[jira] [Commented] (FLINK-10966) Optimize the release blocking logic in BarrierBuffer

["vinoyang (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/FLINK-10966?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16744129#comment-16744129 ] 

vinoyang commented on FLINK-10966:
----------------------------------

[~StephanEwen] Does the actor-style stream task have any plans and schedules? Can I not rely on it to implement the solution 3 I mentioned earlier? I am refactoring the failure handling of the CheckpointCoordinator. Do you think that implementing this issue is a suitable point in time after the refactoring is completed? cc [~till.rohrmann]

> Optimize the release blocking logic in BarrierBuffer
> ----------------------------------------------------
>
>                 Key: FLINK-10966
>                 URL: https://issues.apache.org/jira/browse/FLINK-10966
>             Project: Flink
>          Issue Type: Improvement
>          Components: State Backends, Checkpointing
>            Reporter: vinoyang
>            Assignee: vinoyang
>            Priority: Major
>
> Issue:
> Currently, mixing CancelCheckpointMarker control events with data flow to drive task to release blocking logic in BarrierBuffer may result in blocking logic not being released in time, further leading to a large amount of data being spilled to disk.
> The source code for this problem is as follows:
> {code:java}
> BufferOrEvent bufferOrEvent = next.get();
> if (isBlocked(bufferOrEvent.getChannelIndex())) {          //issue line
>    // if the channel is blocked we, we just store the BufferOrEvent
>    bufferBlocker.add(bufferOrEvent);
>    checkSizeLimit();
> }
> else if (bufferOrEvent.isBuffer()) {
>    return bufferOrEvent;
> }
> else if (bufferOrEvent.getEvent().getClass() == CheckpointBarrier.class) {
>    if (!endOfStream) {
>       // process barriers only if there is a chance of the checkpoint completing
>       processBarrier((CheckpointBarrier) bufferOrEvent.getEvent(), bufferOrEvent.getChannelIndex());
>    }
> }
> else if (bufferOrEvent.getEvent().getClass() == CancelCheckpointMarker.class) {
>    processCancellationBarrier((CancelCheckpointMarker) bufferOrEvent.getEvent());
> }
> else {
>    if (bufferOrEvent.getEvent().getClass() == EndOfPartitionEvent.class) {
>       processEndOfPartition();
>    }
>    return bufferOrEvent;
> }
> {code}
> Scenarios:
> Considering a simple DAG:source->map (network shuffle), the degree of parallelism is 10. The checkpoint semantics is exactly once.
> The first checkpoint: barriers of 9 source subtask are received by all map subtask. One of the source subtasks is blocked, resulting in the failure to send barrier. Eventually, the checkpoint will fail due to timeout. At this point, 9 corresponding input channel are blocked because they have received barrier.
> Second checkpoint: At this point, the special source subtask is still blocked and cannot send any events to downstream, while the nine input channels are still blocked. From the current implementation, the data or events it receives will not be processed, but will be stored directly. Therefore, the barrier of the downstream task will not be released. The only hope is that the cached data reaches the maximum limit.
> I think the main problem here is that we should not store data which comes from blocked input channels directly. Especially when one input channel is blocked by upstream and nine input channels are marked as blocked, we may not always be able to release the blocking.
> A better mechanism might be that we send notifyCheckpointFailed callback via CheckpointCoordinator, allowing each task to unblock itself. This mechanism can make the release of the old checkpoint align independent of the trigger of the new checkpoint. If the interval of the checkpoints are very long but the timeout is very short, then the effect of the optimization will be more obvious.
> Ultimately, we want to reduce unnecessary blocking and data spill to disk.



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