kafka生产者发送消息流程分析

1.消息发送过程

消息的发送可能会经过拦截器、序列化、分区器等过程。消息发送的主要涉及两个线程，分别为main线程和sender线程。

如图所示，主线程由 afkaProducer 创建消息，然后通过可能的拦截器、序列化器和分区器的作用之后缓存到消息累加器RecordAccumulator (也称为消息收集器)中。 Sender 线程负责从RecordAccumulator 获取消息并将其发送到 Kafka中。

1.1拦截器

在消息序列化之前会经过消息拦截器，自定义拦截器需要实现ProducerInterceptor接口，接口主要有两个方案#onSend和#onAcknowledgement，在消息发送之前会调用前者方法，可以在发送之前假如处理逻辑，比如计费。在收到服务端ack响应后会触发后者方法。需要注意的是拦截器中不要加入过多的复杂业务逻辑，以免影响发送效率。

1.2消息分区

消息ProducerRecord会将消息路由到那个分区中，分两种情况：

1.指定了partition字段

如果消息ProducerRecord中指定了 partition字段，那么就不需要走分区器，直接发往指定得partition分区中。

2.没有指定partition，但自定义了分区器

3.没指定parittion，也没有自定义分区器，但key不为空

4.没指定parittion，也没有自定义分区器，key也为空

看源码

// KafkaProducer#partition

private int partition(ProducerRecord<K, V> record, byte[] serializedKey, byte[] serializedValue, Cluster cluster) {
//指定分区partition则直接返回，否则走分区器
        Integer partition = record.partition();
        return partition != null ?
                partition :
                partitioner.partition(
                        record.topic(), record.key(), serializedKey, record.value(),                 serializedValue, cluster);
}

//DefaultPartitioner#partition
public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
        if (keyBytes == null) {
            return stickyPartitionCache.partition(topic, cluster);
        } 
        List<PartitionInfo> partitions = cluster.partitionsForTopic(topic);
        int numPartitions = partitions.size();
        // hash the keyBytes to choose a partition
        return Utils.toPositive(Utils.murmur2(keyBytes)) % numPartitions;
    }

partition方法中定义了分区分配逻辑 如果 ke 不为 null ，那么默认的分区器会对 key 进行哈希（采 MurmurHash2 算法，具备高运算性能及低碰撞率），最终根据得到 哈希值来 算分区号，有相同 key 的消息会被写入同一个分区 如果 key null ，那么消息将会以轮询的方式发往主题内的各个可用分区。

1.3消息累加器

    分区确定好了之后，消息并不是直接发送给broker，因为一个个发送网络消耗太大，而是先缓存到消息累加器RecordAccumulator，RecordAccumulator主要用来缓存消息 Sender 线程可以批量发送，进 减少网络传输 的资源消耗以提升性能 RecordAccumulator 缓存的大 小可以通过生产者客户端参数 buffer memory 配置，默认值为 33554432B ，即 32MB如果生产者发送消息的速度超过发 送到服务器的速度 ，则会导致生产者空间不足，这个时候 KafkaProducer的send()方法调用要么 被阻塞，要么抛出异常，这个取决于参数 max block ms 的配置，此参数的默认值为 60秒。

消息累加器本质上是个ConcurrentMap，

ConcurrentMap<TopicPartition, Deque<ProducerBatch>> batches;

1.4 发送流程源码分析

//KafkaProducer
@Override
public Future<RecordMetadata> send(ProducerRecord<K, V> record, Callback callback) {
    // intercept the record, which can be potentially modified; this method does not throw exceptions
    //首先执行拦截器链
    ProducerRecord<K, V> interceptedRecord = this.interceptors.onSend(record);
    return doSend(interceptedRecord, callback);
}

