Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/50#discussion_r10245880
--- Diff: core/src/main/scala/org/apache/spark/CacheManager.scala ---
@@ -71,10 +71,21 @@ private[spark] class CacheManager(blockManager:
BlockManager) extends Logging {
val computedValues = rdd.computeOrReadCheckpoint(split, context)
// Persist the result, so long as the task is not running locally
if (context.runningLocally) { return computedValues }
- val elements = new ArrayBuffer[Any]
- elements ++= computedValues
- blockManager.put(key, elements, storageLevel, tellMaster = true)
- elements.iterator.asInstanceOf[Iterator[T]]
+ if (storageLevel.useDisk && !storageLevel.useMemory) {
+ blockManager.put(key, computedValues, storageLevel, tellMaster
= true)
+ return blockManager.get(key) match {
+ case Some(values) =>
+ return new InterruptibleIterator(context,
values.asInstanceOf[Iterator[T]])
+ case None =>
+ logInfo("Failure to store %s".format(key))
+ return null
+ }
+ } else {
+ val elements = new ArrayBuffer[Any]
+ elements ++= computedValues
+ blockManager.put(key, elements, storageLevel, tellMaster =
true)
+ return elements.iterator.asInstanceOf[Iterator[T]]
+ }
--- End diff --
Even if we use the same code path for say MEM_ONLY, we won't actually write
the data to disk since we also pass the storage level to BlockManager along
with the data.
But I see your second point about possibly dropping a block before we read
it. That does seem to prevent us from merging the two cases.
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