Fast learning -Flume enterprise development case

Chapter 3 Enterprise Development Case

3.1 Monitoring port official data Case

1. Case requirements: First, the machine Flume monitoring port 44444, then 44444 ports via telnet tool to send a message to this machine, and finally Flume will monitor real-time data displayed on the console.
2. demand analysis:
   
3. Implementation steps:
   1. Installation telnet tool
   would rpm package (xinetd-2.3.14-40.el6.x86_64.rpm, telnet-0.17-48.el6.x86_64.rpm and telnet-server-0.17-48.el6.x86_64.rpm) copyed / opt / software folder. Implementation of RPM package installation command:

[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. 44444 determine whether the port is occupied
   [atguigu @ hadoop102 flume-telnet] $ sudo netstat -tunlp | grep 44444
   Functional Description: netstat command is a very useful tool for monitoring TCP / IP network, it can display the routing table, the actual network connection and status information for each network interface device.

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的程序识别码和程序名称；

3. Creating Flume Agent configuration file flume-telnet-logger.conf create a job folder and enter the job folder in the flume directory.

[atguigu@hadoop102 flume]$ mkdir job
[atguigu@hadoop102 flume]$ cd job/

Creating Flume Agent configuration file flume-telnet-logger.conf in the job folder.
[atguigu @ hadoop102 job] $ touch flume-telnet-logger.conf

Add the following to the flume-telnet-logger.conf file.
[atguigu @ hadoop102 job] $ vim flume-telnet-logger.conf
added as follows:

# 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

Note: The configuration file from the official manual http://flume.apache.org/FlumeUserGuide.html

4. First open flume listening port

[atguigu@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/flume-telnet-logger.conf -Dflume.root.logger=INFO,console

Parameters:
-conf conf /: expressed configuration file is stored in the conf / directory
-name a1: indicates to the agent named A1
-conf-Job File / Flume-telnet.conf: The Flume startup configuration file is read in job folder flume-telnet.conf file under.
-Dflume.root.logger == INFO, console :-D represents the dynamic parameter modifications flume.root.logger flume runtime property values, and the print console log level to INFO level. Log level include: log, info, warn, error .

5. Tool to transmit content using telnet 44444 port of the machine

[atguigu@hadoop102 ~]$ telnet localhost 44444

6. Observe the received data in case Flume monitor page
   

3.2 real-time read local files to HDFS Case

1. Case needs: real-time monitoring Hive log, and upload to the HDFS

2. demand analysis:
   

3. Implementation steps:

   1. Flume To output the data to HDFS, Hadoop must hold a relevant jar package
      to the Configuration-1.6.jar-Commons,
      hadoop-auth-2.7.2.jar,
      hadoop-the Common-2.7.2.jar,
      hadoop-HDFS-2.7 .2.jar,
      Commons-IO-2.4.jar,
      HTRACE-Core-3.1.0-incubating.jar
      copied to the / opt / module / flume / lib folder.

   2. 创建flume-file-hdfs.conf文件
      创建文件
      [atguigu@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。

[atguigu@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 = c2

3. 执行监控配置
   [atguigu@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

[atguigu@hadoop102 hadoop-2.7.2]$ sbin/start-dfs.sh
[atguigu@hadoop103 hadoop-2.7.2]$ sbin/start-yarn.sh

[atguigu@hadoop102 hive]$ bin/hive
hive (default)>

5. 在HDFS上查看文件。

3.3 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：

3. 实现步骤：
   1. 创建配置文件flume-dir-hdfs.conf

创建一个文件
[atguigu@hadoop102 job]$ touch flume-dir-hdfs.conf
打开文件
[atguigu@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 = c3

2. 启动监控文件夹命令
   [atguigu@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

3. 向upload文件夹中添加文件
   在/opt/module/flume目录下创建upload文件夹
   [atguigu@hadoop102 flume]$ mkdir upload
   向upload文件夹中添加文件

[atguigu@hadoop102 upload]$ touch atguigu.txt
[atguigu@hadoop102 upload]$ touch atguigu.tmp
[atguigu@hadoop102 upload]$ touch atguigu.log

4. 查看HDFS上的数据
   
5. 等待1s，再次查询upload文件夹

[atguigu@hadoop102 upload]$ ll
总用量 0
-rw-rw-r--. 1 atguigu atguigu 0 5月  20 22:31 atguigu.log.COMPLETED
-rw-rw-r--. 1 atguigu atguigu 0 5月  20 22:31 atguigu.tmp
-rw-rw-r--. 1 atguigu atguigu 0 5月  20 22:31 atguigu.txt.COMPLETED

3.4 单数据源多出口案例(选择器)

单Source多Channel、Sink如图7-2所示。

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. 需求分析：
   
3. 实现步骤：
   0. 准备工作
      在/opt/module/flume/job目录下创建group1文件夹
      [atguigu@hadoop102 job]$ cd group1/
      在/opt/module/datas/目录下创建flume3文件夹
      [atguigu@hadoop102 datas]$ mkdir flume3
   1. 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。
      创建配置文件并打开

