RocketMq深入分析讲解两种削峰方式

目录

• 何时需要削峰
• 通过消息队列的削峰方法有两种
• 消费延时控流
• 总结

何时需要削峰

当上游调用下游服务速率高于下游服务接口QPS时，那么如果不对调用速率进行控制，那么会发生很多失败请求

通过消息队列的削峰方法有两种

控制消费者消费速率和生产者投放延时消息，本质都是控制消费速度

通过消费者参数控制消费速度

先分析那些参数对控制消费速度有作用

1.PullInterval： 设置消费端，拉取mq消息的间隔时间。

注意：该时间算起时间是rocketMq消费者从broker消息后算起。经过PullInterval再次向broker拉去消息

源码分析:

首先需要了解rocketMq的消息拉去过程

拉去消息的类

PullMessageService

public class PullMessageService extends ServiceThread {
    private final InternalLogger log = ClientLogger.getLog();
    private final LinkedBlockingQueue<PullRequest> pullRequestQueue = new LinkedBlockingQueue<PullRequest>();
    private final MQClientInstance mQClientFactory;
    private final ScheduledExecutorService scheduledExecutorService = Executors
    .newSingleThreadScheduledExecutor(new ThreadFactory() {
        @Override
        public Thread newThread(Runnable r) {
            return new Thread(r, "PullMessageServiceScheduledThread");
        }
    });
    public PullMessageService(MQClientInstance mQClientFactory) {
        this.mQClientFactory = mQClientFactory;
    }
    public void executePullRequestLater(final PullRequest pullRequest, final long timeDelay) {
        if (!isStopped()) {
            this.scheduledExecutorService.schedule(new Runnable() {
                @Override
                public void run() {
                    PullMessageService.this.executePullRequestImmediately(pullRequest);
                }
            }, timeDelay, TimeUnit.MILLISECONDS);
        } else {
            log.warn("PullMessageServiceScheduledThread has shutdown");
        }
    }
    public void executePullRequestImmediately(final PullRequest pullRequest) {
        try {
            this.pullRequestQueue.put(pullRequest);
        } catch (InterruptedException e) {
            log.error("executePullRequestImmediately pullRequestQueue.put", e);
        }
    }
    public void executeTaskLater(final Runnable r, final long timeDelay) {
        if (!isStopped()) {
            this.scheduledExecutorService.schedule(r, timeDelay, TimeUnit.MILLISECONDS);
        } else {
            log.warn("PullMessageServiceScheduledThread has shutdown");
        }
    }
    public ScheduledExecutorService getScheduledExecutorService() {
        return scheduledExecutorService;
    }
    private void pullMessage(final PullRequest pullRequest) {
        final MQConsumerInner consumer = this.mQClientFactory.selectConsumer(pullRequest.getConsumerGroup());
        if (consumer != null) {
            DefaultMQPushConsumerImpl impl = (DefaultMQPushConsumerImpl) consumer;
            impl.pullMessage(pullRequest);
        } else {
            log.warn("No matched consumer for the PullRequest {}, drop it", pullRequest);
        }
    }
    @Override
    public void run() {
        log.info(this.getServiceName() + " service started");
        while (!this.isStopped()) {
            try {
                PullRequest pullRequest = this.pullRequestQueue.take();
                this.pullMessage(pullRequest);
            } catch (InterruptedException ignored) {
            } catch (Exception e) {
                log.error("Pull Message Service Run Method exception", e);
            }
        }
        log.info(this.getServiceName() + " service end");
    }
    @Override
    public void shutdown(boolean interrupt) {
        super.shutdown(interrupt);
                       ThreadUtils.shutdownGracefully(this.scheduledExecutorService, 1000, TimeUnit.MILLISECONDS);
                       }
                       @Override
                       public String getServiceName() {
                       return PullMessageService.class.getSimpleName();
                       }
                       }

