flume安装与使用

日志采集框架Flume

Flume介绍

  • 概述

    Flume是一个分布式、可靠、和高可用的海量日志采集、聚合和传输的系统。

    Flume可以采集文件,socket数据包、文件、文件夹、kafka等各种形式源数据,又可以将采集到的数据(下沉sink)输出到HDFS、hbase、hive、kafka等众多外部存储系统中

  • 运行机制

    Flume分布式系统最核心的角色是agent,flume采集系统就是由一个个agent所连接起来而成

    每一个agent相当于一个数据传递员,内部有三个组件:

    • Source:采集组件,用于跟数据源对接,获取数据
    • Sink:下沉组件,用于往下一级agent传递数据或者往最终存储系统传递数据
    • Channel:传输通道组件,用于从source将数据传递到sink
  • 采集系统结构图

    • 简单结构

    • 复杂结构

      多级agent之间串联

Flume实战案例

安装部署
  • 第一步:下载解压修改配置文件

    Flume的安装非常简单,只需要解压即可,当然,前提是已有hadoop环境

    # 上传安装包到数据源所在节点上 这里采用在第三台机器来进行安装 软件目录 => flume-ng-1.6.0-cdh5.14.0.tar.gz
    tar -zxvf flume-ng-1.6.0-cdh5.14.0.tar.gz -C ../servers/
    cd ../servers/apache-flume-1.6.0-cdh5.14.0-bin/conf/
    cp flume-env.sh.template flume-env.sh
    vim flume-env.sh #只添加一个java环境就可以了
      export JAVA_HOME=/export/servers/jdk1.8.0_141
  • 第二步:开发配置文件

    # 根据数据采集的需求配置采集方案,描述在配置文件中(文件名可任意自定义)
    # 配置我们的网络收集的配置文件
    # 在flume的conf目录下新建一个配置文件(采集方案)
    vim /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf/netcat-logger.conf
        # 定义这个agent中各组件的名字
        a1.sources = r1
        a1.sinks = k1
        a1.channels = c1
    
        # 描述和配置source组件:r1
        a1.sources.r1.type = netcat
        a1.sources.r1.bind = 192.168.52.120
        a1.sources.r1.port = 44444
    
        # 描述和配置sink组件:k1
        a1.sinks.k1.type = logger
    
        # 描述和配置channel组件,此处使用是内存缓存的方式
        a1.channels.c1.type = memory
        a1.channels.c1.capacity = 1000
        a1.channels.c1.transactionCapacity = 100
    
        # 描述和配置source  channel   sink之间的连接关系
        a1.sources.r1.channels = c1
        a1.sinks.k1.channel = c1
  • 启动配置文件

    指定采集方案配置文件,在相应的节点上启动flume agent

    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin
    bin/flume-ng agent -c conf -f conf/netcat-logger.conf -n a1 -Dflume.root.logger=INFO,console
    # -c conf 指定flume自身的配置文件所在目录
    # -f conf/netcat-logger.conf 指定所描述的采集方案
    # -n a1 指定这个agent的名字
  • 安装telent准备测试

    在node02上安装telnet客户端用于模拟数据的发送

    yum -y install telnet
    telnet  node03  44444   # 使用telnet模拟数据发送

采集案例
采集目录到HDFS

某服务器的特定目录下会不断产生新的文件,每当有新文件出现,就需要把文件采集到HDFS中去

  • 根据需求,首先定义以下3大要素

    • 数据源组件,即source -- 监控文件目录:spooldirspooldir特性:
      • 监视一个目录,只要目录中出现新文件,就会采集文件中的内容
      • 采集完成的文件,会被agent自动添加一个后缀:COMPLETED
      • 所监视的目录中不允许重复出现相同文件名的文件
    • 下沉组件,妈sink -- HDFS文件系统:hdfs sink
    • 通道组件,妈channel -- 可用file channel 也可以用内存 memory channel
  • flume配置文件开发

    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
    mkdir -p /export/servers/dirfile
    vim spooldir.conf
        # 定义agent的组件名字
        a1.sources=sr1
        a1.sinks=sk1
        a1.channels=scn1
    
        # 配置数据源source
        a1.sources.sr1.type=spooldir
        a1.sources.sr1.spoolDir=/export/servers/dirfile
        a1.sources.sr1.fileHeader=true
    
