kafka Java实例

import java.util.*;import kafka.javaapi.producer.Producer;import kafka.producer.KeyedMessage;import kafka.producer.Partitioner;import kafka.producer.ProducerConfig;import kafka.utils.VerifiableProperties;public class TestProducer {     class SimplePartitioner implements Partitioner {        public SimplePartitioner (VerifiableProperties props) {        }        public int partition(Object key, int a_numPartitions) {            int partition = 0;            String stringKey = (String) key;            int offset = stringKey.lastIndexOf('.');            if (offset > 0) {                partition = Integer.parseInt( stringKey.substring(offset+1)) % a_numPartitions;            }            return partition;        }    }    public static void main(String[] args) {        long events =30;        Random rnd = new Random();        Properties props = new Properties();        props.put("metadata.broker.list", "kafka1:9092,kafka2:9092,kafka3:9092");        props.put("serializer.class", "kafka.serializer.StringEncoder");        props.put("partitioner.class", "com.xf.kafa.TestProducer.SimplePartitioner");        props.put("request.required.acks", "1");        ProducerConfig config = new ProducerConfig(props);        //The first is the type of the Partition key, the second the type of the message.        Producer<String, String> producer = new Producer<String, String>(config);        for (long nEvents = 0; nEvents < events; nEvents++) {            long runtime = new Date().getTime();            String ip = "192.168.2." + rnd.nextInt(255);            String msg = runtime + ",www.example.com," + ip;            //参数分别为topic，IP as the partition key，msg为消息            KeyedMessage<String, String> data = new KeyedMessage<String, String>("page_visits", ip, msg);            producer.send(data);        }        producer.close();    }}

public class ConsumerGroupExample {    private final ConsumerConnector consumer;    private final String topic;    private ExecutorService executor;    class ConsumerTest implements Runnable {        private KafkaStream m_stream;        //标识线程名称，可以使用Thread.getCurrentTreandName()代替        private int m_threadNumber;        public ConsumerTest(KafkaStream a_stream, int a_threadNumber) {            m_threadNumber = a_threadNumber;            m_stream = a_stream;        }        public void run() {            ConsumerIterator<byte[], byte[]> it = m_stream.iterator();            //有趣的是 while (it.hasNext())部分. 基于此处会不停的读取topic的信息，除非你停止它。            while (it.hasNext())                System.out.println("Thread " + m_threadNumber + ": " + new String(it.next().message()));            System.out.println("Shutting down Thread: " + m_threadNumber);        }    }    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();        try {            if (!executor.awaitTermination(5000, TimeUnit.MILLISECONDS)) {                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");        }    }    public void run(int a_numThreads) {        /**         * First we create a Map that tells Kafka how many threads we are providing for which topics.         * The consumer.createMessageStreams is how we pass this information to Kafka.         * The return is a map of KafkaStream to listen on for each topic.         * (Note here we only asked Kafka for a single Topic but we could have asked for multiple by         * adding another element to the Map.)         Finally we create the thread pool and pass a new ConsumerTest object to each thread as our business logic.         */        //首先我们创建一个map，map能告诉kafka我们为每个topic启动多少个线程        Map<String, Integer> topicCountMap = new HashMap<String, Integer>();        topicCountMap.put(topic, new Integer(a_numThreads));        //consumer.createMessageStreams是我们把信息传递给kafka的方式，返回的是 a map of KafkaStream，来监听每个topic        //注意在这我们仅仅向kafka请求一个topic，但是我们可以通过向map添加另外一个元素来请求多个topic        Map<String, List<KafkaStream<byte[], byte[]>>> consumerMap = consumer.createMessageStreams(topicCountMap);        List<KafkaStream<byte[], byte[]>> streams = consumerMap.get(topic);        // now launch all the threads        executor = Executors.newFixedThreadPool(a_numThreads);        // now create an object to consume the messages        int threadNumber = 0;        for (final KafkaStream stream : streams) {            //最后我们创建线程池，并且传递ConsumerTest线程给线程池            executor.submit(new ConsumerTest(stream, threadNumber));            threadNumber++;        }    }    private static ConsumerConfig createConsumerConfig(String a_zookeeper, String a_groupId) {        Properties props = new Properties();        props.put("zookeeper.connect", a_zookeeper);        props.put("group.id", a_groupId);        props.put("zookeeper.session.timeout.ms", "400");        props.put("zookeeper.sync.time.ms", "200");        props.put("auto.commit.interval.ms", "1000");        return new ConsumerConfig(props);    }    public static void main(String[] args) {        String zooKeeper = "192.168.8.121:2181,192.168.8.122:2181,192.168.8.123:2181";        String groupId = "test";        String topic = "page_visits";        int threads = 1;        ConsumerGroupExample example = new ConsumerGroupExample(zooKeeper, groupId, topic);        example.run(threads);        try {            Thread.sleep(1000000);        } catch (InterruptedException ie) {        }        example.shutdown();    }}