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SpringKafka自动提交源码学习-Spring生态(二)_spring kafka enable-auto-commit

作者：代码探险家 | 2024-07-28 19:42:48

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spring kafka enable-auto-commit

Spring Kafka 可以为我们提供非常简单易用的的上层 API 支持. 在一般处理无需幂等的数据场景下, 我们可以使用默认配置 enable-auto-commit 来使用消息队列. 这里有个疑问, auto-commit 究竟是怎么实现的, 具体在怎样的场景中适用 auto-commit 配置? 带着这些问题, 本节基于 spring-kafka2.7.0 跟踪源码, 观察自动提交的底层实现.

依赖

Spring Kafka 是对 kafka-client 的再封装, 这里列一下, 本文使用的 jar 包版本

  • spring-kafka2.7.0
  • kafka-client2.6.0

结论

为了方便大家往后理解源码实现, 先说结论.
spring-kafka 中并没有实现自动提交的相关功能, 它只是将 ‘enable-auto-commit=true’ 这个参数交给了 kafka-client. kafka-client 消费者每次成功从 Topic 拉取 (poll) 数据后, 都会递增更新消费者偏移量. kafka-client 中有个 SubscriptionState 的类, 专门存储当前消费者监听的 topics、partition、offset 信息.
消费者 poll 消息前, 会触发对 GroupCoordinator 心跳机制校验, 在缺省配置中, kafka-client 每 5 秒会从 SubscriptionState 中获取当前消费者的所有 topics、partition、offset 信息, 并主动发起异步更新的请求.

自动提交架构

源码跟踪

SpringKafka源码doPoll

KafkaMessageListenerContainer.doPoll

前置章节介绍了 spring-kafka 的结构, 我们知道 KafkaMessageListenerContainer 与 kafka-client 实例是一一对应的关系. 并且 KafkaMessageListenerContainer.doPoll() 这个方法是 consumer 获取消息 (Message) 的入口.
在 doPoll() 中, 是调用 this.consumer.poll(this.pollTimeout) 主动拉取数据

@Nullable
private ConsumerRecords<K, V> doPoll() {
	ConsumerRecords<K, V> records;
	if (this.isBatchListener && this.subBatchPerPartition) {
		if (this.batchIterator == null) {
			this.lastBatch = this.consumer.poll(this.pollTimeout);
			if (this.lastBatch.count() == 0) {
				return this.lastBatch;
			}
			else {
				this.batchIterator = this.lastBatch.partitions().iterator();
			}
		}
		TopicPartition next = this.batchIterator.next();
		List<ConsumerRecord<K, V>> subBatch = this.lastBatch.records(next);
		records = new ConsumerRecords<>(Collections.singletonMap(next, subBatch));
		if (!this.batchIterator.hasNext()) {
			this.batchIterator = null;
		}
	}
	else {
		records = this.consumer.poll(this.pollTimeout);
		checkRebalanceCommits();
	}
	return records;
}

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前置章节内容请优先阅读
SpringKafka原理解析及源码学习-Spring生态(一): http://blog.diswares.cn/kafka-spring-kafka-structure/

KafkaConsumer.poll

  • 这个方法中可以看到一个 do while 的结构, while 中的判断我们可以知道, 当一次 poll 超过一定时间还是没有拉取到数据, 会认为本次拉取失败从而不继续 poll
  • do while 里的代码块, 有两个比较关键的方法: updateAssignmentMetadataIfNeeded(timer, false) 和 pollForFetches(timer)
  • updateAssignmentMetadataIfNeeded(timer, false): 此方法为自动提交偏移量的入口, 也是更新 fetcher 信息的入口
  • pollForFetches(timer): 拉取新数据
@Override
public ConsumerRecords<K, V> poll(final Duration timeout) {
    return poll(time.timer(timeout), true);
}

private ConsumerRecords<K, V> poll(final Timer timer, final boolean includeMetadataInTimeout) {
    acquireAndEnsureOpen();
    try {
        // 记录一下 本次 poll 的时间信息
        this.kafkaConsumerMetrics.recordPollStart(timer.currentTimeMs());

        if (this.subscriptions.hasNoSubscriptionOrUserAssignment()) {
            throw new IllegalStateException("Consumer is not subscribed to any topics or assigned any partitions");
        }

