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 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；