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Posted to issues@spark.apache.org by "Hossein Falaki (JIRA)" <ji...@apache.org> on 2016/10/05 21:29:21 UTC

[jira] [Updated] (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:all-tabpanel ]

Hossein Falaki updated SPARK-17790:
-----------------------------------
    Summary: Support for parallelizing R data.frame larger than 2GB  (was: Support for parallelizing data.frame larger than 2GB)

> 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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