继承自ServiceThread，这是一个单线程执行的service，不断获取阻塞队列中的pullRequest，进行消息拉取。

executePullRequestLater会延时将pullrequest放入到pullRequestQueue，达到延时拉去的目的。

那么PullInterval参数就是根据这个功能发挥的作用，在消费者拉去消息成功的回调

 PullCallback pullCallback = new PullCallback() {
            @Override
            public void onSuccess(PullResult pullResult) {
                if (pullResult != null) {
                    pullResult = DefaultMQPushConsumerImpl.this.pullAPIWrapper.processPullResult(pullRequest.getMessageQueue(), pullResult,
                        subscriptionData);
                    switch (pullResult.getPullStatus()) {
                        case FOUND:
                            long prevRequestOffset = pullRequest.getNextOffset();
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());
                            long pullRT = System.currentTimeMillis() - beginTimestamp;
                            DefaultMQPushConsumerImpl.this.getConsumerStatsManager().incPullRT(pullRequest.getConsumerGroup(),
                                pullRequest.getMessageQueue().getTopic(), pullRT);
                            long firstMsgOffset = Long.MAX_VALUE;
                            if (pullResult.getMsgFoundList() == null || pullResult.getMsgFoundList().isEmpty()) {
                                DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            } else {
                                firstMsgOffset = pullResult.getMsgFoundList().get(0).getQueueOffset();
                                DefaultMQPushConsumerImpl.this.getConsumerStatsManager().incPullTPS(pullRequest.getConsumerGroup(),
                                    pullRequest.getMessageQueue().getTopic(), pullResult.getMsgFoundList().size());
                                boolean dispatchToConsume = processQueue.putMessage(pullResult.getMsgFoundList());
                                DefaultMQPushConsumerImpl.this.consumeMessageService.submitConsumeRequest(
                                    pullResult.getMsgFoundList(),
                                    processQueue,
                                    pullRequest.getMessageQueue(),
                                    dispatchToConsume);
                                if (DefaultMQPushConsumerImpl.this.defaultMQPushConsumer.getPullInterval() > 0) {
                                    DefaultMQPushConsumerImpl.this.executePullRequestLater(pullRequest,
                                        DefaultMQPushConsumerImpl.this.defaultMQPushConsumer.getPullInterval());
                                } else {
                                    DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                                }
                            }
                            if (pullResult.getNextBeginOffset() < prevRequestOffset
                                || firstMsgOffset < prevRequestOffset) {
                                log.warn(
                                    "[BUG] pull message result maybe data wrong, nextBeginOffset: {} firstMsgOffset: {} prevRequestOffset: {}",
                                    pullResult.getNextBeginOffset(),
                                    firstMsgOffset,
                                    prevRequestOffset);
                            }
                            break;
                        case NO_NEW_MSG:
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());
                            DefaultMQPushConsumerImpl.this.correctTagsOffset(pullRequest);
                            DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            break;
                        case NO_MATCHED_MSG:
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());
                            DefaultMQPushConsumerImpl.this.correctTagsOffset(pullRequest);
                            DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            break;
                        case OFFSET_ILLEGAL:
                            log.warn("the pull request offset illegal, {} {}",
                                pullRequest.toString(), pullResult.toString());
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());
                            pullRequest.getProcessQueue().setDropped(true);
                            DefaultMQPushConsumerImpl.this.executeTaskLater(new Runnable() {
                                @Override
                                public void run() {
                                    try {
                                        DefaultMQPushConsumerImpl.this.offsetStore.updateOffset(pullRequest.getMessageQueue(),
                                            pullRequest.getNextOffset(), false);
                                        DefaultMQPushConsumerImpl.this.offsetStore.persist(pullRequest.getMessageQueue());
                                        DefaultMQPushConsumerImpl.this.rebalanceImpl.removeProcessQueue(pullRequest.getMessageQueue());
                                        log.warn("fix the pull request offset, {}", pullRequest);
                                    } catch (Throwable e) {
                                        log.error("executeTaskLater Exception", e);
                                    }
                                }
                            }, 10000);
                            break;
                        default:
                            break;
                    }
                }
            }
            @Override
            public void onException(Throwable e) {
                if (!pullRequest.getMessageQueue().getTopic().startsWith(MixAll.RETRY_GROUP_TOPIC_PREFIX)) {
                    log.warn("execute the pull request exception", e);
                }
                DefaultMQPushConsumerImpl.this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_EXCEPTION);
            }
        };

在 case found的情况下,也就是拉取到消息的q情况，在PullInterval>0的情况下，会延时投递到pullRequestQueue中，实现拉取消息的间隔

if (DefaultMQPushConsumerImpl.this.defaultMQPushConsumer.getPullInterval() > 0) {
                                    DefaultMQPushConsumerImpl.this.executePullRequestLater(pullRequest,
                                        DefaultMQPushConsumerImpl.this.defaultMQPushConsumer.getPullInterval());
                                } else {
                                    DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                                }

