日志采集框架Flume
Flume介绍
概述
Flume是一个分布式、可靠、和高可用的海量日志采集、聚合和传输的系统。
Flume可以采集文件，socket数据包、文件、文件夹、kafka等各种形式源数据，又可以将采集到的数据(下沉sink)输出到HDFS、hbase、hive、kafka等众多外部存储系统中
运行机制
Flume分布式系统最核心的角色是agent，flume采集系统就是由一个个agent所连接起来而成
每一个agent相当于一个数据传递员，内部有三个组件：
- Source：采集组件，用于跟数据源对接，获取数据
- Sink：下沉组件，用于往下一级agent传递数据或者往最终存储系统传递数据
- Channel：传输通道组件，用于从source将数据传递到sink
采集系统结构图
简单结构
复杂结构
多级agent之间串联
Flume实战案例
安装部署
第一步：下载解压修改配置文件
Flume的安装非常简单，只需要解压即可，当然，前提是已有hadoop环境
# 上传安装包到数据源所在节点上 这里采用在第三台机器来进行安装 软件目录 => flume-ng-1.6.0-cdh5.14.0.tar.gz tar -zxvf flume-ng-1.6.0-cdh5.14.0.tar.gz -C ../servers/ cd ../servers/apache-flume-1.6.0-cdh5.14.0-bin/conf/ cp flume-env.sh.template flume-env.sh vim flume-env.sh #只添加一个java环境就可以了 export JAVA_HOME=/export/servers/jdk1.8.0_141第二步：开发配置文件
# 根据数据采集的需求配置采集方案，描述在配置文件中(文件名可任意自定义) # 配置我们的网络收集的配置文件 # 在flume的conf目录下新建一个配置文件（采集方案） vim /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf/netcat-logger.conf # 定义这个agent中各组件的名字 a1.sources = r1 a1.sinks = k1 a1.channels = c1 # 描述和配置source组件：r1 a1.sources.r1.type = netcat a1.sources.r1.bind = 192.168.52.120 a1.sources.r1.port = 44444 # 描述和配置sink组件：k1 a1.sinks.k1.type = logger # 描述和配置channel组件，此处使用是内存缓存的方式 a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # 描述和配置source channel sink之间的连接关系 a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1启动配置文件
指定采集方案配置文件，在相应的节点上启动flume agent
cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin bin/flume-ng agent -c conf -f conf/netcat-logger.conf -n a1 -Dflume.root.logger=INFO,console # -c conf 指定flume自身的配置文件所在目录 # -f conf/netcat-logger.conf 指定所描述的采集方案 # -n a1 指定这个agent的名字安装telent准备测试
在node02上安装telnet客户端用于模拟数据的发送
yum -y install telnet telnet node03 44444 # 使用telnet模拟数据发送
采集案例
采集目录到HDFS
某服务器的特定目录下会不断产生新的文件，每当有新文件出现，就需要把文件采集到HDFS中去
根据需求，首先定义以下3大要素
- 数据源组件，即source -- 监控文件目录：spooldirspooldir特性：
- 监视一个目录，只要目录中出现新文件，就会采集文件中的内容
- 采集完成的文件，会被agent自动添加一个后缀：COMPLETED
- 所监视的目录中不允许重复出现相同文件名的文件
- 下沉组件，妈sink -- HDFS文件系统：hdfs sink
- 通道组件，妈channel -- 可用file channel 也可以用内存 memory channel
- 数据源组件，即source -- 监控文件目录：spooldirspooldir特性：
flume配置文件开发
cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf mkdir -p /export/servers/dirfile vim spooldir.conf # 定义agent的组件名字 a1.sources=sr1 a1.sinks=sk1 a1.channels=scn1 # 配置数据源source a1.sources.sr1.type=spooldir a1.sources.sr1.spoolDir=/export/servers/dirfile a1.sources.sr1.fileHeader=true # 配置下沉组件sink a1.sinks.sk1.type=hdfs a1.sinks.sk1.channel=scn1 # hdfs目录路径 a1.sinks.sk1.hdfs.path=hdfs://node01:8020/spooldir/files/%y-%m-%d/%H%M/ # 写入hdfs的文件名前缀 可以使用flume提供的日期及%{host}表达式 a1.sinks.sk1.hdfs.filePrefix=events- # 表示到了需要触发的时间时，是否要更新文件夹，true:表示要 a1.sinks.sk1.hdfs.round=true # 表示每隔value分钟改变一次(在0~24之间) a1.sinks.sk1.hdfs.roundValue=10 # 切换文件的时候的时间单位是分钟 a1.sinks.sk1.hdfs.roundUnit=minute # 多久时间后close hdfs文件。单位是秒，默认30秒。设置为0的话表示不根据时间close hdfs文件 a1.sinks.sk1.hdfs.rollInterval=3 # 文件大小超过一定值后，close文件。默认值1024，单位是字节。设置为0的话表示不基于文件大小,134217728表 示128m，决定了多大块可以切一个文件。 a1.sinks.sk1.hdfs.rollSize=134217728 # 写入了多少个事件后close文件。默认值是10个。设置为0的话表示不基于事件个数 a1.sinks.sk1.hdfs.rollCount=0 # 批次数，HDFS Sink每次从Channel中拿的事件个数。默认值100 a1.sinks.sk1.hdfs.batchSize=100 # 使用本地时间戳 a1.sinks.sk1.hdfs.useLocalTimeStamp=true #生成的文件类型默认是 Sequencefile,可用DataStream则为普通文本 a1.sinks.sk1.hdfs.fileType=DataStream # 配置通道channel a1.channels.scn1.type=memory a1.channels.scn1.capacity=1000 a1.channels.scn1.transactionCapacity=100 bin/flume-ng agent -c ./conf/ -f ./conf/spooldir.conf -n a1 -Dflume.root.logger=INFO,console # 运行flume
采集文件到HDFS
