Flume的学习和使用

比眉伴天荒 2021-09-20 15:00 359阅读 0赞

# Flume的学习和使用 #

本文是基于CentOS 7.3系统环境，进行Flume的学习和使用

*  **CentOS 7.3**

--------------------

# 一、Flume的简介 #

## 1.1 Flume基本概念 ##

### (1) 什么是Flume ###

Flume是Cloudera提供的一个高可用的，高可靠的，分布式的海量日志采集、聚合和传输的系统。

### (2) Flume的目的 ###

Flume最主要的作业就是，实时读取服务器本地磁盘的数据，将数据写入HDFS

## 1.2 Flume基本组件 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70]

### (0) Flume工作流程 ###

Source采集数据并包装成Event，并将Event缓存在Channel中，Sink不断地从Channel获取Event，并解决成数据，最终将数据写入存储或索引系统

### (1) Agent ###

Agent是一个JVM进程，它以事件的形式将数据从源头送至目的。  
Agent主要有3个部分组成，Source、Channel、Sink

### (2) Source ###

Source是负责接收数据到Flume Agent的组件，采集数据并包装成Event。Source组件可以处理各种类型、各种格式的日志数据，包括avro、thrift、exec、jms、spooling directory、netcat、sequence generator、syslog、http、legacy

### (3) Sink ###

Sink不断地轮询Channel中的事件且批量地移除它们，并将这些事件批量写入到存储或索引系统、或者被发送到另一个Flume Agent。  
Sink组件目的地包括hdfs、logger、avro、thrift、ipc、file、HBase、solr、自定义

### (4) Channel ###

Channel是位于Source和Sink之间的缓冲区。因此，Channel允许Source和Sink运作在不同的速率上。Channel是线程安全的，可以同时处理几个Source的写入操作和几个Sink的读取操作

Flume自带两种Channel：Memory Channel和File Channel

1.  Memory Channel是内存中的队列。Memory Channel在不需要关心数据丢失的情景下适用。如果需要关心数据丢失，那么Memory Channel就不应该使用，因为程序死亡、机器宕机或者重启都会导致数据丢失
2.  File Channel将所有事件写到磁盘。因此在程序关闭或机器宕机的情况下不会丢失数据

### (4) Event ###

传输单元，Flume数据传输的基本单元，以Event的形式将数据从源头送至目的地。Event由Header和Body两部分组成，Header用来存放该event的一些属性，为K-V结构，Body用来存放该条数据，形式为字节数组  
![在这里插入图片描述][20200618084704461.png]

# 二、Flume的安装和入门案例 #

## 2.1 Flume安装 ##

### (1) Flume压缩包解压 ###

tar -xzvf apache-flume-1.7.0-bin.tar.gz -C /opt/module/

### (2) 修改Flume名称 ###

cd /opt/module/
    mv apache-flume-1.7.0-bin flume

### (3) 修改Flume配置文件 ###

cd /opt/module/flume/conf
    mv flume-env.sh.template flume-env.sh
    vi flume-env.sh
    # 修改内容如下
    export JAVA_HOME=/opt/module/jdk1.8.0_201

cd /opt/module/flume/conf
    vi log4j.properties
    # 修改内容如下
    flume.log.dir=/opt/module/flume/logs

## 2.1 Flume案例-监听数据端口 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 1]

### (1) 安装nc ###

yum install -y nc

### (2) 安装net-tools ###

yum install -y net-tools

### (3) 检测端口是否被占用 ###

netstat -nltp | grep 444444

### (4) 启动flume-agent ###

cd /opt/module/flume
    bin/flume-ng agent --name a1 --conf conf/ --conf-file job/flume-netcat-logger.conf -Dflume.root.logger=INFO,console

### (5) 开启另一个终端，发送消息 ###

nc localhost 4444
    aaa

## 2.2 Flume案例-实时监控单个追加文件 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 2]

### (1) 拷贝jar包至/opt/module/flume/lib ###

commons-configuration-1.6.jar
    hadoop-auth-2.7.2.jar
    hadoop-common-2.7.2.jar
    hadoop-hdfs-2.7.2.jar
    commons-io-2.4.jar
    htrace-core-3.1.0-incubating.jar

