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Kafka实战——简单易懂的生产者消费者demo_kafka demo

作者：笔触狂放9 | 2024-07-18 14:11:29

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kafka demo

单线程版本适合本地调试，多线程版本适合做压测

1、引入maven依赖

  1. <dependency>
  2.    <groupId>org.apache.kafka</groupId>
  3.    <artifactId>kafka-clients</artifactId>
  4.    <version>1.1.0</version>
  5. </dependency>

2、生产者代码

单线程版

  1. public class MsgProducer {
  2.     public static void main(String[] args) throws InterruptedException, ExecutionException {
  3.         Properties props = new Properties();
  4.         props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "192.168.0.60:9092,192.168.0.60:9093,192.168.0.60:9094");
  5.         /*
  6.          发出消息持久化机制参数
  7.         （1）acks=0： 表示producer不需要等待任何broker确认收到消息的回复，就可以继续发送下一条消息。性能最高，但是最容易丢消息。
  8.         （2）acks=1： 至少要等待leader已经成功将数据写入本地log，但是不需要等待所有follower是否成功写入。就可以继续发送下一条消息。这种情况下，如果follower没有成功备份数据，而此时leader
  9.         又挂掉，则消息会丢失。
  10.         （3）acks=-1或all： 这意味着leader需要等待所有备份(min.insync.replicas配置的备份个数)都成功写入日志，这种策略会保证只要有一个备份存活就不会丢失数据。
  11.                             这是最强的数据保证。一般除非是金融级别，或跟钱打交道的场景才会使用这种配置。
  12.         */
  13.         props.put(ProducerConfig.ACKS_CONFIG, "1");
  14.         //发送失败会重试，默认重试间隔100ms，重试能保证消息发送的可靠性，但是也可能造成消息重复发送，比如网络抖动，所以需要在接收者那边做好消息接收的幂等性处理
  15.         props.put(ProducerConfig.RETRIES_CONFIG, 3);
  16.         //重试间隔设置
  17.         props.put(ProducerConfig.RETRY_BACKOFF_MS_CONFIG, 300);
  18.         //设置发送消息的本地缓冲区，如果设置了该缓冲区，消息会先发送到本地缓冲区，可以提高消息发送性能，默认值是33554432，即32MB
  19.         props.put(ProducerConfig.BUFFER_MEMORY_CONFIG, 33554432);
  20.         //kafka本地线程会从缓冲区取数据，批量发送到broker，
  21.         //设置批量发送消息的大小，默认值是16384，即16kb，就是说一个batch满了16kb就发送出去
  22.         props.put(ProducerConfig.BATCH_SIZE_CONFIG, 16384);
  23.         //默认值是0，意思就是消息必须立即被发送，但这样会影响性能
  24.         //一般设置100毫秒左右，就是说这个消息发送完后会进入本地的一个batch，如果100毫秒内，这个batch满了16kb就会随batch一起被发送出去
  25.         //如果100毫秒内，batch没满，那么也必须把消息发送出去，不能让消息的发送延迟时间太长
  26.         props.put(ProducerConfig.LINGER_MS_CONFIG, 100);
  27.         //把发送的key从字符串序列化为字节数组
  28.         props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
  29.         //把发送消息value从字符串序列化为字节数组
  30.         props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
  31.  
  32.         Producer<String, String> producer = new KafkaProducer<>(props);
  33.  
  34.         int msgNum = 5;
  35.         CountDownLatch countDownLatch = new CountDownLatch(msgNum);
  36.         for (int i = 1; i <= msgNum; i++) {
  37.             Order order = new Order(i, 100 + i, 1, 1000.00);
  38.             //指定发送分区
  39.             ProducerRecord<String, String> producerRecord = new ProducerRecord<String, String>("order-topic"
  40.                     , 0, order.getOrderId().toString(), JSON.toJSONString(order));
  41.             //未指定发送分区，具体发送的分区计算公式：hash(key)%partitionNum
  42.             /*ProducerRecord<String, String> producerRecord = new ProducerRecord<String, String>("my-replicated-topic"
  43.                     , order.getOrderId().toString(), JSON.toJSONString(order));*/
  44.  
  45.             //等待消息发送成功的同步阻塞方法
  46.          /*RecordMetadata metadata = producer.send(producerRecord).get();
  47.          System.out.println("同步方式发送消息结果：" + "topic-" + metadata.topic() + "|partition-"
  48.                  + metadata.partition() + "|offset-" + metadata.offset());*/
  49.  
  50.             //异步方式发送消息
  51.             producer.send(producerRecord, new Callback() {
  52.                 @Override
  53.                 public void onCompletion(RecordMetadata metadata, Exception exception) {
  54.                     if (exception != null) {
  55.                         System.err.println("发送消息失败：" + exception.getStackTrace());
  56.  
  57.                     }
  58.                     if (metadata != null) {
  59.                         System.out.println("异步方式发送消息结果：" + "topic-" + metadata.topic() + "|partition-"
  60.                                 + metadata.partition() + "|offset-" + metadata.offset());
  61.                     }
  62.                     countDownLatch.countDown();
  63.                 }
  64.             });
  65.  
  66.             //送积分 TODO
  67.  
  68.         }
  69.  
  70.         countDownLatch.await(5, TimeUnit.SECONDS);
  71.         producer.close();
  72.     }
  73. }