比如业务系统使用Log4j生成的日志，日志内容不断增加，需要把追加到日志文件中的数据实时采集到hdfs
- 根据需求，首先定义以下3大要素
- 采集源，即source——监控文件内容更新 : exec ‘tail -F file’
- 下沉目标，即sink——HDFS文件系统 : hdfs sink
- Source和sink之间的传递通道——channel，可用filechannel 也可以用 内存channel
定义flume的配置文件
cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf vim tail-file.conf agent1.sources = source1 agent1.sinks = sink1 agent1.channels = channel1 # Describe/configure tail -F source1 agent1.sources.source1.type = exec agent1.sources.source1.command = tail -F /export/servers/taillogs/access_log agent1.sources.source1.channels = channel1 #configure host for source #agent1.sources.source1.interceptors = i1 #agent1.sources.source1.interceptors.i1.type = host #agent1.sources.source1.interceptors.i1.hostHeader = hostname # Describe sink1 agent1.sinks.sink1.type = hdfs #a1.sinks.k1.channel = c1 agent1.sinks.sink1.hdfs.path = hdfs://node01:8020/weblog/flume-collection/%y-%m-%d/%H-%M agent1.sinks.sink1.hdfs.filePrefix = access_log agent1.sinks.sink1.hdfs.maxOpenFiles = 5000 agent1.sinks.sink1.hdfs.batchSize= 100 agent1.sinks.sink1.hdfs.fileType = DataStream agent1.sinks.sink1.hdfs.writeFormat =Text agent1.sinks.sink1.hdfs.rollSize = 102400 agent1.sinks.sink1.hdfs.rollCount = 1000000 agent1.sinks.sink1.hdfs.rollInterval = 60 agent1.sinks.sink1.hdfs.round = true agent1.sinks.sink1.hdfs.roundValue = 10 agent1.sinks.sink1.hdfs.roundUnit = minute agent1.sinks.sink1.hdfs.useLocalTimeStamp = true # Use a channel which buffers events in memory agent1.channels.channel1.type = memory agent1.channels.channel1.keep-alive = 120 agent1.channels.channel1.capacity = 500000 agent1.channels.channel1.transactionCapacity = 600 # Bind the source and sink to the channel agent1.sources.source1.channels = channel1 agent1.sinks.sink1.channel = channel1 bin/flume-ng agent -c conf -f conf/tail-file.conf -n agent1 -Dflume.root.logger=INFO,console #启动Flume # 开发shell脚本定时追加文件内容 mkdir -p /export/servers/shells/ cd /export/servers/shells/ vim tail-file.sh #!/bin/bash while true do date >> /export/servers/taillogs/access_log;
两个agent级联
第一个agent负责收集文件当中的数据，通过网络发送到第二个agent当中去，第二个agent负责接收第一个agent发送的数据，并将数据保存到hdfs上面去
第一步：node02安装flume
cd /export/servers
scp -r apache-flume-1.6.0-cdh5.14.0-bin/ node02:$PWD第二步：node02配置flume配置文件
cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
vim tail-avro-avro-logger.conf
##################
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /export/servers/taillogs/access_log
a1.sources.r1.channels = c1
# Describe the sink
##sink端的avro是一个数据发送者
a1.sinks = k1
a1.sinks.k1.type = avro
a1.sinks.k1.channel = c1
a1.sinks.k1.hostname = 192.168.52.120
a1.sinks.k1.port = 4141
a1.sinks.k1.batch-size = 10
# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1第三步：node02开发脚本文件往文件写入数据
# 直接把node03的脚本拷贝至node02
cd /export/servers
scp -r shells/ taillogs/ node02:$PWD第四步node03开发Flume配置文件
# 在node03机器上开发flume的配置文件
cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
vim avro-hdfs.conf #配置如下
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source
##source中的avro组件是一个接收者服务
a1.sources.r1.type = avro
a1.sources.r1.channels = c1
a1.sources.r1.bind = 192.168.52.120
a1.sources.r1.port = 4141
# Describe the sink
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path = hdfs://node01:8020/avro/hdfs/%y-%m-%d/%H%M/
a1.sinks.k1.hdfs.filePrefix = events-
a1.sinks.k1.hdfs.round = true
a1.sinks.k1.hdfs.roundValue = 10
a1.sinks.k1.hdfs.roundUnit = minute
a1.sinks.k1.hdfs.rollInterval = 3
a1.sinks.k1.hdfs.rollSize = 20
