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https://issues.apache.org/jira/browse/SPARK-29038?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16927258#comment-16927258
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Dilip Biswal edited comment on SPARK-29038 at 9/11/19 5:13 AM:
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[~cltlfcjin]
Actually i had similar question as [~mgaido]. We have been writing the SQL
reference for 3.0 and have recently documented
{code:java}
CACHE TABLE {code}
in [https://github.com/apache/spark/pull/25532]. So in SPARK, it is
possible to cache the result of a complex query involving joins, aggregates
etc, right ?
was (Author: dkbiswal):
[~cltlfcjin]
Actually i had similar question as [~mgaido]. We have been writing the SQL
reference for 3.0 and have recently documented
{code:java}
CACHE TABLE {code}
in [https://github.com/apache/spark/pull/25532]. So in SPARK, it is
possible to cache the result of a complex query involving joins, aggregates
etc.
> SPIP: Support Spark Materialized View
> -------------------------------------
>
> Key: SPARK-29038
> URL: https://issues.apache.org/jira/browse/SPARK-29038
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Lantao Jin
> Priority: Major
>
> Materialized view is an important approach in DBMS to cache data to
> accelerate queries. By creating a materialized view through SQL, the data
> that can be cached is very flexible, and needs to be configured arbitrarily
> according to specific usage scenarios. The Materialization Manager
> automatically updates the cache data according to changes in detail source
> tables, simplifying user work. When user submit query, Spark optimizer
> rewrites the execution plan based on the available materialized view to
> determine the optimal execution plan.
> Details in [design
> doc|https://docs.google.com/document/d/1q5pjSWoTNVc9zsAfbNzJ-guHyVwPsEroIEP8Cca179A/edit?usp=sharing]
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