If you need the functionality I would recommend you just copying the code over to your project and use it that way.
On Wed, Mar 28, 2018 at 9:02 AM Felix Cheung <felixcheun...@hotmail.com> wrote: > I think the difference is py4j is a public library whereas the R backend > is specific to SparkR. > > Can you elaborate what you need JVMObjectTracker for? We have provided R > convenient APIs to call into JVM: sparkR.callJMethod for example > > _____________________________ > From: Jeremy Liu <jeremy.jl....@gmail.com> > Sent: Tuesday, March 27, 2018 12:20 PM > Subject: Re: [Spark R] Proposal: Exposing RBackend in RRunner > To: <dev@spark.apache.org> > > > > Spark Dev, > > On second thought, the below topic seems more appropriate for spark-dev > rather than spark-users: > > Spark Users, >> >> In SparkR, RBackend is created in RRunner.main(). This in particular >> makes it difficult to control or use the RBackend. For my use case, I am >> looking to access the JVMObjectTracker that RBackend maintains for SparkR >> dataframes. >> >> Analogously, pyspark starts a py4j.GatewayServer in PythonRunner.main(). >> It's then possible to start a ClientServer that then has access to the >> object bindings between Python/Java. >> >> Is there something similar for SparkR? Or a reasonable way to expose >> RBackend? >> >> Thanks! >> > -- > ----- > Jeremy Liu > jeremy.jl....@gmail.com > > >
