Thanks, that's very helpful input in terms of debugging performance issues and tuning jobs. So far as I can see there aren't any metrics that provide simple ways to track overall job performance over time on a shared cluster. Aggregated resource usage seems the closest thing, but on a shared cluster I think I will just need to accept that can vary widely depending on the state of the cluster for 2 identical job runs, so while it gives some indication of job performance, there's not really any panacea.   I think I was looking for something like I would usually track for web app (e.g. response times of a web server under a given load), which help me to spot when performance regressions happen. I guess not such a straightforward thing to do with the kinds of workload Spark handles! ... View more