Flume(一)

it2024-04-22 7

Flume的定义

Flume是一个分布式的、高可靠的、高可用的将大批量的不同数据源的日志数据收集、聚合、移动到数据中心（HDFS）进行存储的系统。即是日志采集和汇总的工具Logstash、FileBeat是ES栈的日志数据抽取工具，他们和Flume很类似，前者是轻量级、后者是重量级，若项目组使用的是ES栈技术，那完全可以使用Logstash取代Flume。

版本

NG: 1.x的版本 (N=NEW)OG：0.9.x的版本,不用管(O=OLD)由于我使用的是CDH5.7.0，故选择flume-ng-1.6.0-cdh5.7.0版本，注意此1.6和社区的1.6有差别。

flume的优势：

可以高速采集数据，采集的数据能够以想要的文件格式及压缩方式存储在hdfs上事务功能保证了数据在采集的过程中数据不丢失部分Source保证了Flume挂了以后重启依旧能够继续在上一次采集点采集数据，真正做到数据零丢失

flume的组成

flume有3大组件 source(源端数据采集)：Flume提供了各种各样的Source、同时还提供了自定义的SourceChannel(临时存储聚合数据)：主要用的是memory channel和File channel（生产最常用），生产中channel的数据一定是要监控的，防止sink挂了，撑爆channelSink（移动数据到目标端）：如HDFS、KAFKA、DB以及自定义的sink

flume的架构

flume的agent架构单Agent：

串联Agent：

并联Agent(生产中最多的使用)：

多sinkAgent(也很常见)：

flume部署

打开官网http://archive.cloudera.com/cdh5/cdh/5/

上传并解压

[hadoop@hadoop001 app]$ rz flume-ng-1.6.0-cdh5.7.0.tar.gz [hadoop@hadoop001 app]$tar -xzvf flume-ng-1.6.0-cdh5.7.0.tar.gz

#修改配置文件，添加JAVA_HOME

[hadoop@hadoop001 app]$ cd ~/app/apache-flume-1.6.0-cdh5.7.0-bin [hadoop@hadoop001 apache-flume-1.6.0-cdh5.7.0-bin]$ cp ~/app/apache-flume-1.6.0-cdh5.7.0-bin/conf/flume-env.sh.template ~/app/apache-flume-1.6.0-cdh5.7.0-bin/conf/flume-env.sh [hadoop@hadoop001 apache-flume-1.6.0-cdh5.7.0-bin]$ vim ~/app/apache-flume-1.6.0-cdh5.7.0-bin/conf/flume-env.sh export JAVA_HOME=/usr/java/jdk1.8.0_45

#添加环境变量

hadoop@hadoop001 bin]$ soruce ~/.bash_profile export FLUME_HOME=/home/hadoop/app/apache-flume-1.6.0-cdh5.7.0-bin export PATH=$FLUME_HOME/bin:$PATH [hadoop@hadoop001 bin]$ source ~/.bash_profile [hadoop@hadoop001 bin]$ which flume-ng ~/app/apache-flume-1.6.0-cdh5.7.0-bin/bin/flume-ng

Agent配置使用案列

Flume的使用其实就是Source、Channel、Sink的配置Agent=Source+Channel+Sink，其实agent就是Flume的配置文件一个配置文件可以配置多个Agent的。Event：Flume数据传输的最小单位，一个EVent就是一条记录，由head和body两个部分组成，head存储的是管道，body存储的是字节数组

Flume文件配置

[hadoop@hadoop001 conf]$ vim /home/hadoop/app/apache-flume-1.6.0-cdh5.7.0-bin/conf/example.conf # 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 = localhost 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 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 NetSource:黑色的配置是必填项

memory channel：capatity=>channel的存储最大event(消息)个数，生产至少10万条，transationCapacity=>最多达到多少条必须提交事务。生产也必须调大。

logger：就是控制台类型的sink注意1：一个source可以绑定多个channel,但是一个sink只能绑定一个Channel

启动Agent以及测试

启动 #最后一行是为了方便观察输出INFO日志到控制台，可以去掉 flume-ng agent \ --name a1 \ --conf $FLUME_HOME/conf \ --conf-file $FLUME_HOME/conf/example.conf \ -Dflume.root.logger=INFO,console 使用telnet测试： [hadoop@hadoop001 ~]$ telnet localhost 44444 Trying 127.0.0.1... Connected to localhost. Escape character is '^]'. hello OK

