090-BigData-18Flume
2019-10-07 本文已影响0人
AncientMing
上一篇：089-BigData-17ZooKeeper秒杀和hadoop高可用
一、Flume简介
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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。
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Flume基于流式架构，容错性强，也很灵活简单。
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Flume、Kafka用来实时进行数据收集，Spark、Flink用来实时处理数据，impala用来实时查询。
二、Flume角色
image.png
2.1、Source
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。
2.2、Channel
用于桥接Sources和Sinks，类似于一个队列。
2.3、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)。
2.4、Event
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。
三、Flume传输过程
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。
四、Flume部署及使用
4.1、文件配置
查询JAVA_HOME: echo $JAVA_HOME
/opt/module/jdk1.8.0_144 /opt/module/jdk1.8.0_144
安装Flume
[AncientMing@bigdata113 software]$ tar -zxvf apache-flume1.8.0-bin.tar.gz -C /opt/module/
改名：
[AncientMing@bigdata113 conf]$ mv flume-env.sh.template flume-env.sh
flume-env.sh涉及修改项：
export JAVA_HOME=/opt/module/jdk1.8.0_144
Linux中文件上传命令是：rz，下载：sz 如果没有这两个命令使用yum下载：yum install lrzsz
4.2、案例
4.2.1、案例一：监控端口数据
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：
- 安装telnet工具
image.png
- 创建Flume Agent配置文件flume-telnet.conf
#定义Agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
#定义netcatsource
a1.sources.r1.type = netcat
a1.sources.r1.bind = bigdata111
a1.sources.r1.port = 44445
# 定义sink
a1.sinks.k1.type = logger
# 定义channel
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
# 双向链接
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
- 判断44444端口是否被占用
$ netstat -tunlp | grep 44445
- 启动flume配置文件
/opt/module/flume-1.8.0/bin/flume-ng agent \
--conf /opt/module/flume1.8.0/conf/ \
--name a1 \
--conf-file /opt/module/flume-1.8.0/jobconf/flume-telnet.conf \
-Dflume.root.logger==INFO,console
bin/flume-ng agent --conf conf/ --name a1 --conf-file conf/flume-telnet.conf -Dflume.root.logger==INFO,console
- 使用telnet工具向本机的44444端口发送内容
$ telnet bigdata111 44445
4.2.2、案例二：实时读取本地文件到HDFS
- 创建flume-hdfs.conf文件
# 1 agent
a2.sources = r2
a2.sinks = k2
a2.channels = c2
# 2 source
a2.sources.r2.type = exec
a2.sources.r2.command = tail -F /opt/plus
a2.sources.r2.shell = /bin/bash -c
# 3 sink
a2.sinks.k2.type = hdfs
a2.sinks.k2.hdfs.path = hdfs://bigdata111:9000/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
#最小副本数
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
- 执行监控配置
/opt/module/flume1.8.0/bin/flume-ng agent \
--conf /opt/module/flume1.8.0/conf/ \
--name a2 \
--conf-file /opt/module/flume1.8.0/jobconf/flume-hdfs.conf
4.2.3、案例三：实时读取目录文件到HDFS
目标：使用flume监听整个目录的文件
分步实现：
- 创建配置文件flume-dir.conf
#1 Agent
a3.sources = r3
a3.sinks = k3
a3.channels = c3
#2 source
#监控目录的类型
a3.sources.r3.type = spooldir
#监控目录的路径
a3.sources.r3.spoolDir = /opt/module/flume1.8.0/upload
#哪个文件上传hdfs，然后给这个文件添加一个后缀
a3.sources.r3.fileSuffix = .COMPLETED
a3.sources.r3.fileHeader = true
#忽略所有以.tmp结尾的文件，不上传（可选）
a3.sources.r3.ignorePattern = ([^ ]*\.tmp)
# 3 sink
a3.sinks.k3.type = hdfs
a3.sinks.k3.hdfs.path = hdfs://bigdata111:9000/flume/%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
#最小副本数
a3.sinks.k3.hdfs.minBlockReplicas = 1
- 执行测试：执行如下脚本后，请向upload文件夹中添加文件试试
/opt/module/flume1.8.0/bin/flume-ng agent \
--conf /opt/module/flume1.8.0/conf/ \
