Flume介绍
简述运行机制Flume 结构图
简单结构复杂结构 Flume简单示例
Flume 的安装部署
下载解压修改配置文件开发配置文件启动配置文件安装 Telnet 准备测试 采集示例
采集目录到 HDFS
Flume 配置文件启动 Flume上传文件到指定目录 采集文件到 HDFS
定义 Flume 配置文件启动 Flume开发 Shell 脚本定时追加文件内容启动脚本 Agent级联
Node2安装FlumeNode2配置Flume开发脚本向文件中写入数据第一台Flume配置文件顺序启动 高可用方案
角色分配Node01安装和配置Node02 与 Node03 配置 FlumeCollection顺序启动 Flume 的负载均衡
步骤一：开发node01服务器的flume配置步骤二：开发node02服务器的flume配置步骤三：开发node03服务器flume配置步骤四：准备启动flume服务步骤五：node01服务器运行脚本产生数据 Flume案例
采集端配置文件开发服务端配置文件开发采集端文件生成脚本顺序启动服务 tips：解决中文乱码
Flume介绍 简述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一般的采集需求，通过对flume的简单配置即可实现Flume针对特殊场景也具备良好的自定义扩展能力，因此，flume可以适用于大部分的日常数据采集场景 运行机制
1.Flume分布式系统中最核心的角色是agent，flume采集系统就是由一个个agent所连接起来形成
2.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，以获取数据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 Flume 结构图 简单结构
单个 Agent 采集数据
多级 Agent 之间串联
使用网络telent命令向一台机器发送一些网络数据，然后通过flume采集网络端口数据
Flume 的安装部署 下载解压修改配置文件下载地址： http://archive.apache.org/dist/flume/1.8.0/apache-flume-1.8.0-bin.tar.gz
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
cd /export/softwares/
tar -zxvf apache-flume-1.8.0-bin.tar.gz -C …/servers/
cd /export/servers/apache-flume-1.8.0-bin/conf
cp flume-env.sh.template flume-env.sh
vim flume-env.sh
export JAVA_HOME=/export/servers/jdk1.8.0_141
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-1.8.0-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 = 192.168.174.1 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，先用一个最简单的例子来测试一下程序环境是否正常，启动agent去采集数据
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.con 指定我们所描述的采集方案n a1 指定我们这个agent的名字 安装 Telnet 准备测试
在机器上面安装telnet客户端，用于模拟数据的发送
yum -y install telnet telnet node03 44444 #使用telnet模拟数据发送采集示例 采集目录到 HDFS
某服务器的某特定目录下，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 : spooldir
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，即sink——HDFS文件系统 : hdfs sink通道组件，即channel——可用file channel 也可以用内存channel
cd /export/servers/apache-flume-1.8.0-bin/conf mkdir -p /export/servers/dirfile vim spooldir.conf
# Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source ##注意：不能往监控目中重复丢同名文件 a1.sources.r1.type = spooldir a1.sources.r1.spoolDir = /export/servers/dirfile a1.sources.r1.fileHeader = true # Describe the sink a1.sinks.k1.type = hdfs a1.sinks.k1.channel = c1 a1.sinks.k1.hdfs.path = hdfs://node:8020/spooldir/files/%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
Channel参数解释：
capacity：默认该通道中最大的可以存储的event数量
trasactionCapacity：每次最大可以从source中拿到或者送到sink中的event数量
keep-alive：event添加到通道中或者移出的允许时间
bin/flume-ng agent -c ./conf -f ./conf/spooldir.conf -n a1 -Dflume.root.logger=INFO,console
上传文件到指定目录将不同的文件上传到下面目录里面去，注意文件不能重名
cd /export/servers/dirfile
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 sinkSource和sink之间的传递通道——channel，可用file channel 也可以用 内存channel 定义 Flume 配置文件
cd /export/servers/apache-flume-1.8.0-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 # 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-% 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.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启动 Flume
cd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin
bin/flume-ng agent -c conf -f conf/tail-file.conf -n agent1 -Dflume.root.logger=INFO,console
mkdir -p /export/servers/shells/
cd /export/servers/shells/
vim tail-file.sh
#!/bin/bash while true do date >> /export/servers/taillogs/access_log; sleep 0.5; done启动脚本
# 创建文件夹 mkdir -p /export/servers/taillogs # 启动脚本 sh /export/servers/shells/tail-file.shAgent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
将第一台机器上面解压后的flume文件夹拷贝到第二台机器上面去
cd /export/servers
scp -r apache-flume-1.8.0-bin/ node2:$PWD
在node2机器配置flume
cd /export/servers/ apache-flume-1.8.0-bin/conf
vim tail-avro-avro-logger.conf
# Name the components on this agent a1.sources = r1 a1.sinks = k1 a1.channels = c1 # Describe/configure the source a1.sources.r1.type = exec a1.sources.r1.command = tail -F /export/servers/taillogs/access_log a1.sources.r1.channels = c1 # Describe the sink ##sink端的avro是一个数据发送者 a1.sinks = k1 a1.sinks.k1.type = avro a1.sinks.k1.channel = c1 a1.sinks.k1.hostname = 192.168.174.120 a1.sinks.k1.port = 4141 a1.sinks.k1.batch-size = 10 # Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100 # Bind the source and sink to the channel a1.sources.r1.channels = c1 a1.sinks.k1.channel = c1开发脚本向文件中写入数据
直接将第一台下面的脚本和数据拷贝到node2即可，第一台机器上执行以下命令
