"profiling.sampling" --- Statistical profiler
*********************************************

Added in version 3.15.

**Source code:** Lib/profiling/sampling/

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

[image: Tachyon logo][image]

The "profiling.sampling" module, named **Tachyon**, provides
statistical profiling of Python programs through periodic stack
sampling. Tachyon can run scripts directly or attach to any running
Python process without requiring code changes or restarts. Because
sampling occurs externally to the target process, overhead is
virtually zero, making Tachyon suitable for both development and
production environments.


What is statistical profiling?
==============================

Statistical profiling builds a picture of program behavior by
periodically capturing snapshots of the call stack. Rather than
instrumenting every function call and return as deterministic
profilers do, Tachyon reads the call stack at regular intervals to
record what code is currently running.

This approach rests on a simple principle: functions that consume
significant CPU time will appear frequently in the collected samples.
By gathering thousands of samples over a profiling session, Tachyon
constructs an accurate statistical estimate of where time is spent.
The more samples collected, the more precise this estimate becomes.


How time is estimated
---------------------

The time values shown in Tachyon's output are **estimates derived from
sample counts**, not direct measurements. Tachyon counts how many
times each function appears in the collected samples, then multiplies
by the sampling interval to estimate time.

For example, with a 100 microsecond sampling interval over a 10-second
profile, Tachyon collects approximately 100,000 samples. If a function
appears in 5,000 samples (5% of total), Tachyon estimates it consumed
5% of the 10-second duration, or about 500 milliseconds. This is a
statistical estimate, not a precise measurement.

The accuracy of these estimates depends on sample count. With 100,000
samples, a function showing 5% has a margin of error of roughly ±0.5%.
With only 1,000 samples, the same 5% measurement could actually
represent anywhere from 3% to 7% of real time.

This is why longer profiling durations and shorter sampling intervals
produce more reliable results---they collect more samples. For most
performance analysis, the default settings provide sufficient accuracy
to identify bottlenecks and guide optimization efforts.

Because sampling is statistical, results will vary slightly between
runs. A function showing 12% in one run might show 11% or 13% in the
next. This is normal and expected. Focus on the overall pattern rather
than exact percentages, and don't worry about small variations between
runs.


When to use a different approach
--------------------------------

Statistical sampling is not ideal for every situation.

For very short scripts that complete in under one second, the profiler
may not collect enough samples for reliable results. Use
"profiling.tracing" instead, or run the script in a loop to extend
profiling time.

When you need exact call counts, sampling cannot provide them.
Sampling estimates frequency from snapshots, so if you need to know
precisely how many times a function was called, use
"profiling.tracing".

When comparing two implementations where the difference might be only
1-2%, sampling noise can obscure real differences. Use "timeit" for
micro-benchmarks or "profiling.tracing" for precise measurements.

The key difference from "profiling.tracing" is how measurement
happens. A tracing profiler instruments your code, recording every
function call and return. This provides exact call counts and precise
timing but adds overhead to every function call. A sampling profiler,
by contrast, observes the program from outside at fixed intervals
without modifying its execution. Think of the difference like this:
tracing is like having someone follow you and write down every step
you take, while sampling is like taking photographs every second and
inferring your path from those snapshots.

This external observation model is what makes sampling profiling
practical for production use. The profiled program runs at full speed
because there is no instrumentation code running inside it, and the
target process is never stopped or paused during sampling---Tachyon
reads the call stack directly from the process's memory while it
continues to run. You can attach to a live server, collect data, and
detach without the application ever knowing it was observed. The
trade-off is that very short-lived functions may be missed if they
happen to complete between samples.

Statistical profiling excels at answering the question, "Where is my
program spending time?" It reveals hotspots and bottlenecks in
production code where deterministic profiling overhead would be
unacceptable. For exact call counts and complete call graphs, use
"profiling.tracing" instead.


