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SUMMARY:Profiling Python: From cProfile to Sampling - Farid Mirzayev
DTSTART;TZID=Europe/Stockholm:20261120T120000
DTEND;TZID=Europe/Stockholm:20261120T123000
DTSTAMP:20261111T055033Z
UID:pretalx-pycon-sweden-2026-G9LWHP@pretalx.com
DESCRIPTION:Your Python program is slow\, so you run `cProfile`. It report
 s exact call counts and instrumented timings\, but tracing every function 
 call can change the workload you are trying to understand. Run a sampling 
 profiler on the same program and a different picture emerges. Neither resu
 lt is inherently wrong: each profiler answers a different question.\n\nIn 
 this talk\, we will compare `cProfile`\, external sampling tools such as `
 py-spy` and `austin`\, and Python 3.15's new standard-library sampling pro
 filer\, `profiling.sampling`\, also known as Tachyon.\n\nUsing the same mi
 xed workload throughout\, we will see why call-heavy code is especially vu
 lnerable to tracing overhead\, how periodic stack samples become estimated
  hotspots\, and what sampling can miss. We will then compare Tachyon's wal
 l-clock and CPU modes to reveal an important distinction: time spent waiti
 ng can dominate one profile and disappear from the other.\n\nWe will also 
 see how Tachyon can profile a script or attach to an existing Python proce
 ss without modifying or restarting it.\n\nYou will leave with a practical 
 framework: use tracing when you need exact call counts\, sampling when you
  need representative hotspots or production diagnostics\, and benchmarking
  when you need to compare small performance differences.
LOCATION:Auditorium
URL:https://pretalx.com/pycon-sweden-2026/talk/G9LWHP/
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