        # 配置下沉组件sink
        a1.sinks.sk1.type=hdfs
        a1.sinks.sk1.channel=scn1
        # hdfs目录路径
        a1.sinks.sk1.hdfs.path=hdfs://node01:8020/spooldir/files/%y-%m-%d/%H%M/
        # 写入hdfs的文件名前缀 可以使用flume提供的日期及%{host}表达式
        a1.sinks.sk1.hdfs.filePrefix=events-
        # 表示到了需要触发的时间时,是否要更新文件夹,true:表示要
        a1.sinks.sk1.hdfs.round=true
        # 表示每隔value分钟改变一次(在0~24之间)
        a1.sinks.sk1.hdfs.roundValue=10
        # 切换文件的时候的时间单位是分钟
        a1.sinks.sk1.hdfs.roundUnit=minute
        # 多久时间后close hdfs文件。单位是秒,默认30秒。设置为0的话表示不根据时间close hdfs文件
        a1.sinks.sk1.hdfs.rollInterval=3
        # 文件大小超过一定值后,close文件。默认值1024,单位是字节。设置为0的话表示不基于文件大小,134217728表 示128m,决定了多大块可以切一个文件。
        a1.sinks.sk1.hdfs.rollSize=134217728
        # 写入了多少个事件后close文件。默认值是10个。设置为0的话表示不基于事件个数
        a1.sinks.sk1.hdfs.rollCount=0
        # 批次数,HDFS Sink每次从Channel中拿的事件个数。默认值100
        a1.sinks.sk1.hdfs.batchSize=100
        # 使用本地时间戳
        a1.sinks.sk1.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.sk1.hdfs.fileType=DataStream
    
        # 配置通道channel
        a1.channels.scn1.type=memory
        a1.channels.scn1.capacity=1000
        a1.channels.scn1.transactionCapacity=100
    
    bin/flume-ng agent -c ./conf/ -f ./conf/spooldir.conf -n a1 -Dflume.root.logger=INFO,console # 运行flume
采集文件到HDFS

比如业务系统使用Log4j生成的日志,日志内容不断增加,需要把追加到日志文件中的数据实时采集到hdfs

  • 根据需求,首先定义以下3大要素
    • 采集源,即source——监控文件内容更新 : exec ‘tail -F file’
    • 下沉目标,即sink——HDFS文件系统 : hdfs sink
    • Source和sink之间的传递通道——channel,可用filechannel 也可以用 内存channel
  • 定义flume的配置文件

    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
    vim tail-file.conf
        agent1.sources = source1
        agent1.sinks = sink1
        agent1.channels = channel1
    
        # Describe/configure tail -F source1
        agent1.sources.source1.type = exec
        agent1.sources.source1.command = tail -F /export/servers/taillogs/access_log
        agent1.sources.source1.channels = channel1
    
        #configure host for source
        #agent1.sources.source1.interceptors = i1
        #agent1.sources.source1.interceptors.i1.type = host
        #agent1.sources.source1.interceptors.i1.hostHeader = hostname
    
        # Describe sink1
        agent1.sinks.sink1.type = hdfs
        #a1.sinks.k1.channel = c1
        agent1.sinks.sink1.hdfs.path = hdfs://node01:8020/weblog/flume-collection/%y-%m-%d/%H-%M
        agent1.sinks.sink1.hdfs.filePrefix = access_log
        agent1.sinks.sink1.hdfs.maxOpenFiles = 5000
        agent1.sinks.sink1.hdfs.batchSize= 100
        agent1.sinks.sink1.hdfs.fileType = DataStream
        agent1.sinks.sink1.hdfs.writeFormat =Text
        agent1.sinks.sink1.hdfs.rollSize = 102400
        agent1.sinks.sink1.hdfs.rollCount = 1000000
        agent1.sinks.sink1.hdfs.rollInterval = 60
        agent1.sinks.sink1.hdfs.round = true
        agent1.sinks.sink1.hdfs.roundValue = 10
        agent1.sinks.sink1.hdfs.roundUnit = minute
        agent1.sinks.sink1.hdfs.useLocalTimeStamp = true
    
        # Use a channel which buffers events in memory
        agent1.channels.channel1.type = memory
        agent1.channels.channel1.keep-alive = 120
        agent1.channels.channel1.capacity = 500000
        agent1.channels.channel1.transactionCapacity = 600
    