Quick examples
==============

Profile a script and see the results immediately:

   python -m profiling.sampling run script.py

Profile a module with arguments:

   python -m profiling.sampling run -m mypackage.module arg1 arg2

Generate an interactive flame graph:

   python -m profiling.sampling run --flamegraph -o profile.html script.py

Attach to a running process by PID:

   python -m profiling.sampling attach 12345

Use live mode for real-time monitoring (press "q" to quit):

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

Profile for 60 seconds with a faster sampling rate:

   python -m profiling.sampling run -d 60 -i 50 script.py

Generate a line-by-line heatmap:

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


Commands
========

Tachyon operates through two subcommands that determine how to obtain
the target process.


The "run" command
-----------------

The "run" command launches a Python script or module and profiles it
from startup:

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

When profiling a script, the profiler starts the target in a
subprocess, waits for it to initialize, then begins collecting
samples. The "-m" flag indicates that the target should be run as a
module (equivalent to "python -m"). Arguments after the target are
passed through to the profiled program:

   python -m profiling.sampling run script.py --config settings.yaml


The "attach" command
--------------------

The "attach" command connects to an already-running Python process by
its process ID:

   python -m profiling.sampling attach 12345

This command is particularly valuable for investigating performance
issues in production systems. The target process requires no
modification and need not be restarted. The profiler attaches,
collects samples for the specified duration, then detaches and
produces output.

On most systems, attaching to another process requires appropriate
permissions. See Platform requirements for platform-specific
requirements.


Profiling in production
-----------------------

The sampling profiler is designed for production use. It imposes no
measurable overhead on the target process because it reads memory
externally rather than instrumenting code. The target application
continues running at full speed and is unaware it is being profiled.

When profiling production systems, keep these guidelines in mind:

Start with shorter durations (10-30 seconds) to get quick results,
then extend if you need more statistical accuracy. The default
10-second duration is usually sufficient to identify major hotspots.

If possible, profile during representative load rather than peak
traffic. Profiles collected during normal operation are easier to
interpret than those collected during unusual spikes.

The profiler itself consumes some CPU on the machine where it runs
(not on the target process). On the same machine, this is typically
negligible. When profiling remote processes, network latency does not
affect the target.

Results from production may differ from development due to different
data sizes, concurrent load, or caching effects. This is expected and
is often exactly what you want to capture.


Platform requirements
---------------------

The profiler reads the target process's memory to capture stack
traces. This requires elevated permissions on most operating systems.

**Linux**

On Linux, the profiler uses "ptrace" or "process_vm_readv" to read the
target process's memory. This typically requires one of:

* Running as root

* Having the "CAP_SYS_PTRACE" capability

* Adjusting the Yama ptrace scope:
  "/proc/sys/kernel/yama/ptrace_scope"

The default ptrace_scope of 1 restricts ptrace to parent processes
only. To allow attaching to any process owned by the same user, set it
to 0:

   echo 0 | sudo tee /proc/sys/kernel/yama/ptrace_scope

**macOS**

On macOS, the profiler uses "task_for_pid()" to access the target
process. This requires one of:

* Running as root

* The profiler binary having the "com.apple.security.cs.debugger"
  entitlement

* System Integrity Protection (SIP) being disabled (not recommended)

**Windows**

On Windows, the profiler requires administrative privileges or the
"SeDebugPrivilege" privilege to read another process's memory.


Version compatibility
---------------------

The profiler and target process must run the same Python minor version
(for example, both Python 3.15). Attaching from Python 3.14 to a
Python 3.15 process is not supported.

Additional restrictions apply to pre-release Python versions: if
either the profiler or target is running a pre-release (alpha, beta,
or release candidate), both must run the exact same version.

On free-threaded Python builds, the profiler cannot attach from a
free-threaded build to a standard build, or vice versa.


Sampling configuration
======================

Before exploring the various output formats and visualization options,
it is important to understand how to configure the sampling process
itself. The profiler offers several options that control how
frequently samples are collected, how long profiling runs, which
threads are observed, and what additional context is captured in each
sample.

