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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:38:04 UTC
[jira] [Resolved] (SPARK-18096) Spark on have - 'Update' save mode
[ https://issues.apache.org/jira/browse/SPARK-18096?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-18096.
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Resolution: Incomplete
> 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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