### (2) 创建flume-file-hdfs.conf文件 ###

vi flume-file-hdfs.conf
    # Name the components on this agent
    a2.sources = r2
    a2.sinks = k2
    a2.channels = c2
    
    # Describe/configure the source
    a2.sources.r2.type = exec
    a2.sources.r2.command = tail -F /opt/module/hive/logs/hive.log
    a2.sources.r2.shell = /bin/bash -c
    
    # Describe the sink
    a2.sinks.k2.type = hdfs
    a2.sinks.k2.hdfs.path = hdfs://hadoop102:9000/flume/%Y%m%d/%H
    #上传文件的前缀
    a2.sinks.k2.hdfs.filePrefix = logs-
    #是否按照时间滚动文件夹
    a2.sinks.k2.hdfs.round = true
    #多少时间单位创建一个新的文件夹
    a2.sinks.k2.hdfs.roundValue = 1
    #重新定义时间单位
    a2.sinks.k2.hdfs.roundUnit = hour
    #是否使用本地时间戳
    a2.sinks.k2.hdfs.useLocalTimeStamp = true
    #积攒多少个Event才flush到HDFS一次
    a2.sinks.k2.hdfs.batchSize = 1000
    #设置文件类型，可支持压缩
    a2.sinks.k2.hdfs.fileType = DataStream
    #多久生成一个新的文件
    a2.sinks.k2.hdfs.rollInterval = 60
    #设置每个文件的滚动大小
    a2.sinks.k2.hdfs.rollSize = 134217700
    #文件的滚动与Event数量无关
    a2.sinks.k2.hdfs.rollCount = 0
    
    # Use a channel which buffers events in memory
    a2.channels.c2.type = memory
    a2.channels.c2.capacity = 1000
    a2.channels.c2.transactionCapacity = 100
    	
    # Bind the source and sink to the channel
    a2.sources.r2.channels = c2
    a2.sinks.k2.channel = c2

### (3) 启动flume-agent ###

bin/flume-ng agent -n a2 -c conf/ -f job/flume-file-hdfs.conf

### (4) 开启另一个终端，执行hive命令 ###

hive

## 2.3 Flume案例-实时监控目录下多个新文件 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 3]

### (1) 创建flume-dir-hdfs.conf文件 ###

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://hadoop102:9000/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 = 60
    #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) 启动flume-agent ###

bin/flume-ng agent -n a3 -c conf/ -f job/flume-dir-hdfs.conf

### (3) 开启另一个终端 ###

cd /opt/module/flume/
    mkdir upload
    cp NOTICE upload/

## 2.4 Flume案例-实时监控目录下的多个追加文件 ##

Exec source适用于监控一个实时追加的文件，不能实现断电续传；Spooldir Source适合用于同步新文件，但不适合对实时追加日志的文件进行监听并同步；而Taildir Source适合用于监听多个实时追加的文件，并且能够实现断点续传。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 4]

### (1) 创建flume-dir-hdfs.conf文件 ###

vi flume-taildir-hdfs.conf
    # 添加内容
    a3.sources = r3
    a3.sinks = k3
    a3.channels = c3
    
    # Describe/configure the source
    a3.sources.r3.type = TAILDIR
    a3.sources.r3.positionFile = /opt/module/flume/tail_dir.json
    a3.sources.r3.filegroups = f1 f2
    a3.sources.r3.filegroups.f1 = /opt/module/flume/files/.*file.*
    a3.sources.r3.filegroups.f2 = /opt/module/flume/files/.*log.*
    # 不能写成如下的形式，后面的会覆盖前面的
    # a3.sources.r3.filegroups.f1 = /opt/module/flume/files/file1.txt
    # a3.sources.r3.filegroups.f1 = /opt/module/flume/files/file2.txt
    
    
    # Describe the sink
    a3.sinks.k3.type = hdfs
    a3.sinks.k3.hdfs.path = hdfs://hadoop102:9000/flume/upload2/%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 = 60
    #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) 创建目录和文件 ###

cd /opt/module/flume
    mkdir files
    cp CHANGELOG files/CHANGELOG.log
    cp LICENSE files/LICENSE.log

