"profiling.sampling" --- 统计型性能分析器
*****************************************

Added in version 3.15.

**源代码:** Lib/profiling/sampling/

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

[图片： Tachyon logo][图片]

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.

备注:

  An interactive visualization of sampling profiling is available in
  the HTML version of this documentation.


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 10 kHz sampling rate 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

Print a single snapshot of a running process's stack:

   python -m profiling.sampling dump 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 -r 20khz script.py

Generate a line-by-line heatmap:

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

Enable opcode-level profiling to see which bytecode instructions are
executing:

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


Commands
========

Tachyon operates through several subcommands. "run" and "attach"
collect samples over time; "dump" captures a single snapshot; "replay"
converts binary profiles to other formats.


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.

   python -m profiling.sampling attach --live 12345
   python -m profiling.sampling attach --flamegraph -d 30 -o profile.html 12345

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


The "dump" command
------------------

The "dump" command prints a single snapshot of a running process's
Python stack and exits, similar to a traceback:

   python -m profiling.sampling dump 12345

Unlike "attach", "dump" does not run a sampling loop: it reads the
stack once. This is useful for investigating hung or unresponsive
processes, or for answering "what is this process doing right now?".

The output mirrors a traceback (most recent call last) and annotates
each thread with its current state (main thread, has GIL, on CPU,
waiting for GIL, has exception, or idle):

   Stack dump for PID 12345, thread 140735 (main thread, has GIL, on CPU; most recent call last):
     File "server.py", line 28, in serve
       await handle_request(req)
     File "handler.py", line 91, in handle_request
       result = expensive_call(req)

When the target's source files are readable, "dump" prints the source
line for each frame and highlights the executing expression.

Like "attach", "dump" requires permission to read the target process's
memory. See Platform requirements.

The "dump" command supports the following options:

"-a", "--all-threads"
   Dump every thread in the target process. Without this flag only the
   main thread is shown.

"--native"
   Include synthetic "<native>" frames marking transitions into C
   extensions or other non-Python code.

"--no-gc"
   Hide the synthetic "<GC>" frames that mark active garbage
   collection.

"--opcodes"
   Annotate each frame with the bytecode opcode the thread is
   currently executing (for example, "opcode=CALL_KW"). Useful for
   instruction-level investigation, including identifying
   specializations chosen by the adaptive interpreter.

"--async-aware"
   Reconstruct stacks across "await" boundaries. "dump" walks the task
   graph and emits one section per task, with "<task>" markers
   separating coroutines awaiting each other.

"--async-mode {running,all}"
   Controls which tasks are included when "--async-aware" is enabled.
   "running" shows only the task currently executing on each thread;
   "all" (the default for "dump") also includes tasks suspended on a
   wait. "attach"'s default for this flag is "running"; "dump"
   defaults to "all" because a single snapshot is most useful when it
   shows the full task graph.

"--blocking"
   Pause every thread in the target while reading its stack and resume
   them after. Guarantees a fully consistent snapshot at the cost of
   briefly stopping the target. Without it, "dump" reads memory while
   the target keeps running, which is faster but can occasionally
   produce a torn stack.


The "replay" command
--------------------

The "replay" command converts binary profile files to other output
formats:

   python -m profiling.sampling replay profile.bin
   python -m profiling.sampling replay --flamegraph -o profile.html profile.bin

This command is useful when you have captured profiling data in binary
format and want to analyze it later or convert it to a visualization
format. Binary profiles can be replayed multiple times to different
formats without re-profiling.

   # Convert binary to pstats (default, prints to stdout)
   python -m profiling.sampling replay profile.bin

   # Convert binary to flame graph
   python -m profiling.sampling replay --flamegraph -o output.html profile.bin

   # Convert binary to gecko format for Firefox Profiler
   python -m profiling.sampling replay --gecko -o profile.json profile.bin

   # Convert binary to heatmap
   python -m profiling.sampling replay --heatmap -o my_heatmap profile.bin


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. By default,
profiling runs until the target process completes, which 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:

