1.概述
最近有同学咨询Kafka的消费和心跳机制,今天笔者将通过这篇博客来逐一介绍这些内容。
2.内容
2.1 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 KafkaConsumerconsumer = 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 ConsumerRecordspoll(final long timeoutMs, final boolean includeMetadataInTimeout) { acquireAndEnsureOpen(); try { if (timeoutMs < 0) throw new IllegalArgumentException("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 < timeoutMs); 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。
2.2 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:
![](http://img.e-com-net.com/image/info8/2b246dbcfe82488397e9aa432bbc4e31.gif)
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;
}
2.2.1 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:
2.2.2 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。具体实现,留到下一篇博客,给大家分享《基于Kafka的分布式查询SQL引擎》。
4.结束语
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