flume安装并整合kafka

官方文档：http://flume.apache.org/FlumeUserGuide.html
参考图书：Flume 构建高可用、可拓展的海量日志采集系统
参考文档：http://www.aboutyun.com/forum.php?mod=viewthread&tid=20699

kafka集群部署：https://blog.51cto.com/13323775/2063420

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/hadoop

source /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***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***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
[[e-mail] 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

参考文档：

官方文档：http://flume.apache.org/FlumeUserGuide.html
图书：Flume 构建高可用、可拓展的海量日志采集系统
flume常见配置：http://blog.csdn.net/sang1203/article/details/51474628
flume安装与使用：http://www.aboutyun.com/forum.php?mod=viewthread&tid=20699

转载于:https://blog.51cto.com/13323775/2063751