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516f3b4e-a38e-4280-a6d3-d68f805a2180 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,880 | supabase-export-v2 | 93d90027e487c90b | ``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 stop... | trusted_official_docs | CPython Docs | ``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 stop... | ``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 stop... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
518302ce-56af-48d3-9637-f7931fe9613d | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,104 | supabase-export-v2 | ed102db39ebcf523 | The Flame Graph visualization shows call stacks as nested rectangles. Functions names are visible in the call hierarchy.
.. figure:: tachyon-gecko-opcodes.png
:alt: Firefox Profiler Marker Chart with opcodes
:align: center
:width: 100% | trusted_official_docs | CPython Docs | The Flame Graph visualization shows call stacks as nested rectangles. Functions names are visible in the call hierarchy.
.. figure:: tachyon-gecko-opcodes.png
:alt: Firefox Profiler Marker Chart with opcodes
:align: center
:width: 100% | The Flame Graph visualization shows call stacks as nested rectangles. Functions names are visible in the call hierarchy.
.. figure:: tachyon-gecko-opcodes.png
:alt: Firefox Profiler Marker Chart with opcodes
:align: center
:width: 100% | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
51ee1a04-7cf6-491e-b002-f91d10dacf53 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,159 | supabase-export-v2 | 9da48835ffbdf287 | 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).
:kbd:`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 a... | trusted_official_docs | CPython Docs | 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).
:kbd:`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 a... | 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).
:kbd:`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 a... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
52dc5d0c-9413-448c-8dc1-843bf4aaeec1 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,895 | supabase-export-v2 | aba28e1da34a6fb7 | 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 thos... | trusted_official_docs | CPython Docs | 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 thos... | 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 thos... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
52f3d838-a46d-4a93-85f2-4454c9f65eea | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,870 | supabase-export-v2 | 2fc7c5972e9c4053 | .. code-block:: text
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) | trusted_official_docs | CPython Docs | .. code-block:: text
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) | .. code-block:: text
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) | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
53a37c96-c402-43ba-8102-227b75b000c4 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,132 | supabase-export-v2 | b95460ceda4afb54 | 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 f... | trusted_official_docs | CPython Docs | 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 f... | 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 f... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
53ac8c2a-f4b4-40c6-9194-a6bfde6006c7 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,098 | supabase-export-v2 | c44b94608b233384 | 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 ver... | trusted_official_docs | CPython Docs | 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 ver... | 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 ver... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
53d0c965-03d4-4c8b-9737-496b8b69151a | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,978 | supabase-export-v2 | 4ae45a416e21301b | 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,... | trusted_official_docs | CPython Docs | 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,... | 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,... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
54bf7fa1-b137-409e-a021-247e7e13b34a | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,976 | supabase-export-v2 | 82ee7b07e4610b80 | 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, t... | trusted_official_docs | CPython Docs | 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, t... | 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, t... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
54e70887-769b-4cbf-8bab-d66130ed6e9e | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,032 | supabase-export-v2 | ab5685283fde7522 | 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. | trusted_official_docs | CPython Docs | 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. | 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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
571a2bae-b558-40c8-9439-48512a587014 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,920 | supabase-export-v2 | a94a24aae2a6f05b | samples recorded) * - Default for ``--realtime-stats`` - Disabled * - Default for ``--subprocesses`` - Disabled * - Default for ``--blocking`` - Disabled (non-blocking sampling)
Sampling rate and duration
-------------------------- | trusted_official_docs | CPython Docs | samples recorded) * - Default for ``--realtime-stats`` - Disabled * - Default for ``--subprocesses`` - Disabled * - Default for ``--blocking`` - Disabled (non-blocking sampling)
Sampling rate and duration
-------------------------- | samples recorded) * - Default for ``--realtime-stats`` - Disabled * - Default for ``--subprocesses`` - Disabled * - Default for ``--blocking`` - Disabled (non-blocking sampling)
Sampling rate and duration
-------------------------- | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
574d3aea-beba-49db-a524-bfb6dce0845b | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,970 | supabase-export-v2 | a17dbf287d39ef57 | 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)} facto... | trusted_official_docs | CPython Docs | 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)} facto... | 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)} facto... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
5762dcf8-cecc-49ee-8bfd-cb4e9a6a10c2 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,977 | supabase-export-v2 | 641078d94096d208 | 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 ... | trusted_official_docs | CPython Docs | 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 ... | 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 ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