Exec Source采集文件数据到hdfs

生产的架构是： log数据=》flume=》hdfs，这里我们采用简单的Exec Source通过tail -F 数据文件进行数据采集。

# example.conf: A single-node Flume configuration # Name the components on this agent exec-hdfs-agent.sources = exec-source exec-hdfs-agent.sinks = hdfs-sink exec-hdfs-agent.channels = memory-channel # Describe/configure the source exec-hdfs-agent.sources.exec-source.type = exec exec-hdfs-agent.sources.exec-source.command = tail -F /home/hadoop/data/access_10000.log exec-hdfs-agent.sources.exec-source.shell = /bin/sh -c # Describe the sink exec-hdfs-agent.sinks.hdfs-sink.type = hdfs exec-hdfs-agent.sinks.hdfs-sink.hdfs.path = hdfs://hadoop001:9000/flume/exec exec-hdfs-agent.sinks.hdfs-sink.hdfs.fileType = DataStream exec-hdfs-agent.sinks.hdfs-sink.hdfs.writeFormat = Text # Use a channel which buffers events in memory exec-hdfs-agent.channels.memory-channel.type = memory exec-hdfs-agent.channels.memory-channel.capacity = 1000 exec-hdfs-agent.channels.memory-channel.transactionCapacity = 100 # Bind the source and sink to the channel exec-hdfs-agent.sources.exec-source.channels = memory-channel exec-hdfs-agent.sinks.hdfs-sink.channel = memory-channel

写hdfs文件时先生成创建一个后缀名称为.tmp的文件，当写完成时，去掉了.tmp

缺点：虽然此种tail方式可以将日志数据采集到hdfs，但是tail -F进程挂了咋办，还是会丢数据！生产上是行不通的。无法做到高可用。其次上面的采集流程并未解决生成大量小文件的问题，无法做到高可靠tail只能监控一个文件，生产中更多的是监控一个文件夹。不能满足需求

使用Spooling Directory Source采集文件夹数据到hdfs

写到HDFS上的文件大小最好是100M左右，比blocksize的值（128M）略低一般使用rolllnterval（时间）、rollSize（大小）来控制文件的生成，哪个先触发就会生成HDFS文件，将根据条数的roll关闭。rollSize控制的大小是指的压缩前的，所以若hdfs文件使用了压缩，需调大rollsize的大小当文件夹下的某个文件被采集到hdfs上，会有个。complete的标志使用Spooling Directory Source采集文件数据时若该文件数据已经被采集，再对该文件做修改是会报错的停止的，其次若放进去一个已经完成采集的同名数据文件也是会报错停止的写HDFS数据可按照时间分区，注意改时间刻度内无数据则不会生成该时间文件夹生成的文件名称默认是前缀+时间戳，这个是可以更改的。 # example.conf: A single-node Flume configuration # Name the components on this agent spool-hdfs-agent.sources = spool-source spool-hdfs-agent.sinks = hdfs-sink spool-hdfs-agent.channels = memory-channel # Describe/configure the source spool-hdfs-agent.sources.spool-source.type = spooldir spool-hdfs-agent.sources.spool-source.spoolDir = /home/hadoop/data/flume/spool/input # Describe the sink spool-hdfs-agent.sinks.hdfs-sink.type = hdfs spool-hdfs-agent.sinks.hdfs-sink.hdfs.path = hdfs://hadoop001:9000/flume/spool/%Y%m%d%H%M spool-hdfs-agent.sinks.hdfs-sink.hdfs.useLocalTimeStamp = true spool-hdfs-agent.sinks.hdfs-sink.hdfs.fileType = CompressedStream spool-hdfs-agent.sinks.hdfs-sink.hdfs.writeFormat = Text spool-hdfs-agent.sinks.hdfs-sink.hdfs.codeC = gzip spool-hdfs-agent.sinks.hdfs-sink.hdfs.filePrefix = wsk spool-hdfs-agent.sinks.hdfs-sink.hdfs.rollInterval = 30 spool-hdfs-agent.sinks.hdfs-sink.hdfs.rollSize = 100000000 spool-hdfs-agent.sinks.hdfs-sink.hdfs.rollCount = 0 # Use a channel which buffers events in memory spool-hdfs-agent.channels.memory-channel.type = memory spool-hdfs-agent.channels.memory-channel.capacity = 1000 spool-hdfs-agent.channels.memory-channel.transactionCapacity = 100 # Bind the source and sink to the channel spool-hdfs-agent.sources.spool-source.channels = memory-channel spool-hdfs-agent.sinks.hdfs-sink.channel = memory-channel