[atguigu@hadoop102 group1]$ touch flume-file-flume.conf
[atguigu@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
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。

2. 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
[atguigu@hadoop102 group1]$ touch flume-flume-hdfs.conf
[atguigu@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 = c1

3. 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
[atguigu@hadoop102 group1]$ touch flume-flume-dir.conf
[atguigu@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。
[atguigu@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group1/flume-flume-dir.conf

[atguigu@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group1/flume-flume-hdfs.conf

[atguigu@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group1/flume-file-flume.conf

5. 启动Hadoop和Hive

[atguigu@hadoop102 hadoop-2.7.2]$ sbin/start-dfs.sh
[atguigu@hadoop103 hadoop-2.7.2]$ sbin/start-yarn.sh

[atguigu@hadoop102 hive]$ bin/hive
hive (default)>

6. 检查HDFS上数据
   
7. 检查/opt/module/datas/flume3目录中数据

[atguigu@hadoop102 flume3]$ ll
总用量 8
-rw-rw-r--. 1 atguigu atguigu 5942 5月  22 00:09 1526918887550-3

3.5 单数据源多出口案例(Sink组)

单Source、Channel多Sink(负载均衡)如图7-3所示

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也负责存储到HDFS
2. 需求分析：
   
3. 实现步骤：
   0. 准备工作
      在/opt/module/flume/job目录下创建group2文件夹
      [atguigu@hadoop102 job]$ cd group2/
   1. 创建flume-netcat-flume.conf

配置1个接收日志文件的source和1个channel、两个sink，分别输送给flume-flume-console1和flume-flume-console2。
创建配置文件并打开
[atguigu@hadoop102 group2]$ touch flume-netcat-flume.conf
[atguigu@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。

2. 创建flume-flume-console1.conf

配置上级Flume输出的Source，输出是到本地控制台。
创建配置文件并打开
[atguigu@hadoop102 group2]$ touch flume-flume-console1.conf
[atguigu@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

3. 创建flume-flume-console2.conf

配置上级Flume输出的Source，输出是到本地控制台。
创建配置文件并打开
[atguigu@hadoop102 group2]$ touch flume-flume-console2.conf
[atguigu@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

4. 执行配置文件

分别开启对应配置文件：flume-flume-console2，flume-flume-console1，flume-netcat-flume。
[atguigu@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group2/flume-flume-console2.conf -Dflume.root.logger=INFO,console

[atguigu@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group2/flume-flume-console1.conf -Dflume.root.logger=INFO,console

[atguigu@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group2/flume-netcat-flume.conf

5. 使用telnet工具向本机的44444端口发送内容

$ telnet localhost 44444

6. 查看Flume2及Flume3的控制台打印日志

3.6 多数据源汇总案例

多Source汇总数据到单Flume如图7-4所示。

1. 案例需求：
   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。

2. 需求分析：
   

3. Implementation steps:
   0. Preparations
   distribution Flume
   [atguigu @ hadoop102 Module] $ XSync Flume
   create a folder in group3 hadoop102, hadoop103 and hadoop104 the / opt / module / flume / job directory.
   [atguigu @ hadoop102 the Job] $ mkdir Group3
   [atguigu @ hadoop103 the Job] $ mkdir Group3
   [atguigu @ hadoop104 the Job] $ mkdir Group3

4. Creating flume1-logger-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。
在hadoop103上创建配置文件并打开
[atguigu@hadoop103 group3]$ touch flume1-logger-flume.conf
[atguigu@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

2. Creating flume2-netcat-flume.conf

配置Source监控端口44444数据流，配置Sink数据到下一级Flume：
在hadoop102上创建配置文件并打开
[atguigu@hadoop102 group3]$ touch flume2-netcat-flume.conf
[atguigu@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

3. Creating flume3-flume-logger.conf

配置source用于接收flume1与flume2发送过来的数据流，最终合并后sink到控制台。
在hadoop104上创建配置文件并打开
[atguigu@hadoop104 group3]$ touch flume3-flume-logger.conf
[atguigu@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

4. Execution profile

分别开启对应配置文件：flume3-flume-logger.conf，flume2-netcat-flume.conf，flume1-logger-flume.conf。
[atguigu@hadoop104 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group3/flume3-flume-logger.conf -Dflume.root.logger=INFO,console

[atguigu@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group3/flume2-netcat-flume.conf

[atguigu@hadoop103 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group3/flume1-logger-flume.conf

5. group.log additional content on hadoop103 under the / opt / module directory

[atguigu@hadoop103 module]$ echo 'hello' > group.log

6. Send data to port 44444 on hadoop102

[atguigu@hadoop102 flume]$ telnet hadoop102 44444

7. Check the data on hadoop104