090-BigData-18Flume

作者: AncientMing | 来源:发表于2019-10-07 20:52 被阅读0次

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一、Flume简介

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。
Flume基于流式架构，容错性强，也很灵活简单。
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

下一篇：091-BigData-19Flume与Flume之间数据传递

090-BigData-18Flume

一、Flume简介

二、Flume角色

三、Flume传输过程

四、Flume部署及使用

嗯，需要多练习，实战！

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