Kafka是通过心跳机制来控制消费超时，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。心跳超时会导致消息重复消费。
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
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
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 ConsumerRecords
poll(final long timeoutMs, final boolean includeMetadataInTimeout) { acquireAndEnsureOpen(); try {
if (timeoutMs "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 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、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() {
@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;
}
2.1、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、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。
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