多线程版

  1. package com.test.kafka;
  2.  
  3. import org.apache.kafka.clients.producer.*;
  4. import org.apache.kafka.common.serialization.StringSerializer;
  5.  
  6. import java.util.Properties;
  7. import java.util.concurrent.CountDownLatch;
  8. import java.util.concurrent.ExecutorService;
  9. import java.util.concurrent.Executors;
  10.  
  11. public class MsgProducer {
  12.  
  13.     //发送消息的个数
  14.     private static final int MSG_SIZE = 500000;
  15.     //负责发送消息的线程池
  16.     private static ExecutorService executorService = Executors.newFixedThreadPool(Runtime.getRuntime().availableProcessors());
  17.     private static CountDownLatch countDownLatch = new CountDownLatch(MSG_SIZE);
  18.  
  19.  
  20.     /*发送消息的任务*/
  21.     private static class ProduceWorker implements Runnable {
  22.  
  23.         private ProducerRecord<String, String> record;
  24.         private KafkaProducer<String, String> producer;
  25.  
  26.         public ProduceWorker(ProducerRecord<String, String> record, KafkaProducer<String, String> producer) {
  27.             this.record = record;
  28.             this.producer = producer;
  29.         }
  30.  
  31.         public void run() {
  32.             final String id = Thread.currentThread().getId() + "-" + System.identityHashCode(producer);
  33.             try {
  34.                 producer.send(record, new Callback() {
  35.                     public void onCompletion(RecordMetadata metadata, Exception exception) {
  36.                         if (null != exception) {
  37.                             exception.printStackTrace();
  38.                         }
  39.                         if (null != metadata) {
  40.                             System.out.println(id + "|"
  41.                                     + String.format("偏移量：%s,分区：%s",
  42.                                     metadata.offset(), metadata.partition()));
  43.                         }
  44.                     }
  45.                 });
  46.                 System.out.println(id + ":数据[" + record.key() + "-" + record.value() + "]已发送。");
  47.                 countDownLatch.countDown();
  48.             } catch (Exception e) {
  49.                 e.printStackTrace();
  50.             }
  51.         }
  52.     }
  53.  
  54.     public static void main(String[] args) {
  55.         // 消费主题
  56.         String topicName = "test_datax_kafka_read";
  57.         Properties properties = new Properties();
  58.         properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "10.254.21.6:59292,10.254.21.1:59292,10.254.21.2:59292");
  59.         properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
  60.         properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
  61.         KafkaProducer<String, String> producer = new KafkaProducer(properties);
  62.  
  63.         try {
  64.             //循环发送，通过线程池的方式
  65.             for (int i = 0; i < MSG_SIZE; i++) {
  66.  
  67.                 ProducerRecord<String, String> record = new ProducerRecord(
  68.                         topicName,
  69.                         null,
  70.                         "{\"data\":[{\"byteSize\":5,\"rawData\":28108,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":60,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":99,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":70,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":31,\"type\":\"LONG\"},{\"byteSize\":1,\"rawData\":0,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":82,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":94,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":70,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":22,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":10,\"type\":\"LONG\"},{\"byteSize\":1,\"rawData\":1,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":89,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":14,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":38,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":20,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":50,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":30,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":13,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":36,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":53,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":42,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":11,\"type\":\"LONG\"},{\"byteSize\":1,\"rawData\":4,\"type\":\"LONG\"},{\"byteSize\":1,\"rawData\":6,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":49,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":35,\"type\":\"LONG\"},{\"byteSize\":1,\"rawData\":4,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":48,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":46,\"type\":\"LONG\"},{\"byteSize\":1,\"rawData\":1,\"type\":\"LONG\"},{\"byteSize\":2,\"rawData\":73,\"type\":\"LONG\"},{\"byteSize\":1,\"rawData\":6,\"type\":\"LONG\"},{\"byteSize\":8,\"rawData\":1659515670000,\"subType\":\"DATETIME\",\"type\":\"DATE\"}],\"size\":34}\n"
  71.                 );
  72.                 executorService.submit(new ProduceWorker(record, producer));
  73.             }
  74.             countDownLatch.await();
  75.         } catch (Exception e) {
  76.             e.printStackTrace();
  77.         } finally {
  78.             producer.close();
  79.             executorService.shutdown();
  80.         }
  81.     }
  82. }
  83.

