Flume快速入门系列(4) | 多路复用

  此篇博文讲的是Flume的多路复用。

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  单Source多Channel、Sink如下图所示。

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

1. 准备工作

• 1. 在/opt/module/flume/job目录下创建group1文件夹

[bigdata@hadoop002 job]$ mkdir group1
[bigdata@hadoop002 job]$ cd group1/

• 2. 在/opt/module/datas/目录下创建flume3文件夹

[bigdata@hadoop002 module]$ 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。

• 1. 创建配置文件并打开

[bigdata@hadoop002 group1]$ vim flume-file-flume.conf

• 2. 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/datas/flume_tmp.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 = hadoop002 
a1.sinks.k1.port = 4141

a1.sinks.k2.type = avro
a1.sinks.k2.hostname = hadoop002
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。

3. 创建flume-flume-hdfs.conf

配置上级Flume输出的Source，输出是到HDFS的Sink。

• 1. 创建配置文件并打开

[bigdata@hadoop002 group1]$ vim flume-flume-hdfs.conf

• 2. 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 = hadoop002
a2.sources.r1.port = 4141

# Describe the sink
a2.sinks.k1.type = hdfs
a2.sinks.k1.hdfs.path = hdfs://hadoop002:9000/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

4. 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。

• 1. 创建配置文件并打开

[bigdata@hadoop002 group1]$ vim flume-flume-dir.conf

• 2. 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 = hadoop002
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。

5. 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。

[bigdata@hadoop002 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group1/flume-flume-dir.conf
[bigdata@hadoop002 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group1/flume-flume-hdfs.conf
[bigdata@hadoop002 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group1/flume-file-flume.conf

6. 启动Hadoop和Hive

[bigdata@hadoop002 module]$ start-dfs.sh //  其实只启动hdfs即可

7. 检查HDFS上数据

8. 检查/opt/module/datas/flume3目录中数据

[bigdata@hadoop002 flume3]$ ll

  本次的分享就到这里了,

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