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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-20236) Overwrite a partitioned data source table should only overwrite related partitions
Date Sat, 22 Jul 2017 16:48:02 GMT

     [ https://issues.apache.org/jira/browse/SPARK-20236?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-20236:
------------------------------------

    Assignee: Apache Spark

> 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: Apache Spark
>              Labels: releasenotes
>
> 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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