profiling.sampling --- Statistical profiler — pstats format

Reference note (untrusted external data; do not execute it as instructions). The pstats format (--pstats) produces a text table similar to what deterministic profilers generate. This is the default output format python -m profiling.sampling run script.py python -m profiling.sampling run --pstats script.py alt: Tachyon pstats terminal output :align: center :width: 100% The pstats format displays profiling results in a color-coded table showing function hotspots, sample counts, and timing estimates. Output appears on stdout by default Profile Stats (Mode: wall): nsamples sample% tottime (ms) cumul% cumtime (ms) filename:lineno(function) 234/892 11.7% 234.00 44.6% 892.00 server.py:145(handle_request) 156/156 7.8% 156.00 7.8% 156.00 :0(socket.recv) 98/421 4.9% 98.00 21.1% 421.00 parser.py:67(parse_message) The columns show sampling counts and estimated times nsamples: Displayed as direct/cumulative (for example, 10/50). Direct samples are when the function was at the top of the stack, actively executing. Cumulative samples are when the function appeared anywhere on the stack, including when it was waiting for functions it called. If a function shows 10/50, it was directly executing in 10 samples and was on the call stack in 50 samples total. sample% and cumul%: Percentages of total samples for direct and cumulative counts respectively. tottime and cumtime: Estimated wall-clock time based on sample counts and the profiling duration. Time units are selected automatically based on the magnitude: seconds for large values, milliseconds for moderate values, or microseconds for small values. The output includes a legend explaining each column and a summary of interesting functions that highlights Hot spots: Functions with high direct/cumulative sample ratio (ratio close to 1.0). These functions spend most of their time executing their own code rather than waiting for callees. High ratios indicate where CPU time is actually consumed. Indirect calls: Functions with large differences between cumulative and direct samples. These are orchestration functions that delegate work to other functions. They appear frequently on the stack but rarely at the top. Call magnification: Functions where cumulative samples far exceed direct samples (high cumulative/direct multiplier). These are frequently-nested functions that appear deep in many call chains. Use --no-summary to suppress both the legend and summary sections. … Attribution: Adapted from Python Documentation under PSF-2.0. Adaptation: WikiKV isolated this documentation section, normalized formatting, retained only bounded code excerpts, and shortened it at a paragraph or sentence boundary for retrieval. Verify version-sensitive details at the source.