private Future<RecordMetadata> doSend(ProducerRecord<K, V> record, Callback callback) {
        TopicPartition tp = null;
    try {
        throwIfProducerClosed();
        // first make sure the metadata for the topic is available
        long nowMs = time.milliseconds();
        ClusterAndWaitTime clusterAndWaitTime;
        try {
            clusterAndWaitTime = waitOnMetadata(record.topic(), record.partition(), nowMs, maxBlockTimeMs);
        } catch (KafkaException e) {
            if (metadata.isClosed())
                throw new KafkaException("Producer closed while send in progress", e);
            throw e;
        }
        nowMs += clusterAndWaitTime.waitedOnMetadataMs;
        long remainingWaitMs = Math.max(0, maxBlockTimeMs - clusterAndWaitTime.waitedOnMetadataMs);
        Cluster cluster = clusterAndWaitTime.cluster;
        byte[] serializedKey;
        try {
            //key序列化
            serializedKey = keySerializer.serialize(record.topic(), record.headers(), record.key());
        } catch (ClassCastException cce) {
            throw new SerializationException("Can't convert key of class " + record.key().getClass().getName() +
                    " to class " + producerConfig.getClass(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG).getName() +
                    " specified in key.serializer", cce);
        }
        byte[] serializedValue;
        try {
            //value序列化
            serializedValue = valueSerializer.serialize(record.topic(), record.headers(), record.value());
        } catch (ClassCastException cce) {
            throw new SerializationException("Can't convert value of class " + record.value().getClass().getName() +
                    " to class " + producerConfig.getClass(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG).getName() +
                    " specified in value.serializer", cce);
        }
        //获取分区partition
        int partition = partition(record, serializedKey, serializedValue, cluster);
        tp = new TopicPartition(record.topic(), partition);

        setReadOnly(record.headers());
        Header[] headers = record.headers().toArray();
        //消息压缩
        int serializedSize = AbstractRecords.estimateSizeInBytesUpperBound(apiVersions.maxUsableProduceMagic(),
                compressionType, serializedKey, serializedValue, headers);
        //判断消息是否超过最大允许大小，消息缓存空间是否已满
        ensureValidRecordSize(serializedSize);
        long timestamp = record.timestamp() == null ? nowMs : record.timestamp();
        if (log.isTraceEnabled()) {
            log.trace("Attempting to append record {} with callback {} to topic {} partition {}", record, callback, record.topic(), partition);
        }
        // producer callback will make sure to call both 'callback' and interceptor callback
        Callback interceptCallback = new InterceptorCallback<>(callback, this.interceptors, tp);

        if (transactionManager != null && transactionManager.isTransactional()) {
            transactionManager.failIfNotReadyForSend();
        }
        //将消息缓存在消息累加器RecordAccumulator中
        RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey,
                serializedValue, headers, interceptCallback, remainingWaitMs, true, nowMs);
        //开辟新的ProducerBatch
        if (result.abortForNewBatch) {
            int prevPartition = partition;
            partitioner.onNewBatch(record.topic(), cluster, prevPartition);
            partition = partition(record, serializedKey, serializedValue, cluster);
            tp = new TopicPartition(record.topic(), partition);
            if (log.isTraceEnabled()) {
                log.trace("Retrying append due to new batch creation for topic {} partition {}. The old partition was {}", record.topic(), partition, prevPartition);
            }
            // producer callback will make sure to call both 'callback' and interceptor callback
            interceptCallback = new InterceptorCallback<>(callback, this.interceptors, tp);

            result = accumulator.append(tp, timestamp, serializedKey,
                serializedValue, headers, interceptCallback, remainingWaitMs, false, nowMs);
        }

        if (transactionManager != null && transactionManager.isTransactional())
            transactionManager.maybeAddPartitionToTransaction(tp);
        //判断消息是否已满，唤醒sender线程进行发送消息
        if (result.batchIsFull || result.newBatchCreated) {
            log.trace("Waking up the sender since topic {} partition {} is either full or getting a new batch", record.topic(), partition);
            this.sender.wakeup();
        }
        return result.future;
        // handling exceptions and record the errors;
        // for API exceptions return them in the future,
        // for other exceptions throw directly
    } catch (Exception e) {
        // we notify interceptor about all exceptions, since onSend is called before anything else in this method
        this.interceptors.onSendError(record, tp, e);
        throw e;
    }
}

1.5生产消息的可靠性

消息发送到broker，什么情况下生产者才确定消息写入成功了呢？ack是生产者一个重要的参数，它有三个值，ack=1表示leader副本写入成功服务端即可返回给生产者，是吞吐量和消息可靠性的平衡方案；ack=0表示生产者发送消息之后不需要等服务端响应，这种消息丢失风险最大；ack=-1表示生产者需要等等ISR中所有副本写入成功后才能收到响应，这种消息可靠性最高但吞吐量也是最小的。