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将本地数据从本地导入到HDFS上,其实可以利用IO流的形式,在Inputformat中读取文件,作为输入,在Mapper中输出即可。而Flume就是提供类似功能的框架。
1) Flume 提供一个分布式的,可靠的,对大数据量的日志进行高效收集、聚集、移动的服务, Flume 只能在 Unix 环境下运行。
2) Flume 基于流式架构,容错性强,也很灵活简单。
3) Flume、Kafka 用来实时进行数据收集,Spark、Storm 用来实时处理数据,impala 用来实 时查询。
用于采集数据,Source 是产生数据流的地方,同时 Source 会将产生的数据流传输到 Channel, 这个有点类似于 Java IO 部分的 Channel。
用于桥接 Sources 和 Sinks,类似于一个队列。 (应用的解耦,将source和sink解耦,每个Channel绑定一个sink)
从 Channel 收集数据,将数据写到目标源(可以是下一个 Source,也可以是 HDFS 或者 HBase)。
传输单元,Flume 数据传输的基本单元,以事件的形式将数据从源头送至目的地。
source 监控某个文件或数据流,数据源产生新的数据,拿到该数据后,将数据封装在一个 Event 中,并 put 到 channel 后 commit 提交,channel 队列先进先出,sink 去 channel 队列中 拉取数据,然后写入到 HDFS 中。
flume-env.sh 涉及修改项:
export JAVA_HOME=/home/admin/modules/jdk1.8.0_121
目标:Flume 监控一端 Console,另一端 Console 发送消息,使被监控端实时显示。
分步实现:
1) 安装 telnet 工具
$ sudo rpm -ivh xinetd-2.3.14-40.el6.x86_64.rpm
$ sudo rpm -ivh telnet-0.17-48.el6.x86_64.rpm
$ sudo rpm -ivh telnet-server-0.17-48.el6.x86_64.rpm
2) 创建 Flume Agent 配置文件 flume-telnet.conf
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
#第一步命名agent,source,channel,sink四个变量名称
# Describe/configure the source
a1.sources.r1.type = netcat
a1.sources.r1.bind = localhost
a1.sources.r1.port = 44444
#定义source的相关参数
# Describe the sink
a1.sinks.k1.type = logger
#定义sink的相关参数
# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
#定义channel的相关参数
# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
#定义source和sink连接使用的channel
3) 判断 44444 端口是否被占用
$ netstat -tunlp | grep 44444
4) 先开启 flume 先听端口
$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/flume-telnet.conf -Dflume.root.logger==INFO,console
指定当前配制文件目录 --conf
指定当前运行的agent --name
指定当前运行的job的配制文件 --conf-file
指定java相关参数 -Dflume
目标:实时监控hive日志,并上传到HDFS中
分步实现:
1) 拷贝Hadoop相关jar到Flume的lib目录下(Flume要操作Hadoop,需要相关api,通过 find 路径名 -name 文件名 命令查找)
$ cp share/hadoop/common/lib/hadoop-auth-2.5.0-cdh5.3.6.jar ./lib/ $ cp share/hadoop/common/lib/commons-configuration-1.6.jar ./lib/ $ cp share/hadoop/mapreduce1/lib/hadoop-hdfs-2.5.0-cdh5.3.6.jar ./lib/ $ cp share/hadoop/common/hadoop-common-2.5.0-cdh5.3.6.jar ./lib/ $ cp ./share/hadoop/hdfs/lib/htrace-core-3.1.0-incubating.jar ./lib/ $ cp ./share/hadoop/hdfs/lib/commons-io-2.4.jar ./lib/尖叫提示:标红的jar为1.99版本flume必须引用的jar
2) 创建flume-hdfs.conf文件
# Name the components on this agent
a2.sources = r2
a2.sinks = k2
a2.channels = c2
# Describe/configure the source
a2.sources.r2.type = exec
a2.sources.r2.command = tail -F /home/admin/modules/apache-hive-1.2.2-bin/hive.log
a2.sources.r2.shell = /bin/bash -c
# 如图,利用命令 whereis bash 找到bash绝对路径 /bin/bash -c 执行脚本文件位置+文件名
# Describe the sink
a2.sinks.k2.type = hdfs
a2.sinks.k2.hdfs.path = hdfs://linux01:8020/flume/%Y%m%d/%H
#上传文件的前缀
a2.sinks.k2.hdfs.filePrefix = logs-
#是否按照时间滚动文件夹
a2.sinks.k2.hdfs.round = true
#多少时间单位创建一个新的文件夹
a2.sinks.k2.hdfs.roundValue = 1
#重新定义时间单位
a2.sinks.k2.hdfs.roundUnit = hour
#是否使用本地时间戳
a2.sinks.k2.hdfs.useLocalTimeStamp = true
#积攒多少个Event才flush到HDFS一次
a2.sinks.k2.hdfs.batchSize = 1000
