Github user squito commented on a diff in the pull request:
https://github.com/apache/spark/pull/20940#discussion_r180197576
--- 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 --
ok that makes sense to me. If the logging overhead is small, logging on
every new peak certainly seems simpler.
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