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Kafka源码篇 No.4-Producer消息封装_kafka生产者统一封装

作者：Cpp五条 | 2024-05-24 17:08:56

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kafka生产者统一封装

第1章简介

本篇文章从源码的角度，介绍Kafka生产者如何封装消息，细节详见代码中注释。

第2章详细步骤

2.1 消息大小的校验

在封装前会先进行数据大小的校验

org.apache.kafka.clients.producer.KafkaProducer#doSend


//TODO 校验消息大小
int serializedSize = AbstractRecords.estimateSizeInBytesUpperBound(apiVersions.maxUsableProduceMagic(),
		compressionType, serializedKey, serializedValue, headers);
ensureValidRecordSize(serializedSize);

org.apache.kafka.clients.producer.KafkaProducer#ensureValidRecordSize


private void ensureValidRecordSize(int size) {
	// 一条消息的最大size 默认1M
	if (size > maxRequestSize)
		throw new RecordTooLargeException("The message is " + size +
				" bytes when serialized which is larger than " + maxRequestSize + ", which is the value of the " +
				ProducerConfig.MAX_REQUEST_SIZE_CONFIG + " configuration.");
	// 一条消息的总内存size 默认32M
	if (size > totalMemorySize)
		throw new RecordTooLargeException("The message is " + size +
				" bytes when serialized which is larger than the total memory buffer you have configured with the " +
				ProducerConfig.BUFFER_MEMORY_CONFIG +
				" configuration.");
}

2.2 消息封装

2.2.1 消息封装整体流程

org.apache.kafka.clients.producer.KafkaProducer#doSend


//TODO 把消息封装入RecordAccumulator，内部是多个Deque<ProducerBatch>队列，队列中再分批次；内部定义Bufferpool管理内存
RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey,
		serializedValue, headers, interceptCallback, remainingWaitMs, true, nowMs);
 
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);
}

其中accumulator是在构造函数中已经初始化的对象。

接下来我们进入RecordAccumulator对象，具体看看是如何封装的。

在org.apache.kafka.clients.producer.internals.RecordAccumulator#append方法中进行封装，这里可以看出整体的流程：


public RecordAppendResult append(TopicPartition tp,
								 long timestamp,
								 byte[] key,
								 byte[] value,
								 Header[] headers,
								 Callback callback,
								 long maxTimeToBlock,
								 boolean abortOnNewBatch,
								 long nowMs) throws InterruptedException {
	// We keep track of the number of appending thread to make sure we do not miss batches in
	// abortIncompleteBatches().
	appendsInProgress.incrementAndGet();
	ByteBuffer buffer = null;
	if (headers == null) headers = Record.EMPTY_HEADERS;
	try {
		// check if we have an in-progress batch
		// TODO 根据Partition分区结果，获取到应该加入的队列（双端队列），一个Partition分区对应一个队列
		Deque<ProducerBatch> dq = getOrCreateDeque(tp);
		synchronized (dq) {
			if (closed)
				throw new KafkaException("Producer closed while send in progress");
			// TODO 尝试往队列里面添加数据，如果未申请内存，这里appendResult==null
			RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callback, dq, nowMs);
			if (appendResult != null)
				return appendResult;
		}
 
		// we don't have an in-progress record batch try to allocate a new batch
		if (abortOnNewBatch) {
			// Return a result that will cause another call to append.
			return new RecordAppendResult(null, false, false, true);
		}
 
		byte maxUsableMagic = apiVersions.maxUsableProduceMagic();
		/**
		 * TODO 获得消息的大小，默认配置大小和实际数据大小取最大值
		 * 注意：生成环境要注意发送数据的大小，如果发送的数据过大，而配置的大小只能包含一条消息，则相当于每条消息发送一次，
		 *      就会打破kafka生产者批次发送的设计，从而造成过多的网络IO。
		 */
		int size = Math.max(this.batchSize, AbstractRecords.estimateSizeInBytesUpperBound(maxUsableMagic, compression, key, value, headers));
		log.trace("Allocating a new {} byte message buffer for topic {} partition {} with remaining timeout {}ms", size, tp.topic(), tp.partition(), maxTimeToBlock);
		// TODO 根据计算的消息大小，进行内存分配
		buffer = free.allocate(size, maxTimeToBlock);
 
		// Update the current time in case the buffer allocation blocked above.
		nowMs = time.milliseconds();
		synchronized (dq) {
			// Need to check if producer is closed again after grabbing the dequeue lock.
			if (closed)
				throw new KafkaException("Producer closed while send in progress");
 
			// TODO 尝试往队列里面添加数据,如果未创建batch，这里appendResult==null
			RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callback, dq, nowMs);
			if (appendResult != null) {
				// Somebody else found us a batch, return the one we waited for! Hopefully this doesn't happen often...
				return appendResult;
			}
 