2.PullBatchSize: 设置每次pull消息的数量，该参数设置是针对逻辑消息队列，并不是每次pull消息拉到的总消息数

消费端分配了两个消费队列来监听。那么PullBatchSize 设置为32，那么该消费端每次pull到 64个消息。

消费端每次pull到消息总数=PullBatchSize*监听队列数

源码分析

消费者拉取消息时

org.apache.rocketmq.client.impl.consumer.DefaultMQPushConsumerImpl#pullMessage中

会执行

 this.pullAPIWrapper.pullKernelImpl(
                pullRequest.getMessageQueue(),
                subExpression,
                subscriptionData.getExpressionType(),
                subscriptionData.getSubVersion(),
                pullRequest.getNextOffset(),
                this.defaultMQPushConsumer.getPullBatchSize(),
                sysFlag,
                commitOffsetValue,
                BROKER_SUSPEND_MAX_TIME_MILLIS,
                CONSUMER_TIMEOUT_MILLIS_WHEN_SUSPEND,
                CommunicationMode.ASYNC,
                pullCallback
            );

其中 this.defaultMQPushConsumer.getPullBatchSize(),就是配置的PullBatchSize，代表的是每次从broker的一个队列上拉取的最大消息数。

3.ThreadMin和ThreadMax： 消费端消费pull到的消息需要的线程数量。

源码分析：

还是在消费者拉取消息成功时

  boolean dispatchToConsume = processQueue.putMessage(pullResult.getMsgFoundList());
  DefaultMQPushConsumerImpl.this.consumeMessageService.submitConsumeRequest(
                                    pullResult.getMsgFoundList(),
                                    processQueue,
                                    pullRequest.getMessageQueue(),
                                    dispatchToConsume);

通过consumeMessageService执行

默认情况下是并发消费

org.apache.rocketmq.client.impl.consumer.ConsumeMessageConcurrentlyService#submitConsumeRequest

  @Override
    public void submitConsumeRequest(
        final List<MessageExt> msgs,
        final ProcessQueue processQueue,
        final MessageQueue messageQueue,
        final boolean dispatchToConsume) {
        final int consumeBatchSize = this.defaultMQPushConsumer.getConsumeMessageBatchMaxSize();
        if (msgs.size() <= consumeBatchSize) {
            ConsumeRequest consumeRequest = new ConsumeRequest(msgs, processQueue, messageQueue);
            try {
                this.consumeExecutor.submit(consumeRequest);
            } catch (RejectedExecutionException e) {
                this.submitConsumeRequestLater(consumeRequest);
            }
        } else {
            for (int total = 0; total < msgs.size(); ) {
                List<MessageExt> msgThis = new ArrayList<MessageExt>(consumeBatchSize);
                for (int i = 0; i < consumeBatchSize; i++, total++) {
                    if (total < msgs.size()) {
                        msgThis.add(msgs.get(total));
                    } else {
                        break;
                    }
                }
                ConsumeRequest consumeRequest = new ConsumeRequest(msgThis, processQueue, messageQueue);
                try {
                    this.consumeExecutor.submit(consumeRequest);
                } catch (RejectedExecutionException e) {
                    for (; total < msgs.size(); total++) {
                        msgThis.add(msgs.get(total));
                    }
                    this.submitConsumeRequestLater(consumeRequest);
                }
            }
        }
    }

其中consumeExecutor初始化

this.consumeExecutor = new ThreadPoolExecutor(
            this.defaultMQPushConsumer.getConsumeThreadMin(),
            this.defaultMQPushConsumer.getConsumeThreadMax(),
            1000 * 60,
            TimeUnit.MILLISECONDS,
            this.consumeRequestQueue,
            new ThreadFactoryImpl("ConsumeMessageThread_"));

对象线程池最大和核心线程数。对于顺序消费ConsumeMessageOrderlyService也会使用最大和最小线程数这两个参数，只是消费时会锁定队列。

以上三种情况：是针对参数配置，来调整消费速度。

除了这三种情况外还有两种服务部署情况，可以调整消费速度：

4.rocketMq 逻辑消费队列配置数量 有消费端每次pull到消息总数=PullBatchSize*监听队列数

可知rocketMq 逻辑消费队列配置数量即上图中的 queue1 ，queue2，配置数量越多每次pull到的消息总数也就越多。如果下边配置读队列数量：修改tocpic的逻辑队列数量

5.消费端节点部署数量 ：

部署数量无论一个节点监听所有队列，还是多个节点按照分配策略分配监听队列数量，理论上每秒pull到的数量都一样的，但是多节点消费端消费线程数量要比单节点消费线程数量多，也就是多节点消费速度大于单节点。

