[jira] [Commented] (SPARK-17020) Materialization of RDD via DataFrame.rdd forces a poor re-distribution of data

["Sean Owen (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-17020?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15417194#comment-15417194 ] 

Sean Owen commented on SPARK-17020:
-----------------------------------

Can you provide more detail on how you created the DataFrame, the RDD, where you cached them, etc? just to rule out some other stuff. I can't reproduce this locally but may have a different situation from you.

> Materialization of RDD via DataFrame.rdd forces a poor re-distribution of data
> ------------------------------------------------------------------------------
>
>                 Key: SPARK-17020
>                 URL: https://issues.apache.org/jira/browse/SPARK-17020
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core, SQL
>    Affects Versions: 1.6.1, 1.6.2, 2.0.0
>            Reporter: Roi Reshef
>            Priority: Critical
>         Attachments: dataframe_cache.PNG, rdd_cache.PNG
>
>
> Calling DataFrame's lazy val .rdd results with a new RDD with a poor distribution of partitions across the cluster. Moreover, any attempt to repartition this RDD further will fail.
> Attached are a screenshot of the original DataFrame on cache and the resulting RDD on cache.



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