### (3) 启动flume-agent ###

bin/flume-ng agent -n a3 -c conf/ -f job/flume-taildir-hdfs.conf

### (4) 开启另一个终端 ###

cd /opt/module/flume/files
    vi CHANGELOG.log
    # 添加如下内容
    xxxxx
    sssss
    wwwww

# 三、Flume的进阶 #

## 3.1 Flume事务 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 5]

### (1) Put事务流程 ###

1.  doPut：将批数据先写入临时缓存区putList
2.  doCommit：检查channel内存队列是否足够合并
3.  doRollback：channel内存队列空间不足，回滚数据

### (2) Take事务流程 ###

1.  doTake：将数据取到临时缓存区takeList，并将数据发送到HDFS
2.  doCommit：如果数据全部发送成功，则清除临时缓冲区takeList
3.  doRollback：数据发送过程中如果出现异常，rollback将临时缓冲区takeList中的数据归还给channel内存队列

## 3.2 Flume Agent内部原理 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 6]

### (1) ChannelSelector ###

ChannelSelector的作用就是选出Event将要被发往哪个Channel，其共有两种类型

*  **Replicating（复制）**  
    ReplicatingSelector会将同一个Event发往所有的Channel，
 *  **和Multiplexing（多路复用）**  
    Multiplexing会根据相应的原则，将不同的Event发往不同的Channel

### (2) SinkProcessor ###

SinkProcessor共有三种类型

*  **DefaultSinkProcessor**  
    对应单个sink，发送至单个sink
 *  **LoadBalancingSinkProcessor**  
    LoadBalancingSinkProcessor对应的是Sink Group，LoadBalancingSinkProcessor可以实现负载均衡的功能
 *  **FailoverSinkProcessor**  
    FailoverSinkProcessor对应的是Sink Group，  
    FailoverSinkProcessor可以错误恢复的功能

# 四、Flume的拓扑结构 #

## 4.1 简单串联 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 7]  
这种模式是将多个flume顺序连接起来了，从最初的source开始到最终sink传送的目的存储系统。

*  **优点**  
    多个flume并联，可以增加event缓存数量
 *  **缺点**  
    此模式不建议桥接过多的flume数量， flume数量过多不仅会影响传输速率，而且一旦传输过程中某个节点flume宕机，会影响整个传输系统。

## 4.2 复制和多路复用 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 8]  
Flume支持将事件流向一个或者多个目的地。这种模式可以将相同数据复制到多个channel中，或者将不同数据分发到不同的channel中，sink可以选择传送到不同的目的地。

## 4.3 负载均衡和故障转移 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 9]  
Flume支持使用将多个sink逻辑上分到一个sink组，sink组配合不同的SinkProcessor可以实现负载均衡和错误恢复的功能。

## 4.4 聚合 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 10]  
这种模式是我们最常见的，也非常实用，日常web应用通常分布在上百个服务器，大者甚至上千个、上万个服务器。产生的日志，处理起来也非常麻烦。用flume的这种组合方式能很好的解决这一问题，每台服务器部署一个flume采集日志，传送到一个集中收集日志的flume，再由此flume上传到hdfs、hive、hbase等，进行日志分析。

# 五、Flume的企业开发实例 #

## 5.1 复制和多路复用 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 11]

### (1) 创建flume-file-avro.conf文件 ###

vi flume-file-avro.conf
    # 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/hive/logs/hive.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 = hadoop1021
    a1.sinks.k1.port = 4141
    
    a1.sinks.k2.type = avro
    a1.sinks.k2.hostname = hadoop101
    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

### (2) 创建flume-avro-hdfs.conf文件 ###

vi flume-avro-hdfs.conf
    # 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 = hadoop101
    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
    
    # 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-avro-dir.conf文件 ###

vi flume-avro-dir.conf
    # 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 = hadoop101
    a3.sources.r1.port = 4142
    
    # Describe the sink
    a3.sinks.k1.type = file_roll
    a3.sinks.k1.sink.directory = /opt/module/flume/data/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