        # Bind the source and sink to the channel
        agent1.sources.source1.channels = channel1
        agent1.sinks.sink1.channel = channel1
    
    bin/flume-ng agent -c conf -f conf/tail-file.conf -n agent1  -Dflume.root.logger=INFO,console #启动Flume
    # 开发shell脚本定时追加文件内容
    mkdir -p /export/servers/shells/
    cd /export/servers/shells/
        vim tail-file.sh
        #!/bin/bash
        while true
        do
         date >> /export/servers/taillogs/access_log;
    
两个agent级联

第一个agent负责收集文件当中的数据,通过网络发送到第二个agent当中去,第二个agent负责接收第一个agent发送的数据,并将数据保存到hdfs上面去

第一步:node02安装flume
cd /export/servers
scp -r apache-flume-1.6.0-cdh5.14.0-bin/ node02:$PWD
第二步:node02配置flume配置文件
cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
vim tail-avro-avro-logger.conf
    ##################
    # Name the components on this agent
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1
    # Describe/configure the source
    a1.sources.r1.type = exec
    a1.sources.r1.command = tail -F /export/servers/taillogs/access_log
    a1.sources.r1.channels = c1
    # Describe the sink
    ##sink端的avro是一个数据发送者
    a1.sinks = k1
    a1.sinks.k1.type = avro
    a1.sinks.k1.channel = c1
    a1.sinks.k1.hostname = 192.168.52.120
    a1.sinks.k1.port = 4141
    a1.sinks.k1.batch-size = 10
    # Use a channel which buffers events in memory
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100
    # Bind the source and sink to the channel
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1
第三步:node02开发脚本文件往文件写入数据
# 直接把node03的脚本拷贝至node02
cd /export/servers
scp -r shells/ taillogs/ node02:$PWD
第四步node03开发Flume配置文件
# 在node03机器上开发flume的配置文件
cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
vim avro-hdfs.conf #配置如下
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source
##source中的avro组件是一个接收者服务
a1.sources.r1.type = avro
a1.sources.r1.channels = c1
a1.sources.r1.bind = 192.168.52.120
a1.sources.r1.port = 4141
# Describe the sink
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path = hdfs://node01:8020/avro/hdfs/%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
第五步顺序启动
# node03机器启动flume进程
cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin
bin/flume-ng agent -c conf -f conf/avro-hdfs.conf -n a1  -Dflume.root.logger=INFO,console  

# node02机器启动flume进程
cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/
bin/flume-ng agent -c conf -f conf/tail-avro-avro-logger.conf -n a1  -Dflume.root.logger=INFO,console    

# node02机器启shell脚本生成文件
cd /export/servers/shells
sh tail-file.sh
更多source和sink组件

参见:http://archive.cloudera.com/cdh5/cdh/5/flume-ng-1.6.0-cdh5.14.0/FlumeUserGuide.html

高可用Flume-NG配置案例failover

  • 角色分配

    名称 HOST 角色
    Agent1 node01 Web Server
    Collector1 node02 AgentMstr1
    Collector2 node03 AgentMstr2
  • node01安装配置flume

    # node03机器执行以下命令
    cd /export/servers
    scp -r apache-flume-1.6.0-cdh5.14.0-bin/ node01:$PWD
    scp -r shells/ taillogs/ node01:$PWD
    
    # node01机器配置agent的配置文件
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
    vim agent.conf #配置如下
    
    #agent1 name
    agent1.channels = c1
    agent1.sources = r1
    agent1.sinks = k1 k2
    #
    ##set gruop
    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 /export/servers/taillogs/access_log
    #
    agent1.sources.r1.interceptors = i1 i2
    agent1.sources.r1.interceptors.i1.type = static
    agent1.sources.r1.interceptors.i1.key = Type
    agent1.sources.r1.interceptors.i1.value = LOGIN
    agent1.sources.r1.interceptors.i2.type = timestamp
    #
    ## set sink1
    agent1.sinks.k1.channel = c1
    agent1.sinks.k1.type = avro
    agent1.sinks.k1.hostname = node02
    agent1.sinks.k1.port = 52020
    #
    ## set sink2
    agent1.sinks.k2.channel = c1
    agent1.sinks.k2.type = avro
    agent1.sinks.k2.hostname = node03
    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
    #
  • node02与node03配置flumecollection

