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https://issues.apache.org/jira/browse/FLINK-19382?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Flink Jira Bot updated FLINK-19382:
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Labels: auto-deprioritized-major stale-minor (was:
auto-deprioritized-major)
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> Introducing ReplayableSourceStateBackend
> ----------------------------------------
>
> Key: FLINK-19382
> URL: https://issues.apache.org/jira/browse/FLINK-19382
> Project: Flink
> Issue Type: Improvement
> Components: Runtime / State Backends
> Reporter: Theo Diefenthal
> Priority: Minor
> Labels: auto-deprioritized-major, stale-minor
>
> I got the idea of a new StateBackend simply called "ReplayableSource". This
> statebackend would be bound by a number of limitations, but in the few areas,
> it could improve the pipeline performance by magnitudes which makes me think
> it's worth debating about it.
> I'd like to start with describing two useful scenarios for such a state
> backend before debating more about the backend. Both scenarios share that
> they read data from kafka and process that via Flink.
> Scenario 1: Buffering data. Currently I'm developing a pipeline where
> directly post to reading the data, I need to buffer it for 1 minute in event
> time. This is due to inter-event-dependencies: If within one minute a certain
> event arrives, I have to enrich information to the first event. Right now, I
> store the 1 minute event time data in Flink State leading to checkpointing
> the entire buffer on each checkpoint and making it impossible for me to use
> exactly-once processing (We want to have as low latency as possible, i.e.
> after buffering at maximum a few seconds while simoulatenously having high
> volume of data).
> Scenario 2: Performing Flink SQL CEP Queries. CEP Queries naturally have
> inter-event-dependencies. Having simple MATCH_RECOGNIZE queries directly post
> to a kafka source often lead to requiring RocksDB state backend and slow
> performance.
>
> The idea: Instead of storing the entire state, we could simply store a kafka
> offset. When restoring the state from savepoint, all the state could be
> restored by reading from kafka. The checkpoint size would thus be reduced
> from huge sizes down to just a few numbers which allows frequent and fast
> checkpointing.
>
> Limitations:
> * This would only work for fully deterministic/replayable streaming jobs. If
> a certain operator within the pipeline is not determinstic, a replay could
> cause another result.
> * The source must be replayable, e.g. kafka
> * This would also only work for "short-state-living" pipelines. There are
> many pipelines which build up their state over days, month or even years.
> Restoring such a state by replaying all the data over that time would be
> almost impossible, especially as kafka usually has a retention of something
> like a week configured. However, there are also many queries with
> short-lived-state like the mentioned CEP usecase where one usually have
> patterns defined in timeframes of second, minutes, hours or a few days for
> the event correlation.
> * Not sure if there are more limitations with regards to
> windowing/watermarks or similar things which would make that feature
> impossible!?
> For certain scenarios, this feature would obviously be dumb, most likely for
> windows pipelines. It is certainly much cheapter to e.g. store a COUNT per
> window then replaying all events per window in order to restore that COUNT.
> But I'm focusing on something like CEP.
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