尚硅谷大数据技术之Flume第4章 案例实操

4.1 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)先将rpm软件包(xinetd-2.3.14-40.el6.x86_64.rpm、telnet-0.17-48.el6.x86_64.rpm和telnet-server-0.17-48.el6.x86_64.rpm)拷入Linux系统。执行RPM软件包安装命令:

[atguigu@hadoop102 software]$ sudo rpm -ivh xinetd-2.3.14-40.el6.x86_64.rpm

[atguigu@hadoop102 software]$ sudo rpm -ivh telnet-0.17-48.el6.x86_64.rpm

[atguigu@hadoop102 software]$ sudo rpm -ivh telnet-server-0.17-48.el6.x86_64.rpm

2)在flume目录下创建job文件夹,并在job文件夹下创建Flume Agent配置文件flume_telnet.conf

# Name the components on this agent

a1.sources = r1

a1.sinks = k1

a1.channels = c1

 

# Describe/configure the source

a1.sources.r1.type = netcat

a1.sources.r1.bind = localhost

a1.sources.r1.port = 44444

 

# 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

3)判断44444端口是否被占用

[atguigu@hadoop102 software]$ sudo netstat -tunlp | grep 44444

4)先开启flume先听端口

[atguigu@hadoop102 flume]$ bin/flume-ng agent –conf conf/ –name a1 –conf-file job/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

[atguigu@hadoop102 software]$ telnet localhost 44444

4.2 实时读取本地文件到HDFS

目标:实时监控hive日志,并上传到HDFS中

分步实现:

1)拷贝Hadoop相关jar到Flume的lib目录下(要学会根据自己的目录和版本查找jar包)

hadoop-auth-2.7.2.jar

commons-configuration-1.6.jar

hadoop-hdfs-2.7.2.jar

hadoop-common-2.7.2.jar

htrace-core-3.1.0-incubating.jar

commons-io-2.4.jar

提示:标红的jar为1.99版本flume必须引用的jar

2)创建flume_hdfs.conf文件

# Name the components on this agent

a2.sources = r2

a2.sinks = k2

a2.channels = c2

# Describe/configure the source

a2.sources.r2.type = exec

a2.sources.r2.command = tail -F /opt/module/hive/hive.log

a2.sources.r2.shell = /bin/bash -c

 

# Describe the sink

a2.sinks.k2.type = hdfs

a2.sinks.k2.hdfs.path = hdfs://hadoop102:9000/flume/%Y%m%d/%H

#上传文件的前缀

a2.sinks.k2.hdfs.filePrefix = logs-

#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

#最小冗余数

a2.sinks.k2.hdfs.minBlockReplicas = 1

 

# Use a channel which buffers events in memory

a2.channels.c2.type = memory

a2.channels.c2.capacity = 1000

a2.channels.c2.transactionCapacity = 100

 

# Bind the source and sink to the channel

a2.sources.r2.channels = c2

a2.sinks.k2.channel = c2

 

3)执行监控配置

[atguigu@hadoop102 flume]$ bin/flume-ng agent –conf conf/ –name a2 –conf-file job/flume_hdfs.conf

4)开启hive或者操作hive使其产生日志

4.3 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

a3.sources = r3

a3.sinks = k3

a3.channels = c3

 

# Describe/configure the source

a3.sources.r3.type = spooldir

a3.sources.r3.spoolDir =  tail -F /opt/module/flume/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)

 

# Describe the sink

a3.sinks.k3.type = hdfs

a3.sinks.k3.hdfs.path = hdfs://hadoop102:9000/flume/upload/%Y%m%d/%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 = 134217700

#文件的滚动与Event数量无关

a3.sinks.k3.hdfs.rollCount = 0

#最小冗余数

a3.sinks.k3.hdfs.minBlockReplicas = 1

 

# Use a channel which buffers events in memory

a3.channels.c3.type = memory

a3.channels.c3.capacity = 1000

a3.channels.c3.transactionCapacity = 100

 

