Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/4067#discussion_r23913204
--- Diff: core/src/main/scala/org/apache/spark/CacheManager.scala ---
@@ -47,9 +49,13 @@ private[spark] class CacheManager(blockManager:
BlockManager) extends Logging {
val inputMetrics = blockResult.inputMetrics
val existingMetrics = context.taskMetrics
.getInputMetricsForReadMethod(inputMetrics.readMethod)
- existingMetrics.addBytesRead(inputMetrics.bytesRead)
+ existingMetrics.incBytesRead(inputMetrics.bytesRead)
- new InterruptibleIterator(context,
blockResult.data.asInstanceOf[Iterator[T]])
+ val iter = blockResult.data.asInstanceOf[Iterator[T]]
+ new InterruptibleIterator(context,
AfterNextInterceptingIterator(iter, (next: T) => {
+ existingMetrics.incRecordsRead(1)
--- End diff --
Hm, this seems prohibitively expensive. This will add multiple function
invocations and force hardware locking for every +next+ call (or at least some
internal checks if it's changed to +volatile+). Have you looked at the
performance implications of this? For instance, try reading from a good amount
of off-heap data from many threads and seeing if it regresses performance.
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