Yes, I think exposing SparkR in CRAN can significantly expand the reach of both SparkR and Spark itself to a larger community of data scientists (and statisticians).
I have been getting questions on how to use SparkR in RStudio. Most of these folks have a Spark Cluster and wish to talk to it from RStudio. While that is a bigger task, for now, first step could be not requiring them to download Spark source and run a script that is named install-dev.sh. I filed SPARK-10776 to track this. --Hossein On Tue, Sep 22, 2015 at 7:21 PM, Shivaram Venkataraman < shiva...@eecs.berkeley.edu> wrote: > As Rui says it would be good to understand the use case we want to > support (supporting CRAN installs could be one for example). I don't > think it should be very hard to do as the RBackend itself doesn't use > the R source files. The RRDD does use it and the value comes from > > https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/api/r/RUtils.scala#L29 > AFAIK -- So we could introduce a new config flag that can be used for > this new mode. > > Thanks > Shivaram > > On Mon, Sep 21, 2015 at 8:15 PM, Sun, Rui <rui....@intel.com> wrote: > > Hossein, > > > > > > > > Any strong reason to download and install SparkR source package > separately > > from the Spark distribution? > > > > An R user can simply download the spark distribution, which contains > SparkR > > source and binary package, and directly use sparkR. No need to install > > SparkR package at all. > > > > > > > > From: Hossein [mailto:fal...@gmail.com] > > Sent: Tuesday, September 22, 2015 9:19 AM > > To: dev@spark.apache.org > > Subject: SparkR package path > > > > > > > > Hi dev list, > > > > > > > > SparkR backend assumes SparkR source files are located under > > "SPARK_HOME/R/lib/." This directory is created by running > R/install-dev.sh. > > This setting makes sense for Spark developers, but if an R user downloads > > and installs SparkR source package, the source files are going to be in > > placed different locations. > > > > > > > > In the R runtime it is easy to find location of package files using > > path.package("SparkR"). But we need to make some changes to R backend > and/or > > spark-submit so that, JVM process learns the location of worker.R and > > daemon.R and shell.R from the R runtime. > > > > > > > > Do you think this change is feasible? > > > > > > > > Thanks, > > > > --Hossein >
