首先启动Flume任务,监控本机44444端口,服务端;
然后通过netcat工具向本机44444端口发送消息,客户端;
最后Flume将监听的数据实时显示在控制台。
[liujh@hadoop102 software]$ sudo yum install -y nc
[liujh@hadoop102 flume-telnet]$ sudo netstat -tunlp | grep 44444
功能描述:netstat命令是一个监控TCP/IP网络的非常有用的工具,它可以显示路由表、实际的网络连接以及每一个网络接口设备的状态信息。
基本语法:netstat [选项]
选项参数:
-t或–tcp:显示TCP传输协议的连线状况;
-u或–udp:显示UDP传输协议的连线状况;
-n或–numeric:直接使用ip地址,而不通过域名服务器;
-l或–listening:显示监控中的服务器的Socket;
-p或–programs:显示正在使用Socket的程序识别码(PID)和程序名称;
[liujh@hadoop102 flume]$ mkdir job
[liujh@hadoop102 flume]$ cd job/
在job文件夹下创建Flume Agent配置文件flume-netcat-logger.conf。
[liujh@hadoop102 job]$ touch flume-netcat-logger.conf
在flume-netcat-logger.conf文件中添加如下内容
[liujh@hadoop102 job]$ vim flume-netcat-logger.conf
添加内容如下:
# 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
配置文件来源于官方手册http://flume.apache.org/FlumeUserGuide.html
[liujh@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/flume-netcat-logger.conf -Dflume.root.logger=INFO,console
第二种写法:
[liujh@hadoop102 flume]$ bin/flume-ng agent -c conf/ -n a1 –f job/flume-netcat-logger.conf -Dflume.root.logger=INFO,console
参数说明:
–conf conf/ :表示配置文件存储在conf/目录
–name a1 :表示给agent起名为a1
–conf-file job/flume-netcat.conf :flume本次启动读取的配置文件是在job文件夹下的flume-telnet.conf文件。
-Dflume.root.logger==INFO,console :-D表示flume运行时动态修改flume.root.logger参数属性值,并将控制台日志打印级别设置为INFO级别。日志级别包括:log、info、warn、error。
[liujh@hadoop102 ~]$ nc localhost 44444
hello
liujh
实时监控Hive日志,并上传到HDFS中
将commons-configuration-1.6.jar、
hadoop-auth-2.7.2.jar、
hadoop-common-2.7.2.jar、
hadoop-hdfs-2.7.2.jar、
commons-io-2.4.jar、
htrace-core-3.1.0-incubating.jar
拷贝到/opt/module/flume/lib文件夹下。
[liujh@hadoop102 job]$ touch flume-file-hdfs.conf
注:要想读取Linux系统中的文件,就得按照Linux命令的规则执行命令。由于Hive日志在Linux系统中所以读取文件的类型选择:exec即execute执行的意思。表示执行Linux命令来读取文件。
[liujh@hadoop102 job]$ vim flume-file-hdfs.conf
添加如下内容
# 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/logs/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 = 60
#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
# 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
注意:
对于所有与时间相关的转义序列,Event Header中必须存在以 “timestamp”的key(除非hdfs.useLocalTimeStamp设置为true,此方法会使用TimestampInterceptor自动添加timestamp)。
a3.sinks.k3.hdfs.useLocalTimeStamp = true
[liujh@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/flume-file-hdfs.conf
[liujh@hadoop102 hadoop-2.7.2]$ sbin/start-dfs.sh
[liujh@hadoop103 hadoop-2.7.2]$ sbin/start-yarn.sh
[liujh@hadoop102 hive]$ bin/hive
hive (default)>
使用Flume监听整个目录的文件
创建一个文件
[liujh@hadoop102 job]$ touch flume-dir-hdfs.conf
打开文件
[liujh@hadoop102 job]$ vim flume-dir-hdfs.conf
添加如下内容
a3.sources = r3
a3.sinks = k3
a3.channels = c3
# Describe/configure the source
a3.sources.r3.type = spooldir
a3.sources.r3.spoolDir = /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 = 60
#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
# 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
[liujh@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/flume-dir-hdfs.conf
说明: 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
[liujh@hadoop102 flume]$ mkdir upload
向upload文件夹中添加文件
[liujh@hadoop102 upload]$ touch liujh.txt
[liujh@hadoop102 upload]$ touch liujh.tmp
[liujh@hadoop102 upload]$ touch liujh.log
[liujh@hadoop102 upload]$ ll
总用量 0
-rw-rw-r--. 1 liujh liujh 0 5月 20 22:31 liujh.log.COMPLETED
-rw-rw-r--. 1 liujh liujh 0 5月 20 22:31 liujh.tmp
-rw-rw-r--. 1 liujh liujh 0 5月 20 22:31 liujh.txt.COMPLETED
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。
在/opt/module/flume/job目录下创建group1文件夹
[liujh@hadoop102 job]$ cd group1/
在/opt/module/datas/目录下创建flume3文件夹
[liujh@hadoop102 datas]$ mkdir flume3
[liujh@hadoop102 group1]$ touch flume-file-flume.conf
[liujh@hadoop102 group1]$ vim flume-file-flume.conf
添加如下内容
# Name the components on this agent
a1.sources = r1
a1.sinks = k1 k2
a1.channels = c1 c2
# 将数据流复制给所有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/logs/hive.log
a1.sources.r1.shell = /bin/bash -c
# Describe the sink
# sink端的avro是一个数据发送者
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
注:Avro是由Hadoop创始人Doug Cutting创建的一种语言无关的数据序列化和RPC框架。
注:RPC(Remote Procedure Call)—远程过程调用,它是一种通过网络从远程计算机程序上请求服务,而不需要了解底层网络技术的协议。
[liujh@hadoop102 group1]$ touch flume-flume-hdfs.conf
[liujh@hadoop102 group1]$ vim flume-flume-hdfs.conf
添加如下内容
# Name the components on this agent
a2.sources = r1
a2.sinks = k1
a2.channels = c1
# Describe/configure the source
# source端的avro是一个数据接收服务
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
# 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
[liujh@hadoop102 group1]$ touch flume-flume-dir.conf
[liujh@hadoop102 group1]$ vim flume-flume-dir.conf
添加如下内容
# Name the components on this agent
a3.sources = r1
