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Flume监听多个文件目录，并根据文件名称不同，输出到kafka不同topic中

一、Flume监听多个文件目录

1. flume的环境搭建和基础配置参考

https://blog.csdn.net/qinqinde123/article/details/128130131

2. 修改配置文件flume-conf.properties

#定义两个是数据源source1、source2
agent.sources = source1 source2
agent.channels = channel1
agent.sinks = sink1

#数据源source1：监听/home/sxvbd/bigdata/flumeTestDir目录
agent.sources.source1.type = spooldir
agent.sources.source1.spoolDir = /home/sxvbd/bigdata/flumeTestDir
# 文件名带路径，header中key=filePath
agent.sources.source1.fileHeader = true
agent.sources.source1.fileHeaderKey = filePath
# 文件名不带路径，header中key=fileName
agent.sources.source1.basenameHeader = true
agent.sources.source1.basenameHeaderKey = fileName

#数据源source2：监听/home/sxvbd/bigdata/flumeTestDir/temp目录·
agent.sources.source2.type = spooldir
agent.sources.source2.spoolDir = /home/sxvbd/bigdata/flumeTestDir/temp
# 文件名带路径，header中key=filePaht
agent.sources.source2.fileHeader = true
agent.sources.source2.fileHeaderKey = filePath
# 文件名不带路径，header中key=fileName
agent.sources.source2.basenameHeader = true
agent.sources.source2.basenameHeaderKey = fileName

#定义一个channel
agent.channels.channel1.type = memory
agent.channels.channel1.capacity = 1000000
agent.channels.channel1.transactionCapacity = 10000
agent.channels.channel1.keep-alive = 60

#重写sink，根据文件名称不同，推送到不同topic中
agent.sinks.sink1.type = com.demo.flume.LogToDiffentKafkaTopic
agent.sinks.sink1.kafka.bootstrap.servers = node24:9092,node25:9092,node26:9092
agent.sinks.sink1.parseAsFlumeEvent = false

#定义source channel  sink的关系
agent.sources.source1.channels = channel1
agent.sources.source2.channels = channel1
agent.sinks.sink1.channel = channel1

二、重写Sink，根据文件名称不同，消息发送到不同的topic中

flume监听到有新文件出现的时候，会将文件内容推送到kakfa的topic中，但是如果文件夹中有不同类型的文件，直接推送到kafka的同一个topic中，如果根据内容无法区分不同类型的文件，那就需要根据文件名称来区分。flume本身根据配置无法实现，只能通过重写Sink，根据文件名称，将内容推送到kafka的不同topic。

在这里插入图片描述
看了一下官网的开发文档,要想自定义一个Sink也很简单,只需要继承一个抽象类 AbstractSink 和一个用于接收配置参数的接口 Configurable 即可.然后呢就需要实现两个方法一个就是public Status process() throws EventDeliveryException {}这个方法会被多次调用,反复执行,也就是通过它来实时的获取Channel流出来的数据;第二个就是public void configure(Context context) {} 这个方法主要是通过传入的这个Contex上下文对象.来个获取配置文件中的参数,一些初始化的工作可以写在这个方法里面.

1.创建springboot项目LogToDiffentKafkaTopic

2.pom.xml中引入flume相关依赖

<?xml version="1.0" encoding="UTF-8"?><project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><groupId>com.demo</groupId><artifactId>flume</artifactId><version>1.0</version><properties><maven.compiler.source>8</maven.compiler.source><maven.compiler.target>8</maven.compiler.target><java.version>1.8</java.version></properties><dependencies><!--Flume 依赖--><dependency><groupId>org.apache.flume</groupId><artifactId>flume-ng-core</artifactId><version>1.9.0</version></dependency><dependency><groupId>org.apache.flume</groupId><artifactId>flume-ng-configuration</artifactId><version>1.9.0</version></dependency><!--Kafka 依赖--><dependency><groupId>org.apache.kafka</groupId><artifactId>kafka-clients</artifactId><version>2.4.1</version></dependency><dependency><groupId>org.apache.kafka</groupId><artifactId>kafka_2.11</artifactId><version>2.4.1</version></dependency><dependency><groupId>org.slf4j</groupId><artifactId>slf4j-nop</artifactId><version>1.7.30</version></dependency></dependencies><!--构建--><build><plugins><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-compiler-plugin</artifactId><configuration><source>${java.version}</source><target>${java.version}</target><encoding>UTF-8</encoding></configuration></plugin><plugin><groupId>org.springframework.boot</groupId><artifactId>spring-boot-maven-plugin</artifactId></plugin></plugins></build></project>

