[jira] [Resolved] (FLINK-26306) [Changelog] Thundering herd problem with materialization

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

     [ https://issues.apache.org/jira/browse/FLINK-26306?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Roman Khachatryan resolved FLINK-26306.
---------------------------------------
    Resolution: Fixed

I've extracted non-backend related issue into FLINK-26590.

Backend-related fix (randomized materialization) merged into master as ed5e6144441bfbc020f525f9c10fd29cb3d83cbf.

> [Changelog] Thundering herd problem with materialization
> --------------------------------------------------------
>
>                 Key: FLINK-26306
>                 URL: https://issues.apache.org/jira/browse/FLINK-26306
>             Project: Flink
>          Issue Type: Bug
>          Components: Runtime / State Backends
>    Affects Versions: 1.15.0
>            Reporter: Roman Khachatryan
>            Assignee: Roman Khachatryan
>            Priority: Major
>              Labels: pull-request-available
>             Fix For: 1.15.0
>
>
> Quick note: CheckpointCleaner is not involved here.
> When a checkpoint is subsumed, SharedStateRegistry schedules its unused shared state for async deletion. It uses common IO pool for this and adds a Runnable per state handle. ( see SharedStateRegistryImpl.scheduleAsyncDelete)
> When a checkpoint is started, CheckpointCoordinator uses the same thread pool to initialize the location for it. (see CheckpointCoordinator.initializeCheckpoint)
> The thread pool is of fixed size [jobmanager.io-pool.size|https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/config/#jobmanager-io-pool-size]; by default it's the number of CPU cores) and uses FIFO queue for tasks.
> When there is a spike in state deletion, the next checkpoint is delayed waiting for an available IO thread.
> Back-pressure seems reasonable here (similar to CheckpointCleaner); however, this shared state deletion could be spread across multiple subsequent checkpoints, not neccesarily the next one.
> ---- 
> I believe the issue is an pre-existing one; but it particularly affects changelog state backend, because 1) such spikes are likely there; 2) workloads are latency sensitive.
> In the tests, checkpoint duration grows from seconds to minutes immediately after the materialization.



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