This is for a notebook env that has the spark session/context bootstrapped
for the user. There are settings that are user specific so not all of those
can go into the spark-defaults.conf - such settings need to be dynamically
applied when creating the session/context.

In Scala/Python, I would bootstrap a "spark" handle similar to what
spark-shell / psyspark-shell startup scripts do. In my case the
bootstrapped object could be of a wrapper class that took care of whatever
customization I needed while exposing the regular  SparkSession
scala/python API. The user uses this object as he/she would use a regular
SparkSession to submit work to the Spark cluster. Since I am certain there
is no other way for users to perform Spark work except to go via the
bootstrapped object, I can achieve my objective of delaying creation of
SparkSession/Context until a call comes to my custom spark object.

If I want to do the same in R, and let users write SparkR code as they
normally would, but bootstrapping a SparkContext/Session for them, then I
hit the issues as I explained earlier. There is no single entry point for
SparkContext/Session in SparkR API and so to achieve lazy creation of
SparkContext/session, it looks like the only  option is to do some trickery
with the SparkR:::.sparkREnv$.sparkRjsc and
SparkR:::.sparkREnv$.sparkRsession vars.

Regards,
Vin.

On Sat, Apr 22, 2017 at 3:33 AM, Felix Cheung <felixcheun...@hotmail.com>
wrote:

> How would you handle this in Scala?
>
> If you are adding a wrapper func like getSparkSession for Scala, and have
> your users call it, can't you do that same in SparkR? After all, while true
> you don't need a SparkSession object to call the R API, someone still needs
> to call sparkR.session() to initial the current session?
>
> Also what Spark environment you want to customize?
>
> Can these be set in environment variables or via spark-defaults.conf
> spark.apache.org/docs/latest/configuration.html#dynamically-loading-spark-
> properties
>
>
> _____________________________
> From: Vin J <winjos...@gmail.com>
> Sent: Friday, April 21, 2017 2:22 PM
> Subject: [SparkR] - options around setting up SparkSession / SparkContext
> To: <dev@spark.apache.org>
>
>
>
>
> I need to make an R environment available where the
> SparkSession/SparkContext needs to be setup a specific way. The user simply
> accesses this environment and executes his/her code. If the user code does
> not access any Spark functions, I do not want to create a SparkContext
> unnecessarily.
>
> In Scala/Python environments, the user can't access spark without first
> referencing SparkContext / SparkSession classes. So the above (lazy and/or
> custom SparkSession/Context creation) is easily met by offering
> sparkContext/sparkSession handles to the user that are either wrappers on
> Spark's classes or have lazy evaluation semantics. This way only when the
> user accesses these handles to sparkContext/Session will the
> SparkSession/Context actually get set up without the user needing to know
> all the details about initing the SparkContext/Session.
>
> However, achieving the same doesn't appear to be so straightforward in R.
> From what I see, executing sparkR.session(...) sets up private variables in
> SparkR:::.sparkREnv (.sparkRjsc , .sparkRsession). The way SparkR api
> works, a user doesn't need a handle to the spark session as such. Executing
> functions like so:  "df <- as.DataFrame(..)" implicitly access the private
> vars in SparkR:::.sparkREnv to get access to the sparkContext etc that are
> expected to have been created by a prior call to
> sparkR.session()/sparkR.init() etc.
>
> Therefore, to inject any custom/lazy behavior into this I don't see a way
> except through having my code (that sits outside of Spark) apply a
> delayedAssign() or a makeActiveBinding( ) on SparkR:::.sparkRsession /
> .sparkRjsc  variables. This way when spark code internally references them,
> my wrapper/lazy code gets executed to do whatever I need done.
>
> However, I am seeing some limitations of applying even this approach to
> SparkR - it will not work unless some minor changes are made in the SparkR
> code. But, before I opened a PR that would do these changes in SparkR I
> wanted to check if there was a better way to achieve this? I am far less
> than an R expert, and could be missing something here.
>
> If you'd rather see this in a JIRA and a PR, let me know and I'll go ahead
> and open one.
>
> Regards,
> Vin.
>
>
>
>
>

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