上述的Spooling Directory Source配置虽然解决了小文件过多以及监控多个文件的问题，但是依旧有如下问题。

问题1：虽然能监控一个文件夹，但是无法监控递归的文件夹中的数据问题2：若采集时Flume挂了，无法保证重启时还从之前文件读取的那一行继续采集数据

基于以上两个问题，此凡是生产也是不可接受的

(生产版本)使用Taildir Source采集文件夹数据到hdfs

Taildir Source 是Apache flume1.7新推出的，但是CDH Flume1.6做了集成Taildir Source是高可靠（reliable）的source，他会实时的将文件偏移量写到json文件中并保存到磁盘。下次重启Flume时会读取Json文件获取文件O偏移量，然后从之前的位置读取数据，保证数据零丢失taildir Source可同时监控多个文件夹以及文件。即使文件在实时写入数据。Taildir Source也是无法采集递归文件下的数据，这需要改造源码Taildir Source监控一个文件夹下的所有文件一定要用.*正则

# example.conf: A single-node Flume configuration # Name the components on this agent taildir-hdfs-agent.sources = taildir-source taildir-hdfs-agent.sinks = hdfs-sink taildir-hdfs-agent.channels = memory-channel # Describe/configure the source taildir-hdfs-agent.sources.taildir-source.type = TAILDIR taildir-hdfs-agent.sources.taildir-source.filegroups = f1 taildir-hdfs-agent.sources.taildir-source.filegroups.f1 = /home/hadoop/data/flume/taildir/input/.* taildir-hdfs-agent.sources.taildir-source.positionFile = /home/hadoop/data/flume/taildir/taildir_position/taildir_position.json # Describe the sink taildir-hdfs-agent.sinks.hdfs-sink.type = hdfs taildir-hdfs-agent.sinks.hdfs-sink.hdfs.path = hdfs://hadoop001:9000/flume/taildir/%Y%m%d%H%M taildir-hdfs-agent.sinks.hdfs-sink.hdfs.useLocalTimeStamp = true taildir-hdfs-agent.sinks.hdfs-sink.hdfs.fileType = CompressedStream taildir-hdfs-agent.sinks.hdfs-sink.hdfs.writeFormat = Text taildir-hdfs-agent.sinks.hdfs-sink.hdfs.codeC = gzip taildir-hdfs-agent.sinks.hdfs-sink.hdfs.filePrefix = wsk taildir-hdfs-agent.sinks.hdfs-sink.hdfs.rollInterval = 30 taildir-hdfs-agent.sinks.hdfs-sink.hdfs.rollSize = 100000000 taildir-hdfs-agent.sinks.hdfs-sink.hdfs.rollCount = 0 # Use a channel which buffers events in memory taildir-hdfs-agent.channels.memory-channel.type = memory taildir-hdfs-agent.channels.memory-channel.capacity = 1000 taildir-hdfs-agent.channels.memory-channel.transactionCapacity = 100 # Bind the source and sink to the channel taildir-hdfs-agent.sources.taildir-source.channels = memory-channel taildir-hdfs-agent.sinks.hdfs-sink.channel = memory-channel

转载于:https://www.cnblogs.com/xuziyu/p/11004103.html