源码分析Kafka之Producer

摘要：核心实现是这个方法通过不同的模式可以实现发送即忘忽略返回结果同步发送获取返回的对象，回调函数置为异步发送设置回调函数三种消息模式。

Kafka是一款很棒的消息系统，可以看看我之前写的 后端好书阅读与推荐来了解一下它的整体设计。今天我们就来深入了解一下它的实现细节（我fork了一份代码），首先关注Producer这一方。

要使用kafka首先要实例化一个KafkaProducer，需要有brokerIP、序列化器等必要Properties以及acks（0、1、n）、compression、retries、batch.size等非必要Properties，通过这个简单的接口可以控制Producer大部分行为，实例化后就可以调用send方法发送消息了。

核心实现是这个方法：

public Future send(ProducerRecord record, Callback callback) {
    // intercept the record, which can be potentially modified; this method does not throw exceptions
    ProducerRecord interceptedRecord = this.interceptors.onSend(record);//①
    return doSend(interceptedRecord, callback);//②
}

通过不同的模式可以实现发送即忘（忽略返回结果）、同步发送（获取返回的future对象，回调函数置为null）、异步发送（设置回调函数）三种消息模式。

我们来看看消息类ProducerRecord有哪些属性：

private final String topic;//主题
private final Integer partition;//分区
private final Headers headers;//头
private final K key;//键
private final V value;//值
private final Long timestamp;//时间戳

它有多个构造函数，可以适应不同的消息类型：比如有无分区、有无key等。

①中ProducerInterceptors（有0 ~ 无穷多个，形成一个拦截链）对ProducerRecord进行拦截处理（比如打上时间戳，进行审计与统计等操作）

public ProducerRecord onSend(ProducerRecord record) {
    ProducerRecord interceptRecord = record;
    for (ProducerInterceptor interceptor : this.interceptors) {
        try {
            interceptRecord = interceptor.onSend(interceptRecord);
        } catch (Exception e) {
            // 不抛出异常，继续执行下一个拦截器
            if (record != null)
                log.warn("Error executing interceptor onSend callback for topic: {}, partition: {}", record.topic(), record.partition(), e);
            else
                log.warn("Error executing interceptor onSend callback", e);
        }
    }
    return interceptRecord;
}

如果用户有定义就进行处理并返回处理后的ProducerRecord，否则直接返回本身。
然后②中doSend真正发送消息，并且是异步的（源码太长只保留关键）：

private Future doSend(ProducerRecord record, Callback callback) {
    TopicPartition tp = null;
    try {
        // 序列化 key 和 value
        byte[] serializedKey;
        try {
            serializedKey = keySerializer.serialize(record.topic(), record.headers(), record.key());
        } catch (ClassCastException cce) {
        }
        byte[] serializedValue;
        try {
            serializedValue = valueSerializer.serialize(record.topic(), record.headers(), record.value());
        } catch (ClassCastException cce) {
        }
        // 计算分区获得主题与分区
        int partition = partition(record, serializedKey, serializedValue, cluster);
        tp = new TopicPartition(record.topic(), partition);
        // 回调与事务处理省略。
        Header[] headers = record.headers().toArray();
        // 消息追加到RecordAccumulator中
        RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey,
                serializedValue, headers, interceptCallback, remainingWaitMs);
        // 该批次满了或者创建了新的批次就要唤醒IO线程发送该批次了，也就是sender的wakeup方法
        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;
    } catch (Exception e) {
        // 拦截异常并抛出
        this.interceptors.onSendError(record, tp, e);
        throw e;
    }
}

下面是计算分区的方法：

private int partition(ProducerRecord record, 
byte[] serializedKey, byte[] serializedValue, Cluster cluster) {
    Integer partition = record.partition();
    // 消息有分区就直接使用，否则就使用分区器计算
    return partition != null ?
            partition :
            partitioner.partition(
                    record.topic(), record.key(), serializedKey,
                     record.value(), serializedValue, cluster);
}

默认的分区器DefaultPartitioner实现方式是如果partition存在就直接使用，否则根据key计算partition，如果key也不存在就使用round robin算法分配partition。

/**
 * The default partitioning strategy:
 * 

 * 
If a partition is specified in the record, use it
 * 
If no partition is specified but a key is present choose a partition based on a hash of the key
 * 
If no partition or key is present choose a partition in a round-robin fashion
 */
public class DefaultPartitioner implements Partitioner {

    private final ConcurrentMap topicCounterMap = new ConcurrentHashMap<>();
    
    public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
        List partitions = cluster.partitionsForTopic(topic);
        int numPartitions = partitions.size();
        if (keyBytes == null) {//key为空 
            int nextValue = nextValue(topic);
            List availablePartitions = cluster.availablePartitionsForTopic(topic);//可用的分区
            if (availablePartitions.size() > 0) {//有分区，取模就行
                int part = Utils.toPositive(nextValue) % availablePartitions.size();
                return availablePartitions.get(part).partition();
            } else {// 无分区，
                return Utils.toPositive(nextValue) % numPartitions;
            }
        } else {// key 不为空，计算key的hash并取模获得分区
            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();//返回并加一，在取模的配合下就是round robin
    }
}

