1 files changed, 5 insertions, 3 deletions
diff --git a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
index cd96250389..09d0a8189d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
@@ -20,13 +20,15 @@ package org.apache.spark.rdd
 import scala.collection.immutable.NumericRange
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.Map
+import scala.reflect.ClassTag
+
 import org.apache.spark._
 import java.io._
 import scala.Serializable
 import org.apache.spark.serializer.JavaSerializer
 import org.apache.spark.util.Utils
 
-private[spark] class ParallelCollectionPartition[T: ClassManifest](
+private[spark] class ParallelCollectionPartition[T: ClassTag](
     var rddId: Long,
     var slice: Int,
     var values: Seq[T])
@@ -78,7 +80,7 @@ private[spark] class ParallelCollectionPartition[T: ClassManifest](
   }
 }
 
-private[spark] class ParallelCollectionRDD[T: ClassManifest](
+private[spark] class ParallelCollectionRDD[T: ClassTag](
     @transient sc: SparkContext,
     @transient data: Seq[T],
     numSlices: Int,
@@ -109,7 +111,7 @@ private object ParallelCollectionRDD {
    * collections specially, encoding the slices as other Ranges to minimize memory cost. This makes
    * it efficient to run Spark over RDDs representing large sets of numbers.
    */
-  def slice[T: ClassManifest](seq: Seq[T], numSlices: Int): Seq[Seq[T]] = {
+  def slice[T: ClassTag](seq: Seq[T], numSlices: Int): Seq[Seq[T]] = {
     if (numSlices < 1) {
       throw new IllegalArgumentException("Positive number of slices required")
     }