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From "Nong Li (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-12394) Support writing out pre-hash-partitioned data and exploit that in join optimizations to avoid shuffle (i.e. bucketing in Hive)
Date Mon, 21 Dec 2015 19:31:46 GMT

     [ https://issues.apache.org/jira/browse/SPARK-12394?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Nong Li updated SPARK-12394:
----------------------------
    Attachment: BucketedTables.pdf

Here is a design for how we can supported bucketed tables.

> Support writing out pre-hash-partitioned data and exploit that in join optimizations
to avoid shuffle (i.e. bucketing in Hive)
> ------------------------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-12394
>                 URL: https://issues.apache.org/jira/browse/SPARK-12394
>             Project: Spark
>          Issue Type: New Feature
>          Components: SQL
>            Reporter: Reynold Xin
>         Attachments: BucketedTables.pdf
>
>
> In many cases users know ahead of time the columns that they will be joining or aggregating
on.  Ideally they should be able to leverage this information and pre-shuffle the data so
that subsequent queries do not require a shuffle.  Hive supports this functionality by allowing
the user to define buckets, which are hash partitioning of the data based on some key.
>  - Allow the user to specify a set of columns when caching or writing out data
>  - Allow the user to specify some parallelism
>  - Shuffle the data when writing / caching such that its distributed by these columns
>  - When planning/executing  a query, use this distribution to avoid another shuffle when
reading, assuming the join or aggregation is compatible with the columns specified
>  - Should work with existing save modes: append, overwrite, etc
>  - Should work at least with all Hadoops FS data sources
>  - Should work with any data source when caching



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