The default configuration works well for most use cases:

+---------------------------+-----------------------------------------------------------------------------+
| Option                    | Default behavior                                                            |
|===========================|=============================================================================|
| "--interval" / "-i"       | 100 µs between samples (~10,000 samples/sec)                                |
+---------------------------+-----------------------------------------------------------------------------+
| "--duration" / "-d"       | Profile for 10 seconds                                                      |
+---------------------------+-----------------------------------------------------------------------------+
| "--all-threads" / "-a"    | Sample main thread only                                                     |
+---------------------------+-----------------------------------------------------------------------------+
| "--native"                | No "<native>" frames (C code time attributed to caller)                     |
+---------------------------+-----------------------------------------------------------------------------+
| "--no-gc"                 | Include "<GC>" frames when garbage collection is active                     |
+---------------------------+-----------------------------------------------------------------------------+
| "--mode"                  | Wall-clock mode (all samples recorded)                                      |
+---------------------------+-----------------------------------------------------------------------------+
| "--realtime-stats"        | No live statistics display during profiling                                 |
+---------------------------+-----------------------------------------------------------------------------+


Sampling interval and duration
------------------------------

The two most fundamental parameters are the sampling interval and
duration. Together, these determine how many samples will be collected
during a profiling session.

The "--interval" option ("-i") sets the time between samples in
microseconds. The default is 100 microseconds, which produces
approximately 10,000 samples per second:

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

Lower intervals capture more samples and provide finer-grained data at
the cost of slightly higher profiler CPU usage. Higher intervals
reduce profiler overhead but may miss short-lived functions. For most
applications, the default interval provides a good balance between
accuracy and overhead.

The "--duration" option ("-d") sets how long to profile in seconds.
The default is 10 seconds:

   python -m profiling.sampling run -d 60 script.py

Longer durations collect more samples and produce more statistically
reliable results, especially for code paths that execute infrequently.
When profiling a program that runs for a fixed time, you may want to
set the duration to match or exceed the expected runtime.


Thread selection
----------------

Python programs often use multiple threads, whether explicitly through
the "threading" module or implicitly through libraries that manage
thread pools.

By default, the profiler samples only the main thread. The "--all-
threads" option ("-a") enables sampling of all threads in the process:

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

Multi-thread profiling reveals how work is distributed across threads
and can identify threads that are blocked or starved. Each thread's
samples are combined in the output, with the ability to filter by
thread in some formats. This option is particularly useful when
investigating concurrency issues or when work is distributed across a
thread pool.


Special frames
--------------

The profiler can inject artificial frames into the captured stacks to
provide additional context about what the interpreter is doing at the
moment each sample is taken. These synthetic frames help distinguish
different types of execution that would otherwise be invisible.

The "--native" option adds "<native>" frames to indicate when Python
has called into C code (extension modules, built-in functions, or the
interpreter itself):

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

These frames help distinguish time spent in Python code versus time
spent in native libraries. Without this option, native code execution
appears as time in the Python function that made the call. This is
useful when optimizing code that makes heavy use of C extensions like
NumPy or database drivers.

By default, the profiler includes "<GC>" frames when garbage
collection is active. The "--no-gc" option suppresses these frames:

   python -m profiling.sampling run --no-gc script.py

GC frames help identify programs where garbage collection consumes
significant time, which may indicate memory allocation patterns worth
optimizing. If you see substantial time in "<GC>" frames, consider
investigating object allocation rates or using object pooling.


Real-time statistics
--------------------

The "--realtime-stats" option displays sampling rate statistics during
profiling:

   python -m profiling.sampling run --realtime-stats script.py

This shows the actual achieved sampling rate, which may be lower than
requested if the profiler cannot keep up. The statistics help verify
that profiling is working correctly and that sufficient samples are
being collected. See Sampling efficiency for details on interpreting
these metrics.


Sampling efficiency
-------------------

Sampling efficiency metrics help assess the quality of the collected
data. These metrics appear in the profiler's terminal output and in
the flame graph sidebar.

**Sampling efficiency** is the percentage of sample attempts that
succeeded. Each sample attempt reads the target process's call stack
from memory. An attempt can fail if the process is in an inconsistent
state at the moment of reading, such as during a context switch or
while the interpreter is updating its internal structures. A low
efficiency may indicate that the profiler could not keep up with the
requested sampling rate, often due to system load or an overly
aggressive interval setting.

