You are viewing a plain text version of this content. The canonical link for it is here.

Posted to issues@spark.apache.org by "Roi Reshef (JIRA)" <ji...@apache.org> on 2016/08/11 12:46:20 UTC

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

     [ https://issues.apache.org/jira/browse/SPARK-17020?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Roi Reshef updated SPARK-17020:
-------------------------------
    Attachment: rdd_cache.PNG
                dataframe_cache.PNG

> 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
>            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.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org