• Flume学习笔记

    目录

    一、Flume简介

      1. Flume提供一个分布式的，可靠的，对大数据量的日志进行高效收集、聚集、移动的服务，Flume只能在Unix环境下运行。
      1. Flume基于流式架构，容错性强，也很灵活简单。
      1. Flume、Kafka用来实时进行数据收集，Spark、Flink用来实时处理数据，impala用来实时查询。

    二、Flume角色

    1、Source

      用于采集数据，Source是产生数据流的地方，同时Source会将产生的数据流传输到Channel，这个有点类似于Java IO部分的Channel。

    2、Channel

      用于桥接Sources和Sinks，类似于一个队列。

    3、Sink

      从Channel收集数据，将数据写到目标源(可以是下一个Source，也可以是HDFS或者HBase)。

    4、Event

      传输单元，Flume数据传输的基本单元，以事件的形式将数据从源头送至目的地。

    三、Flume传输过程

      source监控某个文件或数据流，数据源产生新的数据，拿到该数据后，将数据封装在一个Event中，并put到channel后commit提交，channel队列先进先出，sink去channel队列中拉取数据，然后写入到HDFS中。

    四、Flume部署及使用

    1、文件配置

    • 查询JAVA_HOME: echo $JAVA_HOME
    显示/opt/module/jdk1.8.0_144  /opt/module/jdk1.8.0_144
    • 安装Flume
    [itstar@bigdata113 software]$ tar -zxvf apache-flume1.8.0-bin.tar.gz -C /opt/module/
    • 改名：
    [itstar@bigdata113 conf]$ mv flume-env.sh.template flume-env.sh
    • flume-env.sh涉及修改项：
    export JAVA_HOME=/opt/module/jdk1.8.0_144

    2、案例

    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. 安装telnet工具【联网状态】
    yum -y install telnet

      【安装完成】

      1. 创建Flume Agent配置文件flume-telnet.conf
    #1.定义Agent ===> a1
a1.sources = r1
a1.sinks = k1
a1.channels = c1

#2.定义source
a1.sources.r1.type = netcat
a1.sources.r1.bind = bigdata112
a1.sources.r1.port = 44445

#3.定义sink
a1.sinks.k1.type = logger

#4.定义channel
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

#5.双向链接
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
      1. 判断44445端口是否被占用
    netstat -tunlp | grep 44445
      1. 启动flume配置文件
    /opt/module/flume1.8.0/bin/flume-ng agent 
--conf /opt/module/flume1.8.0/conf/ 
--name a1 
--conf-file /opt/module/flume1.8.0/jobconf/flume-telnet.conf 
-Dflume.root.logger==INFO,console
      1. 使用telnet工具向本机的44444端口发送内容
    telnet bigdata112 44445
    2.2、案例二：实时读取本地文件到HDFS
      1. 创建flume-hdfs.conf文件
    # 1.定义agent的名字a2
a2.sources = r2
a2.sinks = k2
a2.channels = c2

#2.定义Source
a2.sources.r2.type = exec
a2.sources.r2.command = tail -F /opt/Andy
a2.sources.r2.shell = /bin/bash -c

#3.定义sink
a2.sinks.k2.type = hdfs
a2.sinks.k2.hdfs.path = hdfs://bigdata111:9000/flume/%H
#上传文件的前缀
a2.sinks.k2.hdfs.filePrefix = Andy-
#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
#最小副本数
a2.sinks.k2.hdfs.minBlockReplicas = 1

# 4.定义Channel 
a2.channels.c2.type = memory
a2.channels.c2.capacity = 1000
a2.channels.c2.transactionCapacity = 100

# 5.链接
a2.sources.r2.channels = c2
a2.sinks.k2.channel = c2
      1. 执行监控配置
    /opt/module/flume1.8.0/bin/flume-ng agent 
--conf /opt/module/flume1.8.0/conf/ 
--name a2 
--conf-file /opt/module/flume1.8.0/jobconf/flume-hdfs.conf
    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

      1. 创建配置文件flume-dir.conf
    #1.定义Agent a3
a3.sources = r3
a3.sinks = k3
a3.channels = c3

# 2.定义Source
a3.sources.r3.type = spooldir
a3.sources.r3.spoolDir = /opt/module/flume1.8.0/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)

# 3.Sink
a3.sinks.k3.type = hdfs
a3.sinks.k3.hdfs.path = hdfs://bigdata111:9000/flume/%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 = 134217728
#文件的滚动与Event数量无关
a3.sinks.k3.hdfs.rollCount = 0
#最小副本数
a3.sinks.k3.hdfs.minBlockReplicas = 1

#4.定义Channel
a3.channels.c3.type = memory
a3.channels.c3.capacity = 1000
a3.channels.c3.transactionCapacity = 100

