Kafka心跳与消费机制是什么

Kafka是通过心跳机制来控制消费超时，心跳机制对于消费者客户端来说是无感的，它是一个异步线程，当我们启动一个消费者实例时，心跳线程就开始工作了。心跳超时会导致消息重复消费。

1、Kafka消费

首先，我们来看看消费。Kafka提供了非常简单的消费API，使用者只需初始化Kafka的Broker Server地址，然后实例化KafkaConsumer类即可拿到Topic中的数据。一个简单的Kafka消费实例代码如下所示：

public class JConsumerSubscribe extends Thread {   public static void main(String[] args) {        JConsumerSubscribe jconsumer = new JConsumerSubscribe();        jconsumer.start();    }    /** 初始化Kafka集群信息. */    private Properties configure() {        Properties props = new Properties();        props.put("bootstrap.servers", "dn1:9092,dn2:9092,dn3:9092");// 指定Kafka集群地址       props.put("group.id", "ke");// 指定消费者组       props.put("enable.auto.commit", "true");// 开启自动提交       props.put("auto.commit.interval.ms", "1000");// 自动提交的时间间隔       // 反序列化消息主键        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");       // 反序列化消费记录        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");       return props;   }    /** 实现一个单线程消费者. */    @Override    public void run() {        // 创建一个消费者实例对象        KafkaConsumer consumer = new KafkaConsumer(configure());        // 订阅消费主题集合        consumer.subscribe(Arrays.asList("test_kafka_topic"));       // 实时消费标识        boolean flag = true;       while (flag) {           // 获取主题消息数据            ConsumerRecords records = consumer.poll(Duration.ofMillis(100));           for (ConsumerRecord record : records)               // 循环打印消息记录                System.out.printf("offset = %d, key = %s, value = %s%n", record.offset(), record.key(), record.value());       }        // 出现异常关闭消费者对象        consumer.close();   }}

上述代码我们就可以非常便捷的拿到Topic中的数据。但是，当我们调用poll方法拉取数据的时候，Kafka Broker Server做了那些事情。接下来，我们可以去看看源代码的实现细节。核心代码如下： org.apache.kafka.clients.consumer.KafkaConsumer

private ConsumerRecords poll(final long timeoutMs, final boolean includeMetadataInTimeout) {       acquireAndEnsureOpen();        try {           if (timeoutMs "Timeout must not be negative");           if (this.subscriptions.hasNoSubscriptionOrUserAssignment()) {               throw new IllegalStateException("Consumer is not subscribed to any topics or assigned any partitions");           }            // poll for new data until the timeout expires           long elapsedTime = 0L;           do {               client.maybeTriggerWakeup();                final long metadataEnd;                if (includeMetadataInTimeout) {                   final long metadataStart = time.milliseconds();                    if (!updateAssignmentMetadataIfNeeded(remainingTimeAtLeastZero(timeoutMs, elapsedTime))) {                       return ConsumerRecords.empty();                   }                    metadataEnd = time.milliseconds();                    elapsedTime += metadataEnd - metadataStart;                } else {                   while (!updateAssignmentMetadataIfNeeded(Long.MAX_VALUE)) {                       log.warn("Still waiting for metadata");                   }                    metadataEnd = time.milliseconds();                }                final Map>> records = pollForFetches(remainingTimeAtLeastZero(timeoutMs, elapsedTime));                if (!records.isEmpty()) {                   // before returning the fetched records, we can send off the next round of fetches                   // and avoid block waiting for their responses to enable pipelining while the user                   // is handling the fetched records.                   //                   // NOTE: since the consumed position has already been updated, we must not allow                   // wakeups or any other errors to be triggered prior to returning the fetched records.                   if (fetcher.sendFetches() > 0 || client.hasPendingRequests()) {                       client.pollNoWakeup();                    }                    return this.interceptors.onConsume(new ConsumerRecords(records));               }                final long fetchEnd = time.milliseconds();                elapsedTime += fetchEnd - metadataEnd;            } while (elapsedTime return ConsumerRecords.empty();       } finally {           release();        }    }

