kafka复习:(3)自定义序列化器和反序列化器

一、实体类定义:

复制代码
public class Company {
    private String name;
    private String address;

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }

    public String getAddress() {
        return address;
    }

    public void setAddress(String address) {
        this.address = address;
    }

    @Override
    public String toString() {
        return "Company{" +
                "name='" + name + '\'' +
                ", address='" + address + '\'' +
                '}';
    }

    public Company(String name, String address) {
        this.name = name;
        this.address = address;
    }

    public Company() {
    }
}

二、自定义序列化器和反序列化器

复制代码
import org.apache.kafka.common.serialization.Serializer;

import java.io.UnsupportedEncodingException;
import java.nio.ByteBuffer;
import java.util.Map;

public class CompanySerializer implements Serializer<Company> {
    @Override
    public void configure(Map<String, ?> configs, boolean isKey) {

    }

    //进行字节数组序列化
    @Override
    public byte[] serialize(String topic, Company data) {
        if(data == null){
            return null;
        }
        byte[] name, address;
        try{
            if(data.getName() != null){
                name = data.getName().getBytes("UTF-8");
            }else {
                name = new byte[0];
            }
            if(data.getAddress() != null){
                address = data.getAddress().getBytes("UTF-8");
            }else{
                address = new byte[0];
            }
            ByteBuffer byteBuffer = ByteBuffer.allocate(4 + 4+ name.length + address.length);

            byteBuffer.putInt(name.length);
            byteBuffer.put(name);
            byteBuffer.putInt(address.length);
            byteBuffer.put(address);
            return byteBuffer.array();
        }catch (UnsupportedEncodingException e){
            e.printStackTrace();
        }
        return new byte[0];
    }

    @Override
    public void close() {

    }
}


import org.apache.kafka.common.errors.SerializationException;
import org.apache.kafka.common.serialization.Deserializer;

import java.io.UnsupportedEncodingException;
import java.nio.ByteBuffer;
import java.util.Map;

public class CompanyDeserializer implements Deserializer<Company> {
    @Override
    public void configure(Map<String, ?> configs, boolean isKey) {

    }

    @Override
    public Company deserialize(String topic, byte[] data) {
        if (data == null) {
            return null;
        }
        ByteBuffer buffer = ByteBuffer.wrap(data);
        int nameLen, addressLen;
        String name, address;
        nameLen = buffer.getInt();
        byte[] nameBytes = new byte[nameLen];
        buffer.get(nameBytes);
        addressLen = buffer.getInt();
        byte[] addressBytes = new byte[addressLen];
        buffer.get(addressBytes);
        try {
            name = new String(nameBytes, "UTF-8");
            address = new String(addressBytes, "UTF-8");
        } catch (UnsupportedEncodingException ex) {
            throw new SerializationException("Error:"+ex.getMessage());
        }
        return new Company(name,address);

    }

    @Override
    public void close() {

    }
}

三、定义生产者和消费者

复制代码
package com.cisdi.dsp.modules.metaAnalysis.rest;

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;

import java.util.Properties;

public class CompanyProducer {
    public static void main(String[] args) throws Exception{
        Properties properties = new Properties();
        properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
        //设置value的序列化器
        properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, CompanySerializer.class.getName());
        properties.put("bootstrap.servers", "xxx.xxx.xxx.xxx:9092");
        KafkaProducer<String, Company> producer = new KafkaProducer<>(properties);
        Company company = new Company();
        company.setAddress("Beijing");
        company.setName("Connection");
        ProducerRecord<String, Company> record = new ProducerRecord<>("companyTopic", company);
        producer.send(record).get();
    }
}

package com.cisdi.dsp.modules.metaAnalysis.rest;

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.serialization.StringDeserializer;

import java.time.Duration;
import java.util.Collections;
import java.util.Properties;

public class CompanyConsumer {
    public static void main(String[] args) {
        Properties properties=new Properties();
        properties.setProperty(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
        properties.setProperty(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, CompanyDeserializer.class.getName());
        properties.setProperty(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"xxx.xxx.xxx.xxx:9092");
        properties.setProperty(ConsumerConfig.GROUP_ID_CONFIG,"my");
        KafkaConsumer<String,Company> kafkaConsumer=new KafkaConsumer<>(properties);
        kafkaConsumer.subscribe(Collections.singletonList("companyTopic"));
        while(true){
            ConsumerRecords<String,Company> consumerRecords=kafkaConsumer.poll(Duration.ofMillis(1000));
            for(ConsumerRecord<String,Company> consumerRecord: consumerRecords){
                System.out.println(consumerRecord.value());
            }
        }
    }
}
相关推荐
却道天凉_好个秋5 小时前
系统架构设计(九):分布式架构与微服务
分布式·架构·系统架构
predisw8 小时前
kafka connect 大概了解
分布式·kafka
计算机毕设定制辅导-无忧学长10 小时前
RabbitMQ 快速上手:安装配置与 HelloWorld 实践(二)
分布式·rabbitmq·ruby
啾啾Fun10 小时前
【Java微服务组件】分布式协调P1-数据共享中心简单设计与实现
java·分布式·微服务
梦想画家13 小时前
Scrapy进阶实践指南:从脚本运行到分布式爬取
分布式·scrapy·数据工程
东阳马生架构14 小时前
Seata源码—5.全局事务的创建与返回处理二
分布式·seata·分布式事务
张伯毅16 小时前
Flink SQL 将kafka topic的数据写到另外一个topic里面
sql·flink·kafka
掘金-我是哪吒16 小时前
分布式微服务系统架构第133集:运维服务器6年经验,高并发,大数据量系统
运维·服务器·分布式·微服务·系统架构
尘世壹俗人18 小时前
hadoop.proxyuser.代理用户.授信域 用来干什么的
大数据·hadoop·分布式
白露与泡影19 小时前
基于Mongodb的分布式文件存储实现
分布式·mongodb·wpf