+---------------------------+-----------------------------------------------------------------------------+
| 选项                      | 默认值                                                                      |
|===========================|=============================================================================|
| Default for "--sampling-  | 1 kHz                                                                       |
| rate" / "-r"              |                                                                             |
+---------------------------+-----------------------------------------------------------------------------+
| Default for "--duration"  | Run to completion                                                           |
| / "-d"                    |                                                                             |
+---------------------------+-----------------------------------------------------------------------------+
| Default for "--all-       | Main thread only                                                            |
| threads" / "-a"           |                                                                             |
+---------------------------+-----------------------------------------------------------------------------+
| Default for "--native"    | No "<native>" frames (C code time attributed to caller)                     |
+---------------------------+-----------------------------------------------------------------------------+
| Default for "--no-gc"     | "<GC>" frames included when garbage collection is active                    |
+---------------------------+-----------------------------------------------------------------------------+
| Default for "--mode"      | Wall-clock mode (all samples recorded)                                      |
+---------------------------+-----------------------------------------------------------------------------+
| Default for "--realtime-  | Disabled                                                                    |
| stats"                    |                                                                             |
+---------------------------+-----------------------------------------------------------------------------+
| Default for "--           | Disabled                                                                    |
| subprocesses"             |                                                                             |
+---------------------------+-----------------------------------------------------------------------------+
| Default for "--blocking"  | Disabled (non-blocking sampling)                                            |
+---------------------------+-----------------------------------------------------------------------------+


Sampling rate and duration
--------------------------

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

The "--sampling-rate" option ("-r") sets how frequently samples are
collected. The default is 1 kHz (1,000 samples per second):

   python -m profiling.sampling run -r 20khz script.py

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

The "--duration" option ("-d") sets how long to profile in seconds. By
default, profiling continues until the target process exits or is
interrupted:

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

Specifying a duration is useful when attaching to long-running
processes or when you want to limit profiling to a specific time
window. When profiling a script, the default behavior of running to
completion is usually what you want.


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.


Blocking mode
-------------

By default, Tachyon reads the target process's memory without stopping
it. This non-blocking approach is ideal for most profiling scenarios
because it imposes virtually zero overhead on the target application:
the profiled program runs at full speed and is unaware it is being
observed.

However, non-blocking sampling can occasionally produce incomplete or
inconsistent stack traces in applications with many generators or
coroutines that rapidly switch between yield points, or in programs
with very fast-changing call stacks where functions enter and exit
between the start and end of a single stack read, resulting in
reconstructed stacks that mix frames from different execution states
or that never actually existed.

For these cases, the "--blocking" option stops the target process
during each sample:

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

When blocking mode is enabled, the profiler suspends the target
process, reads its stack, then resumes it. This guarantees that each
captured stack represents a real, consistent snapshot of what the
process was doing at that instant. The trade-off is that the target
process runs slower because it is repeatedly paused.

警告:

  Do not use very high sample rates (low "--interval" values) with
  blocking mode. Suspending and resuming a process takes time, and if
  the sampling interval is too short, the target will spend more time
  stopped than running. For blocking mode, intervals of 1000
  microseconds (1 millisecond) or higher are recommended. The default
  100 microsecond interval may cause noticeable slowdown in the target
  application.

Use blocking mode only when you observe inconsistent stacks in your
profiles, particularly with generator-heavy or coroutine-heavy code.
For most applications, the default non-blocking mode provides accurate
results with zero impact on the target process.


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.


Opcode-aware profiling
----------------------

The "--opcodes" option enables instruction-level profiling that
captures which Python bytecode instructions are executing at each
sample:

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

This feature provides visibility into Python's bytecode execution,
including adaptive specialization optimizations. When a generic
instruction like "LOAD_ATTR" is specialized at runtime into a more
efficient variant like "LOAD_ATTR_INSTANCE_VALUE", the profiler shows
both the specialized name and the base instruction.

Opcode information appears in several output formats:

* **Flame graphs**: Hovering over a frame displays a tooltip with a
  bytecode instruction breakdown, showing which opcodes consumed time
  in that function

* **Heatmap**: Expandable bytecode panels per source line show
  instruction breakdown with specialization percentages

* **Live mode**: An opcode panel shows instruction-level statistics
  for the selected function, accessible via keyboard navigation

* **Gecko format**: Opcode transitions are emitted as interval markers
  in the Firefox Profiler timeline

This level of detail is particularly useful for:

* Understanding the performance impact of Python's adaptive
  specialization

* Identifying hot bytecode instructions that might benefit from
  optimization

* Analyzing the effectiveness of different code patterns at the
  instruction level

* Debugging performance issues that occur at the bytecode level

The "--opcodes" option is compatible with "--live", "--flamegraph", "
--heatmap", and "--gecko" formats. It requires additional memory to
store opcode information and may slightly reduce sampling performance,
but provides unprecedented visibility into Python's execution model.


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.