**Missed samples** is the percentage of expected samples that were not
collected. Based on the configured interval and duration, the profiler
expects to collect a certain number of samples. Some samples may be
missed if the profiler falls behind schedule, for example when the
system is under heavy load. A small percentage of missed samples is
normal and does not significantly affect the statistical accuracy of
the profile.

Both metrics are informational. Even with some failed attempts or
missed samples, the profile remains statistically valid as long as
enough samples were collected. The profiler reports the actual number
of samples captured, which you can use to judge whether the data is
sufficient for your analysis.


Profiling modes
===============

The sampling profiler supports three modes that control which samples
are recorded. The mode determines what the profile measures: total
elapsed time, CPU execution time, or time spent holding the global
interpreter lock.


Wall-clock mode
---------------

Wall-clock mode ("--mode=wall") captures all samples regardless of
what the thread is doing. This is the default mode and provides a
complete picture of where time passes during program execution:

   python -m profiling.sampling run --mode=wall script.py

In wall-clock mode, samples are recorded whether the thread is
actively executing Python code, waiting for I/O, blocked on a lock, or
sleeping. This makes wall-clock profiling ideal for understanding the
overall time distribution in your program, including time spent
waiting.

If your program spends significant time in I/O operations, network
calls, or sleep, wall-clock mode will show these waits as time
attributed to the calling function. This is often exactly what you
want when optimizing end-to-end latency.


CPU mode
--------

CPU mode ("--mode=cpu") records samples only when the thread is
actually executing on a CPU core:

   python -m profiling.sampling run --mode=cpu script.py

Samples taken while the thread is sleeping, blocked on I/O, or waiting
for a lock are discarded. The resulting profile shows where CPU cycles
are consumed, filtering out idle time.

CPU mode is useful when you want to focus on computational hotspots
without being distracted by I/O waits. If your program alternates
between computation and network calls, CPU mode reveals which
computational sections are most expensive.


Comparing wall-clock and CPU profiles
-------------------------------------

Running both wall-clock and CPU mode profiles can reveal whether a
function's time is spent computing or waiting.

If a function appears prominently in both profiles, it is a true
computational hotspot---actively using the CPU. Optimization should
focus on algorithmic improvements or more efficient code.

If a function is high in wall-clock mode but low or absent in CPU
mode, it is I/O-bound or waiting. The function spends most of its time
waiting for network, disk, locks, or sleep. CPU optimization won't
help here; consider async I/O, connection pooling, or reducing wait
time instead.


GIL mode
--------

GIL mode ("--mode=gil") records samples only when the thread holds
Python's global interpreter lock:

   python -m profiling.sampling run --mode=gil script.py

The GIL is held only while executing Python bytecode. When Python
calls into C extensions, performs I/O operations, or executes native
code, the GIL is typically released. This means GIL mode effectively
measures time spent running Python code specifically, filtering out
time in native libraries.

In multi-threaded programs, GIL mode reveals which code is preventing
other threads from running Python bytecode. Since only one thread can
hold the GIL at a time, functions that appear frequently in GIL mode
profiles are monopolizing the interpreter.

GIL mode helps answer questions like "which functions are monopolizing
the GIL?" and "why are my other threads starving?" It can also be
useful in single-threaded programs to distinguish Python execution
time from time spent in C extensions or I/O.


Output formats
==============

The profiler produces output in several formats, each suited to
different analysis workflows. The format is selected with a command-
line flag, and output goes to stdout, a file, or a directory depending
on the format.


pstats format
-------------

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

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    <built-in>: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.

To save pstats output to a file instead of stdout:

   python -m profiling.sampling run -o profile.txt script.py

The pstats format supports several options for controlling the
display. The "--sort" option determines the column used for ordering
results:

   python -m profiling.sampling run --sort=tottime script.py
   python -m profiling.sampling run --sort=cumtime script.py
   python -m profiling.sampling run --sort=nsamples script.py

The "--limit" option restricts output to the top N entries:

   python -m profiling.sampling run --limit=30 script.py

The "--no-summary" option suppresses the header summary that precedes
the statistics table.