消费延时控流

针对消息订阅者的消费延时流控的基本原理是，每次消费时在客户端增加一个延时来控制消费速度，此时理论上消费并发最快速度为：

单节点部署：

ConsumInterval ：延时时间单位毫秒

ConcurrentThreadNumber：消费端线程数量

MaxRate ：理论每秒处理数量

MaxRate = 1 / ConsumInterval * ConcurrentThreadNumber

如果消息并发消费线程（ConcurrentThreadNumber）为 20，延时（ConsumInterval）为 100 ms，代入上述公式可得

如果消息并发消费线程（ConcurrentThreadNumber）为 20，延时（ConsumInterval）为 100 ms，代入上述公式可得

200 = 1 / 0.1 * 20

由上可知，理论上可以将并发消费控制在 200 以下

如果是多个节点部署如两个节点，理论消费速度最高为每秒处理400个消息。

如下延时流控代码：

 /**
     * 测试mq 并发 接受
     */
    @Component
    @RocketMQMessageListener(topic = ConstantTopic.WRITING_LIKE_TOPIC,selectorExpression = ConstantTopic.WRITING_LIKE_ADD_TAG, consumerGroup = "writing_like_topic_add_group")
    class ConsumerLikeSave implements RocketMQListener<LikeWritingParams>, RocketMQPushConsumerLifecycleListener{
        @SneakyThrows
        @Override
        public void onMessage(LikeWritingParams params) {
            System.out.println("睡上0.1秒");
            Thread.sleep(100);
            long begin = System.currentTimeMillis();
            System.out.println("mq消费速度"+Thread.currentThread().getName()+"  "+DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss.SSS").format(LocalDateTime.now()));
            //writingLikeService.saveLike2Db(params.getUserId(),params.getWritingId());
            long end = System.currentTimeMillis();
          //  System.out.println("消费：: " +Thread.currentThread().getName()+ "毫秒："+(end - begin));
        }
        @Override
        public void prepareStart(DefaultMQPushConsumer defaultMQPushConsumer) {
            defaultMQPushConsumer.setConsumeThreadMin(20); //消费端拉去到消息以后分配线索去消费
            defaultMQPushConsumer.setConsumeThreadMax(50);//最大消费线程，一般情况下，默认队列没有塞满，是不会启用新的线程的
            defaultMQPushConsumer.setPullInterval(0);//消费端多久一次去rocketMq 拉去消息
            defaultMQPushConsumer.setPullBatchSize(32);     //消费端每个队列一次拉去多少个消息，若该消费端分赔了N个监控队列，那么消费端每次去rocketMq拉去消息说为N*1
            defaultMQPushConsumer.setConsumeFromWhere(ConsumeFromWhere.CONSUME_FROM_TIMESTAMP);
            defaultMQPushConsumer.setConsumeTimestamp(UtilAll.timeMillisToHumanString3(System.currentTimeMillis()));
            defaultMQPushConsumer.setConsumeMessageBatchMaxSize(2);
        }
    }

注释：如上消费端，单节点每秒处理速度也就是最高200个消息，实际上要小于200，业务代码执行也是需要时间。

但是要注意实际操作中并发流控实际是默认存在的，

spring boot 消费端默认配置

this.consumeThreadMin = 20;

this.consumeThreadMax = 20;

this.pullInterval = 0L;

this.pullBatchSize = 32;

若业务逻辑执行需要20ms，那么单节点处理速度就是：1/0.02*20=1000

这里默认拉去的速度1s内远大于1000

注意： 这里虽然pullInterval 等于0 当时受限于每次拉去64个，处理完也是需要一端时间才能回复ack，才能再次拉取，所以消费速度应该小于1000

所以并发流控要消费速度大于消费延时流控 ，那么消费延时流控才有意义

使用rokcetMq支持的延时消息也可以实现消息的延时消费，通过对delayLevel对应的时间进行配置为我们的需求。为不同的消息设置不同delayLevel，达到延时消费的目的。

总结

rocketMq 肖锋流控两种方式：

并发流控：就是根据业务流控速率要求，来调整topic 消费队列数量(read queue),消费端部署节点，消费端拉去间隔时间，消费端消费线程数量等，来达到要求的速率内

延时消费流控：就是在消费端延时消费消息(sleep),具体延时多少要根据业务要求速率，和消费端线程数量，和节点部署数量来控制

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