关于高并发下kafka producer send异步发送耗时问题的分析
最近开发网关服务的过程当中，需要用到kafka转发消息与保存日志，在进行压测的过程中由于是多线程并发操作kafka producer 进行异步send，发现send耗时有时会达到几十毫秒的阻塞，很大程度上上影响了并发的性能，而在后续的测试中发现单线程发送反而比多线程发送效率高出几倍。所以就对kafka API send 的源码进行了一下跟踪和分析，在此总结记录一下。首先看springboot下 .
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最近开发网关服务的过程当中，需要用到kafka转发消息与保存日志，在进行压测的过程中由于是多线程并发操作kafka producer 进行异步send，发现send耗时有时会达到几十毫秒的阻塞，很大程度上上影响了并发的性能，而在后续的测试中发现单线程发送反而比多线程发送效率高出几倍。所以就对kafka API send 的源码进行了一下跟踪和分析，在此总结记录一下。Kafka消息系统基础知识索引
首先看springboot下 kafka producer 的使用
在config中进行配置，向IOC容器中注入DefaultKafkaProducerFactory生产者工厂的实例
@Bean
public ProducerFactory<Object, Object> producerFactory() {
return new DefaultKafkaProducerFactory<>(producerConfigs());
}
创建producer
this.producer = producerFactory.createProducer();
大家都知道springboot下IOC容器管理的实例默认都是单例模式；而DefaultKafkaProducerFactory本身也是一个单例工厂
@Override
public Producer<K, V> createProducer() {
if (this.transactionIdPrefix != null) {
return createTransactionalProducer();
}
if (this.producer == null) {
synchronized (this) {
if (this.producer == null) {
this.producer = new CloseSafeProducer<K, V>(createKafkaProducer());
}
}
}
return this.producer;
}
我们创建的producer也是个单例。
接下来就是具体的发送，用过kafka的小伙伴都知道producer.send是个异步操作，会返回一个Future<RecordMetadata> 类型的结果。那么为什么单线程和多线程send效率会较大的差距呢，我们进入KafkaProducer内部看下producer.send的具体源码实现来找下答案
private Future<RecordMetadata> doSend(ProducerRecord<K, V> record, Callback callback) {
TopicPartition tp = null;
try {
//保证主题的元数据可用
ClusterAndWaitTime clusterAndWaitTime = waitOnMetadata(record.topic(), record.partition(), maxBlockTimeMs);
long remainingWaitMs = Math.max(0, maxBlockTimeMs - clusterAndWaitTime.waitedOnMetadataMs);
Cluster cluster = clusterAndWaitTime.cluster;
byte[] serializedKey;
try {
//序列化key
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 {
//序列化Value
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);
}
//计算出具体的partition
int partition = partition(record, serializedKey, serializedValue, cluster);
tp = new TopicPartition(record.topic(), partition);
setReadOnly(record.headers());
Header[] headers = record.headers().toArray();
int serializedSize = AbstractRecords.estimateSizeInBytesUpperBound(apiVersions.maxUsableProduceMagic(),
compressionType, serializedKey, serializedValue, headers);
ensureValidRecordSize(serializedSize);
long timestamp = record.timestamp() == null ? time.milliseconds() : record.timestamp();
log.trace("Sending 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.maybeAddPartitionToTransaction(tp);
//向队列容器中添加数据
RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey,
serializedValue, headers, interceptCallback, remainingWaitMs);
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 (ApiException e) {
log.debug("Exception occurred during message send:", e);
if (callback != null)
callback.onCompletion(null, e);
this.errors.record();
this.interceptors.onSendError(record, tp, e);
return new FutureFailure(e);
} catch (InterruptedException e) {
this.errors.record();
this.interceptors.onSendError(record, tp, e);
throw new InterruptException(e);
} catch (BufferExhaustedException e) {
this.errors.record();
this.metrics.sensor("buffer-exhausted-records").record();
this.interceptors.onSendError(record, tp, e);
throw e;
} catch (KafkaException e) {
this.errors.record();
this.interceptors.onSendError(record, tp, e);
throw e;
} 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;
}
}
这里除了前面做的一些序列化操作和判断，最关键的就是向队列容器中执行添加数据操作
RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey,
serializedValue, headers, interceptCallback, remainingWaitMs);
accumulator是RecordAccumulator这个类的一个实例，RecordAccumulator类是一个队列容器类；它的内部维护了一个ConcurrentMap，每一个TopicPartition都对应一个专属的消息队列。
private final ConcurrentMap<TopicPartition, Deque<ProducerBatch>> batches;
我们进入accumulator.append内部看下具体的实现
public RecordAppendResult append(TopicPartition tp,
long timestamp,
byte[] key,
byte[] value,
Header[] headers,
Callback callback,
long maxTimeToBlock) 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 {
//根据TopicPartition拿到对应的批处理队列
Deque<ProducerBatch> dq = getOrCreateDeque(tp);
//同步队列，保证线程安全
synchronized (dq) {
if (closed)
throw new IllegalStateException("Cannot send after the producer is closed.");
//把序列化后的数据放入队列，并返回结果
RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callback, dq);
if (appendResult != null)
return appendResult;
}
// we don't have an in-progress record batch try to allocate a new batch
byte maxUsableMagic = apiVersions.maxUsableProduceMagic();
int size = Math.max(this.batchSize, AbstractRecords.estimateSizeInBytesUpperBound(maxUsableMagic, compression, key, value, headers));
log.trace("Allocating a new {} byte message buffer for topic {} partition {}", size, tp.topic(), tp.partition());
buffer = free.allocate(size, maxTimeToBlock);
synchronized (dq) {
// Need to check if producer is closed again after grabbing the dequeue lock.
