Flume学习笔记(2)—— Flume进阶

Flume进阶

Flume 事务

事务处理流程如下:

Put

  • doPut:将批数据先写入临时缓冲区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

Take

  • doTake:将数据取到临时缓冲区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

Flume Agent 内部原理

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,分别是 Replicating(复制)和 Multiplexing(多路复用)

  • ReplicatingSelector 会将同一个 Event 发往所有的 Channel
  • Multiplexing 会根据相应的原则 ,将不同的 Event 发往不同的 Channel

SinkProcessor

SinkProcessor 共有三种类型 , 分别是 DefaultSinkProcessor 、LoadBalancingSinkProcessor、FailoverSinkProcessor

  • DefaultSinkProcessor 对应的是单个的Sink
  • LoadBalancingSinkProcessor 可以实现负载均衡的功能
  • FailoverSinkProcessor 可以实现错误恢复的功能

Flume 拓扑结构

简单串联

将多个 flume 顺序连接起来,从最初的 source 开始到最终 sink 传送的目的存储系统

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

复制和多路复用

(单 source,多 channel、sink)

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

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、

这里的agent1有三个sink,分别连接agent2,agent3,agent4,即使其中有的sink出现了故障,数据还是能同步到hdfs

聚合

业务中常用,比如说日志采集功能:

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

可以采用聚合的方式,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

Flume实战案例

复制和多路复用

**需求:**使用 Flume-1 监控文件变动

  1. Flume-1 将变动内容传递给 Flume-2,Flume-2 负责存储到 HDFS
  2. Flume-1 将变动内容传递给 Flume-3,Flume-3 负责输出到 Local FileSystem

实现流程:

1.在job下创建文件夹group1,并在其中创建配置文件flume-file-flume.conf

配置文件中需要有1个source,2个channel,2个sink

bash 复制代码
# 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/apache-hive-3.1.2-bin/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 = 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

该配置文件的作用是将数据发送到两个不同的sink,再由sink发送到其他的agent进行处理

2.创建配置文件flume-flume-hdfs.conf

bash 复制代码
# 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 = hadoop102
a2.sources.r1.port = 4141

# Describe the sink
a2.sinks.k1.type = hdfs
a2.sinks.k1.hdfs.path = hdfs://hadoop102:8020/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 = 30
#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

source绑定上一个agent的sink1,然后上传到hdfs

3.创建配置文件:flume-flume-dir.conf

bash 复制代码
# 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 = /home/why/data/flumeDemo/test1

# 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

参数说明:

sink类型为file_roll:Flume 1.11.0 User Guide --- Apache Flume

可以将events保存到本地文件系统

  • directory:本地文件系统保存数据的路径(注意,该路径必须已经存在才可以)

4.分别启动相应的flume进程:

nohup bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group1/flume-flume-dir.conf &

nohup bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group1/flume-flume-hdfs.conf &

nohup bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group1/flume-file-flume.conf &

5.在hdfs和文件夹中都能看到相应的内容:

hdfs:

文件系统:

负载均衡和故障转移

需求: 使用 Flume1 监控一个端口,其 sink 组中的 sink 分别对接 Flume2 和 Flume3,采用FailoverSinkProcessor,实现故障转移的功能

实现流程:

1.在/opt/module/flume/job 目录下创建 group2 文件夹,创建配置文件flume-netcat-flume.conf

配置 1 个 netcat source 和 1 个 channel、1 个 sink group(2 个 sink),分别输送给flume-flume-console1 和 flume-flume-console2

bash 复制代码
# Name the components on this agent
a1.sources = r1
a1.channels = c1
a1.sinkgroups = g1
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
a1.sinks = k1 k2

# 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 = 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

# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinkgroups.g1.sinks = k1 k2
a1.sinks.k1.channel = c1
a1.sinks.k2.channel = c1

参数说明:Flume 1.11.0 User Guide --- Apache Flume

通过sink groups在一个agent中定义多个sink,并可以配置sink processor使用:Flume 1.11.0 User Guide --- Apache Flume

2.创建 flume-flume-console1.conf

bash 复制代码
# 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 = 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

sink输出到本地的控制台

3.创建 flume-flume-console2.conf

bash 复制代码
# 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 = 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

sink输出到本地的控制台

4.执行指令:

bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group2/flume-flume-console2.conf -Dflume.root.logger=INFO,console

bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group2/flume-flume-console1.conf -Dflume.root.logger=INFO,console

bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group2/flume-netcat-flume.conf

5.使用nc localhost 44444发送数据

由于console2设置的优先级高于console1,因此数据由console2接收到;

接下来将console2进程kill掉,数据就由console1接收了:

聚合

需求:

hadoop102 上的 Flume-1 监控文件/home/why/data/flumeDemo/test3/test3.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 hadoop104 上的 Flume-3,Flume-3 将最终数据打印到控制台

实现流程:

1.首先在三台服务器的job文件夹先创建目录group3

2.在hadoop102上,创建配置文件flume1-logger-flume.conf,source用于监控log日志文件,sink用于输出数据到下一级的Flume

bash 复制代码
# 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/why/data/flumeDemo/test3/test3.log
a1.sources.r1.shell = /bin/bash -c

# Describe the sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = hadoop104
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

3.在hadoop103上,创建配置文件flume2-netcat-flume.conf,source用于监控端口44444的数据流,sink用于将数据传输到下一级的flume

bash 复制代码
# 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 = localhost
a2.sources.r1.port = 44444

# Describe the sink
a2.sinks.k1.type = avro
a2.sinks.k1.hostname = hadoop104
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

注意,这两个agent的sink目的地都是hadoop104这一个服务器,因此hostname和port都相同

4.在hadoop104上创建配置文件flume3-flume-logger.conf,source用于接收flume1和flume2发送来的数据流,sink用于输出数据到控制台;

bash 复制代码
# 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 = hadoop104
a3.sources.r1.port = 4141

# 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

5.分别在三台服务器上执行指令

hadoop104: bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group3/flume3-flume-logger.conf -Dflume.root.logger=INFO,console

hadoop102:bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group3/flume1-logger-flume.conf

hadoop103:bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group3/flume2-netcat-flume.conf

6.在hadoop102上向日志文件中追加内容:

echo "hello" > /home/why/data/flumeDemo/test3/test3.log

在hadoop103中通过nc hadoop103 44444向44444端口发送数据;

然后在hadoop104中即可接收到数据:

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