			// TODO 根据申请的内存大小，封装batch
			MemoryRecordsBuilder recordsBuilder = recordsBuilder(buffer, maxUsableMagic);
			ProducerBatch batch = new ProducerBatch(tp, recordsBuilder, nowMs);
			// TODO 尝试往batch里添加数据
			FutureRecordMetadata future = Objects.requireNonNull(batch.tryAppend(timestamp, key, value, headers,
					callback, nowMs));
 
			// TODO 往队列尾端添加batch
			dq.addLast(batch);
			incomplete.add(batch);
 
			// Don't deallocate this buffer in the finally block as it's being used in the record batch
			// TODO batch在使用，设置buffer为null，防止在finally里中释放内存
			buffer = null;
			return new RecordAppendResult(future, dq.size() > 1 || batch.isFull(), true, false);
		}
	} finally {
		// buffer!=null，没有新创建batch，则可以释放掉本次申请的内存
		if (buffer != null)
			free.deallocate(buffer);
		appendsInProgress.decrementAndGet();
	}
}

接下来从这个整体流程中，找重点的方法进行分解。

2.2.2 getOrCreateDeque获取队列

org.apache.kafka.clients.producer.internals.RecordAccumulator#getOrCreateDeque


private Deque<ProducerBatch> getOrCreateDeque(TopicPartition tp) {
	// TODO 从batches的数据结构 ConcurrentMap<TopicPartition, Deque<ProducerBatch>>，可以看出是一个Parition分区对应一个Deque队列，并且是线程安全的
    // TODO 注意这是一个高频的操作！！
	Deque<ProducerBatch> d = this.batches.get(tp);
	if (d != null)
		return d;
	// TODO 如果没有该partition对应的队列，则新建一个ArrayDeque队列
	d = new ArrayDeque<>();
	// TODO 将新建的队列加入到batches中，并返回
    // TODO 注意这是一个低频的操作！！
	Deque<ProducerBatch> previous = this.batches.putIfAbsent(tp, d);
	if (previous == null)
		return d;
	else
		return previous;
}

其中，batches的数据结构是一个实现了ConcurrentMap接口的Map


// TODO 线程安全的Map，partition分区->Deque队列
private final ConcurrentMap<TopicPartition, Deque<ProducerBatch>> batches;

batches在构造函数中初始化是一个CopyOnWriteMap：


/**
 * TODO 线程安全的Map
 * TODO write：synchronized加锁并copy一个新的对象，在新copy的map中进行写操作，写操作完毕后赋值给原有的Map（volatile修饰）
 * TODO read: 不加锁，直接从map中获取值
 * TODO 适合读多写少的场景
 */
this.batches = new CopyOnWriteMap<>();

CopyOnWriteMap是kafka自定义的一个数据结构：


public class CopyOnWriteMap<K, V> implements ConcurrentMap<K, V> {
 
    private volatile Map<K, V> map;
 
	//...
	
}

2.2.3 tryAppend填充数据

org.apache.kafka.clients.producer.internals.RecordAccumulator#tryAppend


private RecordAppendResult tryAppend(long timestamp, byte[] key, byte[] value, Header[] headers,
									 Callback callback, Deque<ProducerBatch> deque, long nowMs) {
	// TODO 获取队列尾端的ProducerBatch
	ProducerBatch last = deque.peekLast();
	// TODO 如果获取到ProducerBatch，则往ProducerBatch中添加数据
	if (last != null) {
		FutureRecordMetadata future = last.tryAppend(timestamp, key, value, headers, callback, nowMs);
		if (future == null)
			last.closeForRecordAppends();
		else
			return new RecordAppendResult(future, deque.size() > 1 || last.isFull(), false, false);
	}
	return null;
}

2.2.4 allocate分配内存

org.apache.kafka.clients.producer.internals.BufferPool#allocate


public ByteBuffer allocate(int size, long maxTimeToBlockMs) throws InterruptedException {
	// TODO 如果申请的size大于最大内存（默认32M）
	if (size > this.totalMemory)
		throw new IllegalArgumentException("Attempt to allocate " + size
										   + " bytes, but there is a hard limit of "
										   + this.totalMemory
										   + " on memory allocations.");
 
	ByteBuffer buffer = null;
	this.lock.lock();
 
	if (this.closed) {
		this.lock.unlock();
		throw new KafkaException("Producer closed while allocating memory");
	}
 
	try {
		// check if we have a free buffer of the right size pooled
		// TODO 如果申请的size==ProducerBatch && Deque<ByteBuffer> free不是空，则直接从队列头部返回ByteBuffer
		if (size == poolableSize && !this.free.isEmpty())
			return this.free.pollFirst();
 
