tgravescs commented on a change in pull request #25047:
[WIP][SPARK-27371][CORE] Support GPU-aware resources scheduling in Standalone
URL: https://github.com/apache/spark/pull/25047#discussion_r303965288
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File path: core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala
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@@ -89,17 +125,51 @@ private[spark] class WorkerInfo(
drivers(driver.id) = driver
memoryUsed += driver.desc.mem
coresUsed += driver.desc.cores
+ driverToResourcesUsed(driver.id) = driver.resources
}
def removeDriver(driver: DriverInfo) {
drivers -= driver.id
memoryUsed -= driver.desc.mem
coresUsed -= driver.desc.cores
+ driverToResourcesUsed.remove(driver.id)
}
def setState(state: WorkerState.Value): Unit = {
this.state = state
}
def isAlive(): Boolean = this.state == WorkerState.ALIVE
+
+ /**
+ * acquire specified amount resources for driver/executor from the worker
+ * @param resourceReqs the resources requirement from driver/executor
+ */
+ def acquireResources(resourceReqs: Seq[ResourceRequirement])
+ : Map[String, ResourceInformation] = {
+ resourceReqs.map { req =>
+ val rName = req.resourceName
+ val amount = req.amount
+ rName -> resources(rName).acquire(amount)
+ }.toMap
+ }
+
+ /**
+ * used during master recovery
+ */
+ def notifyResources(expected: Map[String, ResourceInformation]): Unit = {
Review comment:
call this recoverResources instead since it looks like its only used for
recovery
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