Tudor Miu created SPARK-21641:
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Summary: 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
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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