1.监控端口数据官方案例
(1) 案例需求：首先，Flume监控本机44444端口，然后通过telnet工具向本机44444端口发送消息，最后Flume将监听的数据实时显示在控制台。
(2) 需求分析：
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(3) 实现步骤：
① 安装telnet工具
在/opt/software目录下创建flume-telnet文件夹
[luomk@hadoop102 software]$ mkdir flume-telnet
再将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/opt/software/flume-telnet文件夹下面。执行RPM软件包安装命令：
[luomk@hadoop102 software]$ sudo rpm -ivh xinetd-2.3.14-40.el6.x86_64.rpm
[luomk@hadoop102 software]$ sudo rpm -ivh telnet-0.17-48.el6.x86_64.rpm
[luomk@hadoop102 software]$ sudo rpm -ivh telnet-server-0.17-48.el6.x86_64.rpm
② 判断44444端口是否被占用
[luomk@hadoop102 flume-telnet]$ sudo netstat -tunlp | grep 44444
③ 创建Flume Agent配置文件flume-telnet-logger.conf
在flume目录下创建job文件夹并进入job文件夹。
[luomk@hadoop102 flume]$ mkdir job
[luomk@hadoop102 flume]$ cd job/
在job文件夹下创建Flume Agent配置文件flume-telnet-logger.conf。
[luomk@hadoop102 job]$ touch flume-telnet-logger.conf
在flume-telnet-logger.conf文件中添加如下内容。
[luomk@hadoop102 job]$ vim flume-telnet-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注：配置文件来源于官方手册http://flume.apache.org/FlumeUserGuide.html
④ 开启flume监听端口
[luomk@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/flume-telnet-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-telnet.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。
⑤ 使用telnet工具向本机的44444端口发送内容
$ telnet localhost 44444
⑥ 效果展示：
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2.实时读取本地文件到HDFS案例
(1) 案例需求：实时监控Hive日志，并上传到HDFS中
(2) 需求分析：
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(3) 实现步骤：
① Flume要想将数据输出到HDFS，必须持有Hadoop相关jar包
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。
尖叫提示：标红的jar为1.99版本flume必须引用的jar。其他版本可以不引用。
② 创建flume-file-hdfs.conf文件
[luomk@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。
[luomk@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 = 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 = c2③ 执行监控配置
[luomk@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/flume-file-hdfs.conf
④ 开启hadoop和hive并操作hive产生日志
[luomk@hadoop102 hadoop-2.7.2]$ sbin/start-dfs.sh
[luomk@hadoop103 hadoop-2.7.2]$ sbin/start-yarn.sh
[luomk@hadoop102 hive]$ bin/hive
⑤ 在HDFS上查看文件。
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3.实时读取目录文件到HDFS案例
(1) 案例需求：使用flume监听整个目录的文件
(2) 需求分析：
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(3) 实现步骤：
① 创建配置文件flume-dir-hdfs.conf
[luomk@hadoop102 job]$ touch flume-dir-hdfs.conf
[luomk@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 = 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 = c3② 启动监控文件夹命令
[luomk@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
不要在监控目录中创建并持续修改文件
上传完成的文件会以.COMPLETED结尾
被监控文件夹每600毫秒扫描一次文件变动
③ 向upload文件夹中添加文件
在/opt/module/flume目录下创建upload文件夹
[luomk@hadoop102 flume]$ mkdir upload
向upload文件夹中添加文件
[luomk@hadoop102 upload]$ touch luomk.txt
[luomk@hadoop102 upload]$ touch luomk.tmp
[luomk@hadoop102 upload]$ touch luomk.log
④ 查看HDFS上的数据
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4.单数据源多出口案例
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(1) 案例需求：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。
(2) 实现步骤：
① 准备工作
在/opt/module/flume/job目录下创建group1文件夹
[luomk@hadoop102 job]$ cd group1/
在/opt/module/datas/目录下创建flume3文件夹
[luomk@hadoop102 datas]$ mkdir flume3
② 创建flume-file-flume.conf
配置1个接收日志文件的source和两个channel、两个sink，分别输送给flume-flume-hdfs和flume-flume-dir。
[luomk@hadoop102 group1]$ touch flume-file-flume.conf
[luomk@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/logs/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。
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。
③ 创建flume-flume-hdfs.conf
配置上级flume输出的source，输出是到hdfs的sink。
创建配置文件并打开
[luomk@hadoop102 group1]$ touch flume-flume-hdfs.conf
[luomk@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
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 = c1④ 创建flume-flume-dir.conf
配置上级flume输出的source，输出是到本地目录的sink。
创建配置文件并打开
[luomk@hadoop102 group1]$ touch flume-flume-dir.conf
[luomk@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/datas/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。
④ 执行配置文件
分别开启对应配置文件：flume-flume-dir，flume-flume-hdfs，flume-file-flume。
[luomk@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group1/flume-flume-dir.conf
[luomk@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group1/flume-flume-hdfs.conf
[luomk@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group1/flume-file-flume.conf
⑤ 启动hadoop和hive
[luomk@hadoop102 hadoop-2.7.2]$ sbin/start-dfs.sh
[luomk@hadoop103 hadoop-2.7.2]$ sbin/start-yarn.sh
[luomk@hadoop102 hive]$ bin/hive
hive (default)>
⑥ 检查HDFS上数据
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5.多数据源汇总案例
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(1) 案例需求：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。
(2) 实现步骤：
① 准备工作
在/opt/module/flume/job目录下创建一个group2文件夹
[luomk@hadoop102 job]$ mkdir group2
② 创建flume-file-flume.conf
配置source用于监控hive.log文件，配置sink输出数据到下一级flume。
创建配置文件并打开
[luomk@hadoop102 group2]$ touch flume-file-flume.conf
[luomk@hadoop102 group2]$ vim flume-file-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/hive/logs/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 = c1③ 创建flume-telnet-flume.conf
配置source监控端口44444数据流，配置sink数据到下一级flume：
创建配置文件并打开
[luomk@hadoop102 group2]$ touch flume-telnet-flume.conf
[luomk@hadoop102 group2]$ vim flume-telnet-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 = 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 = c1④ 创建flume-flume-hdfs.conf
配置source用于接收flume-file-flume与flume-telnet-flume发送过来的数据流，最终合并后sink到HDFS。
创建配置文件并打开
[luomk@hadoop102 group2]$ touch flume-flume-hdfs.conf
[luomk@hadoop102 group2]$ vim flume-flume-hdfs.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 = 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 = c1⑤ 执行配置文件
分别开启对应配置文件：flume-flume-hdfs.conf，flume-telnet-flume.conf，flume-file-flume.conf。
[luomk@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group2/flume-flume-hdfs.conf
[luomk@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group2/flume-telnet-flume.conf
[luomk@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group2/flume-file-flume.conf
⑥ 启动hadoop和hive
[luomk@hadoop102 hadoop-2.7.2]$ sbin/start-dfs.sh
[luomk@hadoop103 hadoop-2.7.2]$ sbin/start-yarn.sh
[luomk@hadoop102 hive]$ bin/hive
hive (default)>
⑦ 向44444端口发送数据
[luomk@hadoop102 flume]$ telnet hadoop102 44444
⑧ 检查HDFS上数据
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