kafka_2.11-0.10.2.1 的生产者 消费者的示例（new producer api）

环境，以及单独的pom.xml文件

环境：java 1.8 ，kafka_2.11-0.10.2.1

pom.xml文件如下

    4.0.0

    kafka_demo
    kafka_demo
    1.0-SNAPSHOT

    
        0.10.2.1
    

    
        
            org.apache.kafka
            kafka_2.11
            ${kafka.version}
        
    

1、生产者代码

package p_c_demo1;

import org.apache.kafka.clients.producer.Callback;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;
import utils.KafkaProperties;

import java.util.Properties;
import java.util.concurrent.ExecutionException;

/**
 *
 */
public class Producer_demo1 extends Thread{
    public static void main(String[] args) {

        //boolean isAsync = args.length == 0 || !args[0].trim().equalsIgnoreCase("sync");
        boolean isAsync = true;
        Producer_demo1 producerThread = new Producer_demo1(KafkaProperties.INTOPIC, isAsync);
        producerThread.start();
    }
    private final KafkaProducer producer;
    private final String topic;
    private final Boolean isAsync;

    public Producer_demo1(String topic, Boolean isAsync) {
        Properties props = new Properties();
        props.put("bootstrap.servers",
                KafkaProperties.KAFKA_SERVER_URL + ":" + KafkaProperties.KAFKA_SERVER_PORT0
                        + "," + KafkaProperties.KAFKA_SERVER_URL + ":" + KafkaProperties.KAFKA_SERVER_PORT1
                        + "," + KafkaProperties.KAFKA_SERVER_URL + ":" + KafkaProperties.KAFKA_SERVER_PORT2);
        props.put("client.id", "Producer_demo1");

        //开始的时候下面5个参数未设置，导致消费时取不到数据，需要注意
       /* acks=0时，producer不会等待确认，直接添加到socket等待发送；
        acks=1时，等待leader写到local log就行；
        acks=all或acks=-1时，等待isr中所有副本确认
        */
        props.put("acks", "all");
        //發送失敗重試
        props.put("retries", 0);
        //批次发送，不会尝试大于此值的容量
        props.put("batch.size", 16384);
        //默认设置为0，
        // 具体参数参考：http://kafka.apache.org/0102/documentation.html#producerconfigs
        props.put("linger.ms", 1);

        props.put("buffer.memory", 33554432);

        props.put("key.serializer", "org.apache.kafka.common.serialization.IntegerSerializer");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

        producer = new KafkaProducer(props);
        //方法传进来的参数
        this.topic = topic;
        this.isAsync = isAsync;
    }
    
    //持续保持数据发送
    public void run() {
        System.out.println("ProducerThread--run");
        int messageNo = 1;
        //这里面应该是自己的数据逻辑处理
        while (true) {
            String messageStr = "Message_" + messageNo;
            long startTime = System.currentTimeMillis();
            /*
            查看源码可以知道第二个send其实就是调用的第一个send 但是callback为null
            send
            public Future send(ProducerRecord record) {
                return this.send(record, (Callback)null);
    }
             */
            if (isAsync) { // Send asynchronously
                producer.send(
                        new ProducerRecord(topic, messageNo, messageStr),
                        new DemoCallBack(startTime, messageNo, messageStr));
            } else { // Send synchronously

                try {
                    producer.send(new ProducerRecord(topic,messageNo,messageStr)).get();
                } catch (InterruptedException e) {
                    // TODO Auto-generated catch block
                    e.printStackTrace();
                } catch (ExecutionException e) {
                    // TODO Auto-generated catch block
                    e.printStackTrace();
                }
                System.out.println("Sent message: (" + messageNo + ", " + messageStr + ")");

            }
            //messageNo;
            System.out.println("Sent message: (" + messageNo++ + ", " + messageStr + ")");
            //休息0.5秒
            try {
                Thread.sleep(500);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }

            if(messageNo==5000){

                //break;
            }
        }
    }
}

class DemoCallBack implements Callback {

    private final long startTime;
    private final int key;
    private final String message;

    public DemoCallBack(long startTime, int key, String message) {
        this.startTime = startTime;
        this.key = key;
        this.message = message;
    }