3、消费者代码

单线程版

  1. public class MsgConsumer {
  2.     public static void main(String[] args) {
  3.         Properties props = new Properties();
  4.         props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "192.168.0.60:9092,192.168.0.60:9093,192.168.0.60:9094");
  5.         // 消费分组名
  6.         props.put(ConsumerConfig.GROUP_ID_CONFIG, "testGroup");
  7.         // 是否自动提交offset
  8.       /*props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "true");
  9.       // 自动提交offset的间隔时间
  10.       props.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG , "1000");*/
  11.         props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "false");
  12.       /*
  13.       心跳时间，服务端broker通过心跳确认consumer是否故障，如果发现故障，就会通过心跳下发
  14.       rebalance的指令给其他的consumer通知他们进行rebalance操作，这个时间可以稍微短一点
  15.       */
  16.         props.put(ConsumerConfig.HEARTBEAT_INTERVAL_MS_CONFIG, 1000);
  17.         //服务端broker多久感知不到一个consumer心跳就认为他故障了，默认是10秒
  18.         props.put(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG, 10 * 1000);
  19.         /*
  20.         如果两次poll操作间隔超过了这个时间，broker就会认为这个consumer处理能力太弱，
  21.         会将其踢出消费组，将分区分配给别的consumer消费
  22.         */
  23.         props.put(ConsumerConfig.MAX_POLL_INTERVAL_MS_CONFIG, 30 * 1000);
  24.         props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
  25.         props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
  26.         KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
  27.         // 消费主题
  28.         String topicName = "order-topic";
  29.         //consumer.subscribe(Arrays.asList(topicName));
  30.         // 消费指定分区
  31.         //consumer.assign(Arrays.asList(new TopicPartition(topicName, 0)));
  32.  
  33.         //消息回溯消费
  34.         consumer.assign(Arrays.asList(new TopicPartition(topicName, 0)));
  35.         consumer.seekToBeginning(Arrays.asList(new TopicPartition(topicName, 0)));
  36.         //指定offset消费
  37.         //consumer.seek(new TopicPartition(topicName, 0), 10);
  38.  
  39.         while (true) {
  40.             /*
  41.              * poll() API 是拉取消息的长轮询，主要是判断consumer是否还活着，只要我们持续调用poll()，
  42.              * 消费者就会存活在自己所在的group中，并且持续的消费指定partition的消息。
  43.              * 底层是这么做的：消费者向server持续发送心跳，如果一个时间段（session.
  44.              * timeout.ms）consumer挂掉或是不能发送心跳，这个消费者会被认为是挂掉了，
  45.              * 这个Partition也会被重新分配给其他consumer
  46.              */
  47.             ConsumerRecords<String, String> records = consumer.poll(Integer.MAX_VALUE);
  48.             for (ConsumerRecord<String, String> record : records) {
  49.                 System.out.printf("收到消息：offset = %d, key = %s, value = %s%n", record.offset(), record.key(),
  50.                         record.value());
  51.             }
  52.  
  53.             if (records.count() > 0) {
  54.                 // 提交offset
  55.                 consumer.commitSync();
  56.             }
  57.         }
  58.     }
  59. }