#设置文件类型,可支持压缩
a2.sinks.k2.hdfs.fileType = DataStream
#多久生成一个新的文件
a2.sinks.k2.hdfs.rollInterval = 600
#设置每个文件的滚动大小
a2.sinks.k2.hdfs.rollSize = 134217700
#文件的滚动与Event数量无关
a2.sinks.k2.hdfs.rollCount = 0
#最小冗余数
a2.sinks.k2.hdfs.minBlockReplicas = 1
# Use a channel which buffers events in memory
a2.channels.c2.type = memory
a2.channels.c2.capacity = 1000
a2.channels.c2.transactionCapacity = 100
# Bind the source and sink to the channel
a2.sources.r2.channels = c2
a2.sinks.k2.channel = c2
3) 执行监控配置
$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/flume-hdfs.conf
目标:使用flume监听整个目录的文件
分步实现:
1) 创建配置文件flume-dir.conf
a3.sources = r3
a3.sinks = k3
a3.channels = c3
# Describe/configure the source
a3.sources.r3.type = spooldir
a3.sources.r3.spoolDir = /home/admin/modules/apache-flume-1.7.0-bin/upload
a3.sources.r3.fileSuffix = .COMPLETED
a3.sources.r3.fileHeader = true
#忽略所有以.tmp结尾的文件,不上传
a3.sources.r3.ignorePattern = ([^ ]*\.tmp)
# Describe the sink
a3.sinks.k3.type = hdfs
a3.sinks.k3.hdfs.path = hdfs://linux01:8020/flume/upload/%Y%m%d/%H
#上传文件的前缀
a3.sinks.k3.hdfs.filePrefix = upload-
#是否按照时间滚动文件夹
a3.sinks.k3.hdfs.round = true
#多少时间单位创建一个新的文件夹
a3.sinks.k3.hdfs.roundValue = 1
#重新定义时间单位
a3.sinks.k3.hdfs.roundUnit = hour
#是否使用本地时间戳
a3.sinks.k3.hdfs.useLocalTimeStamp = true
#积攒多少个Event才flush到HDFS一次
a3.sinks.k3.hdfs.batchSize = 100
#设置文件类型,可支持压缩
a3.sinks.k3.hdfs.fileType = DataStream
#多久生成一个新的文件
a3.sinks.k3.hdfs.rollInterval = 600
#设置每个文件的滚动大小大概是128M
a3.sinks.k3.hdfs.rollSize = 134217700
#文件的滚动与Event数量无关
a3.sinks.k3.hdfs.rollCount = 0
#最小冗余数
a3.sinks.k3.hdfs.minBlockReplicas = 1
# Use a channel which buffers events in memory
a3.channels.c3.type = memory
a3.channels.c3.capacity = 1000
a3.channels.c3.transactionCapacity = 100
# Bind the source and sink to the channel
a3.sources.r3.channels = c3
a3.sinks.k3.channel = c3
2) 执行测试:执行如下脚本后,请向upload文件夹中添加文件试试
$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/flume-dir.conf
尖叫提示: 在使用Spooling Directory Source时
1) 不要在监控目录中创建并持续修改文件
2) 上传完成的文件会以.COMPLETED结尾
3) 被监控文件夹每600毫秒扫描一次文件变动
目标:使用flume-1监控文件变动,flume-1将变动内容传递给flume-2,flume-2负责存储到HDFS。同时flume-1将变动内容传递给flume-3,flume-3负责输出到。
local filesystem。
分步实现:
1) 创建flume-1.conf,用于监控hive.log文件的变动,同时产生两个channel和两个sink分别输送给flume-2和flume3:
# Name the components on this agent
a1.sources = r1
a1.sinks = k1 k2
a1.channels = c1 c2
# 将数据流复制给多个channel
a1.sources.r1.selector.type = replicating
# Describe/configure the source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /home/admin/modules/apache-hive-1.2.2-bin/hive.log
a1.sources.r1.shell = /bin/bash -c
# Describe the sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = linux01
a1.sinks.k1.port = 4141
a1.sinks.k2.type = avro
a1.sinks.k2.hostname = linux01
a1.sinks.k2.port = 4142
#flume1占用的俩个端口
# 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
2) 创建flume-2.conf,用于接收flume-1的event,同时产生1个channel和1个sink,将数据输送给hdfs:
# 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 = linux01 #去哪个ip抓取数据
a2.sources.r1.port = 4141 #去哪个端口抓取数据