### (4) 执行配置文件 ###

bin/flume-ng agent -n a3 -c conf/ -f job/group1/flume-avro-dir.conf
    bin/flume-ng agent -n a2 -c conf/ -f job/group1/flume-avro-hdfs.conf
    bin/flume-ng agent -n a1 -c conf/ -f job/group1/flume-file-avro.conf

### (5) 启动Hadoop和Hive ###

sbin/start-dfs.sh
    sbin/start-yarn.sh
    bin/hive

## 5.2 故障转移 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 12]

### (1) 创建a1.conf文件 ###

vi a1.conf
    # Name the components on this agent
    a1.sources = r1
    a1.channels = c1
    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
    a1.sinkgroups.g1.sinks = k1 k2
    a1.sinkgroups.g1.processor.type = failover
    a1.sinkgroups.g1.processor.priority.k1 = 5
    a1.sinkgroups.g1.processor.priority.k2 = 10
    a1.sinkgroups.g1.processor.maxpenalty = 10000
    
    # Describe the sink
    a1.sinks.k1.type = avro
    a1.sinks.k1.hostname = hadoop101
    a1.sinks.k1.port = 4141
    
    a1.sinks.k2.type = avro
    a1.sinks.k2.hostname = hadoop101
    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.sinks.k1.channel = c1
    a1.sinks.k2.channel = c1

### (2) 创建a2.conf文件 ###

vi a2.conf
    # 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 = hadoop101
    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a3.conf文件 ###

vi a3.conf
    # 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 = hadoop101
    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 ###

bin/flume-ng agent -n a3 -c conf/ -f job/group2/a3.conf -Dflume.root.logger=INFO,console
    bin/flume-ng agent -n a2 -c conf/ -f job/group2/a2.conf -Dflume.root.logger=INFO,console
    bin/flume-ng agent -n a1 -c conf/ -f job/group2/a1.conf

### (5) 开启另一个终端，发送消息 ###

nc localhost 4444
    aaa

### (6) 杀死a3后，通过故障转移，a2能正常工作 ###

kill -9 a3-pid

## 5.3 负载均衡 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 13]

### (1) 创建a1.conf文件 ###

vi a1.conf
    # Name the components on this agent
    a1.sources = r1
    a1.channels = c1
    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
    a1.sinkgroups.g1.sinks = k1 k2
    a1.sinkgroups.g1.processor.type = load_balance
    a1.sinkgroups.g1.processor.backoff = true
    a1.sinkgroups.g1.processor.selector = random
    
    # Describe the sink
    a1.sinks.k1.type = avro
    a1.sinks.k1.hostname = hadoop101
    a1.sinks.k1.port = 4141
    
    a1.sinks.k2.type = avro
    a1.sinks.k2.hostname = hadoop101
    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.sinks.k1.channel = c1
    a1.sinks.k2.channel = c1

### (2) 创建a2.conf文件 ###

### (3) 创建a3.conf文件 ###

### (4) 执行配置文件 ###

### (5) 开启另一个终端，不断发送消息 ###

nc localhost 4444
    aaa

## 5.4 聚合 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 14]

### (1) 创建a1.conf文件 ###

vi a1.conf
    # 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/flume/group.log
    a1.sources.r1.shell = /bin/bash -c
    
    # Describe the sink
    a1.sinks.k1.type = avro
    a1.sinks.k1.hostname = hadoop103
    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a2.conf文件 ###

vi a2.conf
    # 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 = hadoop103
    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a3.conf文件 ###

vi a3.conf
    # 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 = hadoop103
    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 ###

*  **hadoop103**

bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group4/a3.conf -Dflume.root.logger=INFO,console

*  **hadoop102**

bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group4/a2.conf

*  **hadoop101**

bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group4/a1.conf

### (5) 开启另一个终端，不断发送消息 ###

*  **hadoop101**

nc hadoop102 44444
    aaa

### (6) 向group.log文件中，添加内容 ###

*  **hadoop101**

cd /opt/module/flume
    echo 222 >> group.log

## 5.5 自定义Interceptor案例 ##

根据日志不同的类型（type），将日志进行分流，分入到不同的sink

### (1) 实现一个Interceptor接口 ###

package com.inspur.flume.interceptor;
    
    import org.apache.flume.Context;
    import org.apache.flume.Event;
    import org.apache.flume.interceptor.Interceptor;
    
    import java.util.List;
    import java.util.Map;
    
    public class MyInterceptor implements Interceptor { 
        public void initialize() { 
    
