本文是对《【硬刚大数据之学习路线篇】从零到大数据专家的学习指南(全面升级版)》的Kafka部分补充。

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

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