Comments (1)
Hi @mansenfranzen,
I have read with interest your ideas and questions. Unfortunately, Spark metrics are not yet well documented (besides the short description in the monitoring documentation). Further investigations in the source code and coming up with reference workloads that can be used to clarify what a particular metric measures, may be quite good to have.
These days, for Spark performance troubleshooting, I am trying to focus more on metrics that address time-based drilldown, for example to understand how much of the executor time was CPU, how much I/O how much GC, how much shuffle-related operations. etc. Workload-type metrics are also useful, of course, notably memory and I/O related ones. I find more convenient to use the Spark metrics system for that (which anyways is also linked with the metrics used here in SparkMeasure), see https://github.com/cerndb/spark-dashboard
I'll be interested to know if you make further progress on a deeper understanding of the metrics.
Best,
L.
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