Collapsed stacks format
-----------------------

Collapsed stacks format ("--collapsed") produces one line per unique
call stack, with a count of how many times that stack was sampled:

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

The output looks like:

   main;process_data;parse_json;decode_utf8 42
   main;process_data;parse_json 156
   main;handle_request;send_response 89

Each line contains semicolon-separated function names representing the
call stack from bottom to top, followed by a space and the sample
count. This format is designed for compatibility with external flame
graph tools, particularly Brendan Gregg's "flamegraph.pl" script.

To generate a flame graph from collapsed stacks:

   python -m profiling.sampling run --collapsed script.py > stacks.txt
   flamegraph.pl stacks.txt > profile.svg

The resulting SVG can be viewed in any web browser and provides an
interactive visualization where you can click to zoom into specific
call paths.


Flame graph format
------------------

Flame graph format ("--flamegraph") produces a self-contained HTML
file with an interactive flame graph visualization:

   python -m profiling.sampling run --flamegraph script.py
   python -m profiling.sampling run --flamegraph -o profile.html script.py

If no output file is specified, the profiler generates a filename
based on the process ID (for example, "flamegraph.12345.html").

The generated HTML file requires no external dependencies and can be
opened directly in a web browser. The visualization displays call
stacks as nested rectangles, with width proportional to time spent.
Hovering over a rectangle shows details about that function including
source code context, and clicking zooms into that portion of the call
tree.

The flame graph interface includes:

* A sidebar showing profile summary, thread statistics, sampling
  efficiency metrics (see Sampling efficiency), and top hotspot
  functions

* Search functionality supporting both function name matching and
  "file.py:42" line patterns

* Per-thread filtering via dropdown

* Dark/light theme toggle (preference saved across sessions)

* SVG export for saving the current view

The thread statistics section shows runtime behavior metrics:

* **GIL Held**: percentage of samples where a thread held the global
  interpreter lock (actively running Python code)

* **GIL Released**: percentage of samples where no thread held the GIL

* **Waiting GIL**: percentage of samples where a thread was waiting to
  acquire the GIL

* **GC**: percentage of samples during garbage collection

These statistics help identify GIL contention and understand how time
is distributed between Python execution, native code, and waiting.

Flame graphs are particularly effective for identifying deep call
stacks and understanding the hierarchical structure of time
consumption. Wide rectangles at the top indicate functions that
consume significant time either directly or through their callees.


Gecko format
------------

Gecko format ("--gecko") produces JSON output compatible with the
Firefox Profiler:

   python -m profiling.sampling run --gecko script.py
   python -m profiling.sampling run --gecko -o profile.json script.py

The Firefox Profiler is a sophisticated web-based tool originally
built for profiling Firefox itself. It provides features beyond basic
flame graphs, including a timeline view, call tree exploration, and
marker visualization. See the Firefox Profiler documentation for
detailed usage instructions.

To use the output, open the Firefox Profiler in your browser and load
the JSON file. The profiler runs entirely client-side, so your
profiling data never leaves your machine.

Gecko format automatically collects additional metadata about GIL
state and CPU activity, enabling analysis features specific to
Python's threading model. The profiler emits interval markers that
appear as colored bands in the Firefox Profiler timeline:

* **GIL markers**: show when threads hold or release the global
  interpreter lock

* **CPU markers**: show when threads are executing on CPU versus idle

* **Code type markers**: distinguish Python code from native (C
  extension) code

* **GC markers**: indicate garbage collection activity

For this reason, the "--mode" option is not available with Gecko
format; all relevant data is captured automatically.


Heatmap format
--------------

Heatmap format ("--heatmap") generates an interactive HTML
visualization showing sample counts at the source line level:

   python -m profiling.sampling run --heatmap script.py
   python -m profiling.sampling run --heatmap -o my_heatmap script.py

Unlike other formats that produce a single file, heatmap output
creates a directory containing HTML files for each profiled source
file. If no output path is specified, the directory is named
"heatmap_PID".

The heatmap visualization displays your source code with a color
gradient indicating how many samples were collected at each line. Hot
lines (many samples) appear in warm colors, while cold lines (few or
no samples) appear in cool colors. This view helps pinpoint exactly
which lines of code are responsible for time consumption.