--name a3 \
--conf-file /opt/module/flume1.8.0/jobconf/flume-dir.conf
尖叫提示： 在使用Spooling Directory Source时
- 不要在监控目录中创建并持续修改文件
- 上传完成的文件会以.COMPLETED结尾
- 被监控文件夹每500毫秒扫描一次文件变动
4.2.4、案例四：Flume与Flume之间数据传递：单Flume多Channel、Sink
image.png
目标：使用flume1监控文件变动，flume1将变动内容传递给flume-2，flume-2负责存储到HDFS。同时flume1将变动内容传递给flume-3，flume-3负责输出到local
分步实现：
- 创建flume1.conf，用于监控某文件的变动，同时产生两个channel和两个sink分别输送给flume2和flume3：
# 1.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
# 2.source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /opt/Andy
a1.sources.r1.shell = /bin/bash -c
# 3.sink1
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = bigdata111
a1.sinks.k1.port = 4141
# sink2
a1.sinks.k2.type = avro
a1.sinks.k2.hostname = bigdata111
a1.sinks.k2.port = 4142
# 4.channel—1
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
# 4.channel—2
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
- 创建flume-2.conf，用于接收flume1的event，同时产生1个channel和1个sink，将数据输送给hdfs：
# 1 agent
a2.sources = r1
a2.sinks = k1
a2.channels = c1
# 2 source
a2.sources.r1.type = avro
a2.sources.r1.bind = bigdata111
a2.sources.r1.port = 4141
# 3 sink
a2.sinks.k1.type = hdfs
a2.sinks.k1.hdfs.path = hdfs://bigdata111:9000/flume2/%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
#最小副本数
a2.sinks.k1.hdfs.minBlockReplicas = 1
# 4 channel
a2.channels.c1.type = memory
a2.channels.c1.capacity = 1000
a2.channels.c1.transactionCapacity = 100
- 创建flume-3.conf，用于接收flume1的event，同时产生1个channel和1个sink，将数据输送给本地目录：
#1 agent
a3.sources = r1
a3.sinks = k1
a3.channels = c1
# 2 source
a3.sources.r1.type = avro
a3.sources.r1.bind = bigdata111
a3.sources.r1.port = 4142
#3 sink
a3.sinks.k1.type = file_roll
#备注：此处的文件夹需要先创建好
a3.sinks.k1.sink.directory = /opt/flume3
# 4 channel
a3.channels.c1.type = memory
a3.channels.c1.capacity = 1000
a3.channels.c1.transactionCapacity = 100
# 5 Bind
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。
- 执行测试：分别开启对应flume-job（依次启动flume1，flume-2，flume-3），同时产生文件变动并观察结果：
$ bin/flume-ng agent --conf conf/ --name a1 --conf-file jobconf/flume1.conf
$ bin/flume-ng agent --conf conf/ --name a2 --conf-file jobconf/flume2.conf
$ bin/flume-ng agent --conf conf/ --name a3 --conf-file jobconf/flume3.conf
4.2.5、案例五：Flume与Flume之间数据传递，多Flume汇总数据到单Flume
image.png
目标：flume11监控文件hive.log，flume-22监控某一个端口的数据流，flume11与flume-22将数据发送给flume-33，flume33将最终数据写入到HDFS。
分步实现：
- 创建flume11.conf，用于监控hive.log文件，同时sink数据到flume-33：
# 1 agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# 2 source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /opt/Andy
a1.sources.r1.shell = /bin/bash -c
# 3 sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = bigdata111
a1.sinks.k1.port = 4141
# 4 channel
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
# 5. Bind
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
- 创建flume-22.conf，用于监控端口44444数据流，同时sink数据到flume-33：
# 1 agent
a2.sources = r1
a2.sinks = k1
a2.channels = c1
# 2 source
a2.sources.r1.type = netcat
a2.sources.r1.bind = bigdata111
a2.sources.r1.port = 44444
#3 sink
a2.sinks.k1.type = avro
a2.sinks.k1.hostname = bigdata111
a2.sinks.k1.port = 4141
# 4 channel
a2.channels.c1.type = memory
a2.channels.c1.capacity = 1000
a2.channels.c1.transactionCapacity = 100
# 5 Bind
a2.sources.r1.channels = c1
