Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/1165#discussion_r17645275
--- Diff: core/src/main/scala/org/apache/spark/CacheManager.scala ---
@@ -118,21 +118,29 @@ private[spark] class CacheManager(blockManager:
BlockManager) extends Logging {
}
/**
- * Cache the values of a partition, keeping track of any updates in the
storage statuses
- * of other blocks along the way.
+ * Cache the values of a partition, keeping track of any updates in the
storage statuses of
+ * other blocks along the way.
+ *
+ * The effective storage level refers to the level that actually
specifies BlockManager put
+ * behavior, not the level originally specified by the user. This is
mainly for forcing a
+ * MEMORY_AND_DISK partition to disk if there is not enough room to
unroll the partition,
+ * while preserving the the original semantics of the RDD as specified
by the application.
*/
private def putInBlockManager[T](
--- End diff --
Ah, got it. So it's a follow of the origin implementation:
val elements = new ArrayBuffer[Any]
elements ++= computedValues
...
return elements.iterator.asInstanceOf[Iterator[T]]
Then we can ensure the returned iterator will always have data for user.
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