Kafka - 分区算法

目录

一  默认分区策略：

二  自定义分区策略：

三  murmur2 hash算法： 

---

一  默认分区策略：

序列化key存在时，对其采用murmur2 hash算法，再对总分区数取模。得到分区数。

序列化key不存在时，(轮询，round robin)

1.   可用分区数大于0时，用线程安全生成的随机数的绝对值 对 可用分区数 取模，在总分区列表中，找到对应的分区数。
2.   可用分区数等于0时，用线程安全生成的随机数的绝对值 对 总分区数 取模，得到分区数。              

    /**
     * Compute the partition for the given record.
     *
     * @param topic The topic name
     * @param key The key to partition on (or null if no key)
     * @param keyBytes serialized key to partition on (or null if no key)
     * @param value The value to partition on or null
     * @param valueBytes serialized value to partition on or null
     * @param cluster The current cluster metadata
     */
    public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
        List<PartitionInfo> partitions = cluster.partitionsForTopic(topic);
        int numPartitions = partitions.size();
        if (keyBytes == null) {
            int nextValue = nextValue(topic);
            List<PartitionInfo> availablePartitions = cluster.availablePartitionsForTopic(topic);
            if (availablePartitions.size() > 0) {
                int part = Utils.toPositive(nextValue) % availablePartitions.size();
                return availablePartitions.get(part).partition();
            } else {
                // no partitions are available, give a non-available partition
                return Utils.toPositive(nextValue) % numPartitions;
            }
        } else {
            // hash the keyBytes to choose a partition
            return Utils.toPositive(Utils.murmur2(keyBytes)) % numPartitions;
        }
    }

private int nextValue(String topic) {
        AtomicInteger counter = topicCounterMap.get(topic);
        if (null == counter) {
            counter = new AtomicInteger(ThreadLocalRandom.current().nextInt());
            AtomicInteger currentCounter = topicCounterMap.putIfAbsent(topic, counter);
            if (currentCounter != null) {
                counter = currentCounter;
            }
        }
        return counter.getAndIncrement();
    }

---

二  自定义分区策略：

public class MyPartitioner implements Partitioner {
    public static void main(String[] args) {
        //org.apache.kafka.clients.producer.internals.DefaultPartitioner
    }

    @Override
    public void configure(Map<String, ?> configs) {
    }


    @Override
    public int partition(String topic, Object key, byte[] keyBytes,
            Object value, byte[] valueBytes, Cluster cluster) {
        List<PartitionInfo> partitions = cluster.partitionsForTopic(topic);
        int numPartitions = partitions.size();
        /**
         *由于我们按key分区，在这里我们规定：key值不允许为null。在实际项目中，key为null的消息*，可以发送到同一个分区。
         */
        if(keyBytes == null) {
            throw new InvalidRecordException("key cannot be null");
        }
        if(((String)key).equals("1")) {
            return 1;
        }
        //如果消息的key值不为1，那么使用hash值取模，确定分区。
        return Utils.toPositive(Utils.murmur2(keyBytes)) % numPartitions;
    }

    @Override
    public void close() {
    }

}

转载：https://blog.csdn.net/wuxintdrh/article/details/78971308 

//KafkaProducer设置自定义分区

kafkaProperties.put("partitioner.class", "自定义partitioner实现类的完全限定类名");

---

三  murmur2 hash算法： 

/**
     * Generates 32 bit murmur2 hash from byte array
     * @param data byte array to hash
     * @return 32 bit hash of the given array
     */
    public static int murmur2(final byte[] data) {
        int length = data.length;
        int seed = 0x9747b28c;
        // 'm' and 'r' are mixing constants generated offline.
        // They're not really 'magic', they just happen to work well.
        final int m = 0x5bd1e995;
        final int r = 24;

        // Initialize the hash to a random value
        int h = seed ^ length;
        int length4 = length / 4;

        for (int i = 0; i < length4; i++) {
            final int i4 = i * 4;
            int k = (data[i4 + 0] & 0xff) + ((data[i4 + 1] & 0xff) << 8) + ((data[i4 + 2] & 0xff) << 16) + ((data[i4 + 3] & 0xff) << 24);
            k *= m;
            k ^= k >>> r;
            k *= m;
            h *= m;
            h ^= k;
        }

        // Handle the last few bytes of the input array
        switch (length % 4) {
            case 3:
                h ^= (data[(length & ~3) + 2] & 0xff) << 16;
            case 2:
                h ^= (data[(length & ~3) + 1] & 0xff) << 8;
            case 1:
                h ^= data[length & ~3] & 0xff;
                h *= m;
        }

        h ^= h >>> 13;
        h *= m;
        h ^= h >>> 15;

        return h;
    }