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。
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 mode
this.offsetCommitMode = OffsetCommitModes.fromConfiguration(
getIsAutoCommitEnabled(),
enableCommitOnCheckpoints,
((StreamingRuntimeContext) getRuntimeContext()).isCheckpointingEnabled());
// create the partition discoverer
this.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 pattern
if (topicsDescriptor.isFixedTopics()) {
// 对于没有使用通配符的topic,直接获取topic的所有partition
newDiscoveredPartitions = getAllPartitionsForTopics(topicsDescriptor.getFixedTopics());
} else {
// 对于使用了通配符的topic, 先找到所有topic,再一一match
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();
// 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。