Thanks for your questions Mridul.
I assume you are referring to how the functionality to query system state works
in Yarn and Mesos?
The API's used are the standard JVM API's so the functionality will work
without change. There is no real use case for using 'physicalMemoryBytes' in
these cases though, as the JVM size has already been limited by the resource
manager.
Regards,Dale.
> Date: Fri, 13 Mar 2015 08:20:33 -0700
> Subject: Re: Spark config option 'expression language' feedback request
> From: mri...@gmail.com
> To: dale...@hotmail.com
> CC: dev@spark.apache.org
>
> I am curious how you are going to support these over mesos and yarn.
> Any configure change like this should be applicable to all of them, not
> just local and standalone modes.
>
> Regards
> Mridul
>
> On Friday, March 13, 2015, Dale Richardson <dale...@hotmail.com> wrote:
>
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> > PR#4937 ( https://github.com/apache/spark/pull/4937) is a feature to
> > allow for Spark configuration options (whether on command line, environment
> > variable or a configuration file) to be specified via a simple expression
> > language.
> >
> >
> > Such a feature has the following end-user benefits:
> > - Allows for the flexibility in specifying time intervals or byte
> > quantities in appropriate and easy to follow units e.g. 1 week rather
> > rather then 604800 seconds
> >
> > - Allows for the scaling of a configuration option in relation to a system
> > attributes. e.g.
> >
> > SPARK_WORKER_CORES = numCores - 1
> >
> > SPARK_WORKER_MEMORY = physicalMemoryBytes - 1.5 GB
> >
> > - Gives the ability to scale multiple configuration options together eg:
> >
> > spark.driver.memory = 0.75 * physicalMemoryBytes
> >
> > spark.driver.maxResultSize = spark.driver.memory * 0.8
> >
> >
> > The following functions are currently supported by this PR:
> > NumCores: Number of cores assigned to the JVM (usually ==
> > Physical machine cores)
> > PhysicalMemoryBytes: Memory size of hosting machine
> >
> > JVMTotalMemoryBytes: Current bytes of memory allocated to the JVM
> >
> > JVMMaxMemoryBytes: Maximum number of bytes of memory available to the
> > JVM
> >
> > JVMFreeMemoryBytes: maxMemoryBytes - totalMemoryBytes
> >
> >
> > I was wondering if anybody on the mailing list has any further ideas on
> > other functions that could be useful to have when specifying spark
> > configuration options?
> > Regards,Dale.
> >