        // do while 直至超时
        do {
            client.maybeTriggerWakeup();

            if (includeMetadataInTimeout) {
                // 更新 Fetcher 的元信息并自动提交 offset
                updateAssignmentMetadataIfNeeded(timer, false);
            } else {
                while (!updateAssignmentMetadataIfNeeded(time.timer(Long.MAX_VALUE), true)) {
                    log.warn("Still waiting for metadata");
                }
            }

            // nio 拉取新消息
            final Map<TopicPartition, List<ConsumerRecord<K, V>>> records = pollForFetches(timer);
            if (!records.isEmpty()) {
                if (fetcher.sendFetches() > 0 || client.hasPendingRequests()) {
                    client.transmitSends();
                }

                return this.interceptors.onConsume(new ConsumerRecords<>(records));
            }
            // 超时校验
        } while (timer.notExpired());

        return ConsumerRecords.empty();
    } finally {
        release();
        this.kafkaConsumerMetrics.recordPollEnd(timer.currentTimeMs());
    }
}

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拉取消息源码阅读

本节先讲解 KafkaConsumer.poll 中 pollForFetches 分支的代码. 因为顺序思考 Kafka Consumer 拉取消息并提交偏移量的过程, 应该是先拉取消息, 后提交偏移量

KafkaConsumer.pollForFetches

private Map<TopicPartition, List<ConsumerRecord<K, V>>> pollForFetches(Timer timer) {
    long pollTimeout = coordinator == null ? timer.remainingMs() :
            Math.min(coordinator.timeToNextPoll(timer.currentTimeMs()), timer.remainingMs());

    // 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;
    }

    Timer pollTimer = time.timer(pollTimeout);
    client.poll(pollTimer, () -> {
        // 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.hasAvailableFetches();
    });
    timer.update(pollTimer.currentTimeMs());

    return fetcher.fetchedRecords();
}

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Fetcher.fetchRecords

继续跟踪上面代码块中的方法 fetchRecords 该方法是从 kafka-server 拉取数据和更新偏移量的入口

public Map<TopicPartition, List<ConsumerRecord<K, V>>> fetchedRecords() {
    // fetched 应该是设计者认为 poll 下来的消息有很大可能是属于同一个 topic 的, 所以缓存了一个 map 方便查询
    Map<TopicPartition, List<ConsumerRecord<K, V>>> fetched = new HashMap<>();
    Queue<CompletedFetch> pausedCompletedFetches = new ArrayDeque<>();
    int recordsRemaining = maxPollRecords;

    try {
        // 这里会记录一个最大拉取条数
        while (recordsRemaining > 0) {
            if (nextInLineFetch == null || nextInLineFetch.isConsumed) {
                // 获取同步队列中的数据
                CompletedFetch records = completedFetches.peek();
                if (records == null) break; // 同步队列数据消费光了

                if (records.notInitialized()) {
                    try {
                        nextInLineFetch = initializeCompletedFetch(records);
                    } catch (Exception e) {
                        FetchResponse.PartitionData partition = records.partitionData;
                        if (fetched.isEmpty() && (partition.records == null || partition.records.sizeInBytes() == 0)) {
                            completedFetches.poll();
                        }
                        throw e;
                    }
                } else {
                    nextInLineFetch = records;
                }
                completedFetches.poll();
            } else if (subscriptions.isPaused(nextInLineFetch.partition)) {
                // ... 暂停消费时, 此处逻辑暂时不用深入
                log.debug("Skipping fetching records for assigned partition {} because it is paused", nextInLineFetch.partition);
                pausedCompletedFetches.add(nextInLineFetch);
                nextInLineFetch = null;
            } else {
                // fetchRecords() 会去拉取记录, 并修改本地缓存的偏移量
                // 下文会详细解释
                List<ConsumerRecord<K, V>> records = fetchRecords(nextInLineFetch, recordsRemaining);

                if (!records.isEmpty()) {
                    // 成功拉取到过数据, 将其记录下来
                    TopicPartition partition = nextInLineFetch.partition;
                    List<ConsumerRecord<K, V>> currentRecords = fetched.get(partition);
        
