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https://issues.apache.org/jira/browse/SPARK-29427?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun updated SPARK-29427:
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Summary: Add API to convert RelationalGroupedDataset to
KeyValueGroupedDataset (was: Create KeyValueGroupedDataset in a relational way)
> Add API to convert RelationalGroupedDataset to KeyValueGroupedDataset
> ---------------------------------------------------------------------
>
> Key: SPARK-29427
> URL: https://issues.apache.org/jira/browse/SPARK-29427
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 2.4.4
> Reporter: Alexander Hagerf
> Assignee: L. C. Hsieh
> Priority: Major
> Fix For: 3.0.0
>
>
> The scenario I'm having is that I'm reading two huge bucketed tables and
> since a regular join is not performant enough for my cases, I'm using
> groupByKey to generate two KeyValueGroupedDatasets and cogroup them to
> implement the merging logic I need.
> The issue with this approach is that I'm only grouping by the column that the
> tables are bucketed by but since I'm using groupByKey the bucketing is
> completely ignored and I still get a full shuffle.
> What I'm looking for is some functionality to tell Catalyst to group by a
> column in a relational way but then give the user a possibility to utilize
> the functions of the KeyValueGroupedDataset e.g. cogroup (which is not
> available for dataframes)
>
> At current spark (2.4.4) I see no way to do this efficiently. I think this is
> a valid use case which if solved would have huge performance benefits.
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