Github user pwendell commented on a diff in the pull request: https://github.com/apache/spark/pull/4067#discussion_r23951392 --- 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 -- The question is - how expensive is the thing we are doing inside of the override method? In those other cases I think we're just checking the value of a single variable that doesn't change often (i.e. checking for interrupted). In the past we've seen performance regressions from anything more expensive than this: See https://github.com/apache/spark/commit/f708dda81ed5004325591fcc31cd79a8afa580db. The cost of CAS is hardware dependent, but can be expensive on machines with large numbers of cores because in many case there is a single shared bus lock. Volatile is similar, basically if you think about it maybe you have 16 cores and they each need to constantly invalidate each-other's local copy of the variable.
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