Kafka Java Producer代码实例详解

根据业务需要可以使用Kafka提供的Java Producer API进行产生数据，并将产生的数据发送到Kafka对应Topic的对应分区中，入口类为：Producer

Kafka的Producer API主要提供下列三个方法：

• 　　public void send(KeyedMessage<K,V> message) 发送单条数据到Kafka集群
• 　　public void send(List<KeyedMessage<K,V>> messages) 发送多条数据(数据集)到Kafka集群
• 　　public void close() 关闭Kafka连接资源

一、JavaKafkaProducerPartitioner：自定义的数据分区器，功能是：决定输入的key/value键值对的message发送到Topic的那个分区中，返回分区id，范围:[0,分区数量); 这里的实现比较简单，根据key中的数字决定分区的值。具体代码如下：

import kafka.producer.Partitioner;
import kafka.utils.VerifiableProperties;

/**
 * Created by gerry on 12/21.
 */
public class JavaKafkaProducerPartitioner implements Partitioner {

  /**
   * 无参构造函数
   */
  public JavaKafkaProducerPartitioner() {
    this(new VerifiableProperties());
  }

  /**
   * 构造函数，必须给定
   *
   * @param properties 上下文
   */
  public JavaKafkaProducerPartitioner(VerifiableProperties properties) {
    // nothings
  }

  @Override
  public int partition(Object key, int numPartitions) {
    int num = Integer.valueOf(((String) key).replaceAll("key_", "").trim());
    return num % numPartitions;
  }
}

二、 JavaKafkaProducer：通过Kafka提供的API进行数据产生操作的测试类；具体代码如下：

import kafka.javaapi.producer.Producer;
import kafka.producer.KeyedMessage;
import kafka.producer.ProducerConfig;
import org.apache.log4j.Logger;

import java.util.Properties;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicBoolean;
import java.util.concurrent.ThreadLocalRandom;

/**
 * Created by gerry on 12/21.
 */
public class JavaKafkaProducer {
  private Logger logger = Logger.getLogger(JavaKafkaProducer.class);
  public static final String TOPIC_NAME = "test";
  public static final char[] charts = "qazwsxedcrfvtgbyhnujmikolp1234567890".toCharArray();
  public static final int chartsLength = charts.length;

  public static void main(String[] args) {
    String brokerList = "192.168.187.149:9092";
    brokerList = "192.168.187.149:9092,192.168.187.149:9093,192.168.187.149:9094,192.168.187.149:9095";
    brokerList = "192.168.187.146:9092";
    Properties props = new Properties();
    props.put("metadata.broker.list", brokerList);
    /**
     * 0表示不等待结果返回<br/>
     * 1表示等待至少有一个服务器返回数据接收标识<br/>
     * -1表示必须接收到所有的服务器返回标识，及同步写入<br/>
     * */
    props.put("request.required.acks", "0");
    /**
     * 内部发送数据是异步还是同步
     * sync：同步, 默认
     * async：异步
     */
    props.put("producer.type", "async");
    /**
     * 设置序列化的类
     * 可选：kafka.serializer.StringEncoder
     * 默认：kafka.serializer.DefaultEncoder
     */
    props.put("serializer.class", "kafka.serializer.StringEncoder");
    /**
     * 设置分区类
     * 根据key进行数据分区
     * 默认是：kafka.producer.DefaultPartitioner ==> 安装key的hash进行分区
     * 可选:kafka.serializer.ByteArrayPartitioner ==> 转换为字节数组后进行hash分区
     */
    props.put("partitioner.class", "JavaKafkaProducerPartitioner");

    // 重试次数
    props.put("message.send.max.retries", "3");

    // 异步提交的时候(async)，并发提交的记录数
    props.put("batch.num.messages", "200");

    // 设置缓冲区大小，默认10KB
    props.put("send.buffer.bytes", "102400");

    // 2. 构建Kafka Producer Configuration上下文
    ProducerConfig config = new ProducerConfig(props);

    // 3. 构建Producer对象
    final Producer<String, String> producer = new Producer<String, String>(config);

    // 4. 发送数据到服务器，并发线程发送
    final AtomicBoolean flag = new AtomicBoolean(true);
    int numThreads = 50;
    ExecutorService pool = Executors.newFixedThreadPool(numThreads);
    for (int i = 0; i < 5; i++) {
      pool.submit(new Thread(new Runnable() {
        @Override
        public void run() {
          while (flag.get()) {
            // 发送数据
            KeyedMessage message = generateKeyedMessage();
            producer.send(message);
            System.out.println("发送数据:" + message);

            // 休眠一下
            try {
              int least = 10;
              int bound = 100;
              Thread.sleep(ThreadLocalRandom.current().nextInt(least, bound));
            } catch (InterruptedException e) {
              e.printStackTrace();
            }
          }

          System.out.println(Thread.currentThread().getName() + " shutdown....");
        }
      }, "Thread-" + i));

    }

    // 5. 等待执行完成
    long sleepMillis = 600000;
    try {
      Thread.sleep(sleepMillis);
    } catch (InterruptedException e) {
      e.printStackTrace();
    }
    flag.set(false);

    // 6. 关闭资源

    pool.shutdown();
    try {
      pool.awaitTermination(6, TimeUnit.SECONDS);
    } catch (InterruptedException e) {
    } finally {
      producer.close(); // 最后之后调用
    }
  }

  /**
   * 产生一个消息
   *
   * @return
   */
  private static KeyedMessage<String, String> generateKeyedMessage() {
    String key = "key_" + ThreadLocalRandom.current().nextInt(10, 99);
    StringBuilder sb = new StringBuilder();
    int num = ThreadLocalRandom.current().nextInt(1, 5);
    for (int i = 0; i < num; i++) {
      sb.append(generateStringMessage(ThreadLocalRandom.current().nextInt(3, 20))).append(" ");
    }
    String message = sb.toString().trim();
    return new KeyedMessage(TOPIC_NAME, key, message);
  }

  /**
   * 产生一个给定长度的字符串
   *
   * @param numItems
   * @return
   */
  private static String generateStringMessage(int numItems) {
    StringBuilder sb = new StringBuilder();
    for (int i = 0; i < numItems; i++) {
      sb.append(charts[ThreadLocalRandom.current().nextInt(chartsLength)]);
    }
    return sb.toString();
  }
}

三、Pom.xml依赖配置如下

<properties>
  <kafka.version>0.8.2.1</kafka.version>
</properties>

<dependencies>
  <dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka_2.10</artifactId>
    <version>${kafka.version}</version>
  </dependency>
</dependencies>

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