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From "Vladimir Ozerov (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (IGNITE-3084) Investigate how Ignite can support Spark DataFrame
Date Tue, 03 Jan 2017 06:57:58 GMT

    [ https://issues.apache.org/jira/browse/IGNITE-3084?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15794332#comment-15794332
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Vladimir Ozerov commented on IGNITE-3084:
-----------------------------------------

Val,

Cool analysis! I would say that executing query-on-partition is very useful feature. Not only
it will help us with Spark, but will allow us to perform certain useful SQL optimizations
(e.g. IGNITE-4509 and IGNITE-4510). 

I am not quite sure I understand how to work with plans and strategies. Does it mean that
we will have to analyze SQL somehow (e.g. build AST) to give correct hints to Spark?


> Investigate how Ignite can support Spark DataFrame
> --------------------------------------------------
>
>                 Key: IGNITE-3084
>                 URL: https://issues.apache.org/jira/browse/IGNITE-3084
>             Project: Ignite
>          Issue Type: Task
>          Components: Ignite RDD
>    Affects Versions: 1.5.0.final
>            Reporter: Vladimir Ozerov
>            Assignee: Valentin Kulichenko
>              Labels: bigdata
>             Fix For: 2.0
>
>
> We see increasing demand on nice DataFrame support for our Spark integration. Need to
investigate how could we do that.
> Looks like we can investigate how MemSQL do that and take it as a starting point.



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