a2.sinks.k1.channel = c1
- 创建flume33.conf，用于接收flume11与flume22发送过来的数据流，最终合并后sink到HDFS：
# 1 agent
a3.sources = r1
a3.sinks = k1
a3.channels = c1
# 2 source
a3.sources.r1.type = avro
a3.sources.r1.bind = bigdata111
a3.sources.r1.port = 4141
# 3 sink
a3.sinks.k1.type = hdfs
a3.sinks.k1.hdfs.path = hdfs://bigdata111:9000/flume3/%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
# 4 channel
a3.channels.c1.type = memory
a3.channels.c1.capacity = 1000
a3.channels.c1.transactionCapacity = 100
# 5 Bind
- 执行测试：分别开启对应flume-job（依次启动flume-33，flume-22，flume11），同时产生文件变动并观察结果：
$ bin/flume-ng agent --conf conf/ --name a3 --conf-file jobconf/flume33.conf
$ bin/flume-ng agent --conf conf/ --name a2 --conf-file jobconf/flume22.conf
$ bin/flume-ng agent --conf conf/ --name a1 --conf-file jobconf/flume11.conf
数据发送
telnet bigdata111 44444 打开后发送5555555
在/opt/Andy 中追加666666
4.2.6、案例六：Flume拦截器
时间戳拦截器
Timestamp.conf
#1.定义agent名， source、channel、sink的名称
a4.sources = r1
a4.channels = c1
a4.sinks = k1
#2.具体定义source
a4.sources.r1.type = spooldir
a4.sources.r1.spoolDir = /opt/module/flume-1.8.0/upload
#定义拦截器，为文件最后添加时间戳
a4.sources.r1.interceptors = i1
a4.sources.r1.interceptors.i1.type = org.apache.flume.interceptor.TimestampInterceptor$Builder
#具体定义channel
a4.channels.c1.type = memory
a4.channels.c1.capacity = 10000
a4.channels.c1.transactionCapacity = 100
#具体定义sink
a4.sinks.k1.type = hdfs
a4.sinks.k1.hdfs.path = hdfs://bigdata111:9000/flume-interceptors/%H
a4.sinks.k1.hdfs.filePrefix = events-
a4.sinks.k1.hdfs.fileType = DataStream
#不按照条数生成文件
a4.sinks.k1.hdfs.rollCount = 0
#HDFS上的文件达到128M时生成一个文件
a4.sinks.k1.hdfs.rollSize = 134217728
#HDFS上的文件达到60秒生成一个文件
a4.sinks.k1.hdfs.rollInterval = 60
#组装source、channel、sink
a4.sources.r1.channels = c1
a4.sinks.k1.channel = c1
启动命令
/opt/module/flume-1.8.0/bin/flume-ng agent -n a4 \
-f /opt/module/flume-1.8.0/jobconf/flume-interceptors.conf \
-c /opt/module/flume-1.8.0/conf \
-Dflume.root.logger=INFO,console
主机名拦截器
Host.conf
#1.定义agent
a1.sources= r1
a1.sinks = k1
a1.channels = c1
#2.定义source
a1.sources.r1.type = exec
a1.sources.r1.channels = c1
a1.sources.r1.command = tail -F /opt/Andy
#拦截器
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = host
#参数为true时用IP192.168.1.111，参数为false时用主机名，默认为true
a1.sources.r1.interceptors.i1.useIP = false
a1.sources.r1.interceptors.i1.hostHeader = agentHost
#3.定义sinks
a1.sinks.k1.type=hdfs
a1.sinks.k1.channel = c1
a1.sinks.k1.hdfs.path = hdfs://bigdata111:9000/flumehost/%H
a1.sinks.k1.hdfs.filePrefix = Andy_%{agentHost}
#往生成的文件加后缀名.log
a1.sinks.k1.hdfs.fileSuffix = .log
a1.sinks.k1.hdfs.fileType = DataStream
a1.sinks.k1.hdfs.writeFormat = Text
a1.sinks.k1.hdfs.rollInterval = 10
a1.sinks.k1.hdfs.useLocalTimeStamp = true
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
启动命令：
bin/flume-ng agent -c conf/ -f jobconf/host.conf -n a1 -Dflume.root.logger=INFO,console
UUID拦截器
uuid.conf
a1.sources = r1
a1.sinks = k1
a1.channels = c1
a1.sources.r1.type = exec
a1.sources.r1.channels = c1
a1.sources.r1.command = tail -F /opt/Andy
a1.sources.r1.interceptors = i1
#type的参数不能写成uuid，得写具体，否则找不到类
a1.sources.r1.interceptors.i1.type = org.apache.flume.sink.solr.morphline.UUIDInterceptor$Builder
#如果UUID头已经存在,它应该保存
a1.sources.r1.interceptors.i1.preserveExisting = true
