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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