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 <sunrise_...@163.com> 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 > https://issues.apache.org/jira/browse/SPARK-14746 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 <xinh.hu...@gmail.com> wrote: > > It looks like it. "DataFrame UDFs in R" is resolved in Spark 2.0: > https://issues.apache.org/jira/browse/SPARK-6817 > > Here's some of the code: > > https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/r/MapPartitionsRWrapper.scala > > /** > * 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]) > > Xinh > > On Wed, Jun 29, 2016 at 2:59 PM, Sean Owen <so...@cloudera.com> 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 <john.ahe...@justenough.com >> > 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: >>> >>> https://blogs.msdn.microsoft.com/uk_faculty_connection/2016/05/10/r-server-for-hdinsight-running-on-microsoft-azure-cloud-data-science-challenges/ >>> >>> >>> >>> On Wed, Jun 29, 2016 at 3:29 PM, Sean Owen <so...@cloudera.com> 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 <xinh.hu...@gmail.com> >>>> 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: >>>> > >>>> http://people.apache.org/~pwendell/spark-releases/spark-2.0.0-rc1-docs/sparkr.html#from-data-sources >>>> > >>>> > API doc for "dapply": >>>> > >>>> http://people.apache.org/~pwendell/spark-releases/spark-2.0.0-rc1-docs/api/R/index.html >>>> > >>>> > Xinh >>>> > >>>> > On Wed, Jun 29, 2016 at 6:54 AM, sujeet jog <sujeet....@gmail.com> >>>> 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 < >>>> gilad.lan...@clicktale.com> >>>> >> 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 >>>> >>> >>>> >>> >>>> >> >>>> >> >>>> > >>>> >>>> --------------------------------------------------------------------- >>>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org >>>> >>>> >>> >>> >>> -- >>> >>> John Aherne >>> Big Data and SQL Developer >>> >>> [image: JustEnough Logo] >>> >>> Cell: >>> Email: >>> Skype: >>> Web: >>> >>> +1 (303) 809-9718 >>> john.ahe...@justenough.com >>> john.aherne.je >>> www.justenough.com >>> >>> >>> Confidentiality Note: The information contained in this email and >>> document(s) attached are for the exclusive use of the addressee and may >>> contain confidential, privileged and non-disclosable information. If the >>> recipient of this email is not the addressee, such recipient is strictly >>> prohibited from reading, photocopying, distribution or otherwise using this >>> email or its contents in any way. >>> >>> >> > >
