Flume 实战练习

前期准备

了解Flume 架构及核心组件

Flume 架构及核心组件

Source : 收集(指定数据源从哪里获取)

Channel : 聚集

Sink : 输出(把数据写到哪里去)

学习使用 Flume

通过一个简单的小例子学习使用 Flume

使用 Flume 的关键就是写配置文件

配置文件的构成:

A) 配置 Source

B) 配置 Channel

C) 配置 Sink

D) 把以上三个组件串起来

A simple example

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
# example.conf: A single-node Flume configuration
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# a1: agent 的名称
# r1: source 的名称
# k1: sink 的名称
# c1: channel 的名称

# Describe/configure the source
a1.sources.r1.type = netcat
a1.sources.r1.bind = localhost
a1.sources.r1.port = 44444
# type: source组件的类型
# bind: source绑定的主机或IP
# port: source绑定的端口号

# Describe the sink
a1.sinks.k1.type = logger
# 把日志输出到控制台

# Use a channel which buffers events in memory
a1.channels.c1.type = memory
# 存放在内存队列

# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
# r1的channels指定到c1
# k1的channel从c1得到
# 一个source可以输出到多个channel
# 一个channel只能输出一个sink

实战一

需求

需求:从指定网络端口采集数据输出到控制台

写配置文件

/abs/app/apache-flume-1.6.0-cdh5.7.0-bin/conf 目录中新建 example.conf 如下:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
# example.conf: A single-node Flume configuration
# 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 = hadoop
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

# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

启动 agent

Flume 官网启动 agent 的命令:

1
$ bin/flume-ng agent -n $agent_name -c conf -f conf/flume-conf.properties.template

agent options:

1
2
3
--name,-n <name>          the name of this agent (required)
--conf,-c <conf> use configs in <conf> directory
--conf-file,-f <file> specify a config file (required if -z missing)

实际用的启动 agent 的命令:

1
flume-ng agent -n a1 -c $FLUME_HOME $FLUME_HOME/conf/example.conf -Dflume.root.logger=INFO,console

// Dflume.root.logger=INFO,console 为将输出结果显示到控制台

启动失败

1
2
3
4
5
Info: Including Hive libraries found via () for Hive access
+ exec /abs/app/jdk1.8.0_161/bin/java -Xmx20m -Dflume.root.logger=INFO,console -cp '/abs/app/apache-flume-1.6.0-cdh5.7.0-bin:/abs/app/apache-flume-1.6.0-cdh5.7.0-bin/lib/*:/lib/*' -Djava.library.path= org.apache.flume.node.Application -n a1 -f /abs/app/apache-flume-1.6.0-cdh5.7.0-bin/conf/example.conf
log4j:WARN No appenders could be found for logger (org.apache.flume.lifecycle.LifecycleSupervisor).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.

上网查了一下,别人是 -c 的路径指定错误,我的也错了。

-c 后面跟的是 Flumeconf 目录

所以正确的启动命令为:

1
flume-ng agent -n a1 -c $FLUME_HOME/conf -f $FLUME_HOME/conf/example.conf -Dflume.root.logger=INFO,console

正常启动后可以看到如下:

可以看到 SinkSource 都启动了

绑定的主机名为 hadoop 的 IP 和绑定的端口号都有显示

验证

1
2
[root@hadoop ~]# telnet hadoop 44444
-bash: telnet: command not found

显示找不到 telnet ,用 yum install telnet 安装telnet

telnet 进入 hadoop 的 44444 端口进行输入单词按 Enter

agent 的那一端显示如下:

从图中可以看到如下:

1
Event: { headers:{} body: 73 70 61 72 6B 0D   spark. }

Event 是 Flume 数据传输的基本单元

Event = 可选的 header + byte array

以上实现了从指定网络端口采集数据输出到控制台的需求。






实战二

需求

需求:监控一个文件实时采集新增的数据输出到控制台

根据需求可以采用以下方案实现:

Agent 选型: exec source + memory channel + logger sink

写配置文件

/abs/data 目录新建 data.log

1
touch data.log

/abs/app/apache-flume-1.6.0-cdh5.7.0-bin/conf 目录中新建 exec-memory-logger.conf 如下:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
# exec-memory-logger.conf: A realtime single-node Flume configuration
# 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 /abs/data/data.log
a1.sources.r1.shell = /bin/sh -c

