Github user mridulm commented on a diff in the pull request:
https://github.com/apache/spark/pull/1165#discussion_r14738375
--- Diff: core/src/main/scala/org/apache/spark/CacheManager.scala ---
@@ -142,10 +151,76 @@ private[spark] class CacheManager(blockManager:
BlockManager) extends Logging {
* to the BlockManager as an iterator and expect to read it back
later. This is because
* we may end up dropping a partition from memory store before
getting it back, e.g.
* when the entirety of the RDD does not fit in memory. */
- val elements = new ArrayBuffer[Any]
- elements ++= values
- updatedBlocks ++= blockManager.put(key, elements, storageLevel,
tellMaster = true)
- elements.iterator.asInstanceOf[Iterator[T]]
+
--- End diff --
To add, this specific issue I am referring to does not take away from rest
of the patch.
My concern is : if iterator cant be dropped to DISK/etc, then value in
trying to determine if it can be cached in memory or not is not very high in my
opinion.
If it fails, it fails - a recomputation might succeed; and if no -
reporting to user the failure and getting him to change to MEMORY_OR_DISK is
better than suboptimal computational overheads.
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