if (closed)
throw new IllegalStateException("Cannot send after the producer is closed.");
RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callback, dq);
if (appendResult != null) {
// Somebody else found us a batch, return the one we waited for! Hopefully this doesn't happen often...
return appendResult;
}
MemoryRecordsBuilder recordsBuilder = recordsBuilder(buffer, maxUsableMagic);
ProducerBatch batch = new ProducerBatch(tp, recordsBuilder, time.milliseconds());
FutureRecordMetadata future = Utils.notNull(batch.tryAppend(timestamp, key, value, headers, callback, time.milliseconds()));
dq.addLast(batch);
incomplete.add(batch);
// Don't deallocate this buffer in the finally block as it's being used in the record batch
buffer = null;
return new RecordAppendResult(future, dq.size() > 1 || batch.isFull(), true);
}
} finally {
if (buffer != null)
free.deallocate(buffer);
appendsInProgress.decrementAndGet();
}
}
在getOrCreateDeque中我们根据TopicPartition从ConcurrentMap获取对应队列，没有的话就初始化一个。
private Deque<ProducerBatch> getOrCreateDeque(TopicPartition tp) {
Deque<ProducerBatch> d = this.batches.get(tp);
if (d != null)
return d;
d = new ArrayDeque<>();
Deque<ProducerBatch> previous = this.batches.putIfAbsent(tp, d);
if (previous == null)
return d;
else
return previous;
}
更关键的是为了保证并发时的线程安全，执行 RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callback, dq)时，Deque<ProducerBatch>必然需要同步处理。
synchronized (dq) {
if (closed)
throw new IllegalStateException("Cannot send after the producer is closed.");
RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callback, dq);
if (appendResult != null)
return appendResult;
}
在这里我们可以看出，多线程高并发情况下，dq会存在比较大的资源竞争，虽然是基于内存的操作，每个线程持有锁的时间极短，但相比单线程情况，高并发情况下线程开辟较多，锁竞争和cpu上下文切换都比较频繁，会造成一定的性能损耗，产生阻塞耗时。
分析到这里你就会发现，其实KafkaProducer这个异步发送是建立在生产者和消费者模式上的，send的真正操作并不是直接异步发送，而是把数据放在一个中间队列中。那么既然有生产者在往内存队列中放入数据，那么必然会有一个专有的线程负责把这些数据真正发送出去。我们通过监控jvm线程信息可以看到，KafkaProducer创建后确实会启动一个守护线程用于消息的发送。
OK，我们再回到 KafkaProducer中，会看到里面有这样两个对象，Sender就是kafka发送数据的后台线程
private final Sender sender;
private final Thread ioThread;
在KafkaProducer的构造函数中会启动Sender线程
this.sender = new Sender(logContext,
client,
this.metadata,
this.accumulator,
maxInflightRequests == 1,
config.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG),
acks,
retries,
metricsRegistry.senderMetrics,
Time.SYSTEM,
this.requestTimeoutMs,
config.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG),
this.transactionManager,
apiVersions);
String ioThreadName = NETWORK_THREAD_PREFIX + " | " + clientId;
this.ioThread = new KafkaThread(ioThreadName, this.sender, true);
this.ioThread.start();
进入Sender内部可以看到这个线程的作用就是一直轮询发送数据。
public void run() {
log.debug("Starting Kafka producer I/O thread.");
// main loop, runs until close is called
while (running) {
try {
run(time.milliseconds());
} catch (Exception e) {
log.error("Uncaught error in kafka producer I/O thread: ", e);
}
}
log.debug("Beginning shutdown of Kafka producer I/O thread, sending remaining records.");
// okay we stopped accepting requests but there may still be
// requests in the accumulator or waiting for acknowledgment,
// wait until these are completed.