    # node02机器修改配置文件
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
    vim collector.conf
    #set Agent name
    a1.sources = r1
    a1.channels = c1
    a1.sinks = k1
    #
    ##set channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100
    #
    ## other node,nna to nns
    a1.sources.r1.type = avro
    a1.sources.r1.bind = node02
    a1.sources.r1.port = 52020
    a1.sources.r1.interceptors = i1
    a1.sources.r1.interceptors.i1.type = static
    a1.sources.r1.interceptors.i1.key = Collector
    a1.sources.r1.interceptors.i1.value = node02
    a1.sources.r1.channels = c1
    #
    ##set sink to hdfs
    a1.sinks.k1.type=hdfs
    a1.sinks.k1.hdfs.path= hdfs://node01:8020/flume/failover/
    a1.sinks.k1.hdfs.fileType=DataStream
    a1.sinks.k1.hdfs.writeFormat=TEXT
    a1.sinks.k1.hdfs.rollInterval=10
    a1.sinks.k1.channel=c1
    a1.sinks.k1.hdfs.filePrefix=%Y-%m-%d
    
    # node03机器修改配置文件
    cd  /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
    vim collector.conf
    #set Agent name
    a1.sources = r1
    a1.channels = c1
    a1.sinks = k1
    #
    ##set channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100
    #
    ## other node,nna to nns
    a1.sources.r1.type = avro
    a1.sources.r1.bind = node03
    a1.sources.r1.port = 52020
    a1.sources.r1.interceptors = i1
    a1.sources.r1.interceptors.i1.type = static
    a1.sources.r1.interceptors.i1.key = Collector
    a1.sources.r1.interceptors.i1.value = node03
    a1.sources.r1.channels = c1
    #
    ##set sink to hdfs
    a1.sinks.k1.type=hdfs
    a1.sinks.k1.hdfs.path= hdfs://node01:8020/flume/failover/
    a1.sinks.k1.hdfs.fileType=DataStream
    a1.sinks.k1.hdfs.writeFormat=TEXT
    a1.sinks.k1.hdfs.rollInterval=10
    a1.sinks.k1.channel=c1
    a1.sinks.k1.hdfs.filePrefix=%Y-%m-%d
  • 顺序启动命令

    # node03机器上面启动flume
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin
    bin/flume-ng agent -n a1 -c conf -f conf/collector.conf -Dflume.root.logger=DEBUG,console
    
    # node02机器上面启动flume
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin
    bin/flume-ng agent -n a1 -c conf -f conf/collector.conf -Dflume.root.logger=DEBUG,console
    
    # node01机器上面启动flume
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin
    bin/flume-ng agent -n agent1 -c conf -f conf/agent.conf -Dflume.root.logger=DEBUG,console
    
    # node01机器启动文件产生脚本
    cd  /export/servers/shells
    sh tail-file.sh
    
  • FAILOVER测试

    • Collector1宕机,Collector2获取优先上传权限
    • 重启Collector1服务,Collector1重新获得优先上传的权限

Flume的负载均衡 load balancer

负载均衡是用于解决一台机器(一个进程)无法解决所有请求而产生的一种算法。Load balancing Sink Processor 能够实现 load balance 功能,如下图Agent1 是一个路由节点,负责将
Channel 暂存的 Event 均衡到对应的多个 Sink组件上,而每个 Sink 组件分别连接到一个独立的 Agent 上,示例配置,如下所示:

在此处我们通过三台机器来进行模拟flume的负载均衡

三台机器规划如下:

node01:采集数据,发送到node02和node03机器上去

node02:接收node01的部分数据

node03:接收node01的部分数据

  • 第一步:开发node01服务器的flume配置

    # node01服务器配置:
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
    vim load_banlancer_client.conf
    
    #agent name
    a1.channels = c1
    a1.sources = r1
    a1.sinks = k1 k2
    
    #set gruop
    a1.sinkgroups = g1
    
    #set channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100
    
    a1.sources.r1.channels = c1
    a1.sources.r1.type = exec
    a1.sources.r1.command = tail -F /export/servers/taillogs/access_log
    
    # set sink1
    a1.sinks.k1.channel = c1
    a1.sinks.k1.type = avro
    a1.sinks.k1.hostname = node02
    a1.sinks.k1.port = 52020
    
