Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/12899#discussion_r62614867
--- Diff: core/src/main/scala/org/apache/spark/scheduler/Task.scala ---
@@ -155,7 +155,13 @@ private[spark] abstract class Task[T](
*/
def collectAccumulatorUpdates(taskFailed: Boolean = false):
Seq[AccumulatorV2[_, _]] = {
if (context != null) {
- context.taskMetrics.accumulators().filter { a => !taskFailed ||
a.countFailedValues }
+ context.taskMetrics.internalAccums.filter { a =>
+ // RESULT_SIZE accumulator is always zero at executor, we need to
send it back as its
+ // value will be updated at driver side.
+ !a.isZero || a.name == Some(InternalAccumulator.RESULT_SIZE)
+ // zero value external accumulators may still be useful, e.g.
SQLMetrics, we should not filter
--- End diff --
We could see zero value as `null`, it's reasonable to not include the
`null` in sum/avg. Is there any downside to exclude these zeros ?
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