squito commented on a change in pull request #23767: [SPARK-26329][CORE] Faster
polling of executor memory metrics.
URL: https://github.com/apache/spark/pull/23767#discussion_r268871304
##########
File path:
core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
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@@ -267,12 +277,17 @@ private[spark] class EventLoggingListener(
override def onExecutorMetricsUpdate(event:
SparkListenerExecutorMetricsUpdate): Unit = {
if (shouldLogStageExecutorMetrics) {
- // For the active stages, record any new peak values for the memory
metrics for the executor
- event.executorUpdates.foreach { executorUpdates =>
- liveStageExecutorMetrics.values.foreach { peakExecutorMetrics =>
- val peakMetrics = peakExecutorMetrics.getOrElseUpdate(
- event.execId, new ExecutorMetrics())
- peakMetrics.compareAndUpdatePeakValues(executorUpdates)
+ event.executorUpdates.foreach { case (stageKey1, peaks) =>
+ liveStageExecutorMetrics.foreach { case (stageKey2,
metricsPerExecutor) =>
+ // If the update came from the driver, stageKey1 will be the dummy
key (-1, -1),
+ // so record those peaks for all active stages.
+ // Otherwise, record the peaks for the matching stage.
+ val driverStageKey = (-1, -1)
+ if (stageKey1 == driverStageKey || stageKey1 == stageKey2) {
+ val metrics = metricsPerExecutor.getOrElseUpdate(
+ event.execId, new ExecutorMetrics())
+ metrics.compareAndUpdatePeakValues(peaks)
Review comment:
so the trick here is having the DAGScheduler expose the set of running
stages in a thread-safe manner. a ConcurrentSkipListSet might be a good
choice, but I will keep thinking about this.
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