kafka java 生产消费程序示例

自已测试通过的

import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;

import kafka.consumer.ConsumerConfig;
import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;
import kafka.serializer.StringDecoder;
import kafka.utils.VerifiableProperties;

public class KafkaConsumer {

    private final ConsumerConnector consumer;

    private KafkaConsumer() {
        Properties props = new Properties();
        //zookeeper 配置
        //props.put("zookeeper.connect", "103.29.134.193:2181");
        props.put("zookeeper.connect", "103.7.221.141:2181");
        //group 代表一个消费组
        props.put("group.id", "jd-group");

        //zk连接超时
        props.put("zookeeper.session.timeout.ms", "4000");
        props.put("zookeeper.sync.time.ms", "200");
        props.put("auto.commit.interval.ms", "1000");
        props.put("auto.offset.reset", "smallest");
        //序列化类
        props.put("serializer.class", "kafka.serializer.StringEncoder");

        ConsumerConfig config = new ConsumerConfig(props);

        consumer = kafka.consumer.Consumer.createJavaConsumerConnector(config);
    }

    void consume() {
        Map<String, Integer> topicCountMap = new HashMap<String, Integer>();
        topicCountMap.put("srp_word", new Integer(1));

        StringDecoder keyDecoder = new StringDecoder(new VerifiableProperties());
        StringDecoder valueDecoder = new StringDecoder(new VerifiableProperties());

        Map<String, List<KafkaStream<String, String>>> consumerMap =
                consumer.createMessageStreams(topicCountMap,keyDecoder,valueDecoder);
        KafkaStream<String, String> stream = consumerMap.get("srp_word").get(0);
        ConsumerIterator<String, String> it = stream.iterator();
        //进行入库操作
        while (it.hasNext()) {

            System.out.println("=====标示：" + it.next().message());
        }
    }

    public static void main(String[] args) {
        new KafkaConsumer().consume();
    }
}

上面上单线程的处理端，但是在实际的应用中，只有多线程的处理才能够提高性能。
 

 void consume() {
        Map<String, Integer> topicCountMap = new HashMap<String, Integer>();
//传入参数,为分区数量，多线程取多分区
        topicCountMap.put("srp_word", paritonsNum);

        StringDecoder keyDecoder = new StringDecoder(new VerifiableProperties());
        StringDecoder valueDecoder = new StringDecoder(new VerifiableProperties());

        Map<String, List<KafkaStream<String, String>>> consumerMap =
                consumer.createMessageStreams(topicCountMap,keyDecoder,valueDecoder);
        List<KafkaStream<String, String>> streamList = consumerMap.get("srp_word");

//启动多个线程来处理list
ExecutorService threadpool = Executors.newFixedThreadPool(paritonsNum);
for(KafkaStream<String, String> partition : streamList){
threadPool.execute(new MessageRunner(partition));
}