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SUMMARY:Profiling Python: From cProfile to Sampling - Farid Mirzayev
DTSTART;TZID=Europe/Zurich:20261022T143000
DTEND;TZID=Europe/Zurich:20261022T150000
DTSTAMP:20261111T040115Z
UID:pretalx-swiss-python-summit-2026-WJCKGM@talks.python-summit.ch
DESCRIPTION:Python developers have long faced a frustrating tradeoff: the 
 more precisely you measured performance\, the more you risked changing it.
 \n\nTraditional profilers like `profile` and `cProfile` trace every functi
 on call\, providing exact call counts and instrumented timings at the cost
  of significant overhead.\n\nThe ecosystem fought back. Tools like `py-spy
 ` and `austin` embraced statistical sampling\, attaching to running proces
 ses and gathering useful insights with minimal impact. Yet statistical sam
 pling remained outside the Python standard library.\n\nPython 3.15 changes
  that.\n\nThe new `profiling.sampling` module\, also known as Tachyon\, br
 ings statistical profiling into the standard library\, building on years o
 f community experimentation.\n\nIn this talk\, we'll compare tracing and s
 ampling profilers\, exploring how each approach works\, where it struggles
 \, and how sampled stacks become flame graphs.\n\nBy profiling the same co
 de with different tools\, we'll see how each profiler reveals a different 
 picture of performance — and why choosing the right tool matters.\n\n## 
 Problem Statement\n\nMost Python developers reach for `cProfile` because i
 t's built in\, often without realizing its limitations or knowing modern a
 lternatives exist. Sampling profilers have been available for years\, but 
 many developers have not encountered them. This talk closes that gap.\n\n#
 # Attendees will leave knowing\n\n- Deterministic vs statistical profiling
  and why it matters\n- When `cProfile` helps — and when it can mislead\n
 - How and when to use Python 3.15's `profiling.sampling`\n- How to choose 
 the right profiler for the job
LOCATION:Aula 4.101
URL:https://talks.python-summit.ch/swiss-python-summit-2026/talk/WJCKGM/
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