a1.sinks.k1.hdfs.rollCount = 5
a1.sinks.k1.hdfs.batchSize = 1
a1.sinks.k1.hdfs.useLocalTimeStamp = true
#生成的文件类型，默认是Sequencefile，可用DataStream，则为普通文本
a1.sinks.k1.hdfs.fileType = DataStream
# 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第五步顺序启动
# node03机器启动flume进程
cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin
bin/flume-ng agent -c conf -f conf/avro-hdfs.conf -n a1 -Dflume.root.logger=INFO,console
# node02机器启动flume进程
cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/
bin/flume-ng agent -c conf -f conf/tail-avro-avro-logger.conf -n a1 -Dflume.root.logger=INFO,console
# node02机器启shell脚本生成文件
cd /export/servers/shells
sh tail-file.sh更多source和sink组件
参见：http://archive.cloudera.com/cdh5/cdh/5/flume-ng-1.6.0-cdh5.14.0/FlumeUserGuide.html
高可用Flume-NG配置案例failover
角色分配
名称 HOST 角色 Agent1 node01 Web Server Collector1 node02 AgentMstr1 Collector2 node03 AgentMstr2 node01安装配置flume
# node03机器执行以下命令 cd /export/servers scp -r apache-flume-1.6.0-cdh5.14.0-bin/ node01:$PWD scp -r shells/ taillogs/ node01:$PWD # node01机器配置agent的配置文件 cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf vim agent.conf #配置如下 #agent1 name agent1.channels = c1 agent1.sources = r1 agent1.sinks = k1 k2 # ##set gruop agent1.sinkgroups = g1 # ##set channel agent1.channels.c1.type = memory agent1.channels.c1.capacity = 1000 agent1.channels.c1.transactionCapacity = 100 # agent1.sources.r1.channels = c1 agent1.sources.r1.type = exec agent1.sources.r1.command = tail -F /export/servers/taillogs/access_log # agent1.sources.r1.interceptors = i1 i2 agent1.sources.r1.interceptors.i1.type = static agent1.sources.r1.interceptors.i1.key = Type agent1.sources.r1.interceptors.i1.value = LOGIN agent1.sources.r1.interceptors.i2.type = timestamp # ## set sink1 agent1.sinks.k1.channel = c1 agent1.sinks.k1.type = avro agent1.sinks.k1.hostname = node02 agent1.sinks.k1.port = 52020 # ## set sink2 agent1.sinks.k2.channel = c1 agent1.sinks.k2.type = avro agent1.sinks.k2.hostname = node03 agent1.sinks.k2.port = 52020 # ##set sink group agent1.sinkgroups.g1.sinks = k1 k2 # ##set failover agent1.sinkgroups.g1.processor.type = failover agent1.sinkgroups.g1.processor.priority.k1 = 10 agent1.sinkgroups.g1.processor.priority.k2 = 1 agent1.sinkgroups.g1.processor.maxpenalty = 10000 #node02与node03配置flumecollection
# node02机器修改配置文件 cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf vim collector.conf #set Agent name a1.sources = r1 a1.channels = c1 a1.sinks = k1 # ##set channel a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # ## other node,nna to nns a1.sources.r1.type = avro a1.sources.r1.bind = node02 a1.sources.r1.port = 52020 a1.sources.r1.interceptors = i1 a1.sources.r1.interceptors.i1.type = static a1.sources.r1.interceptors.i1.key = Collector a1.sources.r1.interceptors.i1.value = node02 a1.sources.r1.channels = c1 # ##set sink to hdfs a1.sinks.k1.type=hdfs a1.sinks.k1.hdfs.path= hdfs://node01:8020/flume/failover/ a1.sinks.k1.hdfs.fileType=DataStream a1.sinks.k1.hdfs.writeFormat=TEXT a1.sinks.k1.hdfs.rollInterval=10 a1.sinks.k1.channel=c1 a1.sinks.k1.hdfs.filePrefix=%Y-%m-%d # node03机器修改配置文件 cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf vim collector.conf #set Agent name a1.sources = r1 a1.channels = c1 a1.sinks = k1 # ##set channel a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # ## other node,nna to nns a1.sources.r1.type = avro a1.sources.r1.bind = node03 a1.sources.r1.port = 52020 a1.sources.r1.interceptors = i1 