Subprocess profiling
--------------------

The "--subprocesses" option enables automatic profiling of
subprocesses spawned by the target:

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

When enabled, the profiler monitors the target process for child
process creation. When a new Python child process is detected, a
separate profiler instance is automatically spawned to profile it.
This is useful for applications that use "multiprocessing",
"subprocess", "concurrent.futures" with "ProcessPoolExecutor", or
other process spawning mechanisms.

worker_pool.py

   from concurrent.futures import ProcessPoolExecutor
   import math

   def compute_factorial(n):
       total = 0
       for i in range(50):
           total += math.factorial(n)
       return total

   if __name__ == "__main__":
       numbers = [5000 + i * 100 for i in range(50)]
       with ProcessPoolExecutor(max_workers=4) as executor:
           results = list(executor.map(compute_factorial, numbers))
       print(f"Computed {len(results)} factorials")

   python -m profiling.sampling run --subprocesses --flamegraph worker_pool.py

This produces separate flame graphs for the main process and each
worker process: "flamegraph_<main_pid>.html",
"flamegraph_<worker1_pid>.html", and so on.

Each subprocess receives its own output file. The filename is derived
from the specified output path (or the default) with the subprocess's
process ID appended:

* If you specify "-o profile.html", subprocesses produce
  "profile_12345.html", "profile_12346.html", and so on

* With default output, subprocesses produce files like
  "flamegraph_12345.html" or directories like "heatmap_12345"

* For pstats format (which defaults to stdout), subprocesses produce
  files like "profile_12345.pstats"

The subprocess profilers inherit most sampling options from the parent
(sampling rate, duration, thread selection, native frames, GC frames,
async-aware mode, and output format). All Python descendant processes
are profiled recursively, including grandchildren and further
descendants.

Subprocess detection works by periodically scanning for new
descendants of the target process and checking whether each new
process is a Python process by probing the process memory for Python
runtime structures. Non-Python subprocesses (such as shell commands or
external tools) are ignored.

There is a limit of 100 concurrent subprocess profilers to prevent
resource exhaustion in programs that spawn many processes. If this
limit is reached, additional subprocesses are not profiled and a
warning is printed.

The "--subprocesses" option is incompatible with "--live" mode because
live mode uses an interactive terminal interface that cannot
accommodate multiple concurrent profiler displays.


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


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.

   import time

   def do_sleep():
       time.sleep(2)

   def do_compute():
       sum(i**2 for i in range(1000000))

   if __name__ == "__main__":
       do_sleep()
       do_compute()

   python -m profiling.sampling run --mode=wall script.py  # do_sleep ~98%, do_compute ~1%
   python -m profiling.sampling run --mode=cpu script.py   # do_sleep absent, do_compute dominates


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.

   import hashlib

   def hash_work():
       # C extension - releases GIL during computation
       for _ in range(200):
           hashlib.sha256(b"data" * 250000).hexdigest()

   def python_work():
       # Pure Python - holds GIL during computation
       for _ in range(3):
           sum(i**2 for i in range(1000000))

   if __name__ == "__main__":
       hash_work()
       python_work()

   python -m profiling.sampling run --mode=cpu script.py  # hash_work ~42%, python_work ~38%
   python -m profiling.sampling run --mode=gil script.py  # hash_work ~5%, python_work ~60%


Exception mode
--------------

Exception mode ("--mode=exception") records samples only when a thread
has an active exception:

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

Samples are recorded in two situations: when an exception is being
propagated up the call stack (after "raise" but before being caught),
or when code is executing inside an "except" block where exception
information is still present in the thread state.

The following example illustrates which code regions are captured:

   def example():
       try:
           raise ValueError("error")    # Captured: exception being raised
       except ValueError:
           process_error()              # Captured: inside except block
       finally:
           cleanup()                    # NOT captured: exception already handled

   def example_propagating():
       try:
           try:
               raise ValueError("error")
           finally:
               cleanup()                # Captured: exception propagating through
       except ValueError:
           pass

   def example_no_exception():
       try:
           do_work()
       finally:
           cleanup()                    # NOT captured: no exception involved

Note that "finally" blocks are only captured when an exception is
actively propagating through them. Once an "except" block finishes
executing, Python clears the exception information before running any
subsequent "finally" block. Similarly, "finally" blocks that run
during normal execution (when no exception was raised) are not
captured because no exception state is present.

This mode is useful for understanding where your program spends time
handling errors. Exception handling can be a significant source of
overhead in code that uses exceptions for flow control (such as
"StopIteration" in iterators) or in applications that process many
error conditions (such as network servers handling connection
failures).

