Kafka源码研究--Comsumer获取partition下标

背景

由于项目上Flink在设置parallel多于1的情况下，job没法正确地获取watermark，所以周末来研究一下一部分，大概已经锁定了原因：
虽然我们的topic只设置了1的partition，但是Kafka的Comsumer还是起了好几个subtask去读索引是2、3的partition，然后这几个subtask的watermark一直不更新，导致我们job整体的watermark一直是Long.MIN_VALUE。现在需要去了解一下subtask获取partition的流程，等上班的时候debug一遍应该就可以知道原因。

翻源码的过程

通过log找到分配partition的大概位置

从图中可以看到，在org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase这个类中可以找到一些关键信息。

跟踪源码

往上翻翻，看有没有有用信息

关键源码，附上注释

 public void open(Configuration configuration) throws Exception {// determine the offset commit modethis.offsetCommitMode = OffsetCommitModes.fromConfiguration(getIsAutoCommitEnabled(),enableCommitOnCheckpoints,((StreamingRuntimeContext) getRuntimeContext()).isCheckpointingEnabled());// create the partition discovererthis.partitionDiscoverer = createPartitionDiscoverer(topicsDescriptor,getRuntimeContext().getIndexOfThisSubtask(),getRuntimeContext().getNumberOfParallelSubtasks());this.partitionDiscoverer.open();subscribedPartitionsToStartOffsets = new HashMap<>();// 重点函数，这个函数或获取到subtask的所有partition。final List<KafkaTopicPartition> allPartitions = partitionDiscoverer.discoverPartitions();if (restoredState != null) {...} else {// use the partition discoverer to fetch the initial seed partitions,// and set their initial offsets depending on the startup mode.// for SPECIFIC_OFFSETS and TIMESTAMP modes, we set the specific offsets now;// for other modes (EARLIEST, LATEST, and GROUP_OFFSETS), the offset is lazily determined// when the partition is actually read.switch (startupMode) {...default:for (KafkaTopicPartition seedPartition : allPartitions) {subscribedPartitionsToStartOffsets.put(seedPartition, startupMode.getStateSentinel());}}if (!subscribedPartitionsToStartOffsets.isEmpty()) {switch (startupMode) {...case GROUP_OFFSETS:LOG.info("Consumer subtask {} will start reading the following {} partitions from the committed group offsets in Kafka: {}",getRuntimeContext().getIndexOfThisSubtask(),subscribedPartitionsToStartOffsets.size(),subscribedPartitionsToStartOffsets.keySet());}} else {LOG.info("Consumer subtask {} initially has no partitions to read from.",getRuntimeContext().getIndexOfThisSubtask());}}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 patternif (topicsDescriptor.isFixedTopics()) {// 对于没有使用通配符的topic，直接获取topic的所有partitionnewDiscoveredPartitions = getAllPartitionsForTopics(topicsDescriptor.getFixedTopics());} else {// 对于使用了通配符的topic, 先找到所有topic，再一一matchList<String> matchedTopics = getAllTopics();// retain topics that match the patternIterator<String> iter = matchedTopics.iterator();while (iter.hasNext()) {if (!topicsDescriptor.isMatchingTopic(iter.next())) {iter.remove();}}if (matchedTopics.size() != 0) {// get partitions only for matched topicsnewDiscoveredPartitions = getAllPartitionsForTopics(matchedTopics);} else {newDiscoveredPartitions = null;}}// (2) eliminate partition that are old partitions or should not be subscribed by this subtaskif (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();// 只保留符合要求的partition，这就是我们要找的函数if (!setAndCheckDiscoveredPartition(nextPartition)) {iter.remove();}}}return newDiscoveredPartitions;}...}...}public boolean setAndCheckDiscoveredPartition(KafkaTopicPartition partition) {if (isUndiscoveredPartition(partition)) {discoveredPartitions.add(partition);// 在这return KafkaTopicPartitionAssigner.assign(partition, numParallelSubtasks) == indexOfThisSubtask;}return false;}public static int assign(KafkaTopicPartition partition, int numParallelSubtasks) {// 先算出此topic的hash(partition.getTopic().hashCode() * 31)，这里不知道为什么不直接用hash，还要再*31，然后取正数(& 0x7FFFFFFF)，最后获取到此topic的起始位置。int startIndex = ((partition.getTopic().hashCode() * 31) & 0x7FFFFFFF) % numParallelSubtasks;// here, the assumption is that the id of Kafka partitions are always ascending// starting from 0, and therefore can be used directly as the offset clockwise from the start index// 计算当前的partition应该属于哪个subtask。例如：一共有20个subtask，算出来的起始位置是5，partition是5，那么最后就是// (5 + 5) % 20 = 10， 这个partition应该分给10号subtask。return (startIndex + partition.getPartition()) % numParallelSubtasks;}

思考

某topic的每个partition会分给哪个subtask其实是确定的

topic名字是确定的 -> topic的hashCode是确定的 && subtask的数量是确定的 -> startIndex是确定的 -> 某partition会分给哪个subtask其实是确定的

为什么要算startIndex

大概是为了平均分配不同的topic，如果topic很多，每个topic都只从0开始，那么subtask 0，1，2之类的靠前subtask就需要读大量的partition。