Kafka-Partitions分区分配计算

KafkaProducer在调用send方法发送消息至broker的过程中，首先是经过拦截器Inteceptors处理，然后是经过序列化Serializer处理，之后就到了Partitions阶段，即分区分配计算阶段。在某些应用场景下，业务逻辑需要控制每条消息落到合适的分区中，有些情形下则只要根据默认的分配规则即可。在KafkaProducer计算分配时，首先根据的是ProducerRecord中的partition字段指定的序号计算分区。

Kafka默认实现的org.apache.kafka.clients.producer.DefaultPartitioner，其partition()方法实现如下：

/**
 * 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();
}

由上源码可以看出partition的计算方式：

1. 如果key为null，则按照一种轮询的方式来计算分区分配
2. 如果key不为null则使用称之为murmur的Hash算法（非加密型Hash函数，具备高运算性能及低碰撞率）来计算分区分配。

KafkaProducer中还支持自定义分区分配方式，与org.apache.kafka.clients.producer.internals.DefaultPartitioner一样首先实现org.apache.kafka.clients.producer.Partitioner接口，然后在KafkaProducer的配置中指定partitioner.class为对应的自定义分区器（Partitioners）即可，即：

properties.put("partitioner.class","com.hidden.partitioner.DemoPartitioner");
自定义DemoPartitioner主要是实现Partitioner接口的public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster)的方法。DemoPartitioner稍微修改了下DefaultPartitioner的计算方式，详细参考如下：

public class DemoPartitioner implements Partitioner {

    private final AtomicInteger atomicInteger = new AtomicInteger(0);

    @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();
        if (null == keyBytes || keyBytes.length<1) {
            return atomicInteger.getAndIncrement() % numPartitions;
        }
        //借用String的hashCode的计算方式
        int hash = 0;
        for (byte b : keyBytes) {
            hash = 31 * hash + b;
        }
        return hash % numPartitions;
    }

    @Override
    public void close() {}
}