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https://issues.apache.org/jira/browse/SPARK-20236?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16619180#comment-16619180
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Wenchen Fan commented on SPARK-20236:
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It should work for managed table as well. Can you open a JIRA and report the
issues for managed table?
> Overwrite a partitioned data source table should only overwrite related
> partitions
> ----------------------------------------------------------------------------------
>
> Key: SPARK-20236
> URL: https://issues.apache.org/jira/browse/SPARK-20236
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.2.0
> Reporter: Wenchen Fan
> Assignee: Wenchen Fan
> Priority: Major
> Labels: releasenotes
> Fix For: 2.3.0
>
>
> When we overwrite a partitioned data source table, currently Spark will
> truncate the entire table to write new data, or truncate a bunch of
> partitions according to the given static partitions.
> For example, {{INSERT OVERWRITE tbl ...}} will truncate the entire table,
> {{INSERT OVERWRITE tbl PARTITION (a=1, b)}} will truncate all the partitions
> that starts with {{a=1}}.
> This behavior is kind of reasonable as we can know which partitions will be
> overwritten before runtime. However, hive has a different behavior that it
> only overwrites related partitions, e.g. {{INSERT OVERWRITE tbl SELECT
> 1,2,3}} will only overwrite partition {{a=2, b=3}}, assuming {{tbl}} has only
> one data column and is partitioned by {{a}} and {{b}}.
> It seems better if we can follow hive's behavior.
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