                    // 存储本次拉取到的消息的结果集
                    if (currentRecords == null) {
                        fetched.put(partition, records);
                    } else {
                        List<ConsumerRecord<K, V>> newRecords = new ArrayList<>(records.size() + currentRecords.size());
                        newRecords.addAll(currentRecords);
                        newRecords.addAll(records);
                        fetched.put(partition, newRecords);
                    }
                    recordsRemaining -= records.size();
                }
            }
        }
    } catch (KafkaException e) {
        if (fetched.isEmpty())
            throw e;
    } finally {
        completedFetches.addAll(pausedCompletedFetches);
    }

    return fetched;
}

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private List<ConsumerRecord<K, V>> fetchRecords(CompletedFetch completedFetch, int maxRecords) {
    if (!subscriptions.isAssigned(completedFetch.partition)) {
        log.debug("Not returning fetched records for partition {} since it is no longer assigned",
                completedFetch.partition);
    } else if (!subscriptions.isFetchable(completedFetch.partition)) {
        log.debug("Not returning fetched records for assigned partition {} since it is no longer fetchable",
                completedFetch.partition);
    } else {
        // 更新 subscriptions 缓存的偏移量
        FetchPosition position = subscriptions.position(completedFetch.partition);
        if (position == null) {
            throw new IllegalStateException("Missing position for fetchable partition " + completedFetch.partition);
        }
        
        if (completedFetch.nextFetchOffset == position.offset) {
            // 拉取数据, 并更新 nextInLineFetch 中缓存的的偏移量
            List<ConsumerRecord<K, V>> partRecords = completedFetch.fetchRecords(maxRecords);

            log.trace("Returning {} fetched records at offset {} for assigned partition {}",
                    partRecords.size(), position, completedFetch.partition);

            // 如果取到数据, 则更新偏移量 offset
            if (completedFetch.nextFetchOffset > position.offset) {
                FetchPosition nextPosition = new FetchPosition(
                        completedFetch.nextFetchOffset,
                        completedFetch.lastEpoch,
                        position.currentLeader);
                log.trace("Update fetching position to {} for partition {}", nextPosition, completedFetch.partition);
                // 将 offset 的最大值同步给 SubscriptionState
                subscriptions.position(completedFetch.partition, nextPosition);
            }
            
            // 更新各类缓存
            Long partitionLag = subscriptions.partitionLag(completedFetch.partition, isolationLevel);
            if (partitionLag != null)
                this.sensors.recordPartitionLag(completedFetch.partition, partitionLag);
            Long lead = subscriptions.partitionLead(completedFetch.partition);
            if (lead != null) {
                this.sensors.recordPartitionLead(completedFetch.partition, lead);
            }

            // 返回将要被消费的 Messages
            return partRecords;
        } else {
            log.debug("Ignoring fetched records for {} at offset {} since the current position is {}",
                    completedFetch.partition, completedFetch.nextFetchOffset, position);
        }
    }

    log.trace("Draining fetched records for partition {}", completedFetch.partition);
    completedFetch.drain();

    return emptyList();
}

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Fetcher.fetchRecords

private List<ConsumerRecord<K, V>> fetchRecords(int maxRecords) {
    // Error when fetching the next record before deserialization.
    if (corruptLastRecord)
        throw new KafkaException("Received exception when fetching the next record from " + partition
                                     + ". If needed, please seek past the record to "
                                     + "continue consumption.", cachedRecordException);

    if (isConsumed)
        return Collections.emptyList();

    List<ConsumerRecord<K, V>> records = new ArrayList<>();
    try {
        // 遍历请求的最大次数
        for (int i = 0; i < maxRecords; i++) {
            if (cachedRecordException == null) {
                corruptLastRecord = true;
                // 去 server 端 poll 消息
                lastRecord = nextFetchedRecord();
                corruptLastRecord = false;
            }
            if (lastRecord == null)
                break;
            records.add(parseRecord(partition, currentBatch, lastRecord));
            recordsRead++;
            bytesRead += lastRecord.sizeInBytes();
            // 更新 Consumer 内存中缓存的偏移量
            nextFetchOffset = lastRecord.offset() + 1;
            cachedRecordException = null;
        }
    } catch (SerializationException se) {
        cachedRecordException = se;
        if (records.isEmpty())
            throw se;
    } catch (KafkaException e) {
        cachedRecordException = e;
        if (records.isEmpty())
            throw new KafkaException("Received exception when fetching the next record from " + partition
                                         + ". If needed, please seek past the record to "
                                         + "continue consumption.", e);
    }
    return records;
}

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小结

本节以 SpringKafka 调用的其实是 Kafka Client 的 poll API 去远程获取消息. 而且当 poll 成功后, Kafka Client 中消费者对应的 partition 的偏移量会直接更新

自动提交偏移量源码阅读

从上文 KafkaConsumer.poll 这节我们知道, 本段应从 updateAssignmentMetadataIfNeeded 方法入手, 来分析自动提交偏移量的源码