#5.链接
a3.sources.r3.channels = c3
a3.sinks.k3.channel = c3
      1. 执行测试：执行如下脚本后，请向upload文件夹中添加文件试试
    /opt/module/flume1.8.0/bin/flume-ng agent 
--conf /opt/module/flume1.8.0/conf/ 
--name a3 
--conf-file /opt/module/flume1.8.0/jobconf/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500毫秒扫描一次文件变动

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

      目标：使用flume1监控文件变动，flume1将变动内容传递给flume-2，flume-2负责存储到HDFS。同时flume1将变动内容传递给flume-3，flume-3负责输出到local

      1. 创建flume1.conf，用于监控某文件的变动，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 /opt/Andy
a1.sources.r1.shell = /bin/bash -c

# Describe the sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = bigdata111
a1.sinks.k1.port = 4141

a1.sinks.k2.type = avro
a1.sinks.k2.hostname = bigdata111
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
      1. 创建flume-2.conf，用于接收flume1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 = bigdata111
a2.sources.r1.port = 4141

# Describe the sink
a2.sinks.k1.type = hdfs
a2.sinks.k1.hdfs.path = hdfs://bigdata111:9000/flume2/%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
#最小副本数
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
      1. 创建flume-3.conf，用于接收flume1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 = bigdata111
a3.sources.r1.port = 4142

# Describe the sink
a3.sinks.k1.type = file_roll
#备注：此处的文件夹需要先创建好
a3.sinks.k1.sink.directory = /opt/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。

      1. 执行测试：分别开启对应flume-job（依次启动flume1，flume-2，flume-3），同时产生文件变动并观察结果：
    $ bin/flume-ng agent --conf conf/ --name a1 --conf-file jobconf/flume1.conf

$ bin/flume-ng agent --conf conf/ --name a2 --conf-file jobconf/flume2.conf

$ bin/flume-ng agent --conf conf/ --name a3 --conf-file jobconf/flume3.conf
    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：flume11监控文件hive.log，flume-22监控某一个端口的数据流，flume11与flume-22将数据发送给flume-33，flume33将最终数据写入到HDFS。

      1. 创建flume11.conf，用于监控hive.log文件，同时sink数据到flume-33：
    # 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 /opt/Andy
a1.sources.r1.shell = /bin/bash -c

# Describe the sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = bigdata111
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
      1. 创建flume-22.conf，用于监控端口44444数据流，同时sink数据到flume-33：
    # 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 = bigdata111
a2.sources.r1.port = 44444

# Describe the sink
a2.sinks.k1.type = avro
a2.sinks.k1.hostname = bigdata111
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
      1. 创建flume-33.conf，用于接收flume11与flume-22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 = bigdata111
a3.sources.r1.port = 4141

# Describe the sink
a3.sinks.k1.type = hdfs
a3.sinks.k1.hdfs.path = hdfs://bigdata111:9000/flume3/%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
      1. 执行测试：分别开启对应flume-job（依次启动flume-33，flume-22，flume11），同时产生文件变动并观察结果：
    $ bin/flume-ng agent --conf conf/ --name a3 --conf-file jobconf/flume33.conf
$ bin/flume-ng agent --conf conf/ --name a2 --conf-file jobconf/flume22.conf
$ bin/flume-ng agent --conf conf/ --name a1 --conf-file jobconf/flume11.conf
    • 数据发送
    a)telnet bigdata111 44444    打开后发送5555555
在/opt/Andy 中追加666666
    2.6、案例五：Flume自定义拦截器
    • 时间戳拦截器，Timestamp.conf
    #定义agent名， source、channel、sink的名称
a4.sources = r1
a4.channels = c1
a4.sinks = k1

#具体定义source
a4.sources.r1.type = spooldir
a4.sources.r1.spoolDir = /opt/module/flume-1.8.0/upload

#具体定义channel
a4.channels.c1.type = memory
a4.channels.c1.capacity = 10000
a4.channels.c1.transactionCapacity = 100

#定义拦截器，为文件最后添加时间戳
a4.sources.r1.interceptors = i1
a4.sources.r1.interceptors.i1.type = org.apache.flume.interceptor.TimestampInterceptor$Builder

#具体定义sink
a4.sinks.k1.type = hdfs
a4.sinks.k1.hdfs.path = hdfs://bigdata111:9000/flume-interceptors/%H
a4.sinks.k1.hdfs.filePrefix = events-
a4.sinks.k1.hdfs.fileType = DataStream

#不按照条数生成文件
a4.sinks.k1.hdfs.rollCount = 0
#HDFS上的文件达到128M时生成一个文件
a4.sinks.k1.hdfs.rollSize = 134217728
#HDFS上的文件达到60秒生成一个文件
a4.sinks.k1.hdfs.rollInterval = 60

