Github user kayousterhout commented on a diff in the pull request:
https://github.com/apache/spark/pull/3120#discussion_r22809066
--- Diff: core/src/main/scala/org/apache/spark/CacheManager.scala ---
@@ -44,7 +44,14 @@ private[spark] class CacheManager(blockManager:
BlockManager) extends Logging {
blockManager.get(key) match {
case Some(blockResult) =>
// Partition is already materialized, so just return its values
+ val existingMetrics = context.taskMetrics.inputMetrics
+ val prevBytesRead = existingMetrics
+ .filter(_.readMethod == blockResult.inputMetrics.readMethod)
+ .map(_.bytesRead)
+ .getOrElse(0L)
--- End diff --
What if there are 3 input sources that interleave here? Suppose you have
(1) input from cache, (2) input from Hadoop, and (3) input from cache. My
understanding is that when (2) starts being read, it will clobber the input
metrics from (1). Then, when (3) is read, it will again clobber the input
metrics, so the metrics won't properly reflect the total data read from cache
(they'll only reflect the data read from (3)). Is that right?
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