    # set sink2
    a1.sinks.k2.channel = c1
    a1.sinks.k2.type = avro
    a1.sinks.k2.hostname = node03
    a1.sinks.k2.port = 52020
    
    #set sink group
    a1.sinkgroups.g1.sinks = k1 k2
    
    #set failover
    a1.sinkgroups.g1.processor.type = load_balance
    a1.sinkgroups.g1.processor.backoff = true
    a1.sinkgroups.g1.processor.selector = round_robin
    a1.sinkgroups.g1.processor.selector.maxTimeOut=10000
  • 第二步:开发node02服务器的flume配置

    # node02服务器配置:
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
    vim load_banlancer_server.conf
    
    # Name the components on this agent
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1
    
    # Describe/configure the source
    a1.sources.r1.type = avro
    a1.sources.r1.channels = c1
    a1.sources.r1.bind = node02
    a1.sources.r1.port = 52020
    
    # 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
  • 第三步:开发node03服务器flume配置

    # node03服务器配置
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
    vim load_banlancer_server.conf
    
    # Name the components on this agent
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1
    
    # Describe/configure the source
    a1.sources.r1.type = avro
    a1.sources.r1.channels = c1
    a1.sources.r1.bind = node03
    a1.sources.r1.port = 52020
    
    # 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
  • 第四步:准备启动flume服务

    # 启动node03的flume服务
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin
    bin/flume-ng agent -n a1 -c conf -f conf/load_banlancer_server.conf -Dflume.root.logger=DEBUG,console
    
    # 启动node02的flume服务
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin
    bin/flume-ng agent -n a1 -c conf -f conf/load_banlancer_server.conf -Dflume.root.logger=DEBUG,console
    
    # 启动node01的flume服务
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin
    bin/flume-ng agent -n a1 -c conf -f conf/load_banlancer_client.conf -Dflume.root.logger=DEBUG,console
    
    # node01服务器运行脚本产生数据
    cd /export/servers/shells
    sh tail-file.sh

Flume案例一

把A、B 机器中的access.log、nginx.log、web.log 采集汇总到C机器上然后统一收集到hdfs中。

但是在hdfs中要求的目录为:

/source/logs/access/20180101/**

/source/logs/nginx/20180101/**

/source/logs/web/20180101/**

  • 采集端配置文件开发

    # node01与node02服务器开发flume的配置文件
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
    vim exec_source_avro_sink.conf
    
    # 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 /export/servers/taillogs/access.log
    a1.sources.r1.interceptors = i1
    a1.sources.r1.interceptors.i1.type = static
    ##  static拦截器的功能就是往采集到的数据的header中插入自己定## 义的key-value对
    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 /export/servers/taillogs/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 /export/servers/taillogs/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
    a1.sinks.k1.type = avro
    a1.sinks.k1.hostname = node03
    a1.sinks.k1.port = 41414
    
    # Use a channel which buffers events in memory
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 20000
    a1.channels.c1.transactionCapacity = 10000
    
    # Bind the source and sink to the channel
    a1.sources.r1.channels = c1
    a1.sources.r2.channels = c1
    a1.sources.r3.channels = c1
    a1.sinks.k1.channel = c1
    
  • 服务端配置文件开发

    # 在node03上面开发flume配置文件
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
    vim avro_source_hdfs_sink.conf
    
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1
    #定义source
    a1.sources.r1.type = avro
    a1.sources.r1.bind = 192.168.52.120
    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://192.168.52.100:8020/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 = 10000
    #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
  • 采集端文件生成脚本

    cd /export/servers/shells
    vim server.sh 
    
    #!/bin/bash
    while true
    do  
     date >> /export/servers/taillogs/access.log; 
     date >> /export/servers/taillogs/web.log;
     date >> /export/servers/taillogs/nginx.log;
      sleep 0.5;
    done
  • 顺序启动服务

    # node03启动flume实现数据收集
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin
    bin/flume-ng agent -c conf -f conf/avro_source_hdfs_sink.conf -name a1 -Dflume.root.logger=DEBUG,console
    
    # node01与node02启动flume实现数据监控
    cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin
    bin/flume-ng agent -c conf -f conf/exec_source_avro_sink.conf -name a1 -Dflume.root.logger=DEBUG,console
    
    # node01与node02启动生成文件脚本
    cd /export/servers/shells
    sh server.sh

转载于:https://www.cnblogs.com/winter-shadow/p/11444572.html

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