Kafka消费与心跳机制

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
        KafkaConsumer<String, String> 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<String, String> records = consumer.poll(Duration.ofMillis(100));
            for (ConsumerRecord<String, String> 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<K, V> poll(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<TopicPartition, List<ConsumerRecord<K, V>>> 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<TopicPartition, List<ConsumerRecord<K, V>>> 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<TopicPartition, List<ConsumerRecord<K, V>>> 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:

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<Void>() {
                                @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");
            }
        }
View Code

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

这篇博客就和大家分享到这里,如果大家在研究学习的过程当中有什么问题,可以加群进行讨论或发送邮件给我,我会尽我所能为您解答,与君共勉!

另外,博主出书了《Kafka并不难学》和《Hadoop大数据挖掘从入门到进阶实战》,喜欢的朋友或同学, 可以在公告栏那里点击购买链接购买博主的书进行学习,在此感谢大家的支持。关注下面公众号,根据提示,可免费获取书籍的教学视频。

posted @ 2020-09-28 08:35  哥不是小萝莉  阅读(513)  评论(0编辑  收藏