"profiling" --- Python profilers
********************************

Adicionado na versão 3.15.

**Source code:** Lib/profiling/

======================================================================


Introduction to profiling
=========================

A *profile* is a set of statistics that describes how often and for
how long various parts of a program execute. These statistics help
identify performance bottlenecks and guide optimization efforts.
Python provides two fundamentally different approaches to collecting
this information: statistical sampling and deterministic tracing.

The "profiling" package organizes Python's built-in profiling tools
under a single namespace. It contains two submodules, each
implementing a different profiling methodology:

"profiling.sampling"
   A statistical profiler that periodically samples the call stack.
   Run scripts directly or attach to running processes by PID.
   Provides multiple output formats (flame graphs, heatmaps, Firefox
   Profiler), GIL analysis, GC tracking, and multiple profiling modes
   (wall-clock, CPU, GIL) with virtually no overhead.

"profiling.tracing"
   A deterministic profiler that traces every function call, return,
   and exception event. Provides exact call counts and precise timing
   information, capturing every invocation including very fast
   functions.

Nota:

  The profiler modules are designed to provide an execution profile
  for a given program, not for benchmarking purposes. For
  benchmarking, use the "timeit" module, which provides reasonably
  accurate timing measurements. This distinction is particularly
  important when comparing Python code against C code: deterministic
  profilers introduce overhead for Python code but not for C-level
  functions, which can skew comparisons.


Choosing a profiler
===================

For most performance analysis, use the statistical profiler
("profiling.sampling"). It has minimal overhead, works for both
development and production, and provides rich visualization options
including flame graphs, heatmaps, GIL analysis, and more.

Use the deterministic profiler ("profiling.tracing") when you need
**exact call counts** and cannot afford to miss any function calls.
Since it instruments every function call and return, it will capture
even very fast functions that complete between sampling intervals. The
tradeoff is higher overhead.

The following table summarizes the key differences:

+----------------------+--------------------------------+--------------------------------+
| Recurso              | Statistical sampling           | Deterministic                  |
|                      | ("profiling.sampling")         | ("profiling.tracing")          |
|======================|================================|================================|
| **Overhead**         | Virtually none                 | Moderate                       |
+----------------------+--------------------------------+--------------------------------+
| **Accuracy**         | Statistical estimate           | Exact call counts              |
+----------------------+--------------------------------+--------------------------------+
| **Output formats**   | pstats, flame graph, heatmap,  | pstats                         |
|                      | gecko, collapsed               |                                |
+----------------------+--------------------------------+--------------------------------+
| **Profiling modes**  | Wall-clock, CPU, GIL           | Wall-clock                     |
+----------------------+--------------------------------+--------------------------------+
| **Special frames**   | GC, native (C extensions)      | N/D                            |
+----------------------+--------------------------------+--------------------------------+
| **Attach to PID**    | Yes                            | No                             |
+----------------------+--------------------------------+--------------------------------+


When to use statistical sampling
--------------------------------

The statistical profiler ("profiling.sampling") is recommended for
most performance analysis tasks. Use it the same way you would use
"profiling.tracing":

   python -m profiling.sampling run script.py

One of the main strengths of the sampling profiler is its variety of
output formats. Beyond traditional pstats tables, it can generate
interactive flame graphs that visualize call hierarchies, line-level
source heatmaps that show exactly where time is spent in your code,
and Firefox Profiler output for timeline-based analysis.

The profiler also provides insight into Python interpreter behavior
that deterministic profiling cannot capture. Use "--mode gil" to
identify GIL contention in multi-threaded code, "--mode cpu" to
measure actual CPU time excluding I/O waits, or inspect "<GC>" frames
to understand garbage collection overhead. The "--native" option
reveals time spent in C extensions, helping distinguish Python
overhead from library performance.

For multi-threaded applications, the "-a" option samples all threads
simultaneously, showing how work is distributed. And for production
debugging, the "attach" command connects to any running Python process
by PID without requiring a restart or code changes.