3. 创建一个类LogToDiffentKafkaTopic.java，继承自AbstractSink

publicclassLogToDiffentKafkaTopicextendsAbstractSinkimplementsConfigurable{privateMessageClassifier messageClassifier;@OverridepublicStatusprocess()throwsEventDeliveryException{System.out.println("========>process");Status status =null;Channel channel =getChannel();Transaction transaction = channel.getTransaction();
        transaction.begin();try{Event event = channel.take();if(event ==null){
                transaction.rollback();
                status =Status.BACKOFF;return status;}System.out.println("========>event:"+ event.toString());//根据配置文件中定义的agent.sources.source1.basenameHeader = true和agent.sources.source1.basenameHeaderKey = fileName获取文件名称String fileName = event.getHeaders().get("fileName");byte[] body = event.getBody();finalString msg =newString(body);System.out.println("========>msg:"+ msg.toString());
            status = messageClassifier.startClassifier(msg, fileName);// 提交事务
            transaction.commit();}catch(Exception e){
            transaction.rollback();
            e.printStackTrace();
            status =Status.BACKOFF;}finally{
            transaction.close();}return status;}@Overridepublicvoidconfigure(Context context){ImmutableMap<String,String> parameters = context.getParameters();//启动的时候，从配置文件flume-conf.properties中读取的配置信息System.out.println("========>parameters: "+ parameters.toString());Properties properties =newProperties();
        properties.put("bootstrap.servers", context.getString("kafka.bootstrap.servers","localhost:9092"));
        properties.put("acks", context.getString("acks","all"));
        properties.put("retries",Integer.parseInt(context.getString("retries","0")));
        properties.put("batch.size",Integer.parseInt(context.getString("batch.size","16384")));
        properties.put("linger.ms",Integer.parseInt(context.getString("linger.ms","1")));
        properties.put("buffer.memory",Integer.parseInt(context.getString("buffer.memory","33554432")));
        properties.put("key.serializer","org.apache.kafka.common.serialization.StringSerializer");
        properties.put("value.serializer","org.apache.kafka.common.serialization.StringSerializer");
        messageClassifier =newMessageClassifier(properties);}

4. 创建一个类MessageClassifier.java，继承自AbstractSink

publicclassMessageClassifier{/*文件名称中包含_CDSS_,则消息推送到data-ncm-hljk-cdss-topic*/privatestaticfinalStringHJSJ_SSSJ_CDSS=".*_CDSS_.*";privatestaticfinalStringHJSJ_SSSJ_CDSS_TOPIC="data-ncm-hljk-cdss-topic";/*文件名称中包含_FZSS_,则消息推送到data-ncm-hljk-fzss-topic*/privatestaticfinalStringHJSJ_SSSJ_FZSS=".*_FZSS_.*";privatestaticfinalStringHJSJ_SSSJ_FZSS_TOPIC="data-ncm-hljk-fzss-topic";privatefinalKafkaProducer<String,String> producer;publicMessageClassifier(Properties kafkaConf){
        producer =newKafkaProducer<>(kafkaConf);}publicSink.StatusstartClassifier(String msg,String fileName){System.out.println("===========>msg: "+ msg);System.out.println("===========>fileName: "+ fileName);try{if(Pattern.matches(HJSJ_SSSJ_CDSS, fileName)){System.out.println("===========>HJSJ_SSSJ_CDSS");
                producer.send(newProducerRecord<>(HJSJ_SSSJ_CDSS_TOPIC, msg));}elseif(Pattern.matches(HJSJ_SSSJ_FZSS, fileName)){System.out.println("===========>HJSJ_SSSJ_FZSS");
                producer.send(newProducerRecord<>(HJSJ_SSSJ_FZSS_TOPIC, msg));}}catch(Exception e){
            e.printStackTrace();System.out.println("===========>exception: "+ e.getMessage());returnSink.Status.BACKOFF;}returnSink.Status.READY;}}

5. 打jar包: flume-1.0.jar

mvn clean install-DskipTests

6. 在flume的安装目录下创建plugins.d目录

mkdir-p /home/sxvbd/bigdata/flume-1.9.0/plugins.d

7. 在plugins.d目录下创建一个目录(名字任意，例如demo)

mkdir-p /home/sxvbd/bigdata/flume-1.9.0/plugins.d/demo

8. 在demo目录下创建两个目录：lib和libext

mkdir-p /home/sxvbd/bigdata/flume-1.9.0/plugins.d/demo/lib
mkdir-p /home/sxvbd/bigdata/flume-1.9.0/plugins.d/demo/libext

9. 将jar包上传到lib目录下(libext不用管)

10. 在配置文件flume-conf.properties中配置自定义sink

#Each channel's type is defined.
agent.sinks.sink1.type = com.demo.flume.LogToDiffentKafkaTopic
agent.sinks.sink1.kafka.bootstrap.servers = node24:9092,node25:9092,node26:9092
agent.sinks.sink1.parseAsFlumeEvent =false

11.启动

nohup../bin/flume-ng agent --conf conf -f /home/sxvbd/bigdata/flume-1.9.0/conf/flume-conf.properties -n agent -Dflume.root.logger=INFO,console > flume.log 2>&1&

12.在对应的目录下拖入文件

目录/home/sxvbd/bigdata/flumeTestDir/和目录/home/sxvbd/bigdata/flumeTestDir/temp

13.监听kafka的topic

在这里插入图片描述

标签： kafka flume 大数据
本文转载自: https://blog.csdn.net/qinqinde123/article/details/128131260
版权归原作者 victor-维克特 所有， 如有侵权，请联系我们删除。
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