wypoon commented on a change in pull request #23767: [SPARK-26329][CORE][WIP]
Faster polling of executor memory metrics.
URL: https://github.com/apache/spark/pull/23767#discussion_r257419088
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File path: core/src/main/scala/org/apache/spark/executor/Executor.scala
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@@ -840,8 +952,25 @@ private[spark] class Executor(
val accumUpdates = new ArrayBuffer[(Long, Seq[AccumulatorV2[_, _]])]()
val curGCTime = computeTotalGcTime()
- // get executor level memory metrics
- val executorUpdates = heartbeater.getCurrentMetrics()
+ // if not polling in a separater poller, poll here
+ if (poller == null) {
+ poll()
+ }
+
+ // build the executor level memory metrics
+ val executorUpdates = new HashMap[StageKey, ExecutorMetrics]
+
+ def peaksForStage(k: StageKey, v: AtomicLong): (StageKey, AtomicLongArray)
=
+ if (v.get() > 0) (k, stageMetricPeaks.get(k)) else null
+
+ def addPeaks(nested: (StageKey, AtomicLongArray)): Unit = {
+ val (k, v) = nested
+ executorUpdates.put(k, new ExecutorMetrics(v))
+ // at the same time, reset the peaks in stageMetricPeaks
+ stageMetricPeaks.put(k, new
AtomicLongArray(ExecutorMetricType.numMetrics))
+ }
+
+ activeStages.forEach[(StageKey, AtomicLongArray)](LONG_MAX_VALUE,
peaksForStage, addPeaks)
Review comment:
In that case, the per task metrics for any task in that stage will get
updated, and sent in the task result, so we don't lose that information.
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