[jira] [Commented] (FLINK-14197) Increasing trend for state size of keyed stream using ProcessWindowFunction with ProcessingTimeSessionWindows

["Ravi Itha (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/FLINK-14197?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17208458#comment-17208458 ] 

Ravi Itha commented on FLINK-14197:
-----------------------------------

I am impacted with this issue. The checkpoint size increases continuously to a point where application crashes. After a complete restart the Checkpoint size resets back to 0. I have been running 6 different application with steady data load and I can see the same trend in every single application. 

Flink Version = 1.8.2

Window used = SessionWindow

State backend = RocksDB 

> Increasing trend for state size of keyed stream using ProcessWindowFunction with ProcessingTimeSessionWindows
> -------------------------------------------------------------------------------------------------------------
>
>                 Key: FLINK-14197
>                 URL: https://issues.apache.org/jira/browse/FLINK-14197
>             Project: Flink
>          Issue Type: Bug
>          Components: Runtime / Checkpointing, Runtime / State Backends
>    Affects Versions: 1.9.0
>         Environment: Tested with:
>  * Local Flink Mini Cluster running from IDE
>  * Flink standalone cluster run in docker
>            Reporter: Oliver Kostera
>            Priority: Major
>
> I'm using *ProcessWindowFunction* in a keyed stream with the following definition:
> {code:java}
>         final SingleOutputStreamOperator<Message> processWindowFunctionStream =
>             keyedStream.window(ProcessingTimeSessionWindows.withGap(Time.milliseconds(100)))
>                 .process(new CustomProcessWindowFunction()).uid(PROCESS_WINDOW_FUNCTION_OPERATOR_ID)
>                 .name("Process window function");
> {code}
> My checkpointing configuration is set to use RocksDB state backend with incremental checkpointing and EXACTLY_ONCE mode.
> In a runtime I noticed that even though data ingestion is static - same keys and frequency of messages the size of the process window operator keeps increasing. I tried to reproduce it with minimal similar setup here: https://github.com/loliver1234/flink-process-window-function and was successful to do so.
> Testing conditions:
> - RabbitMQ source with Exactly-once guarantee and 65k prefetch count
> - RabbitMQ sink to collect messages
> - Simple ProcessWindowFunction that only pass messages through
> - Stream time characteristic set to TimeCharacteristic.ProcessingTime
> Testing scenario:
> - Start flink job and check initial state size - State Size: 127 KB
> - Start sending messages, 1000 same unique keys every 1s (they are not falling into defined time window gap set to 100ms, each message should create new window)
> - State of the process window operator keeps increasing - after 1mln messages state ended up to be around 2mb
> - Stop sending messages and wait till rabbit queue is fully consumed and few checkpoints go by
> - Was expected to see state size to decrease to base value but it stayed at 2mb
> - Continue to send messages with the same keys and state kept increasing trend.
> What I checked:
> - Registration and deregistration of timestamps set for time windows - each registration matched its deregistration
> - Checked that in fact there are no window merges
> - Tried custom Trigger disabling window merges and setting onProcessingTime trigger to TriggerResult.FIRE_AND_PURGE - same state behavior
> On staging environment, we noticed that state for that operator keeps increasing indefinitely, after some months reaching even 1,5gb for 100k unique keys
> Flink commit id: 9c32ed9
>  



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