Merge branch 'master' of https://github.com/apache/spark into SPARK-1609

core/src/main/scala/org/apache/spark/SparkContext.scala

@@ -300,10 +300,17 @@ class SparkContext(config: SparkConf) extends Logging {
 
   // Create and start the scheduler
   private[spark] var taskScheduler = SparkContext.createTaskScheduler(this, master)
-  taskScheduler.start()
+  @volatile private[spark] var dagScheduler: DAGScheduler = _
+  try {
+    dagScheduler = new DAGScheduler(this)
+  } catch {
+    case e: Exception => throw
+      new SparkException("DAGScheduler cannot be initialized due to %s".format(e.getMessage))
+  }
 
-  @volatile private[spark] var dagScheduler = new DAGScheduler(this)
-  dagScheduler.start()
+  // start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's
+  // constructor
+  taskScheduler.start()
 
   private[spark] val cleaner: Option[ContextCleaner] = {
     if (conf.getBoolean("spark.cleaner.referenceTracking", true)) {
@@ -1022,8 +1029,8 @@ class SparkContext(config: SparkConf) extends Logging {
       partitions: Seq[Int],
       allowLocal: Boolean,
       resultHandler: (Int, U) => Unit) {
-    partitions.foreach{ p =>
-      require(p >= 0 && p < rdd.partitions.size, s"Invalid partition requested: $p")
+    if (dagScheduler == null) {
+      throw new SparkException("SparkContext has been shutdown")
     }
     val callSite = getCallSite
     val cleanedFunc = clean(func)
@@ -1132,9 +1139,6 @@ class SparkContext(config: SparkConf) extends Logging {
       resultHandler: (Int, U) => Unit,
       resultFunc: => R): SimpleFutureAction[R] =
   {
-    partitions.foreach{ p =>
-      require(p >= 0 && p < rdd.partitions.size, s"Invalid partition requested: $p")
-    }
     val cleanF = clean(processPartition)
     val callSite = getCallSite
     val waiter = dagScheduler.submitJob(

core/src/main/scala/org/apache/spark/rdd/RDD.scala

@@ -1142,9 +1142,9 @@ abstract class RDD[T: ClassTag](
   @transient private var doCheckpointCalled = false
 
   /**
-   * Performs the checkpointing of this RDD by saving this. It is called by the DAGScheduler
-   * after a job using this RDD has completed (therefore the RDD has been materialized and
-   * potentially stored in memory). doCheckpoint() is called recursively on the parent RDDs.
+   * Performs the checkpointing of this RDD by saving this. It is called after a job using this RDD
+   * has completed (therefore the RDD has been materialized and potentially stored in memory).
+   * doCheckpoint() is called recursively on the parent RDDs.
    */
   private[spark] def doCheckpoint() {
     if (!doCheckpointCalled) {