标签：load 实战案例 figure 安装配置 数据监控 cal arc 三台 表达式
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：
采集系统结构图
简单结构
复杂结构
多级agent之间串联
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
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
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
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;
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
cd /export/servers
scp -r apache-flume-1.6.0-cdh5.14.0-bin/ node02:$PWDcd /export/servers/apache-flume-1.6.0-cdh5.14.0-bin/conf
vim tail-avro-avro-logger.conf
##################
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /export/servers/taillogs/access_log
a1.sources.r1.channels = c1
# Describe the sink
##sink端的avro是一个数据发送者
a1.sinks = k1
a1.sinks.k1.type = avro
a1.sinks.k1.channel = c1
a1.sinks.k1.hostname = 192.168.52.120
a1.sinks.k1.port = 4141
a1.sinks.k1.batch-size = 10
# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1# 直接把node03的脚本拷贝至node02
cd /export/servers
scp -r shells/ taillogs/ node02:$PWD# 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# 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参见：http://archive.cloudera.com/cdh5/cdh/5/flume-ng-1.6.0-cdh5.14.0/FlumeUserGuide.html
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.sh
FAILOVER测试
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标签：load 实战案例 figure 安装配置 数据监控 cal arc 三台 表达式
原文地址：https://www.cnblogs.com/winter-shadow/p/11444572.html