上述代码中有个方法pollForFetches，它的实现逻辑如下：

private Map>> pollForFetches(final long timeoutMs) {       final long startMs = time.milliseconds();       long pollTimeout = Math.min(coordinator.timeToNextPoll(startMs), timeoutMs);       // if data is available already, return it immediately       final Map>> records = fetcher.fetchedRecords();       if (!records.isEmpty()) {           return records;       }       // send any new fetches (won't resend pending fetches)       fetcher.sendFetches();       // We do not want to be stuck blocking in poll if we are missing some positions       // since the offset lookup may be backing off after a failure       // NOTE: the use of cachedSubscriptionHashAllFetchPositions means we MUST call       // updateAssignmentMetadataIfNeeded before this method.       if (!cachedSubscriptionHashAllFetchPositions && pollTimeout > retryBackoffMs) {           pollTimeout = retryBackoffMs;       }       client.poll(pollTimeout, startMs, () -> {           // since a fetch might be completed by the background thread, we need this poll condition           // to ensure that we do not block unnecessarily in poll()           return !fetcher.hasCompletedFetches();       });       // after the long poll, we should check whether the group needs to rebalance       // prior to returning data so that the group can stabilize faster       if (coordinator.rejoinNeededOrPending()) {           return Collections.emptyMap();       }       return fetcher.fetchedRecords();   }

上述代码中加粗的位置，我们可以看出每次消费者客户端拉取数据时，通过poll方法，先调用fetcher中的fetchedRecords函数，如果获取不到数据，就会发起一个新的sendFetches请求。而在消费数据的时候，每个批次从Kafka Broker Server中拉取数据是有最大数据量限制，默认是500条，由属性(max.poll.records)控制，可以在客户端中设置该属性值来调整我们消费时每次拉取数据的量。

**提示：**这里需要注意的是，max.poll.records返回的是一个poll请求的数据总和，与多少个分区无关。因此，每次消费从所有分区中拉取Topic的数据的总条数不会超过max.poll.records所设置的值。

而在Fetcher的类中，在sendFetches方法中有限制拉取数据容量的限制，由属性(max.partition.fetch.bytes)，默认1MB。可能会有这样一个场景，当满足max.partition.fetch.bytes限制条件，如果需要Fetch出10000条记录，每次默认500条，那么我们需要执行20次才能将这一次通过网络发起的请求全部Fetch完毕。

这里，可能有同学有疑问，我们不能将默认的max.poll.records属性值调到10000吗?可以调，但是还有个属性需要一起配合才可以，这个就是每次poll的超时时间(Duration.ofMillis(100))，这里需要根据你的实际每条数据的容量大小来确定设置超时时间，如果你将最大值调到10000，当你每条记录的容量很大时，超时时间还是100ms，那么可能拉取的数据少于10000条。

而这里，还有另外一个需要注意的事情，就是会话超时的问题。session.timeout.ms默认是10s，group.min.session.timeout.ms默认是6s，group.max.session.timeout.ms默认是30min。当你在处理消费的业务逻辑的时候，如果在10s内没有处理完，那么消费者客户端就会与Kafka Broker Server断开，消费掉的数据，产生的offset就没法提交给Kafka，因为Kafka Broker Server此时认为该消费者程序已经断开，而即使你设置了自动提交属性，或者设置auto.offset.reset属性，你消费的时候还是会出现重复消费的情况，这就是因为session.timeout.ms超时的原因导致的。

2、心跳机制

上面在末尾的时候，说到会话超时的情况导致消息重复消费，为什么会有超时?有同学会有这样的疑问，我的消费者线程明明是启动的，也没有退出，为啥消费不到Kafka的消息呢?消费者组也查不到我的ConsumerGroupID呢?这就有可能是超时导致的，而Kafka是通过心跳机制来控制超时，心跳机制对于消费者客户端来说是无感的，它是一个异步线程，当我们启动一个消费者实例时，心跳线程就开始工作了。

在org.apache.kafka.clients.consumer.internals.AbstractCoordinator中会启动一个HeartbeatThread线程来定时发送心跳和检测消费者的状态。每个消费者都有个org.apache.kafka.clients.consumer.internals.ConsumerCoordinator，而每个ConsumerCoordinator都会启动一个HeartbeatThread线程来维护心跳，心跳信息存放在org.apache.kafka.clients.consumer.internals.Heartbeat中，声明的Schema如下所示：

private final int sessionTimeoutMs;   private final int heartbeatIntervalMs;   private final int maxPollIntervalMs;   private final long retryBackoffMs;   private volatile long lastHeartbeatSend;     private long lastHeartbeatReceive;   private long lastSessionReset;   private long lastPoll;   private boolean heartbeatFailed;

