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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