		// now check if the request is immediately satisfiable with the
		// memory on hand or if we need to block
		// TODO 计算free总内存大小= Deque<ByteBuffer> free的size * 一个ProducerBatch默认的大小（16KB）
		int freeListSize = freeSize() * this.poolableSize;
		// TODO 可用内存大于要申请的内存，备注：总可用内存=nonPooledAvailableMemory + freeListSize
		if (this.nonPooledAvailableMemory + freeListSize >= size) {
			// we have enough unallocated or pooled memory to immediately
			// satisfy the request, but need to allocate the buffer
			freeUp(size);
			// TODO 扣减内存
			this.nonPooledAvailableMemory -= size;
		} else {
			// TODO 总可用内存不足
			// we are out of memory and will have to block
			// 当前分配到的内存大小
			int accumulated = 0;
			Condition moreMemory = this.lock.newCondition();
			try {
				long remainingTimeToBlockNs = TimeUnit.MILLISECONDS.toNanos(maxTimeToBlockMs);
				// TODO 加入到等待队列
				this.waiters.addLast(moreMemory);
				// loop over and over until we have a buffer or have reserved
				// enough memory to allocate one
				// TODO 循环等待其他线程释放内存
				while (accumulated < size) {
					long startWaitNs = time.nanoseconds();
					long timeNs;
					boolean waitingTimeElapsed;
					try {
						// TODO 等待，需要被唤醒：释放内存，这里就被唤醒
						waitingTimeElapsed = !moreMemory.await(remainingTimeToBlockNs, TimeUnit.NANOSECONDS);
					} finally {
						long endWaitNs = time.nanoseconds();
						timeNs = Math.max(0L, endWaitNs - startWaitNs);
						recordWaitTime(timeNs);
					}
 
					if (this.closed)
						throw new KafkaException("Producer closed while allocating memory");
 
					if (waitingTimeElapsed) {
						this.metrics.sensor("buffer-exhausted-records").record();
						throw new BufferExhaustedException("Failed to allocate memory within the configured max blocking time " + maxTimeToBlockMs + " ms.");
					}
 
					remainingTimeToBlockNs -= timeNs;
 
					// check if we can satisfy this request from the free list,
					// otherwise allocate memory
					// TODO 如果申请的size==ProducerBatch && Deque<ByteBuffer> free不是空，则直接从free队列头头部获取ByteBuffer
					if (accumulated == 0 && size == this.poolableSize && !this.free.isEmpty()) {
						// just grab a buffer from the free list
						buffer = this.free.pollFirst();
						accumulated = size;
					} else {
						// TODO 如果依然不够
						// we'll need to allocate memory, but we may only get
						// part of what we need on this iteration
						freeUp(size - accumulated);
						// TODO 计算本次获得到的内存大小got
						int got = (int) Math.min(size - accumulated, this.nonPooledAvailableMemory);
						// TODO 进行内存扣减，占用内存
						this.nonPooledAvailableMemory -= got;
						// TODO 累加获得到的内存，进行下一次循环等待
						accumulated += got;
					}
				}
				// Don't reclaim memory on throwable since nothing was thrown
				accumulated = 0;
			} finally {
				// When this loop was not able to successfully terminate don't loose available memory
				this.nonPooledAvailableMemory += accumulated;
				// TODO 移除等待队列
				this.waiters.remove(moreMemory);
			}
		}
	} finally {
		// signal any additional waiters if there is more memory left
		// over for them
		try {
			if (!(this.nonPooledAvailableMemory == 0 && this.free.isEmpty()) && !this.waiters.isEmpty())
				this.waiters.peekFirst().signal();
		} finally {
			// Another finally... otherwise find bugs complains
			lock.unlock();
		}
	}
 
	if (buffer == null)
		// TODO 分配内存
		return safeAllocateByteBuffer(size);
	else
		return buffer;
}

2.2.5 deallocate释放内存

org.apache.kafka.clients.producer.internals.BufferPool#deallocate(java.nio.ByteBuffer)


public void deallocate(ByteBuffer buffer, int size) {
	lock.lock();
	try {
		// TODO 归还的size==poolableSize（16K）
		if (size == this.poolableSize && size == buffer.capacity()) {
			// TODO 清空buffer
			buffer.clear();
			// TODO 把清空后的buffer放入free队列
			this.free.add(buffer);
		} else {
			// TODO 内存加入到nonPooledAvailableMemory
			this.nonPooledAvailableMemory += size;
		}
		// TODO 获取等待队列中队首的Condition，并唤醒
		Condition moreMem = this.waiters.peekFirst();
		if (moreMem != null)
			moreMem.signal();
	} finally {
		lock.unlock();
	}
}
 
public void deallocate(ByteBuffer buffer) {
	deallocate(buffer, buffer.capacity());
}

总结，经过上面这些步骤，最终封装成RecordAccumulator.RecordAppendResult消息对象，在后续进行发送使用。

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