以上就是发送消息的逻辑处理，接下来我们再看看消息发送的物理处理。

Sender（是一个Runnable，被包含在一个IO线程ioThread中，该线程不断从RecordAccumulator队列中的读取消息并通过Selector将数据发送给Broker）的wakeup方法，实际上是KafkaClient接口的wakeup方法，由NetworkClient类实现，采用了NIO，也就是java.nio.channels.Selector.wakeup()方法实现。

Sender的run中主要逻辑是不停执行准备消息和等待消息：

long pollTimeout = sendProducerData(now);//③
client.poll(pollTimeout, now);//④

③完成消息设置并保存到信道中，然后监听感兴趣的key，由KafkaChannel实现。

public void setSend(Send send) {
    if (this.send != null)
        throw new IllegalStateException("Attempt to begin a send operation with prior send operation still in progress, connection id is " + id);
    this.send = send;
    this.transportLayer.addInterestOps(SelectionKey.OP_WRITE);
}

// transportLayer的一种实现中的相关方法
public void addInterestOps(int ops) {
    key.interestOps(key.interestOps() | ops);
}

④主要是Selector的poll，其select被wakeup唤醒：

public void poll(long timeout) throws IOException {
    /* check ready keys */
    long startSelect = time.nanoseconds();
    int numReadyKeys = select(timeout);//wakeup使其停止阻塞
    long endSelect = time.nanoseconds();
    this.sensors.selectTime.record(endSelect - startSelect, time.milliseconds());

    if (numReadyKeys > 0 || !immediatelyConnectedKeys.isEmpty() || dataInBuffers) {
        Set readyKeys = this.nioSelector.selectedKeys();

        // Poll from channels that have buffered data (but nothing more from the underlying socket)
        if (dataInBuffers) {
            keysWithBufferedRead.removeAll(readyKeys); //so no channel gets polled twice
            Set toPoll = keysWithBufferedRead;
            keysWithBufferedRead = new HashSet<>(); //poll() calls will repopulate if needed
            pollSelectionKeys(toPoll, false, endSelect);
        }

        // Poll from channels where the underlying socket has more data
        pollSelectionKeys(readyKeys, false, endSelect);
        // Clear all selected keys so that they are included in the ready count for the next select
        readyKeys.clear();

        pollSelectionKeys(immediatelyConnectedKeys, true, endSelect);
        immediatelyConnectedKeys.clear();
    } else {
        madeReadProgressLastPoll = true; //no work is also "progress"
    }

    long endIo = time.nanoseconds();
    this.sensors.ioTime.record(endIo - endSelect, time.milliseconds());
}

其中pollSelectionKeys方法会调用如下方法完成消息发送：

public Send write() throws IOException {
    Send result = null;
    if (send != null && send(send)) {
        result = send;
        send = null;
    }
    return result;
}

private boolean send(Send send) throws IOException {
    send.writeTo(transportLayer);
    if (send.completed())
        transportLayer.removeInterestOps(SelectionKey.OP_WRITE);
    return send.completed();
}

Send是一次数据发包，一般由ByteBufferSend或者MultiRecordsSend实现，其writeTo调用transportLayer的write方法，一般由PlaintextTransportLayer或者SslTransportLayer实现，区分是否使用ssl：

public long writeTo(GatheringByteChannel channel) throws IOException {
    long written = channel.write(buffers);
    if (written < 0)
        throw new EOFException("Wrote negative bytes to channel. This shouldn"t happen.");
    remaining -= written;
    pending = TransportLayers.hasPendingWrites(channel);
    return written;
}

public int write(ByteBuffer src) throws IOException {
    return socketChannel.write(src);
}

到此就把Producer的业务相关逻辑处理和非业务相关的网络 2方面的主要流程梳理清楚了。其他额外的功能是通过一些配置保证的。

比如顺序保证就是max.in.flight.requests.per.connection，InFlightRequests的doSend会进行判断（由NetworkClient的canSendRequest调用），只要该参数设为1即可保证当前包未确认就不能发送下一个包从而实现有序性

public boolean canSendMore(String node) {
    Deque queue = requests.get(node);
    return queue == null || queue.isEmpty() ||
           (queue.peekFirst().send.completed() && queue.size() < this.maxInFlightRequestsPerConnection);
}

再比如可靠性，通过设置acks，Sender中sendProduceRequest的clientRequest加入了回调函数：

    RequestCompletionHandler callback = new RequestCompletionHandler() {
        public void onComplete(ClientResponse response) {
            handleProduceResponse(response, recordsByPartition, time.milliseconds());//调用completeBatch
        }
    };
    
     /**
     * 完成或者重试投递，这里如果acks不对就会重试
     *
     * @param batch The record batch
     * @param response The produce response
     * @param correlationId The correlation id for the request
     * @param now The current POSIX timestamp in milliseconds
     */
    private void completeBatch(ProducerBatch batch, ProduceResponse.PartitionResponse response, long correlationId,
                               long now, long throttleUntilTimeMs) {
    }
    
    public class ProduceResponse extends AbstractResponse {
      /**
         * Possible error code:
         * INVALID_REQUIRED_ACKS (21)
         */
    }
    

kafka源码一层一层包装很多，错综复杂，如有错误请大家不吝赐教。