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From "Alexander Hagerf (Jira)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-29427) Create KeyValueGroupedDataset in a relational way
Date Fri, 11 Oct 2019 11:24:00 GMT

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

Alexander Hagerf updated SPARK-29427:
-------------------------------------
    Summary: Create KeyValueGroupedDataset in a relational way  (was: Create KeyValueGroupedDataset
from RelationalGroupedDataset)

> 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
>            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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