[GitHub] HyukjinKwon commented on a change in pull request #23263: [SPARK-23674][ML] Adds Spark ML Events to Instrumentation

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HyukjinKwon commented on a change in pull request #23263: [SPARK-23674][ML] Adds Spark ML Events to Instrumentation
URL: https://github.com/apache/spark/pull/23263#discussion_r248535747
 
 

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 File path: mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
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 @@ -197,10 +200,12 @@ object Pipeline extends MLReadable[Pipeline] {
   @Since("1.6.0")
   override def load(path: String): Pipeline = super.load(path)
 
-  private[Pipeline] class PipelineWriter(instance: Pipeline) extends MLWriter {
+  private[Pipeline] class PipelineWriter(val instance: Pipeline) extends MLWriter {
 
     SharedReadWrite.validateStages(instance.getStages)
 
+    override def save(path: String): Unit =
+      instrumented(_.withSaveInstanceEvent(this, path, logging = true)(super.save(path)))
 
 Review comment:
   Eh, @srowen, `MLEvents` emits log (by leveraging overridden `log[Debug|Error|Warning|Info]` at `Instrumentation`) at top level call for `Pipeline`. Since it emits a debug log, I think it's okay to remove this `logging` flag and log every time it calls.
   
   Did I maybe understand correctly or maybe I'm misunderstanding you?

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