Kafka之消费与心跳

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。

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; 
} 

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：

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。

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。