Kafka并发处理

随着互联网应用的快速发展，处理高并发请求变得尤为重要。分布式消息队列作为一种解决高并发的方式已经广泛应用。Apache Kafka是一个高效、可扩展、支持分布式处理的开源消息队列。Kafka能够处理数百万级别的请求，并保证高可用和高吞吐率。本文将从多个方面详细阐述Kafka并发处理的实现方式。

一、Kafka Producer的并发处理

Kafka Producer是向Kafka写入数据的客户端。在并发场景中，我们需要生产者能够快速发送大量请求，进而提高吞吐量。为了实现这一目标，我们需要使用多线程并发的方式，引入并发包并使用线程池等公共工具类。

import org.apache.kafka.clients.producer.*;
import java.util.concurrent.*;

public class KafkaProducer {
    private static final int MSG_COUNT = 10000;
    private static final String TOPIC = "test-topic";
    private static final String BROKER_LIST = "localhost:9092";
    private static KafkaProducer producer;

    static {
        Properties configs = initConfig();
        producer = new KafkaProducer<>(configs);
    }

    private static Properties initConfig() {
        Properties props = new Properties();
        props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, BROKER_LIST);
        props.put(ProducerConfig.CLIENT_ID_CONFIG, "KafkaProducerDemo");
        props.put(ProducerConfig.ACKS_CONFIG, "all");
        props.put(ProducerConfig.RETRIES_CONFIG, 0);
        props.put(ProducerConfig.BATCH_SIZE_CONFIG, 16384);
        props.put(ProducerConfig.LINGER_MS_CONFIG, 1);
        props.put(ProducerConfig.BUFFER_MEMORY_CONFIG, 33554432);
        props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
        props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
        return props;
    }

    public static void main(String[] args) {
        ExecutorService executorService = new ThreadPoolExecutor(10,10,0L, TimeUnit.MICROSECONDS,new LinkedBlockingQueue<>());
        for(int i=0;i
   (topic,message),new DemoProduceCallback());
        }
    }

    public static class DemoProduceCallback implements Callback {
        @Override
        public void onCompletion(RecordMetadata metadata, Exception exception) {
            if(exception == null){
                System.out.println(String.format("发送成功:topic:%s,partition:%d,offset:%d",metadata.topic(),metadata.partition(),metadata.offset()));
            }else {
                exception.printStackTrace();
            }
        }
    }
}

   
  

上述实现方式使用了线程池，使用submit()方法向线程池提交任务，并使用shutdown()方法关闭线程池。每个任务将一个消息发送到指定的topic中，使用DemoProduceCallback类作为回调函数，接收服务端返回的元数据。

二、Kafka Consumer的并发处理

Kafka Consumer是消费者，用于从Kafka读取消息。在并发场景中，我们需要消费者能够快速处理大量请求，进而提高吞吐量。Kafka Consumer实现并发的方式与Producer有所不同，不需要引入线程池，而是使用多个线程并发地消费Kafka消息。

import org.apache.kafka.clients.consumer.*;
import org.apache.kafka.common.TopicPartition;

import java.util.*;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

public class KafkaConsumer {
    private static final int THREAD_COUNT = 3;
    private static final String TOPIC = "test-topic";
    private static final String BROKER_LIST = "localhost:9092";
    private static final String GROUP_ID = "test-group";
    private static ExecutorService executorService;

    static {
        executorService = Executors.newFixedThreadPool(Runtime.getRuntime().availableProcessors() * THREAD_COUNT);
    }

    public static void main(String[] args) {
        Properties properties = initConfig();
        Consumer consumer = new KafkaConsumer<>(properties);
        consumer.subscribe(Collections.singletonList(TOPIC));
        try{
            while (true){
                ConsumerRecords
    records = consumer.poll(100);
                if(!records.isEmpty()){
                    ListenableFutureTask
     task = new ListenableFutureTask<>(new ConsumerTask(records));
                    executorService.submit(task);
                }
            }
        }finally {
            consumer.close();
        }
    }

    public static class ConsumerTask implements Callable
      {
        private final ConsumerRecords
       records;

