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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