上一篇：091-BigData-19Flume与Flume之间数据传递
一、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
二、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
三、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
四、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
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