# Describe the sink
a1.sinks.k1.type = logger

# Use a channel which buffers events in memory
a1.channels.c1.type = memory

# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

启动 agent

Flume 启动 agent 的命令:

1
flume-ng agent -n a1 -c $FLUME_HOME/conf -f $FLUME_HOME/conf/exec-memory-logger.conf -Dflume.root.logger=INFO,console

// Dflume.root.logger=INFO,console 为将输出结果显示到控制台

正常启动后可以看到如下:

可以看到 SourceChannelSink 的类型和启动类型以及 Source 要执行的命令

验证

/abs/data 目录输入 echo hello >> data.log

agent 的那一端显示如下:

以上实现了监控一个文件实时采集新增的数据输出到控制台的需求。

拓展

参照 Flume 用户指南

如果用 Flume 采集数据做离线处理,可以使用 HDFS Sink

如果用 Flume 采集数据做实时处理,可以使用 Kafka Sink

这里只提供一个拓展,根据具体的需求使用。






实战三

需求

需求:将 A 服务器上的日志实时采集到 B 服务器

根据需求可以采用以下方案实现:

Agent A 选型: exec source + memory channel + avro sink

Agent B 选型: avro source + memory channel + logger sink

写配置文件

/abs/app/apache-flume-1.6.0-cdh5.7.0-bin/conf 目录中新建如下配置文件:

exec-memory-avro.conf:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
# exec-memory-avro.conf: A realtime Flume configuration
# Name the components on this agent
exec-memory-avro.sources = exec-source
exec-memory-avro.sinks = avro-sink
exec-memory-avro.channels = memory-channel

# Describe/configure the source
exec-memory-avro.sources.exec-source.type = exec
exec-memory-avro.sources.exec-source.command = tail -F /abs/data/data.log
exec-memory-avro.sources.exec-source.shell = /bin/sh -c

# Describe the sink
exec-memory-avro.sinks.avro-sink.type = avro
exec-memory-avro.sinks.avro-sink.hostname = hadoop
exec-memory-avro.sinks.avro-sink.port = 44444

# Use a channel which buffers events in memory
exec-memory-avro.channels.memory-channel.type = memory

# Bind the source and sink to the channel
exec-memory-avro.sources.exec-source.channels = memory-channel
exec-memory-avro.sinks.avro-sink.channel = memory-channel

avro-memory-logger.conf:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
# avro-memory-logger.conf: A realtime Flume configuration
# Name the components on this agent
avro-memory-logger.sources = avro-source
avro-memory-logger.sinks = logger-sink
avro-memory-logger.channels = memory-channel

# Describe/configure the source
avro-memory-logger.sources.avro-source.type = avro
avro-memory-logger.sources.avro-source.bind = hadoop
avro-memory-logger.sources.avro-source.port = 44444

# Describe the sink
avro-memory-logger.sinks.logger-sink.type = logger

# Use a channel which buffers events in memory
avro-memory-logger.channels.memory-channel.type = memory

# Bind the source and sink to the channel
avro-memory-logger.sources.avro-source.channels = memory-channel
avro-memory-logger.sinks.logger-sink.channel = memory-channel

启动 agent

两个 Agent ,先启动 Agent A ,再启动 Agent B

先启动 avro-memory-logger:

1
flume-ng agent -n avro-memory-logger -c $FLUME_HOME/conf -f $FLUME_HOME/conf/avro-memory-logger.conf -Dflume.root.logger=INFO,console

再启动 exec-memory-avro:

1
flume-ng agent -n exec-memory-avro -c $FLUME_HOME/conf -f $FLUME_HOME/conf/exec-memory-avro.conf -Dflume.root.logger=INFO,console

验证

/abs/data/ 目录中输入以下命令:

1
2
echo hello spark >> data.log
echo Valentine >> data.log

Agent avro-memory-logger 显示如下:

以上实现了将 A 服务器上的日志实时采集到 B 服务器的需求。

这里采用的是一个服务器开三个窗口,有条件的可以尝试用两台服务器进行这个实战练习





------ 本文结束------
如果对您有帮助的话请我喝瓶水吧!