1. 程式人生 > >Kafka消費與心跳機制

Kafka消費與心跳機制

1.概述

最近有同學諮詢Kafka的消費和心跳機制,今天筆者將通過這篇部落格來逐一介紹這些內容。

2.內容

2.1 Kafka消費

首先,我們來看看消費。Kafka提供了非常簡單的消費API,使用者只需初始化Kafka的Broker Server地址,然後例項化KafkaConsumer類即可拿到Topic中的資料。一個簡單的Kafka消費例項程式碼如下所示:

public class JConsumerSubscribe extends Thread {
    public static void main(String[] args) {
        JConsumerSubscribe jconsumer = new JConsumerSubscribe();
        jconsumer.start();
    }

    /** 初始化Kafka叢集資訊. */
    private Properties configure() {
        Properties props = new Properties();
        props.put("bootstrap.servers", "dn1:9092,dn2:9092,dn3:9092");// 指定Kafka叢集地址
        props.put("group.id", "ke");// 指定消費者組
        props.put("enable.auto.commit", "true");// 開啟自動提交
        props.put("auto.commit.interval.ms", "1000");// 自動提交的時間間隔
        // 反序列化訊息主鍵
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        // 反序列化消費記錄
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        return props;
    }

    /** 實現一個單執行緒消費者. */
    @Override
    public void run() {
        // 建立一個消費者例項物件
        KafkaConsumer<String, String> consumer = new KafkaConsumer<>(configure());
        // 訂閱消費主題集合
        consumer.subscribe(Arrays.asList("test_kafka_topic"));
        // 實時消費標識
        boolean flag = true;
        while (flag) {
            // 獲取主題訊息資料
            ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
            for (ConsumerRecord<String, String> record : records)
                // 迴圈列印訊息記錄
                System.out.printf("offset = %d, key = %s, value = %s%n", record.offset(), record.key(), record.value());
        }
        // 出現異常關閉消費者物件
        consumer.close();
    }
}

上述程式碼我們就可以非常便捷的拿到Topic中的資料。但是,當我們呼叫poll方法拉取資料的時候,Kafka Broker Server做了那些事情。接下來,我們可以去看看原始碼的實現細節。核心程式碼如下:

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();
        }
    }

上述程式碼中有個方法pollForFetches,它的實現邏輯如下:

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();
    }

上述程式碼中加粗的位置,我們可以看出每次消費者客戶端拉取資料時,通過poll方法,先呼叫fetcher中的fetchedRecords函式,如果獲取不到資料,就會發起一個新的sendFetches請求。而在消費資料的時候,每個批次從Kafka Broker Server中拉取資料是有最大資料量限制,預設是500條,由屬性(max.poll.records)控制,可以在客戶端中設定該屬性值來調整我們消費時每次拉取資料的量。

提示:
這裡需要注意的是,max.poll.records返回的是一個poll請求的資料總和,與多少個分割槽無關。因此,每次消費從所有分割槽中拉取Topic的資料的總條數不會超過max.poll.records所設定的值。

而在Fetcher的類中,在sendFetches方法中有限制拉取資料容量的限制,由屬性(max.partition.fetch.bytes),預設1MB。可能會有這樣一個場景,當滿足max.partition.fetch.bytes限制條件,如果需要Fetch出10000條記錄,每次預設500條,那麼我們需要執行20次才能將這一次通過網路發起的請求全部Fetch完畢。

這裡,可能有同學有疑問,我們不能將預設的max.poll.records屬性值調到10000嗎?可以調,但是還有個屬性需要一起配合才可以,這個就是每次poll的超時時間(Duration.ofMillis(100)),這裡需要根據你的實際每條資料的容量大小來確定設定超時時間,如果你將最大值調到10000,當你每條記錄的容量很大時,超時時間還是100ms,那麼可能拉取的資料少於10000條。

而這裡,還有另外一個需要注意的事情,就是會話超時的問題。session.timeout.ms預設是10s,group.min.session.timeout.ms預設是6s,group.max.session.timeout.ms預設是30min。當你在處理消費的業務邏輯的時候,如果在10s內沒有處理完,那麼消費者客戶端就會與Kafka Broker Server斷開,消費掉的資料,產生的offset就沒法提交給Kafka,因為Kafka Broker Server此時認為該消費者程式已經斷開,而即使你設定了自動提交屬性,或者設定auto.offset.reset屬性,你消費的時候還是會出現重複消費的情況,這就是因為session.timeout.ms超時的原因導致的。

2.2 心跳機制

上面在末尾的時候,說到會話超時的情況導致訊息重複消費,為什麼會有超時?有同學會有這樣的疑問,我的消費者執行緒明明是啟動的,也沒有退出,為啥消費不到Kafka的訊息呢?消費者組也查不到我的ConsumerGroupID呢?這就有可能是超時導致的,而Kafka是通過心跳機制來控制超時,心跳機制對於消費者客戶端來說是無感的,它是一個非同步執行緒,當我們啟動一個消費者例項時,心跳執行緒就開始工作了。

在org.apache.kafka.clients.consumer.internals.AbstractCoordinator中會啟動一個HeartbeatThread執行緒來定時傳送心跳和檢測消費者的狀態。每個消費者都有個org.apache.kafka.clients.consumer.internals.ConsumerCoordinator,而每個ConsumerCoordinator都會啟動一個HeartbeatThread執行緒來維護心跳,心跳資訊存放在org.apache.kafka.clients.consumer.internals.Heartbeat中,宣告的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;

心跳執行緒中的run方法實現程式碼如下:

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

在心跳執行緒中這裡麵包含兩個最重要的超時函式,它們是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超時,則會被標記為當前協調器處理斷開,此時,會將消費者移除,重新分配分割槽和消費者的對應關係。在Kafka Broker Server中,Consumer Group定義了5中(如果算上Unknown,應該是6種狀態)狀態,org.apache.kafka.common.ConsumerGroupState,如下圖所示:

 

2.2.2 pollTimeoutExpired

如果觸發了poll超時,此時消費者客戶端會退出ConsumerGroup,當再次poll的時候,會重新加入到ConsumerGroup,觸發RebalanceGroup。而KafkaConsumer Client是不會幫我們重複poll的,需要我們自己在實現的消費邏輯中不停的呼叫poll方法。

3.分割槽與消費執行緒

關於消費分割槽與消費執行緒的對應關係,理論上消費執行緒數應該小於等於分割槽數。之前是有這樣一種觀點,一個消費執行緒對應一個分割槽,當消費執行緒等於分割槽數是最大化執行緒的利用率。直接使用KafkaConsumer Client例項,這樣使用確實沒有什麼問題。但是,如果我們有富裕的CPU,其實還可以使用大於分割槽數的執行緒,來提升消費能力,這就需要我們對KafkaConsumer Client例項進行改造,實現消費策略預計算,利用額外的CPU開啟更多的執行緒,來實現消費任務分片。具體實現,留到下一篇部落格,給大家分享《基於Kafka的分散式查詢SQL引擎》。

4.結束語

這篇部落格就和大家分享到這裡,如果大家在研究學習的過程當中有什麼問題,可以加群進行討論或傳送郵件給我,我會盡我所能為您解答,與君共勉!

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