KafkaConsumer.updateAssignmentMetadataIfNeeded

这个方法是自动提交偏移量的入口类. 其中 coordinator.poll(timer, waitForJoinGroup) 方法才是下级方法的入口. 比较隐蔽

boolean updateAssignmentMetadataIfNeeded(final Timer timer, final boolean waitForJoinGroup) {
    // 自动提交的入口
    if (coordinator != null && !coordinator.poll(timer, waitForJoinGroup)) {
        return false;
    }

    return updateFetchPositions(timer);
}

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ConsumerCoordinator.poll

ConsumerCoordinator 这个类主要是控制 Consumer 的业务流程
这个方法中没有特别需要注意的地方, 只需要知道它是调用 maybeAutoCommitOffsetsAsync 的入口

public boolean poll(Timer timer, boolean waitForJoinGroup) {
    ....dosomething
    // ------------ 我忽略了了大段代码 -----------
    maybeAutoCommitOffsetsAsync(timer.currentTimeMs());
    return true;
}

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ConsumerCoordinator.maybeAutoCommitOffsetsAsync

好的终于进入正题, 这个方法可以说是自动提交的上层入口了吧. 主要方法就是调用了 doAutoCommitOffsetsAsync 方法.
如果提交一条消息, 就需要建立一条链接, 则对 kafka-server 的开销太大. 这里用心跳机制, 维护了长链接. 我们的自动提交偏移量, 也正是心跳机制的部分实现.
这里设计了一个 Timer 的工具, 每次自动提交时, 都会更新 Timer 记录的当前时间, 根据这个 currentTime, Timer 就可以处理一系列时间是否超时问题, 还是比较巧妙的.

public void maybeAutoCommitOffsetsAsync(long now) {
    // 判断用户是否开启了自动提交. 缺省为开启
    if (autoCommitEnabled) {
        // 记录当前时间
        nextAutoCommitTimer.update(now);
        // 是否到了自动提交的时间
        if (nextAutoCommitTimer.isExpired()) {
            // 重置过期时间
            nextAutoCommitTimer.reset(autoCommitIntervalMs);
            // 异步自动提交偏移量
            doAutoCommitOffsetsAsync();
        }
    }
}

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ConsumerCoordinator.doAutoCommitOffsetsAsync

上文讲过, OffsetAndMetadata 偏移量和元数据信息, KafkaClient 是交给 SubscriptionState 这个类去维护的, 这里通过调用 subscriptions.allConsumed() 即可获得当前 Consumer 中所有 TopicPartition 的偏移量信息.

private void doAutoCommitOffsetsAsync() {
    // 获取所有偏移量和元数据信息
    Map<TopicPartition, OffsetAndMetadata> allConsumedOffsets = subscriptions.allConsumed();
    log.debug("Sending asynchronous auto-commit of offsets {}", allConsumedOffsets);

    // commitOffsetsAsync 异步提交偏移量.
    commitOffsetsAsync(allConsumedOffsets, (offsets, exception) -> {
        // 下面的逻辑是提交成功后的回调, 不用太关注
        if (exception != null) {
            if (exception instanceof RetriableCommitFailedException) {
                log.debug("Asynchronous auto-commit of offsets {} failed due to retriable error: {}", offsets,
                    exception);
                nextAutoCommitTimer.updateAndReset(rebalanceConfig.retryBackoffMs);
            } else {
                log.warn("Asynchronous auto-commit of offsets {} failed: {}", offsets, exception.getMessage());
            }
        } else {
            log.debug("Completed asynchronous auto-commit of offsets {}", offsets);
        }
    });
}

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ConsumerCoordinator.commitOffsetsAsync

这个方法异步调用了 doCommitOffsetsAsync.
值得注意的是 lookupCoordinator() 方法. 在 kafka 0.10 版本之后, offset 偏移量不再提交至 zookeeper, 新版本记录偏移量的是 KafkaServer 中的 GroupCoordinator. 故 lookupCoordinator() 实则为申请一个 GroupCoordinator 的连接

public void commitOffsetsAsync(final Map<TopicPartition, OffsetAndMetadata> offsets, final OffsetCommitCallback callback) {
    invokeCompletedOffsetCommitCallbacks();

    if (!coordinatorUnknown()) {
        doCommitOffsetsAsync(offsets, callback);
    } else {
        // 这里由于一个 ConsumerCoordinator 可能会被多处调用
        // pendingAsyncCommits 这个对象是用来记录并发度的
        pendingAsyncCommits.incrementAndGet();
        // lookupCoordinator() 实则为申请一个 coordinator 的连接
        lookupCoordinator().addListener(new RequestFutureListener<Void>() {
            @Override
            public void onSuccess(Void value) {
                // 更新并发度
                pendingAsyncCommits.decrementAndGet();
                // 关键, 异步提交 offset
                doCommitOffsetsAsync(offsets, callback);
                client.pollNoWakeup();
            }

