聊聊kafka0.8的topic的partition以及topicCountMap

序

本文主要研究下kafka0.8版本api的topicCountMap与topic的partition的关系。

partition

物理上把topic分成一个或多个partition，每个partition在物理上对应一个文件夹，该文件夹下存储 这个partition的所有消息和索引文件。

partition与consumer

• 如果consumer比partition多，是浪费，因为kafka的设计是在一个partition上是不允许并发的，所以consumer数不要大于partition数
• 如果consumer比partition少，一个consumer会对应于多个partitions，这里主要合理分配consumer数和partition数，否则会导致partition里面的数据被取的不均匀.最好partiton数目是consumer数目的整数倍，所以partition数目很重要，比如取24，就很容易设定consumer数目
• 如果consumer从多个partition读到数据，不保证数据间的顺序性，kafka只保证在一个partition上数据是有序的，但多个partition，根据你读的顺序会有不同

kafka producer发送消息的时候，如果有key的话，根据key进行hash，然后分发到指定的partition；如果没有key则按counter进行partition。

rebalance

如果增减consumer，broker，partition会导致rebalance，rebalance后consumer对应的partition会发生变化。

比如减少一个consumer，然后rebalance之后，consumer对应的partition会进行重新调整映射。

topicCountMap

告诉Kafka我们在Consumer中将用多少个线程来消费该topic。topicCountMap的key是topic name，value针对该topic是线程的数量。

假设有个topic，有6个partiton，然后启动了两个consumer，每个consumer的topicCount为3，则观察会发现，每个consumer的消费线程都在运行；
如果每个consumer的topicCount变为4，则会发现，先启动的consmer中4个线程都在运行，而后启动的consumer中只有2个线程在运行，其他2个被阻塞住了。

也就是说，对于consumer来说，实际的消费个数=consumer实例个数*每个consumer的topicCount个数，如果这个值>partition，则会造成某些消费线程多余，阻塞住。
如果这个值<=partition，则所有消费线程都在消费。
因此实际分布式部署consumer的时候，其consumer实例个数*每个consumer的topicCount个数<=topic的partition值。

代码实例

• 创建topic

sh kafka-topics.sh --create --topic topic20170921 --replication-factor 1 --partitions 6 --zookeeper localhost:2181

• 查看consumer group

sh kafka-run-class.sh kafka.tools.ConsumerOffsetChecker --zookeeper localhost:2181 --group test-group-0921

• consumer

public class NativeConsumer {

    ExecutorService pool = Executors.newFixedThreadPool(10);

    public void exec(String topic,String zk,int consumerCount,String group) throws UnsupportedEncodingException {
        Properties props = new Properties();
        props.put("zookeeper.connect", zk);
//        props.put("auto.offset.reset","smallest");
        props.put("group.id",group);
        props.put("zookeeper.session.timeout.ms", "10000");
        props.put("zookeeper.sync.time.ms", "2000");
        props.put("auto.commit.interval.ms", "10000");
        props.put(org.apache.kafka.clients.consumer.ConsumerConfig.PARTITION_ASSIGNMENT_STRATEGY, "range");
        ConsumerConfig consumerConfig =  new kafka.consumer.ConsumerConfig(props);
        ConsumerConnector consumerConnector = kafka.consumer.Consumer.createJavaConsumerConnector(consumerConfig);
        Map<String, Integer> topicCountMap = new HashMap<String, Integer>();
        topicCountMap.put(topic, consumerCount);
        Map<String, List<KafkaStream<byte[], byte[]>>> consumerMap = consumerConnector
                .createMessageStreams(topicCountMap);
        consumerMap.get(topic).stream().forEach(stream -> {

            pool.submit(new Runnable() {
                @Override
                public void run() {
                    ConsumerIterator<byte[], byte[]> it = stream.iterator();
                    while (it.hasNext()) {
                        System.out.println(Thread.currentThread().getName()+":"+new String(it.next().message()));
                    }
                }
            });

        });
    }
}

• producer

public class NativeProducer {

    public void produce(String topic,String brokerAddr) throws ExecutionException, InterruptedException {
        Properties props = new Properties();
        props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,brokerAddr);
        props.put(ProducerConfig.CLIENT_ID_CONFIG, "DemoProducer");
        props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.ByteArraySerializer");
        props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.ByteArraySerializer");

        try (KafkaProducer<byte[], byte[]> producer = new KafkaProducer<>(props)) {
            int totalCountOfSendedMessages = 0;
            long totalSendTime = 0;

            long timeOfLastUpdate = 0;
            int countOfMessagesInSec = 0;

            for(int i=0;i<1000000;i++){
                //todo key不能相同,否则都发送到同一个partition了,消费者无法scale out
                byte[] dataKey = SerializationUtils.serialize(UUID.randomUUID().toString());
                byte[] dataValue = SerializationUtils.serialize(UUID.randomUUID().toString());

                ProducerRecord<byte[], byte[]> producerRecord = new ProducerRecord<>(
                        topic,
                        dataKey,
                        dataValue
                );

                long sendingStartTime = System.currentTimeMillis();
                // Sync send
                producer.send(producerRecord).get();
                Thread.sleep(100);
                long currentTime = System.currentTimeMillis();

                long sendTime = currentTime - sendingStartTime;

                totalSendTime += sendTime;

                totalCountOfSendedMessages++;
                countOfMessagesInSec++;
                if (currentTime - timeOfLastUpdate > TimeUnit.SECONDS.toMillis(1)) {
                    System.out.println("Average send time: " +
                            (double) (totalSendTime / totalCountOfSendedMessages) + " ms.");
                    System.out.println("Count of messages in second: " + countOfMessagesInSec);

                    timeOfLastUpdate = currentTime;
                    countOfMessagesInSec = 0;
                }
            }

        }
    }
}

• test

    String zkAddr = "localhost:2181";
    String topic = "topic20170921"; //partition 6
    String brokerAddr = "localhost:9092";
    String group = "test-group-0921";

    @Test
    public void testConsumer1() throws InterruptedException {
        NativeConsumer nativeConsumer = new NativeConsumer();
        try {
            nativeConsumer.exec(topic,zkAddr,4,group);
        } catch (UnsupportedEncodingException e1) {
            e1.printStackTrace();
        }
        Thread.sleep(100000);
    }

    @Test
    public void testConsumer2() throws InterruptedException {
        NativeConsumer nativeConsumer = new NativeConsumer();
        try {
            nativeConsumer.exec(topic,zkAddr,4,group);
        } catch (UnsupportedEncodingException e1) {
            e1.printStackTrace();
        }
        Thread.sleep(100000);
    }

    @Test
    public void testProducer() throws UnsupportedEncodingException, InterruptedException {
        NativeProducer producer = new NativeProducer();
        try {
            producer.produce(topic,brokerAddr);
        } catch (ExecutionException e) {
            e.printStackTrace();
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    }

doc

• Kafka Consumer接口
• Kafka分区机制介绍与示例