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kafka消息分发策略分析

bigfan 2019-09-06 12:25:00 阅读数:160 评论数:0 点赞数:0 收藏数:0


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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版权声明
本文为[bigfan]所创,转载请带上原文链接,感谢
https://www.cnblogs.com/dafanjoy/p/11394485.html

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