a1.sources.r1.interceptors.i1.type = static a1.sources.r1.interceptors.i1.key = Collector a1.sources.r1.interceptors.i1.value = node03 a1.sources.r1.channels = c1 # ##set sink to hdfs a1.sinks.k1.type=hdfs a1.sinks.k1.hdfs.path= hdfs://node01:8020/flume/failover/ a1.sinks.k1.hdfs.fileType=DataStream a1.sinks.k1.hdfs.writeFormat=TEXT a1.sinks.k1.hdfs.rollInterval=10 a1.sinks.k1.channel=c1 a1.sinks.k1.hdfs.filePrefix=%Y-%m-%d顺序启动命令
# node03机器上面启动flume cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin bin/flume-ng agent -n a1 -c conf -f conf/collector.conf -Dflume.root.logger=DEBUG,console # node02机器上面启动flume cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin bin/flume-ng agent -n a1 -c conf -f conf/collector.conf -Dflume.root.logger=DEBUG,console # node01机器上面启动flume cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin bin/flume-ng agent -n agent1 -c conf -f conf/agent.conf -Dflume.root.logger=DEBUG,console # node01机器启动文件产生脚本 cd /export/servers/shells sh tail-file.shFAILOVER测试
- Collector1宕机，Collector2获取优先上传权限
- 重启Collector1服务，Collector1重新获得优先上传的权限
Flume的负载均衡 load balancer
负载均衡是用于解决一台机器(一个进程)无法解决所有请求而产生的一种算法。Load balancing Sink Processor 能够实现 load balance 功能，如下图Agent1 是一个路由节点，负责将
Channel 暂存的 Event 均衡到对应的多个 Sink组件上，而每个 Sink 组件分别连接到一个独立的 Agent 上，示例配置，如下所示：
在此处我们通过三台机器来进行模拟flume的负载均衡
三台机器规划如下：
node01：采集数据，发送到node02和node03机器上去
node02：接收node01的部分数据
node03：接收node01的部分数据
第一步：开发node01服务器的flume配置
# node01服务器配置： cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf vim load_banlancer_client.conf #agent name a1.channels = c1 a1.sources = r1 a1.sinks = k1 k2 #set gruop a1.sinkgroups = g1 #set channel a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 a1.sources.r1.channels = c1 a1.sources.r1.type = exec a1.sources.r1.command = tail -F /export/servers/taillogs/access_log # set sink1 a1.sinks.k1.channel = c1 a1.sinks.k1.type = avro a1.sinks.k1.hostname = node02 a1.sinks.k1.port = 52020 # set sink2 a1.sinks.k2.channel = c1 a1.sinks.k2.type = avro a1.sinks.k2.hostname = node03 a1.sinks.k2.port = 52020 #set sink group a1.sinkgroups.g1.sinks = k1 k2 #set failover a1.sinkgroups.g1.processor.type = load_balance a1.sinkgroups.g1.processor.backoff = true a1.sinkgroups.g1.processor.selector = round_robin a1.sinkgroups.g1.processor.selector.maxTimeOut=10000第二步：开发node02服务器的flume配置
# node02服务器配置： cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf vim load_banlancer_server.conf # Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = avro a1.sources.r1.channels = c1 a1.sources.r1.bind = node02 a1.sources.r1.port = 52020 # 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第三步：开发node03服务器flume配置
# node03服务器配置 cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf vim load_banlancer_server.conf # Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = avro a1.sources.r1.channels = c1 a1.sources.r1.bind = node03 a1.sources.r1.port = 52020 # 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第四步：准备启动flume服务
# 启动node03的flume服务 cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin bin/flume-ng agent -n a1 -c conf -f conf/load_banlancer_server.conf -Dflume.root.logger=DEBUG,console # 启动node02的flume服务 cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin bin/flume-ng agent -n a1 -c conf -f conf/load_banlancer_server.conf -Dflume.root.logger=DEBUG,console # 启动node01的flume服务 cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin bin/flume-ng agent -n a1 -c conf -f conf/load_banlancer_client.conf -Dflume.root.logger=DEBUG,console # node01服务器运行脚本产生数据 cd /export/servers/shells sh tail-file.sh
Flume案例一
