Flume 的安装与使用

安装步骤

1. Flume安装地址

1） Flume官网地址
http://flume.apache.org/
2）文档查看地址
http://flume.apache.org/FlumeUserGuide.html
3）下载地址
http://archive.apache.org/dist/flume/

2. 安装部署

1）将apache-flume-1.7.0-bin.tar.gz上传到linux的/opt/software目录下
2）解压apache-flume-1.7.0-bin.tar.gz到/opt/module/目录下
	[root@hadoop102 software]$ tar -zxvf apache-flume-1.7.0-bin.tar.gz -C /opt/module/
3）修改apache-flume-1.7.0-bin的名称为flume
	[root@hadoop102 software]$ mv apache-flume-1.7.0-bin flume
4）将flume/conf下的flume-env.sh.template文件修改为flume-env.sh，并配置flume-env.sh文件
	[root@hadoop102 conf]$ mv flume-env.sh.template flume-env.sh
	[root@hadoop102 conf]$ vim flume-env.sh
	export JAVA_HOME=/opt/module/jdk1.8.0_144

案例实操

1. Flume实时读取目录中文件到HDFS
   案例需求：实时监控Hive日志，并上传到HDFS中

创建一个文件
[root@hadoop102 job]$ touch flume-dir-hdfs.conf
打开文件
[root@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://hadoop101:9000/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 = 30
#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

2)执行监控配置

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

3)开启hadoop和hive并操作hive产生日志

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

[root@hadoop102 hive]$ bin/hive

1. 单数据源多出口案例

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：
在job目录下创建group1文件夹
[root@hadoop102 job]$ cd group1/
在/opt/module/datas/目录下创建flume3文件夹
[root@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
[root@hadoop102 group1]$ touch flume-file-flume.conf
[root@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
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
[root@hadoop102 group1]$ touch flume-flume-hdfs.conf
[root@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://hadoop101:9000/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
[root@hadoop102 group1]$ touch flume-flume-dir.conf
[root@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。
[root@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group1/flume-flume-dir.conf
[root@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group1/flume-flume-hdfs.conf
[root@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group1/flume-file-flume.conf

5．启动hadoop和hive
[root@hadoop102 hadoop-2.7.2]$ sbin/start-dfs.sh
[root@hadoop102 hadoop-2.7.2]$ sbin/start-yarn.sh
[root@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
[root@hadoop102 flume3]$ ll
总用量 8
-rw-rw-r--. 1 root root 5942 5月  22 00:09 1526918887550-3

3. 多数据源汇总案例
   1）案例需求：
   hadoop102上的flume-1监控文件hive.log，
   hadoop103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hadoop102上的flume-3，flume-3将最终数据打印到控制台

0．准备工作
分发flume
[root@hadoop102 module]$ scp flume
	在hadoop102、hadoop103以及hadoop104的/opt/module/flume/job目录下创建一个group2文件夹
[root@hadoop102 job]$ mkdir group2
[root@hadoop102 job]$ mkdir group2
[root@hadoop103 job]$ mkdir group2
1．创建flume1.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hadoop102上创建配置文件并打开
[root@hadoop102 group2]$ touch flume-file.conf
[root@hadoop102 group2]$ vim flume-file.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 = c1
2．创建flume2.conf
配置source监控端口44444数据流，配置sink数据到下一级flume：
在hadoop104上创建配置文件并打开
[root@hadoop103 group2]$ touch flume2.conf
[root@hadoop103 group2]$ vim flume2.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 = c1
3．创建flume3.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hadoop102上创建配置文件并打开
[root@hadoop102 group2]$ touch flume3.conf
[root@hadoop102 group2]$ vim flume3.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
# 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．执行配置文件
分别开启对应配置文件：flume3.conf，flume2.conf，flume1.conf。
[root@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group2/flume3.conf -Dflume.root.logger=INFO,console
[root@hadoop102 flume]$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group2/flume2.conf
[root@hadoop103 flume]$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group3/flume-file.conf 
5．在hadoop102上向/opt/module目录下的group.log追加内容
[root@hadoop102 module]$ echo 'hello' > group.log
6．在hadoop103上向44444端口发送数据
[root@hadoop103 flume]$ telnet hadoop104 44444
7.  检查数据