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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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L. C. Hsieh updated SPARK-29427:
--------------------------------
    Component/s:     (was: Spark Core)

> Create KeyValueGroupedDataset from RelationalGroupedDataset
> -----------------------------------------------------------
>
>                 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
>            Priority: Major
>
> 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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