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https://issues.apache.org/jira/browse/FLINK-22587?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17352575#comment-17352575
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Seth Wiesman commented on FLINK-22587:
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[~echauchot] That makes sense. You can just assign a static timestamp to each
record and it should work. Watermarks are disregarded in batch mode anyway so
the strategy doesn't matter. The preferable other option is to just do a join
manually with a KeyedCoProcessFunction, the reason why is I think the window
join operator in DataStream will eventually be deprecated for all the reasons
you've discovered.
FWIW I would also like to deprecate custom triggers and window assigners, I
work with a lot of Flink users and have found usage of non-standard window
constructs to be an antipattern. The code ends up being convoluted and
(Keyed)ProcessFunction always ends up as a cleaner, more maintainable solution.
> Support aggregations in batch mode with DataStream API
> ------------------------------------------------------
>
> Key: FLINK-22587
> URL: https://issues.apache.org/jira/browse/FLINK-22587
> Project: Flink
> Issue Type: Bug
> Components: API / DataStream
> Affects Versions: 1.12.0, 1.13.0
> Reporter: Etienne Chauchot
> Priority: Major
>
> A pipeline like this *in batch mode* would output no data
> {code:java}
> stream.join(otherStream)
> .where(<KeySelector>)
> .equalTo(<KeySelector>)
> .window(GlobalWindows.create())
> .apply(<JoinFunction>)
> {code}
> Indeed the default trigger for GlobalWindow is NeverTrigger which never
> fires. If we set a _EventTimeTrigger_ it will fire with every element as the
> watermark will be set to +INF (batch mode) and will pass the end of the
> global window with each new element. A _ProcessingTimeTrigger_ never fires
> either and all elapsed time or delta based triggers would not be suited for
> batch.
> Same goes for _reduce()_ instead of join().
> So I guess we miss something for batch support with DataStream.
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