LucaCanali edited a comment on pull request #31367:
URL: https://github.com/apache/spark/pull/31367#issuecomment-769738766


   I have updated PR with the proposed list of metrics. The list currently 
contains 10 metrics.
   I can see the need to have just a few important metrics.
   However, I can also see that the Python UDF implementation is complex with 
many moving parts, metrics can help with troubleshooting with corner cases too, 
in those circumstances more metrics mean more flexibility and more chances to 
find where the root cause of the problem is.
   
   Another point for discussion is how accurate are the metrics in the current 
implementation? I have run a few tests to check that the values measured make 
sense and are in the ballpark of what was expected.  There is room to do more 
tests with some corner cases to understand how the instrumentation copes there.
   
   In particular, measuring execution time can be challenging at times, as with 
this we attempt to do all measuremetns from JVM/Scala code. I am aiming to do a 
“good enough to be useful” job for the timing metrics, rather than a precise 
timing. I have put in the metrics description some hints of the nuances I found 
when testing. 
   
   I think the "time spent sending data" can be useful when troubleshooting 
cases where the performance problem is with sending large amounts of data to 
Python. Time spent executing is the key metric to understand the overall 
performance. Number of rows returned and processed are also useful metric, to 
understand how much work has been done and how much still needs to be done when 
monitoring the progress of an active query.


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