a1.sources.r1.interceptors.i1.prefix = UUID_
#如果sink类型改为HDFS，那么在HDFS的文本中没有headers的信息数据
a1.sinks.k1.type = logger
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
# bin/flume-ng agent -c conf/ -f jobconf/uuid.conf -n a1 -Dflume.root.logger==INFO,console
查询替换拦截器
search.conf
#1 agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
#2 source
a1.sources.r1.type = exec
a1.sources.r1.channels = c1
a1.sources.r1.command = tail -F /opt/Andy
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = search_replace
#遇到数字改成AncientMing，A123会替换为AAncientMing
a1.sources.r1.interceptors.i1.searchPattern = [0-9]+
a1.sources.r1.interceptors.i1.replaceString = AncientMing
a1.sources.r1.interceptors.i1.charset = UTF-8
#3 sink
a1.sinks.k1.type = logger
#4 Chanel
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
#5 bind
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
# bin/flume-ng agent -c conf/ -f jobconf/search.conf -n a1 -Dflume.root.logger=INFO,console
正则过滤拦截器
filter.conf
#1 agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
#2 source
a1.sources.r1.type = exec
a1.sources.r1.channels = c1
a1.sources.r1.command = tail -F /opt/Andy
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = regex_filter
a1.sources.r1.interceptors.i1.regex = ^A.*
#如果excludeEvents设为false,表示过滤掉不是以A开头的events。如果excludeEvents设为true，则表示过滤掉以A开头的events。
a1.sources.r1.interceptors.i1.excludeEvents = true
a1.sinks.k1.type = logger
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
# bin/flume-ng agent -c conf/ -f jobconf/filter.conf -n a1 -Dflume.root.logger=INFO,console
正则抽取拦截器
extractor.conf
#1 agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
#2 source
a1.sources.r1.type = exec
a1.sources.r1.channels = c1
a1.sources.r1.command = tail -F /opt/Andy
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = regex_extractor
a1.sources.r1.interceptors.i1.regex = hostname is (.*?) ip is (.*)
a1.sources.r1.interceptors.i1.serializers = s1 s2
a1.sources.r1.interceptors.i1.serializers.s1.name = cookieid
a1.sources.r1.interceptors.i1.serializers.s2.name = ip
a1.sinks.k1.type = logger
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
# bin/flume-ng agent -c conf/ -f jobconf/extractor.conf -n a1 -Dflume.root.logger=INFO,console
注：正则抽取拦截器的headers不会出现在文件名和文件内容中
4.2.7、案例七：Flume自定义拦截器
字母小写变大写
1.Pom.xml
<dependencies>
<!-- flume核心依赖 -->
<dependency>
<groupId>org.apache.flume</groupId>
<artifactId>flume-ng-core</artifactId>
<version>1.8.0</version>
</dependency>
</dependencies>
<build>
<plugins>
<!-- 打包插件 -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-jar-plugin</artifactId>
<version>2.4</version>
<configuration>
<archive>
<manifest>
<addClasspath>true</addClasspath>
<classpathPrefix>lib/</classpathPrefix>
<mainClass></mainClass>
</manifest>
</archive>
</configuration>
</plugin>
<!-- 编译插件 -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<configuration>
<source>1.8</source>
<target>1.8</target>
<encoding>utf-8</encoding>
</configuration>
</plugin>
</plugins>
</build>
2.自定义实现拦截器
import org.apache.flume.Context;
import org.apache.flume.Event;
import org.apache.flume.interceptor.Interceptor;
import java.util.ArrayList;
import java.util.List;
public class MyInterceptor implements Interceptor {
@Override
