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https://issues.apache.org/jira/browse/SPARK-18096?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-18096:
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Labels: bulk-closed (was: )
> Spark on have - 'Update' save mode
> ----------------------------------
>
> Key: SPARK-18096
> URL: https://issues.apache.org/jira/browse/SPARK-18096
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 2.0.1
> Reporter: David Hodeffi
> Priority: Major
> Labels: bulk-closed
>
> when creating ETL with Spark on Hive, it is needed to update incrementally
> the destination table.
> In case it is partitioned table it means that we don't need to update all
> partitions, but just the one who mutated.
> right now there is only one way to update a Dataframe which is
> SaveMode.Overwrite , the problem is that when doing it incrementally you
> don't need to update all partitions but just those who changed/updated.
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