The heatmap interface provides several interactive features:

* **Coloring modes**: toggle between "Self Time" (direct execution)
  and "Total Time" (cumulative, including time in called functions)

* **Cold code filtering**: show all lines or only lines with samples

* **Call graph navigation**: each line shows navigation buttons (▲ for
  callers, ▼ for callees) that let you trace execution paths through
  your code. When multiple functions called or were called from a
  line, a menu appears showing all options with their sample counts.

* **Scroll minimap**: a vertical overview showing the heat
  distribution across the entire file

* **Hierarchical index**: files organized by type (stdlib, site-
  packages, project) with aggregate sample counts per folder

* **Dark/light theme**: toggle with preference saved across sessions

* **Line linking**: click line numbers to create shareable URLs

Heatmaps are especially useful when you know which file contains a
performance issue but need to identify the specific lines. Many
developers prefer this format because it maps directly to their source
code, making it easy to read and navigate. For smaller scripts and
focused analysis, heatmaps provide an intuitive view that shows
exactly where time is spent without requiring interpretation of
hierarchical visualizations.


Live mode
=========

Live mode ("--live") provides a terminal-based real-time view of
profiling data, similar to the "top" command for system processes:

   python -m profiling.sampling run --live script.py
   python -m profiling.sampling attach --live 12345

The display updates continuously as new samples arrive, showing the
current hottest functions. This mode requires the "curses" module,
which is available on Unix-like systems but not on Windows. The
terminal must be at least 60 columns wide and 12 lines tall; larger
terminals display more columns.

The header displays the top 3 hottest functions, sampling efficiency
metrics, and thread status statistics (GIL held percentage, CPU usage,
GC time). The main table shows function statistics with the currently
sorted column indicated by an arrow (▼).


Keyboard commands
-----------------

Within live mode, keyboard commands control the display:

"q"
   Quit the profiler and return to the shell.

"s" / "S"
   Cycle through sort orders forward/backward (sample count,
   percentage, total time, cumulative percentage, cumulative time).

"p"
   Pause or resume display updates. Sampling continues in the
   background while the display is paused, so you can freeze the view
   to examine results without stopping data collection.

"r"
   Reset all statistics and start fresh. This is disabled after
   profiling finishes to prevent accidental data loss.

"/"
   Enter filter mode to search for functions by name. The filter uses
   case-insensitive substring matching against the filename and
   function name. Type a pattern and press Enter to apply, or Escape
   to cancel. Glob patterns and regular expressions are not supported.

"c"
   Clear the current filter and show all functions again.

"t"
   Toggle between viewing all threads combined or per-thread
   statistics. In per-thread mode, a thread counter (for example,
   "1/4") appears showing your position among the available threads.

"←" "→" or "↑" "↓"
   In per-thread view, navigate between threads. Navigation wraps
   around from the last thread to the first and vice versa.

"+" / "-"
   Increase or decrease the display refresh rate. The range is 0.05
   seconds (20 Hz, very responsive) to 1.0 second (1 Hz, lower
   overhead). Faster refresh rates use more CPU. The default is 0.1
   seconds (10 Hz).

"x"
   Toggle trend indicators that show whether functions are becoming
   hotter or cooler over time. When enabled, increasing metrics appear
   in green and decreasing metrics appear in red, comparing each
   update to the previous one.

"h" or "?"
   Show the help screen with all available commands.

When profiling finishes (duration expires or target process exits),
the display shows a "PROFILING COMPLETE" banner and freezes the final
results. You can still navigate, sort, and filter the results before
pressing "q" to exit.

Live mode is incompatible with output format options ("--collapsed", "
--flamegraph", and so on) because it uses an interactive terminal
interface rather than producing file output.


Async-aware profiling
=====================

For programs using "asyncio", the profiler offers async-aware mode ("
--async-aware") that reconstructs call stacks based on the task
structure rather than the raw Python frames:

   python -m profiling.sampling run --async-aware async_script.py

Standard profiling of async code can be confusing because the physical
call stack often shows event loop internals rather than the logical
flow of your coroutines. Async-aware mode addresses this by tracking
which task is running and presenting stacks that reflect the "await"
chain.

