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From "Alexander Hagerf (Jira)" <j...@apache.org>
Subject [jira] [Created] (SPARK-29427) Create KeyValueGroupedDataset from RelationalGroupedDataset
Date Thu, 10 Oct 2019 10:36:00 GMT
Alexander Hagerf created SPARK-29427:
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             Summary: Create KeyValueGroupedDataset from RelationalGroupedDataset
                 Key: SPARK-29427
                 URL: https://issues.apache.org/jira/browse/SPARK-29427
             Project: Spark
          Issue Type: New Feature
          Components: Spark Core, SQL
    Affects Versions: 2.4.4
            Reporter: Alexander Hagerf


The scenario I'm having is that I'm reading two huge bucketed tables and since a regular join
is not performant enough for these cases I'm using groupByKey to generate two KeyValueGroupedDatasets
and cogroup them to implement the 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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