spark-instrumented-optimizer/project
Thomas Graves 6dbfa2bb9c [SPARK-29306][CORE] Stage Level Sched: Executors need to track what ResourceProfile they are created with
### What changes were proposed in this pull request?

This is the second PR for the Stage Level Scheduling. This is adding in the necessary executor side changes:
1) executors to know what ResourceProfile they should be using
2) handle parsing the resource profile settings - these are not in the global configs
3) then reporting back to the driver what resource profile it was started with.

This PR adds all the piping for YARN to pass the information all the way to executors, but it just uses the default ResourceProfile (which is the global applicatino level configs).

At a high level these changes include:
1) adding a new --resourceProfileId option to the CoarseGrainedExecutorBackend
2) Add the ResourceProfile settings to new internal confs that gets passed into the Executor
3) Executor changes that use the resource profile id passed in to read the corresponding ResourceProfile confs and then parse those requests and discover resources as necessary
4) Executor registers to Driver with the Resource profile id so that the ExecutorMonitor can track how many executor with each profile are running
5) YARN side changes to show that passing the resource profile id and confs actually works. Just uses the DefaultResourceProfile for now.

I also removed a check from the CoarseGrainedExecutorBackend that used to check to make sure there were task requirements before parsing any custom resource executor requests.  With the resource profiles this becomes much more expensive because we would then have to pass the task requests to each executor and the check was just a short cut and not really needed. It was much cleaner just to remove it.

Note there were some changes to the ResourceProfile, ExecutorResourceRequests, and TaskResourceRequests in this PR as well because I discovered some issues with things not being immutable. That api now look like:

val rpBuilder = new ResourceProfileBuilder()
val ereq = new ExecutorResourceRequests()
val treq = new TaskResourceRequests()

ereq.cores(2).memory("6g").memoryOverhead("2g").pysparkMemory("2g").resource("gpu", 2, "/home/tgraves/getGpus")
treq.cpus(2).resource("gpu", 2)

val resourceProfile = rpBuilder.require(ereq).require(treq).build

This makes is so that ResourceProfile is immutable and Spark can use it directly without worrying about the user changing it.

### Why are the changes needed?

These changes are needed for the executor to report which ResourceProfile they are using so that ultimately the dynamic allocation manager can use that information to know how many with a profile are running and how many more it needs to request.  Its also needed to get the resource profile confs to the executor so that it can run the appropriate discovery script if needed.

### Does this PR introduce any user-facing change?

No

### How was this patch tested?

Unit tests and manually on YARN.

Closes #26682 from tgravescs/SPARK-29306.

Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
2020-01-17 08:15:25 -06:00
..
build.properties [SPARK-26317][BUILD] Upgrade SBT to 0.13.18 2018-12-10 12:04:44 -08:00
MimaBuild.scala [SPARK-25956] Make Scala 2.12 as default Scala version in Spark 3.0 2018-11-14 16:22:23 -08:00
MimaExcludes.scala [SPARK-29306][CORE] Stage Level Sched: Executors need to track what ResourceProfile they are created with 2020-01-17 08:15:25 -06:00
plugins.sbt [SPARK-29729][BUILD] Upgrade ASM to 7.2 2019-11-03 10:42:38 -08:00
SparkBuild.scala Revert [SPARK-27300][GRAPH] Add Spark Graph modules and dependencies 2019-12-17 09:06:23 -08:00