public void initialize() {
}
@Override
public void close() {
}
/**
* 拦截source发送到通道channel中的消息
*
* @param event 接收过滤的event
* @return event 根据业务处理后的event
*/
@Override
public Event intercept(Event event) {
// 获取事件对象中的字节数据
byte[] arr = event.getBody();
// 将获取的数据转换成大写
event.setBody(new String(arr).toUpperCase().getBytes());
// 返回到消息中
return event;
}
// 接收被过滤事件集合
@Override
public List<Event> intercept(List<Event> events) {
List<Event> list = new ArrayList<>();
for (Event event : events) {
list.add(intercept(event));
}
return list;
}
public static class Builder implements Interceptor.Builder {
// 获取配置文件的属性
@Override
public Interceptor build() {
return new MyInterceptor();
}
@Override
public void configure(Context context) {
}
}
使用Maven做成Jar包，在flume的目录下mkdir jar，上传此jar到jar目录中
Flume配置文件
ToUpCase.conf
#1.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 /opt/Andy
a1.sources.r1.interceptors = i1
#全类名$Builder
a1.sources.r1.interceptors.i1.type = ToUpCase.MyInterceptor$Builder
# Describe the sink
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path = /ToUpCase1
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
运行命令：
bin/flume-ng agent -c conf/ -n a1 -f jar/ToUpCase.conf -C jar/Flume-1.0-SNAPSHOT.jar -Dflume.root.logger=DEBUG,console
4.2.8、案例七：Flume对接kafka
配置flume(flume-kafka.conf)
# define
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F -c +0 /opt/jars/calllog.csv
a1.sources.r1.shell = /bin/bash -c
# sink
a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.brokerList = bigdata111:9092,bigdata112:9092,bigdata113:9092
a1.sinks.k1.topic = calllog
a1.sinks.k1.batchSize = 20
a1.sinks.k1.requiredAcks = 1
# channel
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
# bind
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
进入flume根目录下，启动flume
/opt/module/flume-1.8.0/bin/flume-ng agent --conf /opt/module/flume-1.8.0/conf/ --name a1 --conf-file /opt/jars/flume2kafka.conf
4.2.9、案例八：kafka对接Flume
kafka2flume.conf
agent.sources = kafkaSource
agent.channels = memoryChannel
agent.sinks = hdfsSink
# The channel can be defined as follows.
agent.sources.kafkaSource.channels = memoryChannel
agent.sources.kafkaSource.type=org.apache.flume.source.kafka.KafkaSource
agent.sources.kafkaSource.zookeeperConnect=bigdata111:2181,bigdata112:2181,bigdata113:2181
agent.sources.kafkaSource.topic=calllog
#agent.sources.kafkaSource.groupId=flume
agent.sources.kafkaSource.kafka.consumer.timeout.ms=100
agent.channels.memoryChannel.type=memory
agent.channels.memoryChannel.capacity=10000
agent.channels.memoryChannel.transactionCapacity=1000
agent.channels.memoryChannel.type=memory
agent.channels.memoryChannel.capacity=10000
agent.channels.memoryChannel.transactionCapacity=1000
# the sink of hdfs
agent.sinks.hdfsSink.type=hdfs
agent.sinks.hdfsSink.channel = memoryChannel
agent.sinks.hdfsSink.hdfs.path=hdfs://bigdata111:9000/kafka2flume
agent.sinks.hdfsSink.hdfs.writeFormat=Text
agent.sinks.hdfsSink.hdfs.fileType=DataStream
#这两个不配置，会产生大量的小文件
agent.sinks.hdfsSink.hdfs.rollSize=0
agent.sinks.hdfsSink.hdfs.rollCount=0
启动命令
bin/flume-ng agent --conf conf --conf-file jobconf/kafka2flume.conf --name agent -Dflume.root.logger=INFO,console
注意：这个配置是从kafka过数据，但是需要重新向kafka的topic灌数据，他才会传到HDFS
嗯，需要多练习，实战！
rz和sz工具，上传和下载工具
yum install -y lrzsz