        }
    
        public Event intercept(Event event) { 
            Map<String, String> headers = event.getHeaders();
            byte[] body = event.getBody();
            if (body[0] <= '9' && body[0] >= '0') { 
                headers.put("type", "number");
            } else { 
                headers.put("type", "not_number");
            }
            return event;
        }
    
        public List<Event> intercept(List<Event> events) { 
            for (Event event : events) { 
                intercept(event);
            }
            return events;
        }
    
        public void close() { 
    
        }
    
        public static class MyBuilder implements Interceptor.Builder{ 
            public Interceptor build() { 
                return new MyInterceptor();
            }
    
            public void configure(Context context) { 
    
            }
        }
    }

### (2) hadoop101创建配置文件a1.conf ###

*  **hadoop101**

cd /opt/module/flume/job/interceptor
    vi a1.conf 
    # Name the components on this agent
    a1.sources = r1
    a1.sinks = k1 k2
    a1.channels = c1 c2
    
    # Describe/configure the source
    a1.sources.r1.type = netcat
    a1.sources.r1.bind = localhost
    a1.sources.r1.port = 44444
    a1.sources.r1.interceptors = i1
    a1.sources.r1.interceptors.i1.type = com.inspur.flume.interceptor.MyInterceptor$MyBuilder
    a1.sources.r1.selector.type = multiplexing
    a1.sources.r1.selector.header = type
    a1.sources.r1.selector.mapping.not_number = c1
    a1.sources.r1.selector.mapping.number = c2
    # 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 = hadoop103
    a1.sinks.k2.port = 4242
    
    # Use a channel which buffers events in memory
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100
    
    # Use a channel which buffers events in memory
    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

### (3) hadoop102创建配置文件a1.conf ###

*  **hadoop102**

cd /opt/module/flume/job/interceptor
    vi a1.conf 
    # Name the components on this agent
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1
    
    a1.sources.r1.type = avro
    a1.sources.r1.bind = hadoop102
    a1.sources.r1.port = 4141
    
    a1.sinks.k1.type = logger
    
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100
    
    a1.sinks.k1.channel = c1
    a1.sources.r1.channels = c1

### (4) hadoop103创建配置文件a1.conf ###

*  **hadoop103**

cd /opt/module/flume/job/interceptor
    vi a1.conf 
    # Name the components on this agent
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1
    
    a1.sources.r1.type = avro
    a1.sources.r1.bind = hadoop103
    a1.sources.r1.port = 4242
    
    a1.sinks.k1.type = logger
    
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100
    
    a1.sinks.k1.channel = c1
    a1.sources.r1.channels = c1

### (5) 分别启动flume进程 ###

*  **hadoop103**

bin/flume-ng agent -n a1 -c conf/ -f job/interceptor/a1.conf -Dflume.root.logger=INFO,console

*  **hadoop102**

bin/flume-ng agent -n a1 -c conf/ -f job/interceptor/a1.conf -Dflume.root.logger=INFO,console

*  **hadoop101**

bin/flume-ng agent -n a1 -c conf/ -f job/interceptor/a1.conf -Dflume.root.logger=INFO,console

### (6) 开启另一个终端，不断发送消息 ###

*  **hadoop101**

nc hadoop102 44444
    aaa
    111
    1ss
    s11

## 5.6 自定义Source案例 ##

### (1) 实现一个Source类 ###

package com.inspur.flume.source;
    
    import org.apache.flume.Context;
    import org.apache.flume.Event;
    import org.apache.flume.EventDeliveryException;
    import org.apache.flume.PollableSource;
    import org.apache.flume.conf.Configurable;
    import org.apache.flume.event.SimpleEvent;
    import org.apache.flume.source.AbstractSource;
    
    import java.util.HashMap;
    
    public class MySource extends AbstractSource implements Configurable, PollableSource { 
    
        private String prefix;
        private long interval;
    