a3.sinks = k1
a3.channels = c2
# 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 = /opt/module/data/flume3
# Describe the channel
a3.channels.c2.type = memory
a3.channels.c2.capacity = 1000
a3.channels.c2.transactionCapacity = 100
# Bind the source and sink to the channel
a3.sources.r1.channels = c2
a3.sinks.k1.channel = c2
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。
[liujh@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group1/flume-flume-dir.conf
[liujh@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group1/flume-flume-hdfs.conf
[liujh@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group1/flume-file-flume.conf
[liujh@hadoop102 hadoop-2.7.2]$ sbin/start-dfs.sh
[liujh@hadoop103 hadoop-2.7.2]$ sbin/start-yarn.sh
[liujh@hadoop102 hive]$ bin/hive
hive (default)>
[liujh@hadoop102 flume3]$ ll
总用量 8
-rw-rw-r--. 1 liujh liujh 5942 5月 22 00:09 1526918887550-3
单Source、Channel多Sink(负载均衡)如图所示
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也负责存储到HDFS
[liujh@hadoop102 job]$ cd group2/
[liujh@hadoop102 group2]$ touch flume-netcat-flume.conf
[liujh@hadoop102 group2]$ vim flume-netcat-flume.conf
添加如下内容
# Name the components on this agent
a1.sources = r1
a1.channels = c1
a1.sinkgroups = g1
a1.sinks = k1 k2
# Describe/configure the source
a1.sources.r1.type = netcat
a1.sources.r1.bind = localhost
a1.sources.r1.port = 44444
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
# 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
# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinkgroups.g1.sinks = k1 k2
a1.sinks.k1.channel = c1
a1.sinks.k2.channel = c1
注:Avro是由Hadoop创始人Doug Cutting创建的一种语言无关的数据序列化和RPC框架。
注:RPC(Remote Procedure Call)—远程过程调用,它是一种通过网络从远程计算机程序上请求服务,而不需要了解底层网络技术的协议
[liujh@hadoop102 group2]$ touch flume-flume-console1.conf
[liujh@hadoop102 group2]$ vim flume-flume-console1.conf
添加如下内容
# 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 = logger
# 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
[liujh@hadoop102 group2]$ touch flume-flume-console2.conf
[liujh@hadoop102 group2]$ vim flume-flume-console2.conf
添加如下内容
# Name the components on this agent
a3.sources = r1
a3.sinks = k1
a3.channels = c2
# 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 = logger
# Describe the channel
a3.channels.c2.type = memory
a3.channels.c2.capacity = 1000
a3.channels.c2.transactionCapacity = 100
# Bind the source and sink to the channel
a3.sources.r1.channels = c2
a3.sinks.k1.channel = c2
[liujh@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group2/flume-flume-console2.conf -Dflume.root.logger=INFO,console
[liujh@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group2/flume-flume-console1.conf -Dflume.root.logger=INFO,console
[liujh@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group2/flume-netcat-flume.conf
$ nc localhost 44444
hadoop103上的Flume-1监控文件/opt/module/group.log,
hadoop102上的Flume-2监控某一个端口的数据流,
Flume-1与Flume-2将数据发送给hadoop104上的Flume-3,Flume-3将最终数据打印到控制台。
[liujh@hadoop102 module]$ xsync flume
在hadoop102、hadoop103以及hadoop104的/opt/module/flume/job目录下创建一个group3文件夹。
[liujh@hadoop102 job]$ mkdir group3
[liujh@hadoop103 job]$ mkdir group3
[liujh@hadoop104 job]$ mkdir group3
[liujh@hadoop103 group3]$ touch flume1-logger-flume.conf
[liujh@hadoop103 group3]$ vim flume1-logger-flume.conf
添加如下内容
# 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/group.log
a1.sources.r1.shell = /bin/bash -c
# Describe the sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = hadoop104
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
[liujh@hadoop102 group3]$ touch flume2-netcat-flume.conf
[liujh@hadoop102 group3]$ vim flume2-netcat-flume.conf
添加如下内容
# 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 = hadoop104
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
[liujh@hadoop104 group3]$ touch flume3-flume-logger.conf
[liujh@hadoop104 group3]$ vim flume3-flume-logger.conf
添加如下内容
# 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 = hadoop104
a3.sources.r1.port = 4141
# Describe the sink
# Describe the sink
a3.sinks.k1.type = logger
# 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
[liujh@hadoop104 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group3/flume3-flume-logger.conf -Dflume.root.logger=INFO,console
[liujh@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group3/flume2-netcat-flume.conf
[liujh@hadoop103 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group3/flume1-logger-flume.conf
[liujh@hadoop103 module]$ echo 'hello' > group.log
[liujh@hadoop102 flume]$ telnet hadoop102 44444
简书:https://www.jianshu.com/u/0278602aea1d
CSDN:https://blog.csdn.net/u012387141