[SPARK-1930] The Container is running beyond physical memory limits, so as to be killed

Author: witgo <witgo@qq.com>

Closes #894 from witgo/SPARK-1930 and squashes the following commits:

564307e [witgo] Update the running-on-yarn.md
3747515 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1930
172647b [witgo] add memoryOverhead docs
a0ff545 [witgo] leaving only two configs
a17bda2 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1930
478ca15 [witgo] Merge branch 'master' into SPARK-1930
d1244a1 [witgo] Merge branch 'master' into SPARK-1930
8b967ae [witgo] Merge branch 'master' into SPARK-1930
655a820 [witgo] review commit
71859a7 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1930
e3c531d [witgo] review commit
e16f190 [witgo] different memoryOverhead
ffa7569 [witgo] review commit
5c9581f [witgo] Merge branch 'master' into SPARK-1930
9a6bcf2 [witgo] review commit
8fae45a [witgo] fix NullPointerException
e0dcc16 [witgo] Adding  configuration items
b6a989c [witgo] Fix container memory beyond limit, were killed

(cherry picked from commit cdf2b04570871848442ca9f9e2316a37e4aaaae0)
Signed-off-by: Thomas Graves <tgraves@apache.org>

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])