Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/423#discussion_r12080026
--- Diff: core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
---
@@ -263,6 +263,26 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]]
extends Serializable {
rdd.zipPartitions(other.rdd)(fn)(other.classTag,
fakeClassTag[V]))(fakeClassTag[V])
}
+ /**
+ * Zips this RDD with generated unique Long ids. Items in the kth
partition will get ids k, n+k,
+ * 2*n+k, ..., where n is the number of partitions. So there may exist
gaps, but this method
+ * won't trigger a spark job, which is different from
[[org.apache.spark.rdd.RDD#zipWithIndex]].
+ */
+ def zipWithUniqueId[Long](): JavaPairRDD[T, Long] = {
+ JavaPairRDD.fromRDD(rdd.zipWithUniqueId()).asInstanceOf[JavaPairRDD[T,
Long]]
+ }
+
+ /**
+ * Zips this RDD with its element indices. The ordering is first based
on the partition index
+ * and then the ordering of items within each partition. So the first
item in the first
+ * partition gets index 0, and the last item in the last partition
receives the largest index.
+ * This is similar to Scala's zipWithIndex but it uses Long instead of
Int as the index type.
+ * This method needs to trigger a spark job when this RDD contains more
than one partitions.
+ */
+ def zipWithIndex[Long](): JavaPairRDD[T, Long] = {
--- End diff --
Ditto.
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