Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/1165#discussion_r17617153
--- 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, this part is actually really tricky. If you just used `putIterator`
here, the result would be incorrect and the reason behind that is quite subtle.
Here in `getOrCompute` we need to return the actual iterator in addition to
storing it in `BlockManager`, so if we just use `putIterator` with
`MEMORY_ONLY` level, then other threads might drop our block before we get to
read it back, in which case we will have nothing to return because our original
iterator was already exhausted.
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