Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/1639#discussion_r15568992
--- Diff: core/src/main/scala/org/apache/spark/rdd/RDD.scala ---
@@ -1239,6 +1239,28 @@ abstract class RDD[T: ClassTag](
/** The [[org.apache.spark.SparkContext]] that this RDD was created on.
*/
def context = sc
+ /**
+ * Private API for changing an RDD's ClassTag.
+ * Used for internal Java <-> Scala API compatibility.
+ */
+ private[spark] def retag(cls: Class[T]): RDD[T] = {
+ val classTag: ClassTag[T] = ClassTag.apply(cls)
+ this.retag(classTag)
+ }
+
+ /**
+ * Private API for changing an RDD's ClassTag.
+ * Used for internal Java <-> Scala API compatibility.
+ */
+ private[spark] def retag(classTag: ClassTag[T]): RDD[T] = {
+ val oldRDD = this
+ new RDD[T](sc, Seq(new OneToOneDependency(this)))(classTag) {
+ override protected def getPartitions: Array[Partition] =
oldRDD.getPartitions
+ override def compute(split: Partition, context: TaskContext):
Iterator[T] =
+ oldRDD.compute(split, context)
+ }
--- End diff --
Actually compute just works at the iterator level, so I don't think
mapPartitions would hurt. All you do is pass through the parent's iterator.
When you call compute() you're already deserializing the RDD, this won't create
extra work.
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