kafka消息分发策略分析
- 2019 年 10 月 3 日
- 笔记
当我们使用kafka向指定Topic发送消息时，如果该Topic具有多个partition，无论消费者有多少，最终都会保证一个partition内的消息只会被一个Consumer group中的一个Consumer消费，也就是说同一Consumer group中的多个Consumer自动会起到负载均衡的效果。
1、消息构造
下面我们就针对调用kafka API发送消息到Topic时partition的分配策略，分析下其内部具体的源码码实现。
首先看下kafka API中消息体ProducerRecord类的构造函数，可以看到构造消息时可指定该消息要发送的Topic、partition、key、value等关键信息。
/** * Creates a record to be sent to a specified topic and partition * * @param topic The topic the record will be appended to * @param partition The partition to which the record should be sent * @param key The key that will be included in the record * @param value The record contents * @param headers The headers that will be included in the record */ public ProducerRecord(String topic, Integer partition, K key, V value, Iterable<Header> headers) { this(topic, partition, null, key, value, headers); } /** * Creates a record to be sent to a specified topic and partition * * @param topic The topic the record will be appended to * @param partition The partition to which the record should be sent * @param key The key that will be included in the record * @param value The record contents */ public ProducerRecord(String topic, Integer partition, K key, V value) { this(topic, partition, null, key, value, null); } /** * Create a record to be sent to Kafka * * @param topic The topic the record will be appended to * @param key The key that will be included in the record * @param value The record contents */ public ProducerRecord(String topic, K key, V value) { this(topic, null, null, key, value, null); }
2、分发策略
在实际使用中，我们一般不会指定消息发送的具体partition，最多只会传入key值，类似下面这种方式：
producer.send(new ProducerRecord<Object, Object>(topic, key, data));
而kafka也会根据你传入key的hash值，通过取余的方法，尽可能保证消息能够相对均匀的分摊到每个可用的partition上；
下面是kafka内部默认的分发策略：
public class DefaultPartitioner implements Partitioner { private final ConcurrentMap<String, AtomicInteger> topicCounterMap = new ConcurrentHashMap<>(); public void configure(Map<String, ?> configs) {} /** * 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) { //获取该topic的分区列表 List<PartitionInfo> partitions = cluster.partitionsForTopic(topic); int numPartitions = partitions.size(); //如果key值为null if (keyBytes == null) { //维护一个key为topic的ConcurrentHashMap，并通过CAS操作的方式对value值执行递增+1操作 int nextValue = nextValue(topic); //获取该topic的可用分区列表 List<PartitionInfo> availablePartitions = cluster.availablePartitionsForTopic(topic); if (availablePartitions.size() > 0) {//如果可用分区大于0 //执行求余操作，保证消息落在可用分区上 int part = Utils.toPositive(nextValue) % availablePartitions.size(); return availablePartitions.get(part).partition(); } else { // 没有可用分区的话，就给出一个不可用分区 return Utils.toPositive(nextValue) % numPartitions; } } else { // 通过计算key的hash，确定消息分区 return Utils.toPositive(Utils.murmur2(keyBytes)) % numPartitions; } } private int nextValue(String topic) { //获取一个AtomicInteger对象 AtomicInteger counter = topicCounterMap.get(topic); if (null == counter) {//如果为空 //生成一个随机数 counter = new AtomicInteger(ThreadLocalRandom.current().nextInt()); //维护到topicCounterMap中 AtomicInteger currentCounter = topicCounterMap.putIfAbsent(topic, counter); if (currentCounter != null) { counter = currentCounter; } } //返回值并执行递增 return counter.getAndIncrement(); } public void close() {} }
3、自定义负载策略
我们也可以通过实现Partitioner接口，自定义分发策略，看下具体实现
自定义实现Partitioner接口
/** * 自定义实现Partitioner接口 * */ public class KeyPartitioner implements Partitioner { /** * 实现具体分发策略 */ @Override public int partition(String topic, Object key, byte[] bytes, Object o1, byte[] bytes1, Cluster cluster) { List<PartitionInfo> availablePartitions = cluster.availablePartitionsForTopic(topic);//拉取可用的partition if (key == null||key.equals("")) { int random = (int) (Math.random() * 10); int part = random % availablePartitions.size(); return availablePartitions.get(part).partition(); } return Math.abs(key.toString().hashCode() % 6); } @Override public void configure(Map<String, ?> configs) { // TODO Auto-generated method stub } @Override public void close() { // TODO Auto-generated method stub } }
同时在初始化kafka生产者时，增加自定义配置
Properties properties = new Properties(); properties.put(ProducerConfig.PARTITIONER_CLASS_CONFIG,KeyPartitioner.class); //加入自定义的配置 producer = new KafkaProducer<Object, Object>(properties);
4、总结
以上是对kafka消息分发的策略进行一定的分析与自定义扩展，希望对大家在使用kafka时有所帮助，其中如有不足与不正确的地方还望指出与海涵。
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