    /**
     * A callback method the user can implement to provide asynchronous handling of request completion. This method will
     * be called when the record sent to the server has been acknowledged. Exactly one of the arguments will be
     * non-null.
     *
     * @param metadata  The metadata for the record that was sent (i.e. the partition and offset). Null if an error
     *                  occurred.
     * @param exception The exception thrown during processing of this record. Null if no error occurred.
     */
    public void onCompletion(RecordMetadata metadata, Exception exception) {
        long elapsedTime = System.currentTimeMillis() - startTime;
        if (metadata != null) {
            System.out.println(
                    "message(" + key + ", " + message + ") sent to partition(" + metadata.partition() +
                            "), " +
                            "offset(" + metadata.offset() + ") in " + elapsedTime + " ms");
        } else {
            exception.printStackTrace();
        }
    }

}

2、消费者代码

package consumerDemo;

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.slf4j.Logger;
import org.slf4j.LoggerFactory;
import utils.KafkaProperties;

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

public class Conumer_demo1 extends Thread{

    public static void main(String[] args) {
        p_c_demo1.Conumer_demo1 consumerThread = new p_c_demo1.Conumer_demo1(KafkaProperties.INTOPIC);
        consumerThread.start();
    }

    private final KafkaConsumer consumer;

    private final String topic;
    private static final Logger LOG = LoggerFactory.getLogger(Conumer_demo1.class);
    public Conumer_demo1(String topic) {

        Properties props = new Properties();
        //bootstrap.servers   必要
        props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,
                KafkaProperties.KAFKA_SERVER_URL + ":" + KafkaProperties.KAFKA_SERVER_PORT0
        + "," + KafkaProperties.KAFKA_SERVER_URL + ":" + KafkaProperties.KAFKA_SERVER_PORT1
        + "," + KafkaProperties.KAFKA_SERVER_URL + ":" + KafkaProperties.KAFKA_SERVER_PORT2);
        //group id
        props.put(ConsumerConfig.GROUP_ID_CONFIG, "producer-consumer-demo1");
        //是否后台自动提交offset 到kafka
        props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "false");
        //消费者偏移自动提交到Kafka的频率（以毫秒为单位enable.auto.commit）设置为true
        props.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, "1000");
        //故障检测，心跳检测机制 的间隔时间，，在该值范围内，没有接收到心跳，则会删除该消费者
        //并启动再平衡（rebanlance）,值必须在group.min.session.timeout 和 group.max.session.timeout.ms之间
        props.put(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG, "30000");
        //key - value 的序列化类
        props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.IntegerDeserializer");
        props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");

        this.consumer = new KafkaConsumer(props);
        this.topic = topic;
    }
    public void run() {
        System.out.println("ConsumerThread--run");

        consumer.subscribe(Arrays.asList(KafkaProperties.INTOPIC));

        // consumer.subscribe(Collections.singletonList(this.topic));
        while (true) {

            //consumer.poll()
            ConsumerRecords records = consumer.poll(200);

            for (ConsumerRecord record : records) {
                System.out.println("Received message: (" + record.key() + ", " + record.value()
                        + ") offset " + record.offset()
                        + " partition " + record.partition() + ")");
            }
        }
    }

}

3、还有一个工具类，放我们的各项参数设置

package utils;

public class KafkaProperties {
    public static final String INTOPIC = "producer_consumer_demo1";
    //public static final String OUTTOPIC = "topic2";
    public static final String KAFKA_SERVER_URL = "make.spark.com";
    public static final int KAFKA_SERVER_PORT0 = 9092;
    public static final int KAFKA_SERVER_PORT1 = 9093;
    public static final int KAFKA_SERVER_PORT2 = 9094;
    public static final int KAFKA_PRODUCER_BUFFER_SIZE = 65536;
    public static final int CONNECTION_TIMEOUT = 100000;
    public static final String CLIENT_ID = "SimpleConsumerDemoClient";

    private KafkaProperties() {}

}

这个时候启动我们，生产者，就会开始生产数据，同时运行我么你的消费者  就可以看到我们的消费信息，具体在哪个分区，消费到那个偏移量，如果同时多开，两个消费者，可以看到rebalane的机制，会重新再平衡每个消费者，消费的分区，这里的前提是，要修改你的topic，改成多个分区才可以，生产者默认创建的topic是只有一个分区的

以上，就是一个生产者，消费者的一个简单示例，做个简单记录,

参考文章地址：  请点这里！！感谢博主