Github user squito commented on a diff in the pull request: https://github.com/apache/spark/pull/20940#discussion_r179795171 --- Diff: core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala --- @@ -234,8 +244,22 @@ private[spark] class EventLoggingListener( } } - // No-op because logging every update would be overkill - override def onExecutorMetricsUpdate(event: SparkListenerExecutorMetricsUpdate): Unit = { } + /** + * Log if there is a new peak value for one of the memory metrics for the given executor. + * Metrics are cleared out when a new stage is started in onStageSubmitted, so this will + * log new peak memory metric values per executor per stage. + */ + override def onExecutorMetricsUpdate(event: SparkListenerExecutorMetricsUpdate): Unit = { --- End diff -- I wouldn't think you'd want to log for every new peak, as I'd expect it would be natural for the peak to keep growing, so you'd just end up with a lot of logs. I'd expect you'd just log the peak when the stage ended, or when the executor died. the downside of that approach is that you never log a peak if the driver dies ... but then you've got to figure out the driver issue anyway.
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