kafka java动态获取topic并动态创建消费者

1.获取所有topic

package com.example.demo;
import java.io.IOException;
import java.util.List;

import org.apache.zookeeper.KeeperException;
import org.apache.zookeeper.WatchedEvent;
import org.apache.zookeeper.Watcher;
import org.apache.zookeeper.ZooKeeper;

public class zookeeper {

	 public static void main(String[] args) {
	        String connectString = "172.16.10.211:2181";
	        int sessionTimeout = 4000;
	        Watcher watcher = new Watcher() {
	            public void process(WatchedEvent event) {
	            }
	        };
	        try {
	            ZooKeeper zooKeeper = new ZooKeeper(connectString, sessionTimeout, watcher);
	            List<String> list = zooKeeper.getChildren("/brokers/topics", false);
	            int len = list.size();
	            for(int i = 1;i < len;i++){
	            	System.out.println(list.get(i));
　　　　　　　　　　　　　　//此处动态生成消费者
	            	//JavaKafkaConsumerHighAPI example = new JavaKafkaConsumerHighAPI(list.get(i), 1);
	                //new Thread(example).start();
	            }	       
	        } catch (IOException e) {
	            e.printStackTrace();
	        } catch (KeeperException e) {
	            e.printStackTrace();
	        } catch (InterruptedException e) {
	            e.printStackTrace();
	        }
	    }
}

 2.参考http://www.cnblogs.com/liuming1992/p/6432626.html生成消费者，这里进行了小小的改造

package com.example.demo;
import kafka.consumer.*;
import kafka.javaapi.consumer.ConsumerConnector;
import kafka.message.MessageAndMetadata;
import kafka.serializer.StringDecoder;
import kafka.utils.VerifiableProperties;

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;
import java.util.concurrent.TimeUnit;

/**
 * 自定义简单Kafka消费者， 使用高级API
 * Created by gerry on 12/21.
 */
public class JavaKafkaConsumerHighAPI implements Runnable {
    /**
     * Kafka数据消费对象
     */
    private ConsumerConnector consumer;

    /**
     * Kafka Topic名称
     */
    private String topic;

    /**
     * 线程数量，一般就是Topic的分区数量
     */
    private int numThreads;

    /**
     * 线程池
     */
    private ExecutorService executorPool;

    /**
     * 构造函数
     *
     * @param topic      Kafka消息Topic主题
     * @param numThreads 处理数据的线程数/可以理解为Topic的分区数
     * @param zookeeper  Kafka的Zookeeper连接字符串
     * @param groupId    该消费者所属group ID的值
     */
    public JavaKafkaConsumerHighAPI(String topic, int numThreads) {
        // 1. 创建Kafka连接器
        this.consumer = Consumer.createJavaConsumerConnector(createConsumerConfig("172.16.10.211:2181", "test-consumer-group"));
        // 2. 数据赋值
        this.topic = topic;
        this.numThreads = numThreads;
    }

    @Override
    public void run() {
        // 1. 指定Topic
        Map<String, Integer> topicCountMap = new HashMap<String, Integer>();
        topicCountMap.put(this.topic, this.numThreads);

        // 2. 指定数据的解码器
        StringDecoder keyDecoder = new StringDecoder(new VerifiableProperties());
        StringDecoder valueDecoder = new StringDecoder(new VerifiableProperties());

        // 3. 获取连接数据的迭代器对象集合
        /**
         * Key: Topic主题
         * Value: 对应Topic的数据流读取器，大小是topicCountMap中指定的topic大小
         */
        Map<String, List<KafkaStream<String, String>>> consumerMap = this.consumer.createMessageStreams(topicCountMap, keyDecoder, valueDecoder);

        // 4. 从返回结果中获取对应topic的数据流处理器
        List<KafkaStream<String, String>> streams = consumerMap.get(this.topic);

        // 5. 创建线程池
        this.executorPool = Executors.newFixedThreadPool(this.numThreads);

        // 6. 构建数据输出对象
        int threadNumber = 0;
        for (final KafkaStream<String, String> stream : streams) {
            this.executorPool.submit(new ConsumerKafkaStreamProcesser(stream, threadNumber,topic));
            threadNumber++;
        }
    }

    public void shutdown() {
        // 1. 关闭和Kafka的连接，这样会导致stream.hashNext返回false
        if (this.consumer != null) {
            this.consumer.shutdown();
        }

        // 2. 关闭线程池，会等待线程的执行完成
        if (this.executorPool != null) {
            // 2.1 关闭线程池
            this.executorPool.shutdown();

            // 2.2. 等待关闭完成, 等待五秒
            try {
                if (!this.executorPool.awaitTermination(5, TimeUnit.SECONDS)) {
                    System.out.println("Timed out waiting for consumer threads to shut down, exiting uncleanly!!");
                }
            } catch (InterruptedException e) {
                System.out.println("Interrupted during shutdown, exiting uncleanly!!");
            }
        }

