[
https://issues.apache.org/jira/browse/FLINK-22587?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17630307#comment-17630307
]
Etienne Chauchot commented on FLINK-22587:
------------------------------------------
Hi [~gaoyunhaii] thanks for the update ! In [my blog
post|https://echauchot.blogspot.com/2022/11/flink-howto-migrate-real-life-batch.html]
there is a point on the join where I worked around this join problem with a
manual KeyedCoProcessFunction and MapStates. What I wonder now is: would this
new workaround with windowing be more efficient ?
> 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.
--
This message was sent by Atlassian Jira
(v8.20.10#820010)
