and sink to the channel agent.sinks = avroSink agent.sources.avroSrc.channels = avroChannel agent.sinks.avroSink.channel = avroChannel
“#测试agent.sources.avroSrc.type用avro，然后报错
#org.apache.avro.AvroRuntimeException: Excessively large list #allocation request detected: 1863125517 items! Connection #closed”
运行flume agent
flume-ng agent -f /data/apache-flume-1.8.0-bin/conf/avro.conf -n agent -Dflume.root.logger=INFO,console
使用Telnet连接测试
telnet localhost 6200
查看
exec监控本地文件
cd ${FLUME_HOME}/conf/
添加配置文件
vim exec.conf
#example.conf: A single-node Flume configuration #Name the components on this agent agentexec.sources = avroexec agentexec.sinks = sinkexec agentexec.channels = channelexec #Describe/configure the sources #Describe/configure the source agentexec.sources.avroexec.bind = localhost agentexec.sources.avroexec.port = 630000 agentexec.sources.avroexec.type = exec agentexec.sources.avroexec.command = tail -F /tmp/testexec.log #Describe the sink agentexec.sinks.sinkexec.type = logger #Use a channel which buffers events in memory agentexec.channels.channelexec.type = memory agentexec.channels.channelexec.capacity = 100000 agentexec.channels.channelexec.transactionCapacity = 10000 #Bind the source and sink to the channel agentexec.sources.avroexec.channels = channelexec agentexec.sinks.sinkexec.channel = channelexec
运行flume agent
flume-ng agent -f /data/apache-flume-1.8.0-bin/conf/exec.conf --name agentexec -Dflume.root.logger=INFO,console
测试
尴尬，只获取到了一部分（暂时没有占到解决方法）
spooldir整合kafka监控日志
前提：安装kafka集群
cd ${FLUME_HOME}/conf/
添加配置文件
vim single_agent.conf
#agent name a1 a1.sources = source1 a1.channels = channel1 a1.sinks = sink1 #set source #“测试使用将数据放在了/tmp目录下，注意设置” a1.sources.source1.type = spooldir a1.sources.source1.spoolDir=/tmp/spooldir a11.sources.source1.fileHeader = false #set sink a1.sinks.sink1.type = org.apache.flume.sink.kafka.KafkaSink a1.sinks.sink1.kafka.bootstrap.servers = master:9092,slave1:9092,slave2:9092 a1.sinks.sink1.topic= spooldir #set channel #“测试使用将数据放在了/tmp目录下，注意设置” a1.channels.channel1.type = file a1.channels.channel1.checkpointDir = /tmp/flume_data/checkpoint a1.channels.channel1.dataDirs= /tmp/flume_data/data #bind a1.sources.source1.channels = channel1 a1.sinks.sink1.channel = channel1
创建文件存放目录
mkdir -pv /tmp/spooldir mkdir -pv /tmp/flume_data/checkpoint mkdir -pv /tmp/flume_data/data
（所有节点）启动kafka集群
kafka-server-start.sh /data/kafka_2.11-1.0.0/config/server.properties
创建kafka的topic
kafka-topics.sh --zookeeper master:2181,slave1:2181,slave2:2181 --create --topic spooldir --replication-factor 1 --partitions 3
查看topic
kafka-topics.sh --list --zookeeper master:2181,slave1:2181,slave2:2181
创建kafka的consumer
kafka-console-consumer.sh --zookeeper master:2181,slave1:2181,slave2:2181 --topic spooldir --from-beginning
（新窗口）启动flume的agent
flume-ng agent -f /data/apache-flume-1.8.0-bin/conf/single_agent.conf --name a1 -Dflume.root.logger=INFO,console
写入测试
[root@master conf]# echo "hello ,test flume spooldir source" >> /tmp/spooldir/spool.txt
flume-ng信息
kafka信息
将日志信息写入hbase
前提：安装hbase集群
cd ${FLUME_HOME}/conf/
mkdir hbase && cd hbase
添加配置文件，这里需要两个agent端
hbase-back.conf用于收集本地数据，hbase-front.conf用于将数据写入hbase
vim hbase-back.conf
agent.sources =backsrc agent.channels=memoryChannel agent.sinks =remotesink #Describe the sources agent.sources.backsrc.type = exec agent.sources.backsrc.command = tail -F /tmp/test/data/data.txt agent.sources.backsrc.checkperiodic = 1000 agent.sources.backsrc.channels=memoryChannel #Describe the channels agent.channels.memoryChannel.type = memory agent.channels.memoryChannel.keep-alive = 30 agent.channels.memoryChannel.capacity = 1000 agent.channels.memoryChannel.transactionCapacity = 1000 #Describe the sinks agent.sinks.remotesink.type = avro agent.sinks.remotesink.hostname = master agent.sinks.remotesink.port = 9999 agent.sinks.remotesink.channel= memoryChannel
vim hbase-front.conf
