[jira] [Resolved] (SPARK-29427) Create KeyValueGroupedDataset in a relational way

["Dongjoon Hyun (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-29427?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Dongjoon Hyun resolved SPARK-29427.
-----------------------------------
    Fix Version/s: 3.0.0
       Resolution: Fixed

Issue resolved by pull request 26509
[https://github.com/apache/spark/pull/26509]

> Create KeyValueGroupedDataset in a relational way
> -------------------------------------------------
>
>                 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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