    }

    /**
     * 根据传入的zk的连接信息和groupID的值创建对应的ConsumerConfig对象
     *
     * @param zookeeper zk的连接信息，类似于：<br/>
     *                  hadoop-senior01.ibeifeng.com:2181,hadoop-senior02.ibeifeng.com:2181/kafka
     * @param groupId   该kafka consumer所属的group id的值， group id值一样的kafka consumer会进行负载均衡
     * @return Kafka连接信息
     */
    private ConsumerConfig createConsumerConfig(String zookeeper, String groupId) {
        // 1. 构建属性对象
        Properties prop = new Properties();
        // 2. 添加相关属性
        prop.put("group.id", groupId); // 指定分组id
        prop.put("zookeeper.connect", zookeeper); // 指定zk的连接url
        prop.put("zookeeper.session.timeout.ms", "400"); //
        prop.put("zookeeper.sync.time.ms", "200");
        prop.put("auto.commit.interval.ms", "1000");
        // 3. 构建ConsumerConfig对象
        return new ConsumerConfig(prop);
    }


    /**
     * Kafka消费者数据处理线程
     */
    public static class ConsumerKafkaStreamProcesser implements Runnable {
        // Kafka数据流
        private KafkaStream<String, String> stream;
        // 线程ID编号
        private int threadNumber;
        private String topic;

        public ConsumerKafkaStreamProcesser(KafkaStream<String, String> stream, int threadNumber,String topic) {
            this.stream = stream;
            this.threadNumber = threadNumber;
            this.topic = topic;
        }

        @Override
        public void run() {
            // 1. 获取数据迭代器
            ConsumerIterator<String, String> iter = this.stream.iterator();
            // 2. 迭代输出数据
            while (iter.hasNext()) {
                // 2.1 获取数据值
                MessageAndMetadata value = iter.next();

                // 2.2 输出
                System.out.println(this.threadNumber + "____" + value.offset() +"_____"+ topic + "____" + value.message());
            }
            // 3. 表示当前线程执行完成
            System.out.println("Shutdown Thread:" + this.threadNumber);
        }
    }
}  

 3.pom

<dependency>
     <groupId>org.apache.kafka</groupId>
     <artifactId>kafka_2.11</artifactId>
     <version>0.8.2.1</version>
</dependency>

 4.

package com.example.text;

import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.TimeUnit;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.example.es.Es;

import kafka.consumer.Consumer;
import kafka.consumer.ConsumerConfig;
import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;

public class KafkaConsumer implements Runnable {
	
	
	
	private static Logger logger = LoggerFactory.getLogger(KafkaConsumer.class);

	private Map<String, Integer> topicCountMap;
	private Properties props;

	public KafkaConsumer(Map<String, Integer> topicCountMap, Properties props) {
		this.topicCountMap = topicCountMap;
		this.props = props;
	}

	@Override
	public void run() {
		ConsumerConnector consumer = null;
		ExecutorService executor = null;
		try {
			consumer = Consumer.createJavaConsumerConnector(new ConsumerConfig(props));
			Map<String, List<KafkaStream<byte[], byte[]>>> msgStreams = consumer.createMessageStreams(topicCountMap);
			for (String topic : topicCountMap.keySet()) {
				List<KafkaStream<byte[], byte[]>> msgStreamList = msgStreams.get(topic);
				// 使用ExecutorService来调度线程
				executor = Executors.newFixedThreadPool(topicCountMap.get(topic));
				for (int i = 0; i < msgStreamList.size(); i++) {
					KafkaStream<byte[], byte[]> kafkaStream = msgStreamList.get(i);
					executor.submit(new HanldMessageThread(kafkaStream, i, topic));
				}
			} 

		} catch (Exception e) {
			if (consumer != null) {
				consumer.shutdown();
			}
			if (executor != null) {
				executor.shutdown();
			}
			try {
			if (!executor.awaitTermination(5000, TimeUnit.MILLISECONDS)) {
				logger.error("Timed out waiting for consumer threads to shutdown, exiting uncleanly");
				}
			} catch (InterruptedException e1) {
				logger.error("Interrupted during shutdown, exiting uncleanly");
		 }
			logger.error(e.getMessage());
		}
	}

}

/**
 * 具体处理message的线程
 * 
 * @author Administrator
 *
 */
class HanldMessageThread implements Runnable {

	private KafkaStream<byte[], byte[]> kafkaStream = null;
	private int num = 0;
	private String topic;

	public HanldMessageThread(KafkaStream<byte[], byte[]> kafkaStream, int num, String topic) {
		super();
		this.kafkaStream = kafkaStream;
		this.num = num;
		this.topic = topic;
	}

	public void run() {
		ConsumerIterator<byte[], byte[]> iterator = kafkaStream.iterator();
		
	
		
		while (iterator.hasNext()) {
			String message = new String(iterator.next().message());
//			System.out.println(Thread.currentThread().getName());  
//			System.out.println(this.num + "____" + topic + "____" + message);
//			System.out.println(Thread.currentThread().getId());
//			System.out.println("Thread no: " + num + ", message: " + message);
			if (topic.startsWith("xrs") || topic.startsWith("meitan") || topic.startsWith("qiyexinxi")) {
				Es.setData(message, "xrs_db", topic);
			} else if (topic.startsWith("search")) {
				Es.setData(message, "pholcus_news_v1", topic);
			} else {
				Es.setData(message, "pholcus_db", topic);
			}
		}
	}

}

private static Properties props;

	static {
		props = new Properties();
		props.put("zookeeper.connect", "172.16.10.211:2181");
		props.put("group.id", "test-consumer-group");
		props.put("zookeeper.session.timeout.ms", "400");
		props.put("zookeeper.sync.time.ms", "200");
		props.put("auto.commit.interval.ms", "1000");
	}

Map<String, Integer> topicCountMap = new HashMap<String, Integer>();