cd /export/servers
scp -r shells/ taillogs/ node2:$PWD
在第一台机器上开发flume的配置文件
cd /export/servers/apache-flume-1.8.0-bin/conf
vim avro-hdfs.conf
# 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.xxx.xxx a1.sources.r1.port = 6666 # Describe the sink a1.sinks.k1.type = hdfs a1.sinks.k1.hdfs.path = hdfs://node01:8020/av /%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顺序启动
第一台机器启动flume进程
cd /export/servers/apache-flume-1.8.0-bin
bin/flume-ng agent -c conf -f conf/avro-hdfs.conf -n a1 -Dflume.root.logger=INFO,console
node2机器启动flume进程
cd /export/servers/apache-flume-1.8.0-bin/
bin/flume-ng agent -c conf -f conf/tail-avro-avro-logger.conf -n a1 -Dflume.root.logger
node2机器启shell脚本生成文件
cd /export/servers/shells
sh tail-file.sh
示例搭建一个高可用的Flume NG集群
角色分配Flume的Agent和Collector分布如下表所示：
| 名称 | HOST | 角色 |
|---|---|---|
| Agent1 | node01 | Web Server |
| Collector1 | node02 | AgentMstr1 |
| Collector2 | node03 | AgentMstr2 |
图中所示，Agent1数据分别流入到Collector1和Collector2，Flume NG本身提供了Failover机制，可以
自动切换和恢复。有3个产生日志服务器分布在不同的机房，要把所有的日志都收集到一个
集群中存储。
将flume安装包以及文件生产的两个目录拷贝到node01机器上面去
node02机器执行以下命令
cd /export/servers scp -r apache-flume-1.8.0-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-1.8.0-bin/conf vim agent.conf
#agent1 name agent1.channels = c1 agent1.sources = r1 agent1.sinks = k1 k2 ##set gruop agent1.sinkgroups = g1 agent1.sources.r1.channels = c1 agent1.sources.r1.type = exec agent1.sources.r1.command = tail -F /export/servers/taillogs/access_log ##set channel agent1.channels.c1.type = memory agent1.channels.c1.capacity = 1000 agent1.channels.c1.transactionCapacity = 100 ## 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 = 10000Node02 与 Node03 配置 FlumeCollection
node02机器修改配置文件
cd /export/servers/apache-flume-1.8.0-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.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-1.8.0-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.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
node03机器上面启动flume
cd /export/servers/apache-flume-1.8.0-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-1.8.0-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-1.8.0-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.sh
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：
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
node01服务器配置：
cd /export/servers/apache-flume-1.8.0-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步骤二：开发node02服务器的flume配置
node02服务器配置：
cd /export/servers/apache-flume-1.8.0-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步骤三：开发node03服务器flume配置
node03服务器配置
cd /export/servers/apache-flume-1.8.0-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步骤四：准备启动flume服务
启动node03的flume服务
cd /export/servers/apache-flume-1.8.0-bin
bin/flume-ng agent -n a1 -c conf -f conf/load_banlancer_server.conf -Dflume.root.logger
启动node02的flume服务
cd /export/servers/apache-flume-1.8.0-bin
bin/flume-ng agent -n a1 -c conf -f conf/load_banlancer_server.conf -Dflume.root.logger
启动node01的flume服务
cd /export/servers/apache-flume-1.8.0-bin
bin/flume-ng agent -n a1 -c conf -f conf/load_banlancer_client.conf -Dflume.root.logger
cd /export/servers/shells
sh tail-file.sh
A、B两台日志服务机器实时生产日志主要类型为access.log、nginx.log、web.log，现在需要把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/20210101/** /source/logs/nginx/20210101/** /source/logs/web/20210101/**
服务器A对应的IP为 192.168.174.1
服务器B对应的IP为 192.168.174.2
服务器C对应的IP为 192.168.174.3
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 = 192.168.174.3
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://192.168.174.1:8020/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
在node01与node02上面开发shell脚本，模拟数据生成
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
