diff --git a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala
index 843a893235e56..926bce6f15a2a 100644
--- a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala
@@ -30,7 +30,7 @@ import org.apache.spark.util.StatCounter
  */
 class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
   /** Add up the elements in this RDD. */
-  def sum(): Double = {
+  def sum(): Double = self.withScope {
     self.fold(0.0)(_ + _)
   }
 
@@ -38,37 +38,49 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
    * Return a [[org.apache.spark.util.StatCounter]] object that captures the mean, variance and
    * count of the RDD's elements in one operation.
    */
-  def stats(): StatCounter = {
+  def stats(): StatCounter = self.withScope {
     self.mapPartitions(nums => Iterator(StatCounter(nums))).reduce((a, b) => a.merge(b))
   }
 
   /** Compute the mean of this RDD's elements. */
-  def mean(): Double = stats().mean
+  def mean(): Double = self.withScope {
+    stats().mean
+  }
 
   /** Compute the variance of this RDD's elements. */
-  def variance(): Double = stats().variance
+  def variance(): Double = self.withScope {
+    stats().variance
+  }
 
   /** Compute the standard deviation of this RDD's elements. */
-  def stdev(): Double = stats().stdev
+  def stdev(): Double = self.withScope {
+    stats().stdev
+  }
 
   /**
    * Compute the sample standard deviation of this RDD's elements (which corrects for bias in
    * estimating the standard deviation by dividing by N-1 instead of N).
    */
-  def sampleStdev(): Double = stats().sampleStdev
+  def sampleStdev(): Double = self.withScope {
+    stats().sampleStdev
+  }
 
   /**
    * Compute the sample variance of this RDD's elements (which corrects for bias in
    * estimating the variance by dividing by N-1 instead of N).
    */
-  def sampleVariance(): Double = stats().sampleVariance
+  def sampleVariance(): Double = self.withScope {
+    stats().sampleVariance
+  }
 
   /**
    * :: Experimental ::
    * Approximate operation to return the mean within a timeout.
    */
   @Experimental
-  def meanApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = {
+  def meanApprox(
+      timeout: Long,
+      confidence: Double = 0.95): PartialResult[BoundedDouble] = self.withScope {
     val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns)
     val evaluator = new MeanEvaluator(self.partitions.length, confidence)
     self.context.runApproximateJob(self, processPartition, evaluator, timeout)
@@ -79,7 +91,9 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
    * Approximate operation to return the sum within a timeout.
    */
   @Experimental
-  def sumApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = {
+  def sumApprox(
+      timeout: Long,
+      confidence: Double = 0.95): PartialResult[BoundedDouble] = self.withScope {
     val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns)
     val evaluator = new SumEvaluator(self.partitions.length, confidence)
     self.context.runApproximateJob(self, processPartition, evaluator, timeout)
@@ -93,7 +107,7 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
    * If the RDD contains infinity, NaN throws an exception
    * If the elements in RDD do not vary (max == min) always returns a single bucket.
    */
-  def histogram(bucketCount: Int): Pair[Array[Double], Array[Long]] = {
+  def histogram(bucketCount: Int): Pair[Array[Double], Array[Long]] = self.withScope {
     // Scala's built-in range has issues. See #SI-8782
     def customRange(min: Double, max: Double, steps: Int): IndexedSeq[Double] = {
       val span = max - min
@@ -140,7 +154,9 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
    * the maximum value of the last position and all NaN entries will be counted
    * in that bucket.
    */
-  def histogram(buckets: Array[Double], evenBuckets: Boolean = false): Array[Long] = {
+  def histogram(
+      buckets: Array[Double],
+      evenBuckets: Boolean = false): Array[Long] = self.withScope {
     if (buckets.length < 2) {
       throw new IllegalArgumentException("buckets array must have at least two elements")
     }
diff --git a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
index b16376f773750..d71bb63000904 100644
--- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
@@ -47,8 +47,6 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
     self: RDD[P])
   extends Logging with Serializable
 {
-  // TODO: Don't forget to scope me later
-
   private val ordering = implicitly[Ordering[K]]
 
   /**
@@ -59,7 +57,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
    */
   // TODO: this currently doesn't work on P other than Tuple2!
   def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.length)
-      : RDD[(K, V)] =
+      : RDD[(K, V)] = self.withScope
   {
     val part = new RangePartitioner(numPartitions, self, ascending)
     new ShuffledRDD[K, V, V](self, part)
@@ -73,7 +71,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
    * This is more efficient than calling `repartition` and then sorting within each partition
    * because it can push the sorting down into the shuffle machinery.
    */
-  def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)] = {
+  def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)] = self.withScope {
     new ShuffledRDD[K, V, V](self, partitioner).setKeyOrdering(ordering)
   }
 
@@ -83,7 +81,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
    * performed efficiently by only scanning the partitions that might contain matching elements.
    * Otherwise, a standard `filter` is applied to all partitions.
    */
-  def filterByRange(lower: K, upper: K): RDD[P] = {
+  def filterByRange(lower: K, upper: K): RDD[P] = self.withScope {
 
     def inRange(k: K): Boolean = ordering.gteq(k, lower) && ordering.lteq(k, upper)
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala
index 059f8963691f0..3dfcf67f0eb66 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala
@@ -85,7 +85,9 @@ class SequenceFileRDDFunctions[K <% Writable: ClassTag, V <% Writable : ClassTag
    * byte arrays to BytesWritable, and Strings to Text. The `path` can be on any Hadoop-supported
    * file system.
    */
-  def saveAsSequenceFile(path: String, codec: Option[Class[_ <: CompressionCodec]] = None) {
+  def saveAsSequenceFile(
+      path: String,
+      codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope {
     def anyToWritable[U <% Writable](u: U): Writable = u
 
     // TODO We cannot force the return type of `anyToWritable` be same as keyWritableClass and