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https://issues.apache.org/jira/browse/SPARK-17428?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15475612#comment-15475612
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Felix Cheung edited comment on SPARK-17428 at 9/9/16 1:59 AM:
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I don't think I see a way to specify a version number for install.packages in R?
Python does compile code - installing packages with pip compiles the python
scripts. https://www.google.com/search?q=pyc
And also many packages have native components which will not work without
installing or compiling as root (or heavy hacking), eg. matplotlib, scipy.
was (Author: felixcheung):
I don't think I see a way to specify a version number for install.packages in R?
Python does compile code - installing packages with pip compiles the python
scripts. https://www.google.com/search?q=pyc
And also many packages have heavy native components which will not work without
installing as root (or heavy hacking), eg. matplotlib, scipy.
> SparkR executors/workers support virtualenv
> -------------------------------------------
>
> Key: SPARK-17428
> URL: https://issues.apache.org/jira/browse/SPARK-17428
> Project: Spark
> Issue Type: New Feature
> Components: SparkR
> Reporter: Yanbo Liang
>
> Many users have requirements to use third party R packages in
> executors/workers, but SparkR can not satisfy this requirements elegantly.
> For example, you should to mess with the IT/administrators of the cluster to
> deploy these R packages on each executors/workers node which is very
> inflexible.
> I think we should support third party R packages for SparkR users as what we
> do for jar packages in the following two scenarios:
> 1, Users can install R packages from CRAN or custom CRAN-like repository for
> each executors.
> 2, Users can load their local R packages and install them on each executors.
> To achieve this goal, the first thing is to make SparkR executors support
> virtualenv like Python conda. I have investigated and found
> packrat(http://rstudio.github.io/packrat/) is one of the candidates to
> support virtualenv for R. Packrat is a dependency management system for R and
> can isolate the dependent R packages in its own private package space. Then
> SparkR users can install third party packages in the application
> scope(destroy after the application exit) and don’t need to bother
> IT/administrators to install these packages manually.
> I would like to know whether it make sense.
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