这里主要是三个常见的需求：
监听端口收集数据
监听文件收集数据
监听文件数据转向其他机器
Flume安装前置条件
Java Runtime Environment - Java 1.7 or later
Memory - Sufficient memory for configurations used by sources, channels or sinks
Disk Space - Sufficient disk space for configurations used by channels or sinks
Directory Permissions - Read/Write permissions for directories used by agent
安装jdk
下载
解压到~/app
将java配置系统环境变量中: ~/.bash_profile
export JAVA_HOME=/home/hadoop/app/jdk1.8.0_144
export PATH=$JAVA_HOME/bin:$PATH
source下让其配置生效
检测: java -version
安装Flume
下载
解压到~/app
将java配置系统环境变量中: ~/.bash_profile
export FLUME_HOME=/home/hadoop/app/apache-flume-1.6.0-cdh5.7.0-bin
export PATH=$FLUME_HOME/bin:$PATH
source下让其配置生效
flume-env.sh的配置：export JAVA_HOME=/home/hadoop/app/jdk1.8.0_144
检测: flume-ng version
example.conf: A single-node Flume configuration
使用Flume的关键就是写配置文件
A） 配置Source
B） 配置Channel
C） 配置Sink
D） 把以上三个组件串起来
需求一:从指定网络端口采集数据输出到控制台
a1: agent名称
r1: source的名称
k1: sink的名称
c1: channel的名称
# 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 = hadoop000
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agentflume-ng agent \--name a1 \--conf $FLUME_HOME/conf \--conf-file $FLUME_HOME/conf/example.conf \-Dflume.root.logger=INFO,console使用telnet进行测试： telnet hadoop000 44444Event: { headers:{} body: 68 65 6C 6C 6F 0D hello. }Event是FLume数据传输的基本单元
Event = 可选的header + byte array
需求二：监控一个文件实时采集新增的数据输出到控制台
Agent选型：exec source + memory channel + logger sink
# 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 /home/hadoop/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-ng agent \
--name a1 \
--conf $FLUME_HOME/conf \
--conf-file $FLUME_HOME/conf/exec-memory-logger.conf \
-Dflume.root.logger=INFO,console
测试：新创建第二个终端，输入echo hello >> exec-memory-logger.conf ，看第一个终端是否能获取到
注意如果走离线的，就将收集到的数据存入到hdfs，如果走实时的，就将数据存入kafka(要设置sink类型和server)
以上只要修改a1.sinks.k1.type即可
需求三：将A服务器上的日志实时采集到B服务器
技术选型：
exec source + memory channel + avro sink
avro source + memory channel + logger sink
技术原理图：
flume配置：
第一台机器上：
配置文件名：exec-memory-avro.conf
exec-memory-avro.sources = exec-source
exec-memory-avro.sinks = avro-sink
exec-memory-avro.channels = memory-channel
exec-memory-avro.sources.exec-source.type =
exec
exec-memory-avro.sources.exec-source.command = tail -F /home/hadoop/data/data.log
exec-memory-avro.sources.exec-source.shell = /bin/sh -c
exec-memory-avro.sinks.avro-sink.type =
avro
exec-memory-avro.sinks.avro-sink.hostname = hadoop000
exec-memory-avro.sinks.avro-sink.port = 44444
exec-memory-avro.channels.memory-channel.type = memory
exec-memory-avro.sources.exec-source.channels = memory-channel
exec-memory-avro.sinks.avro-sink.channel = memory-channel
第二台机器：
配置文件名：avro-memory-logger.conf
avro-memory-logger.sources =
avro-source
avro-memory-logger.sinks = logger-sink
avro-memory-logger.channels = memory-channel
avro-memory-logger.sources.avro-source.type =
avro
avro-memory-logger.sources.avro-source.bind = hadoop000
avro-memory-logger.sources.avro-source.port = 44444
avro-memory-logger.sinks.logger-sink.type = logger
avro-memory-logger.channels.memory-channel.type = memory
avro-memory-logger.sources.avro-source.channels = memory-channel
avro-memory-logger.sinks.logger-sink.channel = memory-channel
先启动avro-memory-logger
flume-ng agent \
--name avro-memory-logger \
--conf $FLUME_HOME/conf \
--conf-file $FLUME_HOME/conf/avro-memory-logger.conf \
-Dflume.root.logger=INFO,console
flume-ng agent \
--name exec-memory-avro \
--conf $FLUME_HOME/conf \
--conf-file $FLUME_HOME/conf/exec-memory-avro.conf \
-Dflume.root.logger=INFO,console
需求三的实现流程：
1）机器上A上监控呢一个文件，当我们访问主站时会有用户行为日志记录到access.log中
2）avro sink把新产生的日志输出到对应的avro source指定的hostname和port上
3）通过avro source对应的agent将我们的日志输出到控制台（kafka）
