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https://issues.apache.org/jira/browse/FLINK-24611?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17438020#comment-17438020
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Roman Khachatryan commented on FLINK-24611:
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{quote}I think there might be left over artefacts currently, but wouldn't this
be significantly more likely with keeping pending/non confirmed files (as
required per changelog)?{quote}
The solution without dealing with left-overs cernainly adds a new case; but I
don't think this case is very likely to happen as both checkpoint abortion and
JM failover in some time window are required.
I'm also not sure how to compare this probability to the existing cases, e.g.
network failure during upload from TM; TM crash during checkpoint; JM failover
with pending checkpoint and non-incremental backend.
>From the offline discussion about dealing with PlaceholderStateHandles:
{quote}
In terms of sending the same data multiple times:
(3) avoids it completely
(1) does this on failover
(2) does this potentially on every checkpoint, if confirmation is slow (and
concurrent checkpoints > 1)
Yes, it's only relevant for "inlined" state (< threshold).
Additional drawback of (1) is code complexity: we'll have to propagate
reconnection event from TaskExecutorall the way to state backends.
Additional advantage of (3) is the ability to discard (more) state after
failover.
{quote}
> 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
> Priority: Major
> Fix For: 1.15.0
>
>
> 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).
> Only JM code is changed.
>
> 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,
> List<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)
> Given the following constraints:
> - 10M state entries at most
> - 10 (retained) checkpoint at most
> - 10 checkpoints per second at most
> - state entry key takes 32B (usually UUID or two UUIDs)
> The extra space for (2) would be in order of 10M*32B=38Mb.
> The extra time for (1) would be in order of 10M * 10 checkpoints per second
> * ratio of outdated entries per checkpoint. Depending on the ratio and the
> hardware, this could take up to hundreds of ms per second, blocking the main
> thread.
> So approach (2) seems reasonable.
>
> 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. PlaceholderStreamStateHandles
> 3. Cross-task state sharing - not an issue as long as everything is managed
> by JM
> 4. Dependencies between SharedStateRegistry and CompletedCheckpointStore -
> simple after FLINK-24086
> 5. 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
>
> For the end users, this change might render as a delay in discarding state of
> aborted checkpoints, which seems acceptable.
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