Exception mode helps answer questions like "how much time is spent
handling exceptions?" and "which exception handlers are the most
expensive?" It can reveal hidden performance costs in code that
catches and processes many exceptions, even when those exceptions are
handled gracefully. For example, if a parsing library uses exceptions
internally to signal format errors, this mode will capture time spent
in those handlers even if the calling code never sees the exceptions.


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

   [图片： Tachyon pstats terminal output][图片]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    <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 binary file instead of stdout:

   python -m profiling.sampling run -o profile.pstats 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

   [图片： Tachyon interactive flame graph][图片]The flame graph
   visualization shows call stacks as nested rectangles, with width
   proportional to time spent. The sidebar displays runtime
   statistics, GIL metrics, and hotspot functions.

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.


Differential flame graphs
~~~~~~~~~~~~~~~~~~~~~~~~~

Differential flame graphs compare two profiling runs to highlight
where performance changed. This helps identify regressions introduced
by code changes and validate that optimizations achieved their
intended effect:

   # Capture baseline profile
   python -m profiling.sampling run --binary -o baseline.bin script.py

   # After modifying code, generate differential flamegraph
   python -m profiling.sampling run --diff-flamegraph baseline.bin -o diff.html script.py

The visualization draws the current profile with frame widths showing
current time consumption, then applies color to indicate how each
function changed relative to the baseline.

**Color coding**:

* **Red**: Functions consuming more time (regressions). Lighter shades
  indicate modest increases, while darker shades show severe
  regressions.

* **Blue**: Functions consuming less time (improvements). Lighter
  shades for modest reductions, darker shades for significant
  speedups.

* **Gray**: Minimal or no change.

* **Purple**: New functions not present in the baseline.

Frame colors indicate changes in **direct time** (time when the
function was at the top of the stack, actively executing), not
cumulative time including callees. Hovering over a frame shows
comparison details including baseline time, current time, and the
percentage change.

Some call paths may disappear entirely between profiles. These are
called **elided stacks** and occur when optimizations eliminate code
paths or certain branches stop executing. If elided stacks are
present, an elided toggle appears allowing you to switch between the
main differential view and an elided-only view that shows just the
removed paths (colored purple).


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.

   [图片： Firefox Profiler Call Tree view][图片]The Call Tree view
   shows the complete call hierarchy with sample counts and
   percentages. The sidebar displays detailed statistics for the
   selected function including running time and sample distribution.

   [图片： Firefox Profiler Flame Graph view][图片]The Flame Graph
   visualization shows call stacks as nested rectangles. Functions
   names are visible in the call hierarchy.

   [图片： Firefox Profiler Marker Chart with opcodes][图片]The Marker
   Chart displays interval markers including CPU state, GIL status,
   and opcodes. With "--opcodes" enabled, bytecode instructions like
   "BINARY_OP_ADD_FLOAT", "CALL_PY_EXACT_ARGS", and "CALL_LIST_APPEND"
   appear as markers showing execution over time.


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

   [图片： Tachyon heatmap visualization][图片]The heatmap overlays
   sample counts directly on your source code. Lines are color-coded
   from cool (few samples) to hot (many samples). Navigation buttons
   (▲▼) let you jump between callers and callees.

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

When opcode-level profiling is enabled with "--opcodes", each hot line
can be expanded to show which bytecode instructions consumed time:

   [图片： Heatmap with expanded bytecode panel][图片]Expanding a hot
   line reveals the bytecode instructions executed, including
   specialized variants. The panel shows sample counts per instruction
   and the overall specialization percentage for the line.

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.


Binary format
-------------

Binary format ("--binary") produces a compact binary file for
efficient storage of profiling data:

   python -m profiling.sampling run --binary -o profile.bin script.py
   python -m profiling.sampling attach --binary -o profile.bin 12345

The "--compression" option controls data compression:

* "auto" (default): Use zstd compression if available, otherwise no
  compression

* "zstd": Force zstd compression (requires "compression.zstd" support)

* "none": Disable compression

   python -m profiling.sampling run --binary --compression=zstd -o profile.bin script.py

To analyze binary profiles, use the The replay command to convert them
to other formats like flame graphs or pstats output.


Record and replay workflow
==========================

The binary format combined with the replay command enables a record-
and-replay workflow that separates data capture from analysis. Rather
than generating visualizations during profiling, you capture raw data
to a compact binary file and convert it to different formats later.

This approach has three main benefits:

* Sampling runs faster because the work of building data structures
  for visualization is deferred until replay.

* A single binary capture can be converted to multiple output formats
