Kafka快速入门系列(13) | Flume对接Kafka

  本篇博主带来的是Flume对接Kafka。

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1. Kafka与Flume比较

在企业中必须要清楚流式数据采集框架flume和kafka的定位是什么：

• 1. flume：cloudera公司研发
  适合多个生产者；
  适合下游数据消费者不多的情况；
  适合数据安全性要求不高的操作；
  适合与Hadoop生态圈对接的操作。
• 2.kafka：linkedin公司研发:
  适合数据下游消费众多的情况；
  适合数据安全性要求较高的操作，支持replication。

因此我们常用的一种模型是：
线上数据 --> flume --> kafka --> flume(根据情景增删该流程) --> HDFS

2. Flume与kafka集成

• 1. 编写代码

package com.buwenbuhuo.flume.interceptor;

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

import java.util.List;

/**
 * @author 卜温不火
 * @create 2020-05-07 18:57
 * com.buwenbuhuo.flume.interceptor - the name of the target package where the new class or interface will be created.
 * kafka0506 - the name of the current project.
 */
public class Customlnterceptor implements Interceptor {
    @Override
    public void initialize() {

    }

    @Override
    public Event intercept(Event event) {

        if (event.getBody()[0] >= '0' && event.getBody()[0] <= '9'){
            event.getHeaders().put("topic","number");
        }else if (event.getBody()[0] >= 'a' && event.getBody()[0] <= 'z'){
            event.getHeaders().put("topic","letter");
        }
        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 Builder implements Interceptor.Builder{
        public Interceptor build(){
            return new Customlnterceptor();
        }

        @Override
        public void configure(Context context) {

        }
    }
}

• 2. 打包上传
  
  
• 3. 配置flume(nc-kafka.conf)

[bigdata@hadoop002 job]$ vim nc-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/module/datas/flume.log
a1.sources.r1.shell = /bin/bash -c

# Describe the source
a1.sources.r1.type = netcat
a1.sources.r1.bind = hadoop002
a1.sources.r1.port = 44444
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = com.buwenbuhuo.flume.interceptor.Customlnterceptor$Builder

# sink
a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.kafka.bootstrap.servers = hadoop002:9092,hadoop003:9092,hadoop004:9092
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

• 4. 启动flume

[bigdata@hadoop002 flume]$ bin/flume-ng agent -n a1 -c conf/ -f job/nc-kafka.conf 

• 5. 分别在hadoop003，hadoop004启动消费者

[bigdata@hadoop003 kafka]$ bin/kafka-console-consumer.sh  --bootstrap-server hadoop002:9092 --topic number
[bigdata@hadoop004 kafka]$ bin/kafka-console-consumer.sh  --bootstrap-server hadoop002:9092 --topic letter

• 6. 启动端口测试

[bigdata@hadoop003 module]$ nc hadoop002 44444

可以看到最终结果图与我们设想是一致的，所以此次实验成功。

  本次的分享就到这里了,

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