Github user adrian-wang commented on a diff in the pull request:
https://github.com/apache/spark/pull/6081#discussion_r30300624
--- Diff: core/src/main/scala/org/apache/spark/SparkContext.scala ---
@@ -689,6 +689,64 @@ class SparkContext(config: SparkConf) extends Logging
with ExecutorAllocationCli
new ParallelCollectionRDD[T](this, seq, numSlices, Map[Int,
Seq[String]]())
}
+ /**
+ * Creates a new RDD[Long] containing elements from `start` to
`end`(exclusive), increased by
+ * `step` every element.
+ *
+ * @note if we need to cache this RDD, we should make sure each
partition contains no more than
+ * 2 billion element.
+ *
+ * @param start the start value.
+ * @param end the end value.
+ * @param step the incremental step
+ * @param numSlices the partition number of the new RDD.
+ * @return
+ */
+ def range(
+ start: Long,
+ end: Long,
+ step: Long = 1,
+ numSlices: Int = defaultParallelism): RDD[Long] = withScope {
+ assertNotStopped()
+ if (step == 0) {
+ // when step is 0, range will run infinite
+ throw new IllegalArgumentException("`step` cannot be 0")
+ }
+ val length =
+ if ((end - start) % step == 0) {
--- End diff --
This is really an issue.
The length could be overflow also. Even if the `length` does not overflow,
the calculation of `partitionStart`/`partitionEnd` could also lead to that. I
am trying to figure out another way to calculate this, since the `length` is
used for this calculation.
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