Github user ksakellis commented on a diff in the pull request:

    https://github.com/apache/spark/pull/4067#discussion_r23950220
  
    --- 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 --
    
    @sryza right. So how do you propose we increment the bytes and records read 
in a threadsafe way? If we use a @volatile Long we can't safely do an increment 
unless we guarantee that only one thread is accessing the InputMetrics at any 
one time. I guess this is an okay assumption now but doesn't that open 
ourselves up to race conditions down the line when we add more multithreading? 
    
    Looking at: 
http://stackoverflow.com/questions/2538070/atomic-operation-cost it doesn't 
seem like the cost of CAS is that high, there is at most 2 cacheline misses for 
this integer and only 1 if other CPUs are not reading and writing from it.  Am 
i misinterpreting this?



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