        public Status process() throws EventDeliveryException { 
            Status status = null;
            try { 
                for (int i = 1; i <= 5; i++) { 
                    Event e = new SimpleEvent();
                    e.setHeaders(new HashMap<String, String>());
                    e.setBody((prefix + i).getBytes());
                    getChannelProcessor().processEvent(e);
                    Thread.sleep(interval);
                }
                status = Status.READY;
            } catch (InterruptedException e) { 
                status = Status.BACKOFF;
            }
    
            return status;
        }
    
        public long getBackOffSleepIncrement() { 
            return 2000;
        }
    
        public long getMaxBackOffSleepInterval() { 
            return 20000;
        }
    
        public void configure(Context context) { 
            prefix = context.getString("source.prefix","Log");
            interval = context.getLong("source.interval",1000L);
        }
    }

### (2) hadoop101创建配置文件a1.conf ###

*  **hadoop101**

cd /opt/module/flume/job/source
    vi a1.conf 
    # Name the components on this agent
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1
    
    # Describe/configure the source
    a1.sources.r1.type = com.inspur.flume.source.MySource
    a1.sources.r1.source.prefix= Log
    a1.sources.r1.source.interval= 1000
    
    # Describe the sink
    a1.sinks.k1.type = logger
    
    # Use a channel which buffers events in memory
    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

### (3) 启动flume进程 ###

*  **hadoop101**

bin/flume-ng agent -n a1 -c conf/ -f job/source/a1.conf -Dflume.root.logger=INFO,console

## 5.7 自定义文件Source案例二 ##

### (1) 实现一个Source类，接收MQTT服务器 ###

1.  引入org.eclipse.paho.client.mqttv3.\* 类包
2.  自定义一个Source类
3.  自定义一个SimpleMqttClient类  
    该类用于配置MQTT服务器的链接（IP地址、端口号、用户名、密码、主题）、订阅主题消息、订阅消息的回调
4.  接收到回调函数的消息，进行event的封装，然后发送

### (2) 实现一个Source类，接收传感器串口数据 ###

1.  自定义一个Source类
2.  自定义一个串口类  
    该类用于配置串口类的链接（串口号、波特率、校验位、数据位、停止位）、数据的回调（数据都是以十六进制进行传递）
3.  接收到回调函数的消息，进行event的封装，然后发送

## 5.8 自定义Sink案例 ##

### (1) 实现一个Sink类 ###

package com.inspur.flume.sink;
    
    import org.apache.flume.*;
    import org.apache.flume.conf.Configurable;
    import org.apache.flume.sink.AbstractSink;
    
    public class MySink extends AbstractSink implements Configurable { 
        private long interval;
        private String prefix;
        private String suffix;
    
        public Status process() throws EventDeliveryException { 
            Status status = null;
            Channel channel = this.getChannel();
            Transaction transaction = channel.getTransaction();
            transaction.begin();
            try { 
                Event event = null;
                while ((event = channel.take()) == null) { 
                    Thread.sleep(interval);
                }
                byte[] body = event.getBody();
                String line = new String(body, "UTF-8");
                System.out.println(prefix + line + suffix);
                status = Status.READY;
                transaction.commit();
            } catch (Exception e) { 
                transaction.rollback();
                status = Status.BACKOFF;
            } finally { 
                transaction.close();
            }
    
            return status;
        }
    
        public void configure(Context context) { 
            prefix = context.getString("source.prefix", "start:");
            suffix = context.getString("source.suffix", ":end");
            interval = context.getLong("source.interval", 1000L);
        }
    }

### (2) hadoop101创建配置文件a1.conf ###

*  **hadoop101**

# 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 = localhost
    a1.sources.r1.port = 44444
    
    # Describe the sink
    a1.sinks.k1.type = com.inspur.flume.sink.MySink
    a1.sinks.k1.source.prefix = xuzheng:
    a1.sinks.k1.source.suffix = :xuzheng
    a1.sinks.k1.source.interval = 1000
    
    # Use a channel which buffers events in memory
    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

### (3) 启动flume进程 ###

*  **hadoop101**

bin/flume-ng agent -n a1 -c conf/ -f job/sink/a1.conf -Dflume.root.logger=INFO,console

