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Posted to issues@spark.apache.org by "Felix Cheung (JIRA)" <ji...@apache.org> on 2016/10/06 00:35:20 UTC
[jira] [Comment Edited] (SPARK-17790) Support for parallelizing R
data.frame larger than 2GB
[ https://issues.apache.org/jira/browse/SPARK-17790?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15550414#comment-15550414 ]
Felix Cheung edited comment on SPARK-17790 at 10/6/16 12:34 AM:
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Yes. Driver R and Driver JVM should be on the same machine.
I have not checked recently but there might be projects changing on how the Backend is connected that could be affected by this though
was (Author: felixcheung):
Yes.
> Support for parallelizing R data.frame larger than 2GB
> ------------------------------------------------------
>
> Key: SPARK-17790
> URL: https://issues.apache.org/jira/browse/SPARK-17790
> Project: Spark
> Issue Type: Sub-task
> Components: SparkR
> Affects Versions: 2.0.1
> Reporter: Hossein Falaki
>
> This issue is a more specific version of SPARK-17762.
> Supporting larger than 2GB arguments is more general and arguably harder to do because the limit exists both in R and JVM (because we receive data as a ByteArray). However, to support parallalizing R data.frames that are larger than 2GB we can do what PySpark does.
> PySpark uses files to transfer bulk data between Python and JVM. It has worked well for the large community of Spark Python users.
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