Flume的学习和使用
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
(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
-
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
-
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
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
(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
(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
(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。
(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.*
# 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
(1) Put事务流程
- doPut：将批数据先写入临时缓存区putList
- doCommit：检查channel内存队列是否足够合并
- doRollback：channel内存队列空间不足，回滚数据
(2) Take事务流程
- doTake：将数据取到临时缓存区takeList，并将数据发送到HDFS
- doCommit：如果数据全部发送成功，则清除临时缓冲区takeList
- 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
(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
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
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
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
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
(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
(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
(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
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，不断发送消息
nc localhost 4444
aaa
5.4 聚合
(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
(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.channel.ChannelProcessor;
import org.apache.flume.conf.Configurable;
import org.apache.flume.event.SimpleEvent;
import org.apache.flume.source.AbstractSource;
import java.io.*;
import java.util.HashMap;
public class MySource extends AbstractSource implements Configurable, PollableSource {
private long interval;
private String file;
public Status process() throws EventDeliveryException {
Status status = null;
ChannelProcessor channelProcessor = getChannelProcessor();
BufferedReader bufferedReader = null;
try {
bufferedReader = new BufferedReader(new InputStreamReader(new FileInputStream(file)));
String line;
while ((line = bufferedReader.readLine()) != null) {
Event event = new SimpleEvent();
event.setHeaders(new HashMap<String, String>());
event.setBody(line.getBytes());
channelProcessor.processEvent(event);
try {
Thread.sleep(interval);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
status = Status.READY;
} catch (IOException e) {
status = Status.BACKOFF;
} finally {
if (bufferedReader != null) {
try {
bufferedReader.close();
} catch (IOException e) {
e.printStackTrace();
}
}
}
return status;
}
public long getBackOffSleepIncrement() {
return 2000;
}
public long getMaxBackOffSleepInterval() {
return 20000;
}
public void configure(Context context) {
file = context.getString("source.file", null);
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.file= /opt/module/flume/group.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
5.8 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 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