多线程版

  1. package com.test.kafka;
  2.  
  3. import org.apache.kafka.clients.consumer.ConsumerConfig;
  4. import org.apache.kafka.clients.consumer.ConsumerRecord;
  5. import org.apache.kafka.clients.consumer.ConsumerRecords;
  6. import org.apache.kafka.clients.consumer.KafkaConsumer;
  7. import org.apache.kafka.common.serialization.StringDeserializer;
  8.  
  9. import java.time.Duration;
  10. import java.util.Arrays;
  11. import java.util.Properties;
  12. import java.util.concurrent.ExecutorService;
  13. import java.util.concurrent.Executors;
  14.  
  15. public class MsgConsumer {
  16.  
  17.  
  18.     private static ExecutorService receiveMsgExecutorService = Executors.newFixedThreadPool(Runtime.getRuntime().availableProcessors());
  19.  
  20.     public static void main(String[] args) {
  21.         // 消费主题
  22.         String topicName = "test_datax_kafka_read";
  23.  
  24.         int consumerThreadNum = 12;
  25.         for (int i = 0; i < consumerThreadNum; i++) {
  26.             receiveMsgExecutorService.submit(new KafkaConsumerThread(initConfig(), topicName));
  27.         }
  28.     }
  29.  
  30.     public static Properties initConfig() {
  31.         Properties properties = new Properties();
  32.         properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "10.254.21.6:59292,10.254.21.1:59292,10.254.21.2:59292");
  33.         // 消费分组名
  34.         properties.put(ConsumerConfig.GROUP_ID_CONFIG, "local-test-2");
  35.         // 是否自动提交offset
  36.         properties.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "true");
  37.         // 自动提交offset的间隔时间
  38.         properties.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, "1000");
  39.         // 心跳时间，服务端broker通过心跳确认consumer是否故障，如果发现故障，
  40.         // 就会通过心跳下发rebalance的指令给其他的consumer通知他们进行rebalance操作，这个时间可以稍微短一点
  41.         properties.put(ConsumerConfig.HEARTBEAT_INTERVAL_MS_CONFIG, 1000);
  42.         // 服务端broker多久感知不到一个consumer心跳就认为他故障了，默认是10秒
  43.         properties.put(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG, 10 * 1000);
  44.         // 如果两次poll操作间隔超过了这个时间，broker就会认为这个consumer处理能力太弱，
  45.         // 会将其踢出消费组，将分区分配给别的consumer消费
  46.         properties.put(ConsumerConfig.MAX_POLL_INTERVAL_MS_CONFIG, 30 * 1000);
  47.  
  48.         properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
  49.         properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
  50.         return properties;
  51.     }
  52.  
  53.  
  54.     static class KafkaConsumerThread implements Runnable {
  55.         private KafkaConsumer<String, String> kafkaConsumer;
  56.  
  57.         public KafkaConsumerThread(Properties properties, String topic) {
  58.             this.kafkaConsumer = new KafkaConsumer<String, String>(properties);
  59.             this.kafkaConsumer.subscribe(Arrays.asList(topic));
  60.         }
  61.  
  62.         @Override
  63.         public void run() {
  64.             try {
  65.                 int consumerCount = 0;
  66.                 int lastConsumerCount = 0;
  67.                 long lastTime = System.currentTimeMillis();
  68.                 while (true) {
  69.                     ConsumerRecords<String, String> records = kafkaConsumer.poll(Duration.ofMillis(1000));
  70.                     for (ConsumerRecord<String, String> record : records) {
  71.                         //处理消息模块
  72.                         System.out.printf("收到消息：partition = %d, offset = %d, key = %s, value = %s%n", record.partition(), record.offset(), record.key(), record.value());
  73.                         consumerCount++;
  74.                     }
  75.                     System.out.println("consumerCount:" + consumerCount);
  76.  
  77.                     long thisTime = System.currentTimeMillis();
  78.                     long speedTime = thisTime - lastTime;
  79.                     if (speedTime >= 1000L) {
  80.                         lastTime = thisTime;
  81.                         long speedCount = (consumerCount - lastConsumerCount)/(speedTime /1000L);
  82.                         lastConsumerCount = consumerCount;
  83.                         if (speedCount > 10) {
  84.                             System.out.println("消费速度：" + speedCount + "条/s");
  85.                         }
  86.                     }
  87.                 }
  88.             } catch (Exception e) {
  89.                 e.printStackTrace();
  90.             } finally {
  91.                 kafkaConsumer.close();
  92.             }
  93.         }
  94.     }
  95.  
  96. }

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