第5章 Flume对接Kafka

1 简单实现
1）配置flume

define

a1.sources = r1
a1.sinks = k1
a1.channels = c1

source

a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /opt/module/data/flume.log

sink

a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.kafka.bootstrap.servers = hadoop102:9092,hadoop103:9092,hadoop104:9092
a1.sinks.k1.kafka.topic = first
a1.sinks.k1.kafka.flumeBatchSize = 20
a1.sinks.k1.kafka.producer.acks = 1
a1.sinks.k1.kafka.producer.linger.ms = 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
2） 启动kafka消费者
3） 进入flume根目录下，启动flume
$ bin/flume-ng agent -c conf/ -n a1 -f jobs/flume-kafka.conf
4） 向 /opt/module/data/flume.log里追加数据，查看kafka消费者消费情况
$ echo hello >> /opt/module/data/flume.log
6.2 数据分离
0）需求: 将flume采集的数据按照不同的类型输入到不同的topic中
将日志数据中带有atguigu的，输入到Kafka的first主题中，
将日志数据中带有shangguigu的,输入到Kafka的second主题中，
其他的数据输入到Kafka的third主题中
1）编写Flume的Interceptor
package com.atguigu.kafka.flumeInterceptor;

import org.apache.flume.Context;
import org.apache.flume.Event;
import org.apache.flume.interceptor.Interceptor;

import javax.swing.text.html.HTMLEditorKit;
import java.util.List;
import java.util.Map;

public class FlumeKafkaInterceptor implements Interceptor {
@Override
public void initialize() {

}

/**
 * 如果包含"atguigu"的数据，发送到first主题
 * 如果包含"sgg"的数据，发送到second主题
 * 其他的数据发送到third主题
 * @param event
 * @return
 */
@Override
public Event intercept(Event event) {
    //1.获取event的header
    Map<String, String> headers = event.getHeaders();
    //2.获取event的body
    String body = new String(event.getBody());
    if(body.contains("atguigu")){
        headers.put("topic","first");
    }else if(body.contains("sgg")){
        headers.put("topic","second");
    }
    return event;

}

@Override
public List<Event> intercept(List<Event> events) {
    for (Event event : events) {
      intercept(event);
    }
    return events;
}

@Override
public void close() {

}

public static class MyBuilder implements  Builder{

    @Override
    public Interceptor build() {
        return  new FlumeKafkaInterceptor();
    }

    @Override
    public void configure(Context context) {

    }
}

}
2）将写好的interceptor打包上传到Flume安装目录的lib目录下
3）配置flume

Name the components on this agent

a1.sources = r1
a1.sinks = k1
a1.channels = c1

Describe/configure the source

a1.sources.r1.type = netcat
a1.sources.r1.bind = 0.0.0.0
a1.sources.r1.port = 6666

Describe the sink

a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.kafka.topic = third
a1.sinks.k1.kafka.bootstrap.servers = hadoop102:9092,hadoop103:9092,hadoop104:9092
a1.sinks.k1.kafka.flumeBatchSize = 20
a1.sinks.k1.kafka.producer.acks = 1
a1.sinks.k1.kafka.producer.linger.ms = 1

#Interceptor
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = com.atguigu.kafka.flumeInterceptor.FlumeKafkaInterceptor$MyBuilder

# 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
4） 启动kafka消费者
5） 进入flume根目录下，启动flume
$ bin/flume-ng agent -c conf/ -n a1 -f jobs/flume-kafka.conf
6） 向6666端口写数据，查看kafka消费者消费情况