把A、B 机器中的access.log、nginx.log、web.log 采集汇总到C机器上然后统一收集到hdfs中。
但是在hdfs中要求的目录为：
/source/logs/access/20180101/**
/source/logs/nginx/20180101/**
/source/logs/web/20180101/**
采集端配置文件开发
# node01与node02服务器开发flume的配置文件 cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf vim exec_source_avro_sink.conf # Name the components on this agent a1.sources = r1 r2 r3 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = exec a1.sources.r1.command = tail -F /export/servers/taillogs/access.log a1.sources.r1.interceptors = i1 a1.sources.r1.interceptors.i1.type = static ## static拦截器的功能就是往采集到的数据的header中插入自己定## 义的key-value对 a1.sources.r1.interceptors.i1.key = type a1.sources.r1.interceptors.i1.value = access a1.sources.r2.type = exec a1.sources.r2.command = tail -F /export/servers/taillogs/nginx.log a1.sources.r2.interceptors = i2 a1.sources.r2.interceptors.i2.type = static a1.sources.r2.interceptors.i2.key = type a1.sources.r2.interceptors.i2.value = nginx a1.sources.r3.type = exec a1.sources.r3.command = tail -F /export/servers/taillogs/web.log a1.sources.r3.interceptors = i3 a1.sources.r3.interceptors.i3.type = static a1.sources.r3.interceptors.i3.key = type a1.sources.r3.interceptors.i3.value = web # Describe the sink a1.sinks.k1.type = avro a1.sinks.k1.hostname = node03 a1.sinks.k1.port = 41414 # Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 20000 a1.channels.c1.transactionCapacity = 10000 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sources.r2.channels = c1 a1.sources.r3.channels = c1 a1.sinks.k1.channel = c1服务端配置文件开发
# 在node03上面开发flume配置文件 cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf vim avro_source_hdfs_sink.conf a1.sources = r1 a1.sinks = k1 a1.channels = c1 #定义source a1.sources.r1.type = avro a1.sources.r1.bind = 192.168.52.120 a1.sources.r1.port =41414 #添加时间拦截器 a1.sources.r1.interceptors = i1 a1.sources.r1.interceptors.i1.type = org.apache.flume.interceptor.TimestampInterceptor$Builder #定义channels a1.channels.c1.type = memory a1.channels.c1.capacity = 20000 a1.channels.c1.transactionCapacity = 10000 #定义sink a1.sinks.k1.type = hdfs a1.sinks.k1.hdfs.path=hdfs://192.168.52.100:8020/source/logs/%{type}/%Y%m%d a1.sinks.k1.hdfs.filePrefix =events a1.sinks.k1.hdfs.fileType = DataStream a1.sinks.k1.hdfs.writeFormat = Text #时间类型 a1.sinks.k1.hdfs.useLocalTimeStamp = true #生成的文件不按条数生成 a1.sinks.k1.hdfs.rollCount = 0 #生成的文件按时间生成 a1.sinks.k1.hdfs.rollInterval = 30 #生成的文件按大小生成 a1.sinks.k1.hdfs.rollSize = 10485760 #批量写入hdfs的个数 a1.sinks.k1.hdfs.batchSize = 10000 #flume操作hdfs的线程数（包括新建，写入等） a1.sinks.k1.hdfs.threadsPoolSize=10 #操作hdfs超时时间 a1.sinks.k1.hdfs.callTimeout=30000 #组装source、channel、sink a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1采集端文件生成脚本
cd /export/servers/shells vim server.sh #!/bin/bash while true do date >> /export/servers/taillogs/access.log; date >> /export/servers/taillogs/web.log; date >> /export/servers/taillogs/nginx.log; sleep 0.5; done顺序启动服务
# node03启动flume实现数据收集 cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin bin/flume-ng agent -c conf -f conf/avro_source_hdfs_sink.conf -name a1 -Dflume.root.logger=DEBUG,console # node01与node02启动flume实现数据监控 cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin bin/flume-ng agent -c conf -f conf/exec_source_avro_sink.conf -name a1 -Dflume.root.logger=DEBUG,console # node01与node02启动生成文件脚本 cd /export/servers/shells sh server.sh