[ https://issues.apache.org/jira/browse/SPARK-29427?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Dongjoon Hyun updated SPARK-29427: ---------------------------------- 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. -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org