srowen 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_r248654526
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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:
I see, yes I think it's good to emit log statements by default to match
`Instrumentation`, and think it's always fine to emit a debug log. I had
imagined that maybe a default implementation of `MLEvents` calls `logInfo` or
similar for these events, and then anyone can provide a different
implementation. Or, always logging at debug level no matter what the
implementation also seems OK.
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