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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
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
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。
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:
@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。
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
到此这篇关于RocketMq深入分析讲解两种削峰方式的文章就介绍到这了,更多相关RocketMq削峰内容请搜索编程网以前的文章或继续浏览下面的相关文章希望大家以后多多支持编程网!
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