            @Override
            public void onFailure(RuntimeException e) {
                pendingAsyncCommits.decrementAndGet();
                completedOffsetCommits.add(new OffsetCommitCompletion(callback, offsets,
                        new RetriableCommitFailedException(e)));
            }
        });
    }

    // ensure the commit has a chance to be transmitted (without blocking on its completion).
    // Note that commits are treated as heartbeats by the coordinator, so there is no need to
    // explicitly allow heartbeats through delayed task execution.
    client.pollNoWakeup();
}

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ConsumerCoordinator.doCommitOffsetsAsync

关键为 sendOffsetCommitRequest(offsets) 这个方法提交了偏移量, 由于是异步的, 所以返回的还是 RequestFuture 这个数据结构.

private void doCommitOffsetsAsync(final Map<TopicPartition, OffsetAndMetadata> offsets, final OffsetCommitCallback callback) {
    // 异步提交
    RequestFuture<Void> future = sendOffsetCommitRequest(offsets);
    final OffsetCommitCallback cb = callback == null ? defaultOffsetCommitCallback : callback;
    // 提交后的回调
    future.addListener(new RequestFutureListener<Void>() {
        @Override
        public void onSuccess(Void value) {
            if (interceptors != null)
                interceptors.onCommit(offsets);
            completedOffsetCommits.add(new OffsetCommitCompletion(cb, offsets, null));
        }

        @Override
        public void onFailure(RuntimeException e) {
            Exception commitException = e;

            if (e instanceof RetriableException) {
                commitException = new RetriableCommitFailedException(e);
            }
            completedOffsetCommits.add(new OffsetCommitCompletion(cb, offsets, commitException));
            if (commitException instanceof FencedInstanceIdException) {
                asyncCommitFenced.set(true);
            }
        }
    });
}

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RequestFuture<Void> sendOffsetCommitRequest(final Map<TopicPartition, OffsetAndMetadata> offsets) {
    if (offsets.isEmpty())
        return RequestFuture.voidSuccess();

    // 获取 Coordinator
    Node coordinator = checkAndGetCoordinator();
    if (coordinator == null)
        return RequestFuture.coordinatorNotAvailable();

    // 封装要发送的 Topic 和偏移量信息
    Map<String, OffsetCommitRequestData.OffsetCommitRequestTopic> requestTopicDataMap = new HashMap<>();
    for (Map.Entry<TopicPartition, OffsetAndMetadata> entry : offsets.entrySet()) {
        // ======== 这里是分割线 =========
        // 隐藏了一大段获取 Topic 信息的代码
        // ======== 这里是分割线 =========
        requestTopicDataMap.put(topicPartition.topic(), topic);
    }

    // 一个提交 offset 偏移量的关键参数
    // 用于识别提交者信息
    final Generation generation;
    // ======== 这里是分割线 =========
    // 隐藏了一大段生成 generation 的代码
    // ======== 这里是分割线 =========
        
    // 封装自动提交请求中的 offset 信息
    OffsetCommitRequest.Builder builder = new OffsetCommitRequest.Builder(
            new OffsetCommitRequestData()
                    .setGroupId(this.rebalanceConfig.groupId)
                    .setGenerationId(generation.generationId)
                    .setMemberId(generation.memberId)
                    .setGroupInstanceId(rebalanceConfig.groupInstanceId.orElse(null))
                    .setTopics(new ArrayList<>(requestTopicDataMap.values()))
    );

    log.trace("Sending OffsetCommit request with {} to coordinator {}", offsets, coordinator);

    // 异步提交
    return client.send(coordinator, builder)
            .compose(new OffsetCommitResponseHandler(offsets, generation));
}

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client.send(coordinator, builder) 即是真正提交偏移量的地方. client 的实现是 ConsumerNetworkClient 这个类如何实现的 nio 有兴趣的同学可以去研究一下.

小结

KafkaClient 的自动提交实现是随着心跳机制异步提交的.

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