while (!forceClose && (this.accumulator.hasUndrained() || this.client.inFlightRequestCount() > 0)) {
try {
run(time.milliseconds());
} catch (Exception e) {
log.error("Uncaught error in kafka producer I/O thread: ", e);
}
}
if (forceClose) {
// We need to fail all the incomplete batches and wake up the threads waiting on
// the futures.
log.debug("Aborting incomplete batches due to forced shutdown");
this.accumulator.abortIncompleteBatches();
}
try {
this.client.close();
} catch (Exception e) {
log.error("Failed to close network client", e);
}
log.debug("Shutdown of Kafka producer I/O thread has completed.");
}
/**
* Run a single iteration of sending
*
* @param now The current POSIX time in milliseconds
*/
void run(long now) {
if (transactionManager != null) {
try {
if (transactionManager.shouldResetProducerStateAfterResolvingSequences())
// Check if the previous run expired batches which requires a reset of the producer state.
transactionManager.resetProducerId();
if (!transactionManager.isTransactional()) {
// this is an idempotent producer, so make sure we have a producer id
maybeWaitForProducerId();
} else if (transactionManager.hasUnresolvedSequences() && !transactionManager.hasFatalError()) {
transactionManager.transitionToFatalError(new KafkaException("The client hasn't received acknowledgment for " +
"some previously sent messages and can no longer retry them. It isn't safe to continue."));
} else if (transactionManager.hasInFlightTransactionalRequest() || maybeSendTransactionalRequest(now)) {
// as long as there are outstanding transactional requests, we simply wait for them to return
client.poll(retryBackoffMs, now);
return;
}
// do not continue sending if the transaction manager is in a failed state or if there
// is no producer id (for the idempotent case).
if (transactionManager.hasFatalError() || !transactionManager.hasProducerId()) {
RuntimeException lastError = transactionManager.lastError();
if (lastError != null)
maybeAbortBatches(lastError);
client.poll(retryBackoffMs, now);
return;
} else if (transactionManager.hasAbortableError()) {
accumulator.abortUndrainedBatches(transactionManager.lastError());
}
} catch (AuthenticationException e) {
// This is already logged as error, but propagated here to perform any clean ups.
log.trace("Authentication exception while processing transactional request: {}", e);
transactionManager.authenticationFailed(e);
}
}
long pollTimeout = sendProducerData(now);
client.poll(pollTimeout, now);
}
通过上面的分析我们可以看出producer.send操作本身其实是个基于内存的存储操作，耗时几乎可以忽略不计，但由于高并发情况下，线程同步会有一定的性能损耗，当然这个损耗在一般的应用场景下几乎是可以忽略不计的，但如果是数据量比较大，高并发的场景下会比较明显。
针对上面的问题分析，这里说下我个人的一些总结：
1、首先避免多线程操作producer发送数据，你可以采用生产者消费者模式把producer.send从你的多线程操作中解耦出来，维护一个你要发送的消息队列，单独开辟一个线程操作；
2、可能有的小伙伴会问，那么多创建几个producer的实例或者维护一个producer池可以吗，我原本也是这个想法，只是在测试中发现效果也不是很理想，我估计是由于创建producer实例过多，导致线程数量也跟着增加，本身的业务线程再加上kafka的线程，线程上下文切换比较频繁，CPU资源压力比较大，效率也不如单线程操作；
3、这个问题其实真是针对API操作来讲的，send操作并不是真正的数据发送，真正的数据发送由守护线程进行；按照kafka本身的设计思想，如果操作本身就成为了你性能的瓶颈，你应该考虑的是集群部署，负载均衡；
4、无锁才是真正的高性能；
Kafka开源项目指南提供详尽教程，助开发者掌握其架构、配置和使用，实现高效数据流管理和实时处理。它高性能、可扩展，适合日志收集和实时数据处理，通过持久化保障数据安全，是企业大数据生态系统的核心。
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