Flum

大数据技术之Flume

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。

2) Flume基于流式架构，容错性强，也很灵活简单。

3) 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

 

2.1、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。

2.2、Channel

用于桥接Sources和Sinks，类似于一个队列。

2.3、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)。

2.4、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

4.1、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

 

4.2、案例

4.2.1、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】

 

2) 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

3) 判断44445端口是否被占用

$ netstat -tunlp | grep 44445

4) 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

5) 使用telnet工具向本机的44444端口发送内容

$ telnet bigdata112 44445

4.2.2、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

3) 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

4.2.3、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

分步实现：

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

2) 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

4.2.4、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

分步实现：

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

 

2) 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

 

3) 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。

4) 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

4.2.5、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。

分步实现：

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

 

2) 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

 

3) 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

 

4) 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

b) 在/opt/Andy 中追加666666

4.2.6、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