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From "Izek Greenfield (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-13346) Using DataFrames iteratively leads to slow query planning
Date Wed, 01 Aug 2018 07:32:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-13346?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16564885#comment-16564885
] 

Izek Greenfield commented on SPARK-13346:
-----------------------------------------

What the status of that? we face this issue too!

> Using DataFrames iteratively leads to slow query planning
> ---------------------------------------------------------
>
>                 Key: SPARK-13346
>                 URL: https://issues.apache.org/jira/browse/SPARK-13346
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 2.0.0
>            Reporter: Joseph K. Bradley
>            Priority: Major
>
> I have an iterative algorithm based on DataFrames, and the query plan grows very quickly
with each iteration.  Caching the current DataFrame at the end of an iteration does not fix
the problem.  However, converting the DataFrame to an RDD and back at the end of each iteration
does fix the problem.
> Printing the query plans shows that the plan explodes quickly (10 lines, to several hundred
lines, to several thousand lines, ...) with successive iterations.
> The desired behavior is for the analyzer to recognize that a big chunk of the query plan
does not need to be computed since it is already cached.  The computation on each iteration
should be the same.
> If useful, I can push (complex) code to reproduce the issue.  But it should be simple
to see if you create an iterative algorithm which produces a new DataFrame from an old one
on each iteration.



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