Note:

  Async-aware profiling requires the target process to have the
  "asyncio" module loaded. If you profile a script before it imports
  asyncio, async-aware mode will not be able to capture task
  information.


Async modes
-----------

The "--async-mode" option controls which tasks appear in the profile:

   python -m profiling.sampling run --async-aware --async-mode=running async_script.py
   python -m profiling.sampling run --async-aware --async-mode=all async_script.py

With "--async-mode=running" (the default), only the task currently
executing on the CPU is profiled. This shows where your program is
actively spending time and is the typical choice for performance
analysis.

With "--async-mode=all", tasks that are suspended (awaiting I/O,
locks, or other tasks) are also included. This mode is useful for
understanding what your program is waiting on, but produces larger
profiles since every suspended task appears in each sample.


Task markers and stack reconstruction
-------------------------------------

In async-aware profiles, you will see "<task>" frames that mark
boundaries between asyncio tasks. These are synthetic frames inserted
by the profiler to show the task structure. The task name appears as
the function name in these frames.

When a task awaits another task, the profiler reconstructs the logical
call chain by following the "await" relationships. Only "leaf" tasks
(tasks that no other task is currently awaiting) generate their own
stack entries. Tasks being awaited by other tasks appear as part of
their awaiter's stack instead.

If a task has multiple awaiters (a diamond pattern in the task graph),
the profiler deterministically selects one parent and annotates the
task marker with the number of parents, for example "MyTask (2
parents)". This indicates that alternate execution paths exist but are
not shown in this particular stack.


Option restrictions
-------------------

Async-aware mode uses a different stack reconstruction mechanism and
is incompatible with: "--native", "--no-gc", "--all-threads", and "--
mode=cpu" or "--mode=gil".


Command-line interface
======================

The complete command-line interface for reference.


Global options
--------------

run

   Run and profile a Python script or module.

attach

   Attach to and profile a running process by PID.


Sampling options
----------------

-i <microseconds>, --interval <microseconds>

   Sampling interval in microseconds. Default: 100.

-d <seconds>, --duration <seconds>

   Profiling duration in seconds. Default: 10.

-a, --all-threads

   Sample all threads, not just the main thread.

--realtime-stats

   Display sampling statistics during profiling.

--native

   Include "<native>" frames for non-Python code.

--no-gc

   Exclude "<GC>" frames for garbage collection.

--async-aware

   Enable async-aware profiling for asyncio programs.


Mode options
------------

--mode <mode>

   Sampling mode: "wall" (default), "cpu", or "gil". The "cpu" and
   "gil" modes are incompatible with "--async-aware".

--async-mode <mode>

   Async profiling mode: "running" (default) or "all". Requires "--
   async-aware".


Output options
--------------

--pstats

   Generate text statistics output. This is the default.

--collapsed

   Generate collapsed stack format for external flame graph tools.

--flamegraph

   Generate self-contained HTML flame graph.

--gecko

   Generate Gecko JSON format for Firefox Profiler.

--heatmap

   Generate HTML heatmap with line-level sample counts.

-o <path>, --output <path>

   Output file or directory path. Default behavior varies by format: "
   --pstats" writes to stdout, "--flamegraph" and "--gecko" generate
   files like "flamegraph.PID.html", and "--heatmap" creates a
   directory named "heatmap_PID".


pstats display options
----------------------

These options apply only to pstats format output.

--sort <key>

   Sort order: "nsamples", "tottime", "cumtime", "sample-pct", "cumul-
   pct", "nsamples-cumul", or "name". Default: "nsamples".

-l <count>, --limit <count>

   Maximum number of entries to display. Default: 15.

--no-summary

   Omit the Legend and Summary of Interesting Functions sections from
   output.


Run command options
-------------------

-m, --module

   Treat the target as a module name rather than a script path.

--live

   Start interactive terminal interface instead of batch profiling.

See also:

  "profiling"
     Overview of Python profiling tools and guidance on choosing a
     profiler.

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

  "pstats"
     Statistics analysis for profile data.

  Firefox Profiler
     Web-based profiler that accepts Gecko format output. See the
     documentation for usage details.

  FlameGraph
     Tools for generating flame graphs from collapsed stack format.