  without re-profiling: pstats for a quick overview, flame graph for
  visual exploration, heatmap for line-level detail.

* Binary files are compact and easy to share with colleagues who can
  convert them to their preferred format.

A typical workflow:

   # Capture profile in production or during tests
   python -m profiling.sampling attach --binary -o profile.bin 12345

   # Later, analyze with different formats
   python -m profiling.sampling replay profile.bin
   python -m profiling.sampling replay --flamegraph -o profile.html profile.bin
   python -m profiling.sampling replay --heatmap -o heatmap profile.bin


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

   [图片： Tachyon live mode showing all threads][图片]

Live mode displays real-time profiling statistics, showing combined
data from multiple threads in a multi-threaded application.

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 (▼).

When "--opcodes" is enabled, an additional opcode panel appears below
the main table, showing instruction-level statistics for the currently
selected function. This panel displays which bytecode instructions are
executing most frequently, including specialized variants and their
base opcodes.

   [图片： Tachyon live mode with opcode panel][图片]

Live mode with "--opcodes" enabled shows an opcode panel with a
bytecode instruction breakdown for the selected function.


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.

"j" / "k" (or "Up" / "Down")
   Navigate through opcode entries in the opcode panel (when "--
   opcodes" is enabled). These keys scroll through the instruction-
   level statistics for the currently selected function.

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.

   import asyncio

   async def fetch(url):
       await asyncio.sleep(0.1)
       return url

   async def main():
       for _ in range(50):
           await asyncio.gather(fetch("a"), fetch("b"), fetch("c"))

   if __name__ == "__main__":
       asyncio.run(main())

   python -m profiling.sampling run --async-aware --flamegraph -o out.html script.py

备注:

  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".


命令行接口
==========

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.

dump

   Print a single one-shot snapshot of a running process's Python
   stack.

replay

   Convert a binary profile file to another output format.


Dump options
------------

The following options apply to the "dump" subcommand:

-a, --all-threads

   Dump all threads in the target process instead of just the main
   thread.

--native

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

--no-gc

   Exclude "<GC>" frames for active garbage collection.

--opcodes

   Show bytecode opcode names when available.

--async-aware

   Reconstruct the stack across "await" boundaries for asyncio
   applications.

--async-mode <mode>

   Async stack mode: "running" (only the running task) or "all" (all
   tasks including waiting). Defaults to "all" for "dump". Requires "
   --async-aware".

--blocking

   Pause all threads in the target process while reading the stack.


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

-r <rate>, --sampling-rate <rate>

   Sampling rate (for example, "10000", "10khz", "10k"). Default:
   "1khz".

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

   Profiling duration in seconds. Default: run to completion.

-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.

--opcodes

   Gather bytecode opcode information for instruction-level profiling.
   Shows which bytecode instructions are executing, including
   specializations. Compatible with "--live", "--flamegraph", "--
   heatmap", and "--gecko" formats only.

--subprocesses

   Also profile subprocesses. Each subprocess gets its own profiler
   instance and output file. Incompatible with "--live".

--blocking

   Pause the target process during each sample. This ensures
   consistent stack traces at the cost of slowing down the target. Use
   with longer intervals (1000 µs or higher) to minimize impact. See
   Blocking mode for details.


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

--mode <mode>

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

--async-mode <mode>

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


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

--pstats

   Generate pstats statistics. This is the default. When written to
   stdout, the output is a text table; with "-o", it is a binary
   pstats file.

--collapsed

   Generate collapsed stack format for external flame graph tools.

--flamegraph

   Generate self-contained HTML flame graph.

--diff-flamegraph <baseline.bin>

   Generate differential flamegraph comparing to a baseline binary
   profile.

--gecko

   Generate Gecko JSON format for Firefox Profiler.

--heatmap

   Generate HTML heatmap with line-level sample counts.

--binary

   Generate high-performance binary format for later conversion with
   the "replay" command.

--compression <type>

   Compression for binary format: "auto" (use zstd if available,
   default), "zstd", or "none".

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

   Output file or directory path. Default behavior varies by format: "
   --pstats" prints a text table to stdout, while "-o" writes a binary
   pstats file. Other formats generate a file named
   "<format>_<PID>.<ext>" (for example, "flamegraph_12345.html"). "--
   heatmap" creates a directory named "heatmap_<PID>".

--browser

   Automatically open HTML output ("--flamegraph" and "--heatmap") in
   your default web browser after generation. When profiling with "--
   subprocesses", only the main process opens the browser; subprocess
   outputs are never auto-opened.


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.

参见:

  "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.
