前言
Sunday night，继续超短文模式（希望下周就可以不这么划水了hhhh
Kafka是我们日常的流处理任务中最为常用的数据源之一。随着数据类型和数据量的增大，难免要增加新的Kafka topic，或者为已有的topic增加更多partition。那么，Kafka后面作为消费者的实时处理引擎是如何感知到topic和partition变化的呢？本文以Spark Streaming和Flink为例来简单探究一下。
Spark Streaming的场合
根据官方文档（如上图），spark-streaming-kafka-0-10才支持Kafka的动态感知（即Dynamic Topic Subscription），翻翻源码，来到o.a.s.streaming.kafka010.DirectKafkaInputDStream类，每个微批次都会调用的compute()方法的第一行。
val untilOffsets = clamp(latestOffsets())
顾名思义，clamp()方法用来限制数据的流量，这里不提。而latestOffsets()方法返回各个partition当前最近的offset值，其具体实现如下（包含它调用的paranoidPoll()方法）。
/**
* Returns the latest (highest) available offsets, taking new partitions into account.
*/
protected def latestOffsets(): Map[TopicPartition, Long] = {
val c = consumer
paranoidPoll(c)
val parts = c.assignment().asScala
// make sure new partitions are reflected in currentOffsets
val newPartitions = parts.diff(currentOffsets.keySet)
// Check if there's any partition been revoked because of consumer rebalance.
val revokedPartitions = currentOffsets.keySet.diff(parts)
if (revokedPartitions.nonEmpty) {
throw new IllegalStateException(s"Previously tracked partitions " +
s"${revokedPartitions.mkString("[", ",", "]")} been revoked by Kafka because of consumer " +
s"rebalance. This is mostly due to another stream with same group id joined, " +
s"please check if there're different streaming application misconfigure to use same " +
s"group id. Fundamentally different stream should use different group id")
}
// position for new partitions determined by auto.offset.reset if no commit
currentOffsets = currentOffsets ++ newPartitions.map(tp => tp -> c.position(tp)).toMap
// find latest available offsets
c.seekToEnd(currentOffsets.keySet.asJava)
parts.map(tp => tp -> c.position(tp)).toMap
}
/**
* The concern here is that poll might consume messages despite being paused,
* which would throw off consumer position. Fix position if this happens.
*/
private def paranoidPoll(c: Consumer[K, V]): Unit = {
// don't actually want to consume any messages, so pause all partitions
c.pause(c.assignment())
val msgs = c.poll(0)
if (!msgs.isEmpty) {
// position should be minimum offset per topicpartition
msgs.asScala.foldLeft(Map[TopicPartition, Long]()) { (acc, m) =>
val tp = new TopicPartition(m.topic, m.partition)
val off = acc.get(tp).map(o => Math.min(o, m.offset)).getOrElse(m.offset)
acc + (tp -> off)
}.foreach { case (tp, off) =>
logInfo(s"poll(0) returned messages, seeking $tp to $off to compensate")
c.seek(tp, off)
}
}
}
可见，在每次compute()方法执行时，都会通过paranoidPoll()方法来seek到每个TopicPartition对应的offset位置，并且通过latestOffsets()方法找出那些新加入的partition，并维护它们的offset，实现了动态感知。
由上也可以看出，Spark Streaming无法处理Kafka Consumer的Rebalance（之前讲过），所以一定要为不同的Streaming App设置不同的group.id。
Flink的场合
根据官方文档（如上图），Flink是支持Topic/Partition Discovery的，但是默认并未开启，需要手动配置flink.partition-discovery.interval-millis参数，即动态感知新topic/partition的间隔，单位毫秒。
Flink Kafka Source的基类时o.a.f.streaming.connectors.kafka.FlinkKafkaConsumerBase抽象类，在其run()方法中，会先创建获取数据的KafkaFetcher，再判断是否启用了动态感知。
this.kafkaFetcher = createFetcher(
sourceContext,
subscribedPartitionsToStartOffsets,
watermarkStrategy,
(StreamingRuntimeContext) getRuntimeContext(),
offsetCommitMode,
getRuntimeContext().getMetricGroup().addGroup(KAFKA_CONSUMER_METRICS_GROUP),
useMetrics);
if (!running) {
return;
}
// depending on whether we were restored with the current state version (1.3),
// remaining logic branches off into 2 paths:
// 1) New state - partition discovery loop executed as separate thread, with this
// thread running the main fetcher loop
// 2) Old state - partition discovery is disabled and only the main fetcher loop is executed
if (discoveryIntervalMillis == PARTITION_DISCOVERY_DISABLED) {
kafkaFetcher.runFetchLoop();
} else {
runWithPartitionDiscovery();
}
如果启用了，最终会调用createAndStartDiscoveryLoop()方法，启动一个单独的线程，负责以discoveryIntervalMillis为周期发现新的topic/partition，并传递给KafkaFetcher。
