springboot集成kafka(实现producer和consumer)

Posted 2023-01-19 互联网极客

本文简单介绍下如何在springboot中集成kafka收发消息

1、先安装依赖的jar包：

    	<dependency>
			<groupId>org.springframework.kafka</groupId>
			<artifactId>spring-kafka</artifactId>
			<version>2.2.4.RELEASE</version>
		</dependency>
		<dependency>
			<groupId>org.apache.kafka</groupId>
			<artifactId>kafka_2.12</artifactId>
			<version>2.2.0</version>
		</dependency>

2、kafka的配置信息如下：

#kafka相关参数配置
kafka:
  consumer:
    servers: 127.0.0.1:9092
    enable:
      auto:
        commit: true #(是否自动提交)
    session:
      timeout: 20000 #连接超时时间
    auto:
      commit:
        interval: 100
      offset:
        reset: latest # (实时生产，实时消费，不会从头开始消费)
    topic: result #消费者的topic
    group:
      id: test   #(消费组)
    concurrency: 10 #(设置消费线程数)
  producer:
    servers: 118.89.28.233:9092
    topic: result #(生产的topic)
    retries: 0
    batch:
      size: 4096
    linger: 1
    buffer:
      memory: 40960

3、configuration:kafka producer

通过@configuration @EnableKafka，声明config并打开kafkaTemplate的能力

通过@value注入application.yml配置文件中的kafka的配置 

生成bean@Bean

生产者类：

/**
 * author jinsq
 *
 * @date 2019/5/22 15:09
 */

import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.common.serialization.StringSerializer;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.annotation.EnableKafka;
import org.springframework.kafka.core.DefaultKafkaProducerFactory;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.kafka.core.ProducerFactory;

import java.util.HashMap;
import java.util.Map;

/**
 * kafka生产配置
 * @author Lvjiapeng
 *
 */
@Configuration
@EnableKafka
public class KafkaProducerConfig 
    @Value("$kafka.producer.servers")
    private String servers;
    @Value("$kafka.producer.retries")
    private int retries;
    @Value("$kafka.producer.batch.size")
    private int batchSize;
    @Value("$kafka.producer.linger")
    private int linger;
    @Value("$kafka.producer.buffer.memory")
    private int bufferMemory;

    public Map<String, Object> producerConfigs() 
        Map<String, Object> props = new HashMap<>();
        props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, servers);
        props.put(ProducerConfig.RETRIES_CONFIG, retries);
        props.put(ProducerConfig.BATCH_SIZE_CONFIG, batchSize);
        props.put(ProducerConfig.LINGER_MS_CONFIG, linger);
        props.put(ProducerConfig.BUFFER_MEMORY_CONFIG, bufferMemory);
        props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        return props;
    

    public ProducerFactory<String, String> producerFactory() 
        return new DefaultKafkaProducerFactory<>(producerConfigs());
    

    @Bean
    public KafkaTemplate<String, String> kafkaTemplate() 
        return new KafkaTemplate<String, String>(producerFactory());
    

消费者类：

/**
 * author jinsq
 *
 * @date 2019/5/22 15:10
 */
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.annotation.EnableKafka;
import org.springframework.kafka.config.ConcurrentKafkaListenerContainerFactory;
import org.springframework.kafka.config.KafkaListenerContainerFactory;
import org.springframework.kafka.core.ConsumerFactory;
import org.springframework.kafka.core.DefaultKafkaConsumerFactory;
import org.springframework.kafka.listener.ConcurrentMessageListenerContainer;

import java.util.HashMap;
import java.util.Map;

/**
 * kafka消费者配置
 * @author Lvjiapeng
 *
 */
@Configuration
@EnableKafka
public class KafkaConsumerConfig 