agent.sources = frontsrc agent.channels = memoryChannel agent.sinks = fileSink #Describe the sources agent.sources.frontsrc.type = avro agent.sources.frontsrc.bind = master agent.sources.frontsrc.port = 9999 agent.sources.frontsrc.channels = memoryChannel #Describe the channels agent.channels.memoryChannel.type = memory agent.channels.memoryChannel.keep-alive = 30 agent.channels.memoryChannel.capacity = 1000 agent.channels.memoryChannel.transactionCapacity =1000 #Describe the sinks agent.sinks.fileSink.type = hbase agent.sinks.fileSink.channel=memoryChannel agent.sinks.fileSink.table = access_log agent.sinks.fileSink.columnFamily = t agent.sinks.fileSink.batchSize= 50 agent.sinks.fileSink.serializer = org.apache.flume.sink.hbase.RegexHbaseEventSerializer agent.sinks.fileSink.zookeeperQuorum = master:2181,slave1:2181,slave2:2181 agent.sinks.fileSink.znodeParent = /hbase agent.sinks.fileSink.timeout = 90000
创建本地文件和目录
mkdir -pv /tmp/test/data && touch /tmp/test/data/data.txt
创建hbase中的表
hbase shell
创建表
create 'access_log','t'
查看
list
启动back agent
flume-ng agent -f /data/apache-flume-1.8.0-bin/conf/hbase/hbase-back.conf --name agent -Dflume.root.logger=INFO,console
启动后会报错
18/01/22 22:29:28 WARN sink.AbstractRpcSink: Unable to create Rpc client using hostname: 192.168.3.58, port: 9999
org.apache.flume.FlumeException: NettyAvroRpcClient { host: master, port: 9999 }: RPC connection error
这是因为avro连接没有完成，现在只启动了sink端，没有source端，等启动了front后就会显示连接上了
启动front agent
flume-ng agent -f /data/apache-flume-1.8.0-bin/conf/hbase/hbase-front.conf --name agent -Dflume.root.logger=INFO,console
向本地文件中追加内容，然后在hbase中查看
echo "hello ,test flush to hbase">>/tmp/test/data/data.txt
写入的过程中两个agent不会打印日志
查看hbase中的数据
hbase shell scan "access_log"
flume向hbase中写入日志会有一定时间的延迟
将日志写入hadoop
原理和写入hbase一样，理解了hbase写入流程就很好理解写入其它服务了，详细配置参考官方文档。
前提：安装hadoop集群
cd ${FLUME_HOME}/conf/
mkdir hdfs && cd hdfs
添加配置文件，这里需要两个agent端
hadoop-back.conf用于收集本地数据，hadoop-front.conf用于将数据写入hadoop
vim hadoop-back.conf
#Namethe components hadoop.sources= backsrc hadoop.sinks= fileSink hadoop.channels= memoryChannel #Source hadoop.sources.backsrc.type= spooldir hadoop.sources.backsrc.spoolDir= /tmp/data/hadoop hadoop.sources.backsrc.channels= memoryChannel hadoop.sources.backsrc.fileHeader = true #Channel hadoop.channels.memoryChannel.type= memory hadoop.channels.memoryChannel.keep-alive = 30 hadoop.channels.memoryChannel.capacity = 1000 hadoop.channels.memoryChannel.transactionCapacity = 1000 #Sink hadoop.sinks.fileSink.type= avro hadoop.sinks.fileSink.hostname= master hadoop.sinks.fileSink.port= 10000 hadoop.sinks.fileSink.channel= memoryChannel
vim hadoop-front.conf
#Namethe components hadoop.sources= frontsrc hadoop.channels= memoryChannel hadoop.sinks= remotesink #Source hadoop.sources.frontsrc.type= avro hadoop.sources.frontsrc.bind= master hadoop.sources.frontsrc.port= 10000 hadoop.sources.frontsrc.channels= memoryChannel #Channel hadoop.channels.memoryChannel.type= memory hadoop.channels.memoryChannel.keep-alive = 30 hadoop.channels.memoryChannel.capacity = 1000 hadoop.channels.memoryChannel.transactionCapacity =1000 #Sink hadoop.sinks.remotesink.type= hdfs hadoop.sinks.remotesink.hdfs.path=hdfs://master/flume hadoop.sinks.remotesink.hdfs.rollInterval = 0 hadoop.sinks.remotesink.hdfs.idleTimeout = 10000 hadoop.sinks.remotesink.hdfs.fileType= DataStream hadoop.sinks.remotesink.hdfs.writeFormat= Text hadoop.sinks.remotesink.hdfs.threadsPoolSize = 20 hadoop.sinks.remotesink.channel= memoryChannel
创建本地目录并修改权限
mkdir -pv /tmp/data/hadoop && chmod -R 777 /tmp/data/
创建hdfs中的目录并修改权限
hadoop fs -mkdir /flume hadoop fs -chmod 777 /flume hadoop fs -ls /
向本地目录中写入文件
echo "hello, test hadoop" >> /tmp/data/hadoop/hadoop.log echo "hello, test flume" >> /tmp/data/hadoop/flume.log echo "hello, test helloworld" >> /tmp/data/hadoop/helloworld.log
查看hdfs中的文件和文件信息
hadoop fs -ls /flume hadoop fs -cat /flume/FlumeData.1516634328510.tmp
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