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-rw-r--r--yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala4
-rw-r--r--yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala12
2 files changed, 10 insertions, 6 deletions
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 1b6bfb42a5..ac46bd39ce 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -84,7 +84,7 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
// Memory for the ApplicationMaster.
val memoryResource = Records.newRecord(classOf[Resource]).asInstanceOf[Resource]
- memoryResource.setMemory(args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD)
+ memoryResource.setMemory(args.amMemory + memoryOverhead)
appContext.setResource(memoryResource)
// Finally, submit and monitor the application.
@@ -118,7 +118,7 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
// val minResMemory: Int = newApp.getMinimumResourceCapability().getMemory()
// var amMemory = ((args.amMemory / minResMemory) * minResMemory) +
// ((if ((args.amMemory % minResMemory) == 0) 0 else minResMemory) -
- // YarnAllocationHandler.MEMORY_OVERHEAD)
+ // memoryOverhead )
args.amMemory
}
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index a979fe4d62..29ccec2adc 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -90,6 +90,10 @@ private[yarn] class YarnAllocationHandler(
// Containers to be released in next request to RM
private val pendingReleaseContainers = new ConcurrentHashMap[ContainerId, Boolean]
+ // Additional memory overhead - in mb.
+ private def memoryOverhead: Int = sparkConf.getInt("spark.yarn.executor.memoryOverhead",
+ YarnAllocationHandler.MEMORY_OVERHEAD)
+
// Number of container requests that have been sent to, but not yet allocated by the
// ApplicationMaster.
private val numPendingAllocate = new AtomicInteger()
@@ -106,7 +110,7 @@ private[yarn] class YarnAllocationHandler(
def getNumExecutorsFailed: Int = numExecutorsFailed.intValue
def isResourceConstraintSatisfied(container: Container): Boolean = {
- container.getResource.getMemory >= (executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD)
+ container.getResource.getMemory >= (executorMemory + memoryOverhead)
}
def releaseContainer(container: Container) {
@@ -248,7 +252,7 @@ private[yarn] class YarnAllocationHandler(
val executorHostname = container.getNodeId.getHost
val containerId = container.getId
- val executorMemoryOverhead = (executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD)
+ val executorMemoryOverhead = (executorMemory + memoryOverhead)
assert(container.getResource.getMemory >= executorMemoryOverhead)
if (numExecutorsRunningNow > maxExecutors) {
@@ -477,7 +481,7 @@ private[yarn] class YarnAllocationHandler(
numPendingAllocate.addAndGet(numExecutors)
logInfo("Will Allocate %d executor containers, each with %d memory".format(
numExecutors,
- (executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD)))
+ (executorMemory + memoryOverhead)))
} else {
logDebug("Empty allocation request ...")
}
@@ -537,7 +541,7 @@ private[yarn] class YarnAllocationHandler(
priority: Int
): ArrayBuffer[ContainerRequest] = {
- val memoryRequest = executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD
+ val memoryRequest = executorMemory + memoryOverhead
val resource = Resource.newInstance(memoryRequest, executorCores)
val prioritySetting = Records.newRecord(classOf[Priority])