21、Flink基础：Sink之Kafka-程序员快看-教程，程序员编程资料站

文章目录

一.Sink概述
二.Sink之Kafka
- 2.1 将文本文件数据写入Kafka
2.2 Java代码准备
2.3 开启生产者
2.4 查看Kafka输出
参考:

备注：
Flink 1.9.0

一.Sink概述

Flink没有类似于spark中foreach方法，让用户进行迭代的操作。虽有对外的输出操作都要利用Sink完成。最后通过类似如下方式完成整个任务最终输出操作。

官方提供了一部分的框架的sink。除此以外，需要用户自定义实现sink。

二.Sink之Kafka

2.1 将文本文件数据写入Kafka

sensor.txt

sensor_1,1547718199,35.8
sensor_6,1547718201,15.4
sensor_7,1547718202,6.7
sensor_10,1547718205,38.1
sensor_1,1547718207,36.3
sensor_1,1547718209,32.8
sensor_1,1547718212,37.1

代码:

package org.zqs.kafka;

import java.io.*;

import java.util.Properties;
import java.util.Random;

import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.serialization.StringSerializer;

public class Producer2 {
   
     
    public static String topic = "sensor4";//定义主题

    public static void main(String[] args) throws IOException {
   
     

        Properties p = new Properties();
        p.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "10.31.1.124:9092,10.31.1.125:9092,10.31.1.126:9092");//kafka地址，多个地址用逗号分割
        p.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        p.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        KafkaProducer<String, String> kafkaProducer = new KafkaProducer<>(p);
        try {
   
     
        //读取文件内容
        String filename = "C:\\Users\\Administrator\\IdeaProjects\\SparkStudy\\src\\main\\resources\\sensor.txt";
        FileInputStream file = new FileInputStream(filename);
        //指定字符缓冲输入流
        BufferedInputStream bis = new BufferedInputStream(file);

        byte[] bys = new byte[1024];
        int len;

            while ((len = bis.read(bys)) != -1) {
   
     
                //一次读取一个字节数组
                String msg = new String(bys, 0, len);
                System.out.println(msg);
                ProducerRecord<String, String> record = new ProducerRecord<String, String>(topic, msg);
                kafkaProducer.send(record);

            }
            bis.close();

        }
        catch (Exception e) {
   
     
        e.getStackTrace();
        } finally {
   
     
            kafkaProducer.close();
        }
    }
}

测试记录:

2.2 Java代码准备

代码:

package org.flink.sink;

import org.flink.beans.SensorReading;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;

import java.util.Properties;

/**
 * @author  只是甲
 * @date    2021-09-13
 * @remark  Kafka Sink
 */
public class SinkTest1_Kafka {
   
     
    public static void main(String[] args) throws Exception{
   
     
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

//        // 从文件读取数据
     //   DataStream<String> inputStream = env.readTextFile("C:\\Users\\Administrator\\IdeaProjects\\FlinkStudy\\src\\main\\resources\\sensor.txt");

        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", "10.31.1.124:9092,10.31.1.125:9092,10.31.1.126:9092");
        properties.setProperty("group.id", "consumer-group");
        properties.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        properties.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        properties.setProperty("auto.offset.reset", "latest");

        // 从Kafka读取数据
        DataStream<String> inputStream = env.addSource( new FlinkKafkaConsumer<String>("sensor4", new SimpleStringSchema(), properties));

        // 转换成SensorReading类型
        DataStream<String> dataStream = inputStream.map(line -> {
   
     
            String[] fields = line.split(",");
            return new SensorReading(fields[0], new Long(fields[1]), new Double(fields[2])).toString();
        });

        dataStream.addSink( new FlinkKafkaProducer<String>("10.31.1.124:9092,10.31.1.125:9092,10.31.1.126:9092", "sinktest", new SimpleStringSchema()));

        env.execute();
    }
}

直接运行代码，后面开启生产者，看输出。

2.3 开启生产者

因为真实环境非离线，来一条处理一条，所以这个地方我们开启一个Kafka的生产者，手工的录入一些数据

/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/kafka/bin/kafka-console-producer.sh --broker-list 10.31.1.124:9092 --topic first

输入:

2.4 查看Kafka输出

/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/kafka/bin/kafka-console-consumer.sh --from-beginning --bootstrap-server 10.31.1.124:9092 --topic sensor4

参考:

1、 https://www.bilibili.com/video/BV1qy4y1q728；
2. https://ashiamd.github.io/docsify-notes/#/study/BigData/Flink/%E5%B0%9A%E7%A1%85%E8%B0%B7Flink%E5%85%A5%E9%97%A8%E5%88%B0%E5%AE%9E%E6%88%98-%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0?id=_521-%e4%bb%8e%e9%9b%86%e5%90%88%e8%af%bb%e5%8f%96%e6%95%b0%e6%8d%ae