# Describe the sink
a2.sinks.k1.type = hdfs
a2.sinks.k1.hdfs.path = hdfs://linux01:8020/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 = 600
#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
#最小冗余数
a2.sinks.k1.hdfs.minBlockReplicas = 1
# 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
3) 创建flume-3.conf,用于接收flume-1的event,同时产生1个channel和1个sink,将数据输送给本地目录:
# 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 = linux01
a3.sources.r1.port = 4142
# Describe the sink
a3.sinks.k1.type = file_roll
a3.sinks.k1.sink.directory = /home/admin/Desktop/flume3
# 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
尖叫提示:输出的本地目录必须是已经存在的目录,如果该目录不存在,并不会创建新的目录。
4) 执行测试:分别开启对应flume-job(依次启动flume-3,flume-2,flume-1),同时产生文件变动并观察结果:
$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group-job1/flume-3.conf
$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group-job1/flume-2.conf
$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group-job1/flume-1.conf
目标:flume-1监控文件hive.log,flume-2监控某一个端口的数据流,flume-1与flume-2将数据发送给flume-3,flume3将最终数据写入到HDFS。
分步实现:
1) 创建flume-1.conf,用于监控hive.log文件,同时sink数据到flume-3:
# 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/admin/modules/apache-hive-1.2.2-bin/hive.log
a1.sources.r1.shell = /bin/bash -c
# Describe the sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = linux01
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
2) 创建flume-2.conf,用于监控端口44444数据流,同时sink数据到flume-3:
# 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 = linux01
a2.sources.r1.port = 44444
# Describe the sink
a2.sinks.k1.type = avro
a2.sinks.k1.hostname = linux01
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
3) 创建flume-3.conf,用于接收flume-1与flume-2发送过来的数据流,最终合并后sink到HDFS:
# 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 = linux01
a3.sources.r1.port = 4141
# Describe the sink
a3.sinks.k1.type = hdfs
a3.sinks.k1.hdfs.path = hdfs://linux01:8020/flume3/%Y%m%d/%H
#上传文件的前缀
a3.sinks.k1.hdfs.filePrefix = flume3-
#是否按照时间滚动文件夹
a3.sinks.k1.hdfs.round = true
#多少时间单位创建一个新的文件夹
a3.sinks.k1.hdfs.roundValue = 1
#重新定义时间单位
a3.sinks.k1.hdfs.roundUnit = hour
#是否使用本地时间戳
a3.sinks.k1.hdfs.useLocalTimeStamp = true
#积攒多少个Event才flush到HDFS一次
a3.sinks.k1.hdfs.batchSize = 100
#设置文件类型,可支持压缩
a3.sinks.k1.hdfs.fileType = DataStream
#多久生成一个新的文件
a3.sinks.k1.hdfs.rollInterval = 600
#设置每个文件的滚动大小大概是128M
a3.sinks.k1.hdfs.rollSize = 134217700
#文件的滚动与Event数量无关
a3.sinks.k1.hdfs.rollCount = 0
#最小冗余数
a3.sinks.k1.hdfs.minBlockReplicas = 1
# 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
4) 执行测试:分别开启对应flume-job(依次启动flume-3,flume-2,flume-1),同时产生文件变动并观察结果:
$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group-job2/flume-3.conf
$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group-job2/flume-2.conf
$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group-job2/flume-1.conf
尖叫提示:测试时记得启动hive产生一些日志,同时使用telnet向44444端口发送内容,如:
$ bin/hive
$ telnet linux01 44444
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