# Bind the source and sink to the channel

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

[atguigu@hadoop102 flume]$ bin/flume-ng agent –conf conf/ –name a3 –conf-file job/flume-dir.conf

说明: 在使用Spooling Directory Source时

a.不要在监控目录中创建并持续修改文件

b.上传完成的文件会以.COMPLETED结尾

c.被监控文件夹每600毫秒扫描一次文件变动

4.4 单Flume多Channel、Sink

目标:使用flume-1监控文件变动,flume-1将变动内容传递给flume-2,flume-2负责存储到HDFS。同时flume-1将变动内容传递给flume-3,flume-3负责输出到local filesystem。

分步实现:

1)创建flume-1.conf,用于监控hive.log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/module/hive/hive.log

a1.sources.r1.shell = /bin/bash -c

 

# Describe the sink

a1.sinks.k1.type = avro

a1.sinks.k1.hostname = hadoop102

a1.sinks.k1.port = 4141

 

a1.sinks.k2.type = avro

a1.sinks.k2.hostname = hadoop102

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flume-1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 = hadoop102

a2.sources.r1.port = 4141

 

# Describe the sink

a2.sinks.k1.type = hdfs

a2.sinks.k1.hdfs.path = hdfs://hadoop102:9000/flume2/%Y%m%d/%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

#最小冗余数

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flume-1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 = hadoop102

a3.sources.r1.port = 4142

 

# Describe the sink

a3.sinks.k1.type = file_roll

a3.sinks.k1.sink.directory = /home/atguigu/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

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flume-3,flume-2,flume-1),同时产生文件变动并观察结果:

[atguigu@hadoop102 flume]$ bin/flume-ng agent –conf conf/ –name a3 –conf-file job/group-job1/flume-3.conf

[atguigu@hadoop102 flume]$ bin/flume-ng agent –conf conf/ –name a2 –conf-file job/group-job1/flume-2.conf

[atguigu@hadoop102 flume]$ bin/flume-ng agent –conf conf/ –name a1 –conf-file job/group-job1/flume-1.conf

4.5 多Flume汇总数据到单Flume

目标:flume-1监控文件hive.log,flume-2监控某一个端口的数据流,flume-1与flume-2将数据发送给flume-3,flume3将最终数据写入到HDFS。

分步实现:

1)创建flume-1.conf,用于监控hive.log文件,同时sink数据到flume-3:

# 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/module/hive/hive.log

a1.sources.r1.shell = /bin/bash -c

 

# Describe the sink

a1.sinks.k1.type = avro

a1.sinks.k1.hostname = hadoop102

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-2.conf,用于监控端口44444数据流,同时sink数据到flume-3:

# 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 = hadoop102

a2.sources.r1.port = 44444

 

# Describe the sink

a2.sinks.k1.type = avro

a2.sinks.k1.hostname = hadoop102

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-3.conf,用于接收flume-1与flume-2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 = hadoop102

a3.sources.r1.port = 4141

 

# Describe the sink

a3.sinks.k1.type = hdfs

a3.sinks.k1.hdfs.path = hdfs://hadoop102:9000/flume3/%Y%m%d/%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-3,flume-2,flume-1),同时产生文件变动并观察结果:

[atguigu@hadoop102 flume]$ bin/flume-ng agent –conf conf/ –name a3 –conf-file job/group-job2/flume-3.conf

[atguigu@hadoop102 flume]$ bin/flume-ng agent –conf conf/ –name a2 –conf-file job/group-job2/flume-2.conf

[atguigu@hadoop102 flume]$ bin/flume-ng agent –conf conf/ –name a1 –conf-file job/group-job2/flume-1.conf

提示:测试时记得启动hive产生一些日志,同时使用telnet向44444端口发送内容,如:

[atguigu@hadoop102 hive]$ bin/hive

[atguigu@hadoop102 flume]$ telnet hadoop102 44444

 

本教程由尚硅谷教育大数据研究院出品,如需转载请注明来源,欢迎大家关注尚硅谷公众号(atguigu)了解更多。


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