#组装source、channel、sink
a4.sources.r1.channels = c1
a4.sinks.k1.channel = c1

    启动命令

    /opt/module/flume-1.8.0/bin/flume-ng agent -n a4 
-f /opt/module/flume-1.8.0/jobconf/flume-interceptors.conf 
-c /opt/module/flume-1.8.0/conf 
-Dflume.root.logger=INFO,console
    • 主机名拦截器，Host.conf
    a1.sources= r1
a1.sinks = k1
a1.channels = c1
 
a1.sources.r1.type = exec
a1.sources.r1.channels = c1
a1.sources.r1.command = tail -F /opt/Andy
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = host

#参数为true时用IP192.168.1.111，参数为false时用主机名，默认为true
a1.sources.r1.interceptors.i1.useIP = false
a1.sources.r1.interceptors.i1.hostHeader = agentHost
 
a1.sinks.k1.type=hdfs
a1.sinks.k1.channel = c1
a1.sinks.k1.hdfs.path = hdfs://bigdata111:9000/flumehost/%H
a1.sinks.k1.hdfs.filePrefix = Andy_%{agentHost}
#往生成的文件加后缀名.log
a1.sinks.k1.hdfs.fileSuffix = .log
a1.sinks.k1.hdfs.fileType = DataStream
a1.sinks.k1.hdfs.writeFormat = Text
a1.sinks.k1.hdfs.rollInterval = 10
a1.sinks.k1.hdfs.useLocalTimeStamp = true
 
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
 
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

    启动命令：

    bin/flume-ng agent -c conf/ -f jobconf/host.conf -n a1 -Dflume.root.logger=INFO,console
    • UUID拦截器，uuid.conf
    a1.sources = r1
a1.sinks = k1
a1.channels = c1

a1.sources.r1.type = exec
a1.sources.r1.channels = c1
a1.sources.r1.command = tail -F /opt/Andy
a1.sources.r1.interceptors = i1
#type的参数不能写成uuid，得写具体，否则找不到类
a1.sources.r1.interceptors.i1.type = org.apache.flume.sink.solr.morphline.UUIDInterceptor$Builder
#如果UUID头已经存在,它应该保存
a1.sources.r1.interceptors.i1.preserveExisting = true
a1.sources.r1.interceptors.i1.prefix = UUID_

a1.sinks.k1.type = logger

a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
# bin/flume-ng agent -c conf/ -f jobconf/uuid.conf -n a1 -Dflume.root.logger==INFO,console
    • 查询替换拦截器，search.conf
    a1.sources = r1
a1.sinks = k1
a1.channels = c1

a1.sources.r1.type = exec
a1.sources.r1.channels = c1
a1.sources.r1.command = tail -F /opt/Andy
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = search_replace
a1.sources.r1.interceptors.i1.searchPattern = [0-9]+
a1.sources.r1.interceptors.i1.replaceString = itstar
a1.sources.r1.interceptors.i1.charset = UTF-8

a1.sinks.k1.type = logger

a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
# bin/flume-ng agent -c conf/ -f jobconf/search.conf -n a1 -Dflume.root.logger=INFO,console
    • 正则过滤拦截器，filter.conf
    a1.sources = r1
a1.sinks = k1
a1.channels = c1

a1.sources.r1.type = exec
a1.sources.r1.channels = c1
a1.sources.r1.command = tail -F /opt/Andy
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = regex_filter
a1.sources.r1.interceptors.i1.regex = ^A.*
#如果excludeEvents设为false,表示过滤掉不是以A开头的events。如果excludeEvents设为true，则表示过滤掉以A开头的events。
a1.sources.r1.interceptors.i1.excludeEvents = true

a1.sinks.k1.type = logger

a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
# bin/flume-ng agent -c conf/ -f jobconf/filter.conf -n a1 -Dflume.root.logger=INFO,console
    • 正则抽取拦截器，extractor.conf
    a1.sources = r1
a1.sinks = k1
a1.channels = c1

a1.sources.r1.type = exec
a1.sources.r1.channels = c1
a1.sources.r1.command = tail -F /opt/Andy
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = regex_extractor
a1.sources.r1.interceptors.i1.regex = hostname is (.*?) ip is (.*)
a1.sources.r1.interceptors.i1.serializers = s1 s2
a1.sources.r1.interceptors.i1.serializers.s1.name = cookieid
a1.sources.r1.interceptors.i1.serializers.s2.name = ip

a1.sinks.k1.type = logger

a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
# bin/flume-ng agent -c conf/ -f jobconf/extractor.conf -n a1 -Dflume.root.logger=INFO,console
    作者：落花桂
    　　　　 　　　　
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  • 原文地址：https://www.cnblogs.com/nthforsth/p/14402614.html
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