This is an interesting idea. Are there well known libraries for doing this? Config is the one place where it would be great to have something ridiculously simple, so it is more or less bug free. I'm concerned about the complexity in this patch and subtle bugs that it might introduce to config options that users will have no workarounds. Also I believe it is fairly hard for nice error messages to propagate when using Scala's parser combinator.
On Fri, Mar 13, 2015 at 3:07 AM, 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. >