# 六、Flume数据流监控 #

## 6.1 Flume自带监控工具 ##

执行如下的命令，并访问 http://IP:1234

bin/flume-ng agent -n a1 -c conf/ -f job/sink/a1.conf -Dflume.monitoring.type=http -Dflume.monitoring.port=1234

## 6.2 Ganglia ##

Ganglia由gmond、gmetad和gweb三部分组成

*  **gmond（Ganglia Monitoring Daemon）**  
    gmond是一种轻量级服务，安装在每台需要收集指标数据的节点主机上。使用gmond，你可以很容易收集很多系统指标数据，如CPU、内存、磁盘、网络和活跃进程的数据等
 *  **gmetad（Ganglia Meta Daemon）**  
    gmetad整合所有信息，并将其以RRD格式存储至磁盘的服务
 *  **gweb（Ganglia Web）**  
    Ganglia可视化工具，gweb是一种利用浏览器显示gmetad所存储数据的PHP前端。在Web界面中以图表方式展现集群的运行状态下收集的多种不同指标数据

## 6.3 Ganglia监控视图 ##

<table> 
 <thead> 
  <tr> 
   <th align="left">监控视图名称</th> 
   <th align="left">描述</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td align="left">EventPutAttemptCount</td> 
   <td align="left">source尝试写入channel的事件总数量</td> 
  </tr> 
  <tr> 
   <td align="left">EventPutSuccessCount</td> 
   <td align="left">成功写入channel且提交的事件总数量</td> 
  </tr> 
  <tr> 
   <td align="left">EventTakeAttemptCount</td> 
   <td align="left">sink尝试从channel拉取事件的总数量</td> 
  </tr> 
  <tr> 
   <td align="left">EventTakeSuccessCount</td> 
   <td align="left">sink成功读取的事件的总数量</td> 
  </tr> 
  <tr> 
   <td align="left">StartTime</td> 
   <td align="left">channel启动的时间（毫秒）</td> 
  </tr> 
  <tr> 
   <td align="left">StopTime</td> 
   <td align="left">channel停止的时间（毫秒）</td> 
  </tr> 
  <tr> 
   <td align="left">ChannelSize</td> 
   <td align="left">目前channel中事件的总数量</td> 
  </tr> 
  <tr> 
   <td align="left">ChannelFillPercentage</td> 
   <td align="left">channel占用百分比</td> 
  </tr> 
  <tr> 
   <td align="left">ChannelCapacity</td> 
   <td align="left">channel的容量</td> 
  </tr> 
 </tbody> 
</table>

EventPutSuccessCount = EventTakeSuccessCount + ChannelSize  
否则，丢数据

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70]: /images/20210920/bb9b9d87dad041f996119dc0b3e3a1db.png
[20200618084704461.png]: /images/20210920/1d55a84adb1443abac81fa4ee9213ae3.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 1]: /images/20210920/d0c315a6df0648599062ff4b4f98f96b.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 2]: /images/20210920/9d4363613414437dabd76327bdacf378.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 3]: /images/20210920/7cf06a5aea9440e09b96f4fe0f5b7f08.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 4]: /images/20210920/70fdbf47c9ac446ca6a136fed10d00f9.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 5]: /images/20210920/367bbc8a6dbf48cf8f7fb6d0ea3b1ae8.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 6]: /images/20210920/10fd92dacc574f7cbbda84d305950482.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 7]: /images/20210920/5a97a52eaf664982baadae75c5c917c3.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 8]: /images/20210920/5bf346ee6a834d50bc918b81fc13ac89.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 9]: /images/20210920/53248bd728eb40d1a4cd9d8ff5082dc4.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 10]: /images/20210920/18b85cbee91d45d79d444c97285bc6ca.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 11]: https://img-blog.csdnimg.cn/20200619100122518.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 12]: https://img-blog.csdnimg.cn/20200619105749441.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 13]: https://img-blog.csdnimg.cn/20200619111514941.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5_size_16_color_FFFFFF_t_70 14]: https://img-blog.csdnimg.cn/20200619131332422.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyNTk5NDc5,size_16,color_FFFFFF,t_70