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https://issues.apache.org/jira/browse/SPARK-19842?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Ioana Delaney updated SPARK-19842:
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Attachment: InformationalRIConstraints.doc
> Informational Referential Integrity Constraints Support in Spark
> ----------------------------------------------------------------
>
> Key: SPARK-19842
> URL: https://issues.apache.org/jira/browse/SPARK-19842
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.2.0
> Reporter: Ioana Delaney
> Attachments: InformationalRIConstraints.doc
>
>
> *Informational Referential Integrity Constraints Support in Spark*
> This work proposes support for _informational primary key_ and _foreign key
> (referential integrity) constraints_ in Spark. The main purpose is to open up
> an area of query optimization techniques that rely on referential integrity
> constraints semantics.
> An _informational_ or _statistical constraint_ is a constraint such as a
> _unique_, _primary key_, _foreign key_, or _check constraint_, that can be
> used by Spark to improve query performance. Informational constraints are not
> enforced by the Spark SQL engine; rather, they are used by Catalyst to
> optimize the query processing. They provide semantics information that allows
> Catalyst to rewrite queries to eliminate joins, push down aggregates, remove
> unnecessary Distinct operations, and perform a number of other optimizations.
> Informational constraints are primarily targeted to applications that load
> and analyze data that originated from a data warehouse. For such
> applications, the conditions for a given constraint are known to be true, so
> the constraint does not need to be enforced during data load operations.
> The attached document covers constraint definition, metastore storage,
> constraint validation, and maintenance. The document shows many examples of
> query performance improvements that utilize referential integrity constraints
> and can be implemented in Spark.
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