Flume案例：2个案例单数据源多出口

一、单数据源多出口案例1

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

[jason@hadoop102 job]$ cd group1/

在/opt/module/datas/目录下创建flume3文件夹

[jason@hadoop102 datas]$ mkdir flume3

 

1．创建flume-file-flume.conf

配置实时接收日志文件的1个source和两个channel、两个sink，分别输送给flume-flume-hdfs和flume-flume-dir这两个agent。

创建配置文件

[jason@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。

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

接收上级flume数据。flume-flume-hdfs输入为avro source，输出是到hdfs的sink。

创建配置文件

[jason@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

接收上级flume数据。flume-flume-dir输入是avro source，输出是到本地目录的sink。

创建配置文件

[jason@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。

[jason@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group1/flume-flume-dir.conf

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

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

 

5．启动hadoop和hive

[jason@hadoop102 hadoop-2.7.2]$ sbin/start-dfs.sh

[jason@hadoop103 hadoop-2.7.2]$ sbin/start-yarn.sh

 

[jason@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

[jason@hadoop102 flume3]$ ll

总用量 8

-rw-rw-r--. 1 jason jason 5942 5月  22 00:09 1526918887550-3

 

 

二、单数据源多出口案例2

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

[jason@hadoop102 job]$ cd group1/

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-flume1和flume-flume2。

创建配置文件

[jason@hadoop102 group1]$ 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-flume1.conf

接收上级flume数据。输入是的avro source，输出是到本地控制台。

创建配置文件

[jason@hadoop102 group1]$ vim flume-flume1.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-flume2.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

[jason@hadoop102 group1]$ vim flume-flume2.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-flume2，flume-flume1，flume-netcat-flume。

[jason@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group1/flume-flume2.conf -Dflume.root.logger=INFO,console

[jason@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group1/flume-flume1.conf -Dflume.root.logger=INFO,console

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