[jira] [Updated] (FLINK-24611) Prevent JM from discarding state on checkpoint abortion

["Roman Khachatryan (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/FLINK-24611?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Roman Khachatryan updated FLINK-24611:
--------------------------------------
    Release Note: 
Shared state tracking changed to use checkpoint ID instead of reference counts. Shared state is not cleaned up on abortion anymore (but rather on subsumption or job termination).

This might result in delays in discarding state of aborted checkpoints.

  was:
Shared state tracking changed to use checkpoint ID instead of reference counts. Shared state is not cleaned up on abortion anymore (but rather on subsumption or job termination).

This might result in:
- Discarding state of aborted checkpoints might be delayed
- Undeleted state might see a slight increase in case of failures
- Checkpointing time should reduce and less IO be used in cases when notifications are delayed, checkpoints are aborted, or new JM is elected.


> Prevent JM from discarding state on checkpoint abortion
> -------------------------------------------------------
>
>                 Key: FLINK-24611
>                 URL: https://issues.apache.org/jira/browse/FLINK-24611
>             Project: Flink
>          Issue Type: Sub-task
>          Components: Runtime / Checkpointing
>    Affects Versions: 1.15.0
>            Reporter: Roman Khachatryan
>            Assignee: Roman Khachatryan
>            Priority: Major
>              Labels: pull-request-available
>             Fix For: 1.15.0
>
>
> ================================================================================
> MOTIVATION
> When a checkpoint is aborted, JM discards any state that was sent to it and wasn't used in other checkpoints. This forces incremental state backends to wait for confirmation from JM before re-using this state. For changelog backend this is even more critical.
> One approach proposed was to make backends/TMs responsible for their state, until it's not shared with other TMs, i.e. until rescaling (private/shared state ownership track: FLINK-23342 and more).
> However, that approach is quite invasive.
> An alternative solution would be:
> 1. SharedStateRegistry remembers the latest checkpoint for each shared state (instead of usage count currently)
> 2. CompletedCheckpointStore notifies it about the lowest valid checkpoint (on subsumption)
> 3. SharedStateRegistry then discards any state associated with the lower (subsumed/aborted) checkpoints
> So the aborted checkpoint can only be discarded after some subsequent successful checkpoint (which can mark state as used).
> Mostly JM code is changed. IncrementalRemoteKeyedStateHandle.discard needs to be adjusted.
> Backends don't re-upload state.
> ================================================================================
> IMPLEMENTATION CONSIDERATIONS
> On subsumption, JM needs to find all the unused state and discard it.
> This can either be done by
> 1) simply traversing all entries; or by 
> 2) maintaining a set of entries per checkpoint (e.g. SortedMap<Long, Set<K>>). This allows to skip unnecessary traversal at the cost of higher memory usage
> In both cases:
> - each entry stores last checkpoint ID it was used in (long)
> - key is hashed (even with plain traversal, map.entrySet.iterator.remove() computes hash internally)
> Because of the need to maintain secondary sets, (2) isn't asymptotically better than (1), and is likely worse in practice and requires more memory (see discussion in the comments). So approach (1) seems reasonable.
> ================================================================================
> CORNER CASES
> The following cases shouldn't pose any difficulties:
> 1. Recovery, re-scaling, and state used by not all or by no tasks - we still register all states on recovery even after FLINK-22483/FLINK-24086
> 2. Cross-task state sharing - not an issue as long as everything is managed by JM
> 3. Dependencies between SharedStateRegistry and CompletedCheckpointStore - simple after FLINK-24086
> 4. Multiple concurrent checkpoints (below)
> Consider the following case:
> (nr. concurrent checkpoints > 1)
> 1. checkpoint 1 starts, TM reports that it uses file f1; checkpoint 1 gets aborted - f1 is now tracked
> 2. savepoint 2 starts, it *will* use f1
> 3. checkpoint 3 starts and finishes; it does NOT use file f1
> When a checkpoint finishes, all pending checkpoints before it are aborted - but not savepoints.
> Savepoints currently are NOT incremental. And in the future, incremental savepoints shouldn't share any artifacts with checkpionts.
> The following should be kept in mind:
> 1. On job cancellation, state of aborted checkpoints should be cleaned up explicitly
> 2. Savepoints should be ignored and not change CheckpointStore.lowestCheckpointID
> 3. In case of JM failover, there might be more state left undeleted (see follow-up FLINK-24852 and/or comments)
> 4. To handle JM leadership change,  backends should NOT send PlaceholderStreamStateHandles unless the checkpoint was confirmed (see comments)
> 5. To handle TM failover after an incomplete checkpoint, JM should replace old state handle with a new one in case of collision (test that old state isn't included into a completed checkpoint) (see comments)
> ================================================================================
> USER IMPACT
> For the end users, this change might render as a delay in discarding state of aborted checkpoints and in slight increase of undeleted state in case of failures; which seems acceptable.
> After updating backends to not re-upload state, checkpointing time should be reduced and less IO will be used (in cases when notifications are delayed or new JM is elected).



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