Thanks for the link Sun,  I believe running external Scripts like R code in Data Frames is a much needed facility,  for example for the algorithms that are not available in MLLIB, invoking such from a R script would definitely be a powerful feature when your APP is Scala/Python based,  you don;t have to use Spark-R for this sake when much of your application code is in Scala/python.

On Thu, Jun 30, 2016 at 8:25 AM, Sun Rui <> wrote:
Hi, Gilad,

You can try the dapply() and gapply() function in SparkR in Spark 2.0. Yes, it is required that R installed on each worker node.

However, if your Spark application is Scala/Java based, it is not supported for now to run R code in DataFrames. There is closed lira which remains discussion purpose. You have to convert DataFrames to RDDs, and use pipe() on RDDs to launch external R processes and run R code.

On Jun 30, 2016, at 07:08, Xinh Huynh <> wrote:

It looks like it. "DataFrame UDFs in R" is resolved in Spark 2.0:

Here's some of the code:

* A function wrapper that applies the given R function to each partition.
private[sql] case class MapPartitionsRWrapper(
func: Array[Byte],
packageNames: Array[Byte],
broadcastVars: Array[Broadcast[Object]],
inputSchema: StructType,
outputSchema: StructType) extends (Iterator[Any] => Iterator[Any])


On Wed, Jun 29, 2016 at 2:59 PM, Sean Owen <> wrote:
Here we (or certainly I) am not talking about R Server, but plain vanilla R, as used with Spark and SparkR. Currently, SparkR doesn't distribute R code at all (it used to, sort of), so I'm wondering if that is changing back.

On Wed, Jun 29, 2016 at 10:53 PM, John Aherne <> wrote:
I don't think R server requires R on the executor nodes. I originally set up a SparkR cluster for our Data Scientist on Azure which required that I install R on each node, but for the R Server set up, there is an extra edge node with R server that they connect to. From what little research I was able to do, it seems that there are some special functions in R Server that can distribute the work to the cluster. 

Documentation is light, and hard to find but I found this helpful:

On Wed, Jun 29, 2016 at 3:29 PM, Sean Owen <> wrote:
Oh, interesting: does this really mean the return of distributing R
code from driver to executors and running it remotely, or do I
misunderstand? this would require having R on the executor nodes like
it used to?

On Wed, Jun 29, 2016 at 5:53 PM, Xinh Huynh <> wrote:
> There is some new SparkR functionality coming in Spark 2.0, such as
> "dapply". You could use SparkR to load a Parquet file and then run "dapply"
> to apply a function to each partition of a DataFrame.
> Info about loading Parquet file:
> API doc for "dapply":
> Xinh
> On Wed, Jun 29, 2016 at 6:54 AM, sujeet jog <> wrote:
>> try Spark pipeRDD's , you can invoke the R script from pipe , push  the
>> stuff you want to do on the Rscript stdin,  p
>> On Wed, Jun 29, 2016 at 7:10 PM, Gilad Landau <>
>> wrote:
>>> Hello,
>>> I want to use R code as part of spark application (the same way I would
>>> do with Scala/Python).  I want to be able to run an R syntax as a map
>>> function on a big Spark dataframe loaded from a parquet file.
>>> Is this even possible or the only way to use R is as part of RStudio
>>> orchestration of our Spark  cluster?
>>> Thanks for the help!
>>> Gilad

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