ivoson commented on code in PR #39459:
URL: https://github.com/apache/spark/pull/39459#discussion_r1090773361
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core/src/main/scala/org/apache/spark/storage/BlockManager.scala:
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@@ -1325,14 +1325,47 @@ private[spark] class BlockManager(
blockInfoManager.releaseAllLocksForTask(taskAttemptId)
}
+ /**
+ * Retrieve the given rdd block if it exists and is visible, otherwise call
the provided
+ * `makeIterator` method to compute the block, persist it, and return its
values.
+ *
+ * @return either a BlockResult if the block was successfully cached, or an
iterator if the block
+ * could not be cached.
+ */
+ def getOrElseUpdateRDDBlock[T](
+ taskId: Long,
+ blockId: RDDBlockId,
+ level: StorageLevel,
+ classTag: ClassTag[T],
+ makeIterator: () => Iterator[T]): Either[BlockResult, Iterator[T]] = {
+ val isCacheVisible = isRDDBlockVisible(blockId)
+ var computed: Boolean = false
+ val getIterator = () => {
+ computed = true
+ makeIterator()
+ }
+
+ val res = getOrElseUpdate(blockId, level, classTag, getIterator)
+ if (res.isLeft && !isCacheVisible) {
+ if (!computed) {
+ // Loaded from cache, re-compute to update accumulators.
+ makeIterator()
+ }
Review Comment:
> Particularly for unordered or indeterminate computation, this can impact
the accumulators generated - and it wont match the result iterator returned.
There could be some other issues with such scenarios, like if we have blocks
generated from different task attempts or cache re-compute, each cache may also
have different data. Would this also be a problem?
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