心跳线程中的run方法实现代码如下：

public void run() {           try {               log.debug("Heartbeat thread started");               while (true) {                   synchronized (AbstractCoordinator.this) {                       if (closed)                           return;                       if (!enabled) {                           AbstractCoordinator.this.wait();                           continue;                       }                        if (state != MemberState.STABLE) {                           // the group is not stable (perhaps because we left the group or because the coordinator                           // kicked us out), so disable heartbeats and wait for the main thread to rejoin.                           disable();                           continue;                       }                       client.pollNoWakeup();                       long now = time.milliseconds();                       if (coordinatorUnknown()) {                           if (findCoordinatorFuture != null || lookupCoordinator().failed())                               // the immediate future check ensures that we backoff properly in the case that no                               // brokers are available to connect to.                               AbstractCoordinator.this.wait(retryBackoffMs);                       } else if (heartbeat.sessionTimeoutExpired(now)) {                           // the session timeout has expired without seeing a successful heartbeat, so we should                           // probably make sure the coordinator is still healthy.                           markCoordinatorUnknown();                       } else if (heartbeat.pollTimeoutExpired(now)) {                           // the poll timeout has expired, which means that the foreground thread has stalled                           // in between calls to poll(), so we explicitly leave the group.                           maybeLeaveGroup();                       } else if (!heartbeat.shouldHeartbeat(now)) {                           // poll again after waiting for the retry backoff in case the heartbeat failed or the                           // coordinator disconnected                           AbstractCoordinator.this.wait(retryBackoffMs);                       } else {                           heartbeat.sentHeartbeat(now);                           sendHeartbeatRequest().addListener(new RequestFutureListener() {                               @Override                               public void onSuccess(Void value) {                                   synchronized (AbstractCoordinator.this) {                                       heartbeat.receiveHeartbeat(time.milliseconds());                                   }                               }                               @Override                               public void onFailure(RuntimeException e) {                                   synchronized (AbstractCoordinator.this) {                                       if (e instanceof RebalanceInProgressException) {                                           // it is valid to continue heartbeating while the group is rebalancing. This                                           // ensures that the coordinator keeps the member in the group for as long                                           // as the duration of the rebalance timeout. If we stop sending heartbeats,                                           // however, then the session timeout may expire before we can rejoin.                                           heartbeat.receiveHeartbeat(time.milliseconds());                                       } else {                                           heartbeat.failHeartbeat();                                           // wake up the thread if it's sleeping to reschedule the heartbeat                                           AbstractCoordinator.this.notify();                                       }                                   }                               }                           });                       }                   }               }           } catch (AuthenticationException e) {               log.error("An authentication error occurred in the heartbeat thread", e);               this.failed.set(e);           } catch (GroupAuthorizationException e) {               log.error("A group authorization error occurred in the heartbeat thread", e);               this.failed.set(e);           } catch (InterruptedException | InterruptException e) {               Thread.interrupted();               log.error("Unexpected interrupt received in heartbeat thread", e);               this.failed.set(new RuntimeException(e));           } catch (Throwable e) {               log.error("Heartbeat thread failed due to unexpected error", e);               if (e instanceof RuntimeException)                   this.failed.set((RuntimeException) e);               else                   this.failed.set(new RuntimeException(e));           } finally {               log.debug("Heartbeat thread has closed");           }       }

在心跳线程中这里面包含两个最重要的超时函数，它们是sessionTimeoutExpired和pollTimeoutExpired。

public boolean sessionTimeoutExpired(long now) {       return now - Math.max(lastSessionReset, lastHeartbeatReceive) > sessionTimeoutMs;}public boolean pollTimeoutExpired(long now) {       return now - lastPoll > maxPollIntervalMs;}

2.1、sessionTimeoutExpired

如果是sessionTimeout超时，则会被标记为当前协调器处理断开，此时，会将消费者移除，重新分配分区和消费者的对应关系。在Kafka Broker Server中，Consumer Group定义了5中(如果算上Unknown，应该是6种状态)状态，org.apache.kafka.common.ConsumerGroupState，如下图所示：

2.2、pollTimeoutExpired

如果触发了poll超时，此时消费者客户端会退出ConsumerGroup，当再次poll的时候，会重新加入到ConsumerGroup，触发RebalanceGroup。而KafkaConsumer Client是不会帮我们重复poll的，需要我们自己在实现的消费逻辑中不停的调用poll方法。

3.分区与3消费线程

关于消费分区与消费线程的对应关系，理论上消费线程数应该小于等于分区数。之前是有这样一种观点，一个消费线程对应一个分区，当消费线程等于分区数是最大化线程的利用率。直接使用KafkaConsumer Client实例，这样使用确实没有什么问题。但是，如果我们有富裕的CPU，其实还可以使用大于分区数的线程，来提升消费能力，这就需要我们对KafkaConsumer Client实例进行改造，实现消费策略预计算，利用额外的CPU开启更多的线程，来实现消费任务分片。