[jira] [Resolved] (SPARK-21641) Combining windowing (groupBy) and mapGroupsWithState (groupByKey) in Spark Structured Streaming

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-21641?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-21641.
----------------------------------
    Resolution: Incomplete

> Combining windowing (groupBy) and mapGroupsWithState (groupByKey) in Spark Structured Streaming
> -----------------------------------------------------------------------------------------------
>
>                 Key: SPARK-21641
>                 URL: https://issues.apache.org/jira/browse/SPARK-21641
>             Project: Spark
>          Issue Type: Improvement
>          Components: Structured Streaming
>    Affects Versions: 2.2.0
>            Reporter: Tudor Miu
>            Priority: Major
>              Labels: bulk-closed
>
> Given a stream of timestamped data with watermarking, there seems to be no way to combine (1) the {{groupBy}} operation to achieve windowing by the timestamp field and other grouping criteria with (2) the {{groupByKey}} operation in order to apply {{mapGroupsWithState }}to the groups for custom sessionization.
> For context:
> - calling {{groupBy}}, which supports windowing, on a Dataset returns a {{RelationalGroupedDataset}} which does not have {{mapGroupsWithState}}.
> - calling {{groupByKey}}, which supports {{mapGroupsWithState}}, returns a {{KeyValueGroupedDataset}}, but that has no support for windowing.
> The suggestion is to _somehow_ unify the two APIs.



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