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Posted to issues@spark.apache.org by "nirav patel (JIRA)" <ji...@apache.org> on 2018/08/01 18:12:00 UTC
[jira] [Commented] (SPARK-17861) Store data source partitions in
metastore and push partition pruning into metastore
[ https://issues.apache.org/jira/browse/SPARK-17861?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16565749#comment-16565749 ]
nirav patel commented on SPARK-17861:
-------------------------------------
[~rxin] can this also be supported via dataframe? so following will also give same behavior?
`df.write.mode(SaveMode.Overwrite).partitionBy(partitionCols : _*).parquet(tableLocation)`
Currently it overwrites all partitions with spark 2.2.1 version
> Store data source partitions in metastore and push partition pruning into metastore
> -----------------------------------------------------------------------------------
>
> Key: SPARK-17861
> URL: https://issues.apache.org/jira/browse/SPARK-17861
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Reporter: Reynold Xin
> Assignee: Eric Liang
> Priority: Critical
> Fix For: 2.1.0
>
>
> Initially, Spark SQL does not store any partition information in the catalog for data source tables, because initially it was designed to work with arbitrary files. This, however, has a few issues for catalog tables:
> 1. Listing partitions for a large table (with millions of partitions) can be very slow during cold start.
> 2. Does not support heterogeneous partition naming schemes.
> 3. Cannot leverage pushing partition pruning into the metastore.
> This ticket tracks the work required to push the tracking of partitions into the metastore. This change should be feature flagged.
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