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https://issues.apache.org/jira/browse/SPARK-17428?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15475943#comment-15475943
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Shivaram Venkataraman commented on SPARK-17428:
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I think there are bunch of issues being discussed here. My initial take would
be to add support for something simple and then iterate based on user feedback.
Given that R users generally don't know / care much about package version
numbers I'd say an initial cut that handles two flags in spark-submit
(a) a list of package names and calls `install.packages` on each machine with
them
(b) a list of package tar.gz that are installed with `R CMD INSTALL` on each
machine
We can also make the package installs lazy, i.e. they only get run on a worker
when there is a R worker process launched there. Will this meet the user needs
you have in mind [~yanboliang] ?
> 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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