    @Value("$kafka.consumer.servers")
    private String servers;
    @Value("$kafka.consumer.enable.auto.commit")
    private boolean enableAutoCommit;
    @Value("$kafka.consumer.session.timeout")
    private String sessionTimeout;
    @Value("$kafka.consumer.auto.commit.interval")
    private String autoCommitInterval;
    @Value("$kafka.consumer.group.id")
    private String groupId;
    @Value("$kafka.consumer.auto.offset.reset")
    private String autoOffsetReset;
    @Value("$kafka.consumer.concurrency")
    private int concurrency;

    public Map<String, Object> consumerConfigs() 
        Map<String, Object> propsMap = new HashMap<>();
        propsMap.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, servers);
        propsMap.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, enableAutoCommit);
        propsMap.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, autoCommitInterval);
        propsMap.put(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG, sessionTimeout);
        propsMap.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        propsMap.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        propsMap.put(ConsumerConfig.GROUP_ID_CONFIG, groupId);
        propsMap.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, autoOffsetReset);
        return propsMap;
    

    public ConsumerFactory<String, String> consumerFactory() 
        return new DefaultKafkaConsumerFactory<>(consumerConfigs());
    

    @Bean
    public KafkaListenerContainerFactory<ConcurrentMessageListenerContainer<String, String>> kafkaListenerContainerFactory() 
        ConcurrentKafkaListenerContainerFactory<String, String> factory = new ConcurrentKafkaListenerContainerFactory<>();
        factory.setConsumerFactory(consumerFactory());
        factory.setConcurrency(concurrency);
        factory.getContainerProperties().setPollTimeout(1500);
        return factory;
    

    /**
     * kafka监听
     * @return
     */
    @Bean
    public RawDataListener listener() 
        return new RawDataListener();
    

实现producer，写一个controller,发送消息

/**
 * author jinsq
 *
 * @date 2019/5/22 10:59
 */
@RestController
@RequestMapping("test")
public class KafkaTestController 

    @Autowired
    private KafkaTemplate kafkaTemplate;

    @RequestMapping(value = "/producer")
    public R consume(@RequestBody String body) throws IOException 
        kafkaTemplate.send("result",body);
        return R.ok();
    

newListener()生成一个bean用来处理从kafka读取数据。Listener的实现demo如下：

@KafkaListener中的topics属于用于指定kafka topic的名称，topic名称是由消息的生产者指定，也就是kafkaTemplate在发送消息的时候指定。

/**
 * author jinsq
 *
 * @date 2019/5/22 17:06
 */
import lombok.extern.slf4j.Slf4j;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.springframework.kafka.annotation.KafkaListener;
import org.springframework.stereotype.Component;

import java.io.IOException;

/**
 * kafka监听
 * @author shangzz
 *
 */
@Component
@Slf4j
public class RawDataListener 

    /**
     * 实时获取kafka数据(生产一条，监听生产topic自动消费一条)
     * @param record
     * @throws IOException
     */
    @KafkaListener(topics = "$kafka.consumer.topic")
    public void listen(ConsumerRecord<?, ?> record) throws IOException 
        String value = (String) record.value();
        String topic = record.topic();
        if("result".equals(topic))
            log.info("接收到的信息为："+value);
        

    

最后再写一个并发测试的demo对我们的代码进行测试。

如下：

import org.junit.Test;
import org.junit.runner.RunWith;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.test.context.junit4.SpringRunner;

import java.util.concurrent.CountDownLatch;

/**
 * author jinsq
 *
 * @date 2019/5/22 17:27
 */
@RunWith(SpringRunner.class)
@SpringBootTest
public class CountDownLatchTest 

    @Autowired
    private KafkaTemplate kafkaTemplate;

    //模拟短时间内的并发请求量
    private static final int threadNum =20000;
    //倒计时器，用于模拟高并发
    private CountDownLatch cdl = new CountDownLatch(threadNum);
    private static int i = 0;

    @Test
    public void test()
        for(int i =0;i<=threadNum;i++)
            MyThread myThread = new MyThread(cdl);
            Thread thread = new Thread(myThread);
            thread.start();
        
        try 
            cdl.await();
        catch (Exception e)
            e.printStackTrace();
        
    

    class MyThread implements Runnable
        private CountDownLatch countDownLatch;
        public MyThread(CountDownLatch countDownLatch)
            this.countDownLatch = countDownLatch;
        
        @Override
        public void run()
            kafkaTemplate.send("result","发送消息为："+(i++));
            countDownLatch.countDown();
        
    

看代码中，我们同时开启了20000个线程进行并发测试，代码都没有问题，消费正常。