private void createAndStartDiscoveryLoop(AtomicReference<Exception> discoveryLoopErrorRef) {
discoveryLoopThread = new Thread(() -> {
try {
// --------------------- partition discovery loop ---------------------
// throughout the loop, we always eagerly check if we are still running before
// performing the next operation, so that we can escape the loop as soon as possible
while (running) {
if (LOG.isDebugEnabled()) {
LOG.debug("Consumer subtask {} is trying to discover new partitions ...", getRuntimeContext().getIndexOfThisSubtask());
}
final List<KafkaTopicPartition> discoveredPartitions;
try {
discoveredPartitions = partitionDiscoverer.discoverPartitions();
} catch (AbstractPartitionDiscoverer.WakeupException | AbstractPartitionDiscoverer.ClosedException e) {
// the partition discoverer may have been closed or woken up before or during the discovery;
// this would only happen if the consumer was canceled; simply escape the loop
break;
}
// no need to add the discovered partitions if we were closed during the meantime
if (running && !discoveredPartitions.isEmpty()) {
kafkaFetcher.addDiscoveredPartitions(discoveredPartitions);
}
// do not waste any time sleeping if we're not running anymore
if (running && discoveryIntervalMillis != 0) {
try {
Thread.sleep(discoveryIntervalMillis);
} catch (InterruptedException iex) {
// may be interrupted if the consumer was canceled midway; simply escape the loop
break;
}
}
}
} catch (Exception e) {
discoveryLoopErrorRef.set(e);
} finally {
// calling cancel will also let the fetcher loop escape
// (if not running, cancel() was already called)
if (running) {
cancel();
}
}
}, "Kafka Partition Discovery for " + getRuntimeContext().getTaskNameWithSubtasks());
discoveryLoopThread.start();
}
可见，Flink通过名为PartitionDiscoverer的组件来实现动态感知。上面的代码中调用了discoverPartitions()方法，其源码如下。
public List<KafkaTopicPartition> discoverPartitions() throws WakeupException, ClosedException {
if (!closed && !wakeup) {
try {
List<KafkaTopicPartition> newDiscoveredPartitions;
// (1) get all possible partitions, based on whether we are subscribed to fixed topics or a topic pattern
if (topicsDescriptor.isFixedTopics()) {
newDiscoveredPartitions = getAllPartitionsForTopics(topicsDescriptor.getFixedTopics());
} else {
List<String> matchedTopics = getAllTopics();
// retain topics that match the pattern
Iterator<String> iter = matchedTopics.iterator();
while (iter.hasNext()) {
if (!topicsDescriptor.isMatchingTopic(iter.next())) {
iter.remove();
}
}
if (matchedTopics.size() != 0) {
// get partitions only for matched topics
newDiscoveredPartitions = getAllPartitionsForTopics(matchedTopics);
} else {
newDiscoveredPartitions = null;
}
}
// (2) eliminate partition that are old partitions or should not be subscribed by this subtask
if (newDiscoveredPartitions == null || newDiscoveredPartitions.isEmpty()) {
throw new RuntimeException("Unable to retrieve any partitions with KafkaTopicsDescriptor: " + topicsDescriptor);
} else {
Iterator<KafkaTopicPartition> iter = newDiscoveredPartitions.iterator();
KafkaTopicPartition nextPartition;
while (iter.hasNext()) {
nextPartition = iter.next();
if (!setAndCheckDiscoveredPartition(nextPartition)) {
iter.remove();
}
}
}
return newDiscoveredPartitions;
} catch (WakeupException e) {
// the actual topic / partition metadata fetching methods
// may be woken up midway; reset the wakeup flag and rethrow
wakeup = false;
throw e;
}
} else if (!closed && wakeup) {
// may have been woken up before the method call
wakeup = false;
throw new WakeupException();
} else {
throw new ClosedException();
}
首先，会根据传入的是单个固定的topic还是由正则表达式指定的多个topics来分别处理，最终都调用getAllPartitionsForTopics()方法来获取这些topic的所有partition（这个方法由抽象类AbstractPartitionDiscoverer的各个子类实现，很简单）。然后会遍历这些partition，并调用setAndCheckDiscoveredPartition()方法来检查之前是否消费过它们，如果是，则移除之，保证方法返回的是新加入的partition。
The End
明天早起搬砖，民那晚安晚安。
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