        ConsumerTask(ConsumerRecords
       
        records) { this.records = records; } @Override public Boolean call() { for (TopicPartition tp : records.partitions()) { List
        
         
          > partitionRecords = records.records(tp); for (ConsumerRecord
          
           record : partitionRecords) { System.out.println(String.format("partition:%d,offset:%d,key:%s,value:%s", record.partition(), record.offset(), record.key(), record.value())); } } return Boolean.TRUE; } } private static Properties initConfig() { Properties props = new Properties(); props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, BROKER_LIST); props.put(ConsumerConfig.GROUP_ID_CONFIG, GROUP_ID); props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "latest"); props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, true); props.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, 1000); props.put(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG, 30000); props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer"); props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer"); return props; } }
          
         
        
       
      
     
    
   
  

上述实现方式使用了ExecutorService作为线程池，使用submit()方法向线程池提交任务。ConsumerTask类是一个实现Callable接口的消费者任务，每个任务将消费一批消息，并返回一个Boolean值。

三、Kafka在分布式任务中的应用

Kafka的并发处理在分布式任务中尤为重要。Kafka可以将一个分布式任务分成多个子任务，每个子任务独立完成，使用并发方式提高吞吐量。

以下是一个分布式任务的代码示例：

import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;

import java.util.Properties;
import java.util.UUID;

public class KafkaDistributedTaskProducer {
    public static final String TOPIC_NAME = "distributed-task-topic";
    private static final String NAME_SERVER = "localhost:9092";
    private static final int PARTITION_COUNT = 3;

    public static void main(String[] args){
        Properties properties = new Properties();
        properties.put("bootstrap.servers", NAME_SERVER);
        properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        properties.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        Producer producer = new KafkaProducer<>(properties);
        for (int i = 0; i < PARTITION_COUNT; i++) {
            ProducerRecord
    record = new ProducerRecord<>(TOPIC_NAME, "id-"+ UUID.randomUUID().toString() , "sub task number "+i);
            producer.send(record);
        }
        producer.close();
    }
}

   
  

上述代码产生了一个分布式任务，将任务拆分为3个子任务，分别将其发送到Kafka中的3个分区。

import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.PartitionInfo;

import java.util.Arrays;
import java.util.List;
import java.util.Properties;

public class KafkaDistributedTaskConsumer {
    public static final String TOPIC_NAME = "distributed-task-topic";
    private static final String NAME_SERVER = "localhost:9092";

    public static void main(String[] args){
        Properties properties = new Properties();
        properties.put("bootstrap.servers", NAME_SERVER);
        properties.put("group.id", "distributed-task-consumer");
        properties.put("auto.offset.reset", "earliest");
        properties.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        properties.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        KafkaConsumer consumer = new KafkaConsumer<>(properties);
        List
    partitions = consumer.partitionsFor(TOPIC_NAME);
        int partitionCount = partitions.size();
        for(int i = 0; i < partitionCount; i++){
            new Thread(() -> {
                consumer.subscribe(Arrays.asList(TOPIC_NAME));
                consumer.seekToBeginning(Arrays.asList(new org.apache.kafka.common.TopicPartition(TOPIC_NAME, i)));
                while(true){
                    ConsumerRecords
     records = consumer.poll(100);
                    if(records != null && !records.isEmpty()){
                        records.forEach(record -> {
                            System.out.println(String.format("线程ID:%s,分区:%d,偏移量:%d,键值:%s,消息值:%s", Thread.currentThread().getId(), record.partition(), record.offset(), record.key(), record.value()));
                        });
                    }
                }
            }).start();
        }
    }
}

    
   
  

上述代码是对分布式任务的消费端的实现，为了提高处理速度，使用了多线程并发的方式，分别监听Kafka的3个分区，可以看到，每个线程消费一个分区，从而实现分布式任务的并发处理。

结尾

本文从Producer、Consumer和分布式任务等多个方面详细阐述了Kafka并发处理的实现方式，Kafka在并发场景中极具优势，能够轻易实现高并发高吞吐的数据流处理。