标签：kafka   high level consumer   

原文：https://cwiki.apache.org/confluence/display/KAFKA/Consumer+Group+Example

为什么使用High Level Consumer

1. 在某些应用场景，我们希望通过多线程读取消息，而我们并不关心从Kafka消费消息的顺序，我们仅仅关心数据能被消费就行。High Level 就是用于抽象这类消费动作的。

2. 消息消费已Consumer Group为单位，每个Consumer Group中可以有多个consumer，每个consumer是一个线程，topic的每个partition同时只能被某一个consumer读 取，Consumer Group对应的每个partition都有一个最新的offset的值，存储在zookeeper上的。所以不会出现重复消费的情况。

3. 因为consumer的offerset并不是实时的传送到zookeeper（通过配置来制定更新周期），所以Consumer如果突然Crash，有可能会读取重复的信息

设计High Level Consumer

High Level Consumer 可以并且应该被使用在多线程的环境，线程模型中线程的数量(也代表group中consumer的数量)和topic的partition数量有关，下面列举一些规则：

1. 当提供的线程数量多于partition的数量，则部分线程将不会接收到消息；
2. 当提供的线程数量少于partition的数量，则部分线程将从多个partition接收消息；
3. 当某个线程从多个partition接收消息时，不保证接收消息的顺序；可能出现从partition3接收5条消息，从partition4接收6条消息，接着又从partition3接收10条消息；
4. 当添加更多线程时，会引起kafka做re-balance, 可能改变partition和线程的对应关系。
5. 因为突然停止Consumer以及Broker会导致消息重复读的情况，为了避免这种情况在shutdown之前通过Thread.sleep(10000)让Consumer有时间将offset同步到zookeeper

例子

      <!--Kafka 消息依赖-->
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka_2.10</artifactId>
            <version>0.8.2.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>0.8.2.0</version>
        </dependency>

import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
import kafka.message.MessageAndMetadata;

public class ConsumerThread implements Runnable {
 private KafkaStream kafkaStream;
 //线程编号
 private int threadNumber;
 public ConsumerThread(KafkaStream kafkaStream, int threadNumber) {
  this.threadNumber = threadNumber;
  this.kafkaStream = kafkaStream;
 }
 public void run() {
  ConsumerIterator<byte[], byte[]> it = kafkaStream.iterator();
  StringBuffer sb = new StringBuffer();
//该循环会持续从Kafka读取数据，直到手工的将进程进行中断
  while (it.hasNext()) {
   MessageAndMetadata metaData = it.next();
   sb.append("Thread: " + threadNumber + " ");
   sb.append("Part: " + metaData.partition() + " ");
   sb.append("Key: " + metaData.key() + " ");
   sb.append("Message: " + metaData.message() + " ");
   sb.append("\n");
   System.out.println(sb.toString());
  }
  System.out.println("Shutting down Thread: " + threadNumber);
 }
}

import kafka.consumer.ConsumerConfig;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;
 
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
 
public class ConsumerGroupExample {
    private final ConsumerConnector consumer;
    private final String topic;
    private  ExecutorService executor;
 
    public ConsumerGroupExample(String a_zookeeper, String a_groupId, String a_topic) {
        consumer = kafka.consumer.Consumer.createJavaConsumerConnector(
                createConsumerConfig(a_zookeeper, a_groupId));
        this.topic = a_topic;
    }
 
    public void shutdown() {
        if (consumer != null) consumer.shutdown();
        if (executor != null) executor.shutdown();
    }
 
    public void run(int a_numThreads) {
        Map<String, Integer> topicCountMap = new HashMap<String, Integer>();
        topicCountMap.put(topic, new Integer(a_numThreads));
        //返回的Map包含所有的Topic以及对应的KafkaStream
        Map<String, List<KafkaStream<byte[], byte[]>>> consumerMap = consumer.createMessageStreams(topicCountMap);
        List<KafkaStream<byte[], byte[]>> streams = consumerMap.get(topic);
 
        //创建Java线程池
        executor = Executors.newFixedThreadPool(a_numThreads);
 
        // 创建 consume 线程消费messages
        int threadNumber = 0;
        for (final KafkaStream stream : streams) {
            executor.submit(new ConsumerTest(stream, threadNumber));
            threadNumber++;
        }
    }
 
    private static ConsumerConfig createConsumerConfig(String a_zookeeper, String a_groupId) {
        Properties props = new Properties();
        //指定连接的Zookeeper集群，通过该集群来存储连接到某个Partition的Consumer的Offerset
        props.put("zookeeper.connect", a_zookeeper);
       //consumer group 的ID
        props.put("group.id", a_groupId);
        //Kafka等待Zookeeper的响应时间（毫秒）
        props.put("zookeeper.session.timeout.ms", "400");
       //ZooKeeper 的‘follower’可以落后Master多少毫秒
        props.put("zookeeper.sync.time.ms", "200");
      //consumer更新offerset到Zookeeper的时间
        props.put("auto.commit.interval.ms", "1000");
 
        return new ConsumerConfig(props);
    }
 
    public static void main(String[] args) {
        String zooKeeper = args[0];
        String groupId = args[1];
        String topic = args[2];
        int threads = Integer.parseInt(args[3]);
 
        ConsumerGroupExample example = new ConsumerGroupExample(zooKeeper, groupId, topic);
        example.run(threads);
         //因为consumer的offerset并不是实时的传送到zookeeper（通过配置来制定更新周期），所以shutdown Consumer的线程，有可能会读取重复的信息
        //增加sleep时间，让consumer把offset同步到zookeeper
        try {
            Thread.sleep(10000);
        } catch (InterruptedException ie) {
 
        }
        example.shutdown();
    }
}

设计Kafka的High Level Consumer

标签：kafka   high level consumer   

原文地址：http://blog.csdn.net/eric_sunah/article/details/44243077