When to use deterministic tracing
---------------------------------

The deterministic profiler ("profiling.tracing") instruments every
function call and return. This approach has higher overhead than
sampling, but guarantees complete coverage of program execution.

The primary reason to choose deterministic tracing is when you need
exact call counts. Statistical profiling estimates frequency based on
sampling, which may undercount short-lived functions that complete
between samples. If you need to verify that an optimization actually
reduced the number of function calls, or if you want to trace the
complete call graph to understand caller-callee relationships,
deterministic tracing is the right choice.

Deterministic tracing also excels at capturing functions that execute
in microseconds. Such functions may not appear frequently enough in
statistical samples, but deterministic tracing records every
invocation regardless of duration.


Quick start
===========

This section provides the minimal steps needed to start profiling. For
complete documentation, see the dedicated pages for each profiler.


Statistical profiling
---------------------

To profile a script, use the "profiling.sampling" module with the
"run" command:

   python -m profiling.sampling run script.py
   python -m profiling.sampling run -m mypackage.module

This runs the script under the profiler and prints a summary of where
time was spent. For an interactive flame graph:

   python -m profiling.sampling run --flamegraph script.py

To profile an already-running process, use the "attach" command with
the process ID:

   python -m profiling.sampling attach 1234

For custom settings, specify the sampling interval (in microseconds)
and duration (in seconds):

   python -m profiling.sampling run -i 50 -d 30 script.py


Deterministic profiling
-----------------------

To profile a script from the command line:

   python -m profiling.tracing script.py

To profile a piece of code programmatically:

   import profiling.tracing
   profiling.tracing.run('my_function()')

This executes the given code under the profiler and prints a summary
showing exact function call counts and timing.


Understanding profile output
============================

Both profilers collect function-level statistics, though they present
them in different formats. The sampling profiler offers multiple
visualizations (flame graphs, heatmaps, Firefox Profiler, pstats
tables), while the deterministic profiler produces pstats-compatible
output. Regardless of format, the underlying concepts are the same.

Key profiling concepts:

**Direct time** (also called *self time* or *tottime*)
   Time spent executing code in the function itself, excluding time
   spent in functions it called. High direct time indicates the
   function contains expensive operations.

**Cumulative time** (also called *total time* or *cumtime*)
   Time spent in the function and all functions it called. This
   measures the total cost of calling a function, including its entire
   call subtree.

**Call count** (also called *ncalls* or *samples*)
   How many times the function was called (deterministic) or sampled
   (statistical). In deterministic profiling, this is exact. In
   statistical profiling, it represents the number of times the
   function appeared in a stack sample.

**Primitive calls**
   Calls that are not induced by recursion. When a function recurses,
   the total call count includes recursive invocations, but primitive
   calls counts only the initial entry. Displayed as "total/primitive"
   (for example, "3/1" means three total calls, one primitive).

**Caller/Callee relationships**
   Which functions called a given function (callers) and which
   functions it called (callees). Flame graphs visualize this as
   nested rectangles; pstats can display it via the "print_callers()"
   and "print_callees()" methods.


Legacy compatibility
====================

For backward compatibility, the "cProfile" module remains available as
an alias to "profiling.tracing". Existing code using "import cProfile"
will continue to work without modification in all future Python
versions.

Descontinuado desde a versão 3.15: The pure Python "profile" module is
deprecated and will be removed in Python 3.17. Use "profiling.tracing"
(or its alias "cProfile") instead. See "profile" for migration
guidance.

Ver também:

  "profiling.sampling"
     Statistical sampling profiler with flame graphs, heatmaps, and
     GIL analysis. Recommended for most users.

  "profiling.tracing"
     Deterministic tracing profiler for exact call counts.

  "pstats"
     Statistics analysis and formatting for profile data.

  "timeit"
     Module for measuring execution time of small code snippets.

-[ Submódulos ]-

* "profiling.tracing" --- Deterministic profiler

* "profiling.sampling" --- Statistical profiler
