Percentiles and other statistics besides mean, min, max for flat profiles about omnitrace HOT 2 OPEN

Hi @pelahi, currently the flat profiles are handled by timemory and the min/max/stddev is stored in a constant size data structure that does not require dynamic allocations: statistics.cpp.

In order to provide the requested functionality, it would have to effectively store every measurement because we wouldn't be able to bin the values until the min/max was established. Another way of explaining it is that we would need collect the full trace before condensing it into a flat profile during post-processing instead of condensing it into a flat profile on the fly. And that would significantly bloat the memory usage which is a non-starter since tracing at scale on supercomputers generates far too much data. So unfortunately, we will not be able to accommodate this request directly.

However, I've just finished work on integrating support for the perfetto trace format into a Python package called hatchet in hatchet/hatchet#501. This package will effectively translate the perfetto traces into a pandas dataframe and allow you to manipulate it into a flat profile and bin the measurements into however many percentiles you desire. Ideally, this capability could even be part of a omnitrace-analysis Python package installable via PyPi with a console script omnitrace-analyze which command-line-izes this functionality.

side note: hatchet already supports the JSON profiles generated by timemory.

from omnitrace.

As you can see in the hatchet docs for generating a flat profile, it is very straightforward to convert the trace to a flat profile and pandas has built-in capabilities for calculating the percentiles/quantiles (tutorial)

from omnitrace.