58051f9a-1111-4ae0-8de3-769e6737e820 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,992 | supabase-export-v2 | 2e839d2944eff124 | 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 ca... | trusted_official_docs | CPython Docs | 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 ca... | 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 ca... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
586a7fb5-837a-49ad-b6c3-edbe8b8794ca | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,922 | supabase-export-v2 | 796aaa2014c3be25 | 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 :option:`--sampling-rate` option (:option:`-r`) sets how frequently samples
are collected. The default is 1 kHz (1,000 samples per second):: | trusted_official_docs | CPython Docs | 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 :option:`--sampling-rate` option (:option:`-r`) sets how frequently samples
are collected. The default is 1 kHz (1,000 samples per second):: | 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 :option:`--sampling-rate` option (:option:`-r`) sets how frequently samples
are collected. The default is 1 kHz (1,000 samples per second):: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
58aed219-b5bd-4a1b-99bb-4f35a7daa37c | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,012 | supabase-export-v2 | 514438e4752c25ab | 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 pr... | trusted_official_docs | CPython Docs | 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 pr... | 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 pr... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
59ea7b70-9519-4110-9f0a-7cc77d0355d8 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,819 | supabase-export-v2 | 92952e1215f5183b | (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
sa... | trusted_official_docs | CPython Docs | (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
sa... | (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
sa... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
5a3030af-d67f-4a3a-855d-66484b6dccbf | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,946 | supabase-export-v2 | d7306251c82c971b | 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 extension... | trusted_official_docs | CPython Docs | 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 extension... | 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 extension... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
5b6dd991-9a83-4936-8fb8-31e625754b0d | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,944 | supabase-export-v2 | 960b5a375388f40a | 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 :option:`--native` option adds ``<native>`` frames to indicate when Python has
called into C code (extension modules, built-in functions, or the interp... | trusted_official_docs | CPython Docs | 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 :option:`--native` option adds ``<native>`` frames to indicate when Python has
called into C code (extension modules, built-in functions, or the interp... | 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 :option:`--native` option adds ``<native>`` frames to indicate when Python has
called into C code (extension modules, built-in functions, or the interp... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
5b92945f-cb64-4b72-bcf9-893bf1ba69a5 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,872 | supabase-export-v2 | 5064114c22ae8556 | 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 :ref:`profiling-permissions`. | trusted_official_docs | CPython Docs | 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 :ref:`profiling-permissions`. | 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 :ref:`profiling-permissions`. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
5ec61ab8-6af7-4aee-a1a5-77ba87fc15ff | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,037 | supabase-export-v2 | 7492c3f2d9760e68 | .. figure:: tachyon-pstats.png :alt: Tachyon pstats terminal output :align: center :width: 100%
The pstats format displays profiling results in a color-coded table showing
function hotspots, sample counts, and timing estimates. | trusted_official_docs | CPython Docs | .. figure:: tachyon-pstats.png :alt: Tachyon pstats terminal output :align: center :width: 100%
The pstats format displays profiling results in a color-coded table showing
function hotspots, sample counts, and timing estimates. | .. figure:: tachyon-pstats.png :alt: Tachyon pstats terminal output :align: center :width: 100%
The pstats format displays profiling results in a color-coded table showing
function hotspots, sample counts, and timing estimates. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6056bf95-a2cc-4741-8852-e616707aab41 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,919 | supabase-export-v2 | d8900d46c977015e | .. list-table:: :header-rows: 1 :widths: 25 75
* - Option
- Default
* - Default for ``--sampling-rate`` / ``-r``
- 1 kHz
* - Default for ``--duration`` / ``-d``
- Run to completion
* - Default for ``--all-threads`` / ``-a``
- Main thread only
* - Default for ``--native``
- No ``<native>`` frames (C code time a... | trusted_official_docs | CPython Docs | .. list-table:: :header-rows: 1 :widths: 25 75
* - Option
- Default
* - Default for ``--sampling-rate`` / ``-r``
- 1 kHz
* - Default for ``--duration`` / ``-d``
- Run to completion
* - Default for ``--all-threads`` / ``-a``
- Main thread only
* - Default for ``--native``
- No ``<native>`` frames (C code time a... | .. list-table:: :header-rows: 1 :widths: 25 75
* - Option
- Default
* - Default for ``--sampling-rate`` / ``-r``
- 1 kHz
* - Default for ``--duration`` / ``-d``
- Run to completion
* - Default for ``--all-threads`` / ``-a``
- Main thread only
* - Default for ``--native``
- No ``<native>`` frames (C code time a... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
608e855e-24ca-4c85-9a56-e08ebbad2e96 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,174 | supabase-export-v2 | aaabef159673d2d7 | .. note::
Async-aware profiling requires the target process to have the :mod:`asyncio`
module loaded. If you profile a script before it imports asyncio, async-aware
mode will not be able to capture task information. | trusted_official_docs | CPython Docs | .. note::
Async-aware profiling requires the target process to have the :mod:`asyncio`
module loaded. If you profile a script before it imports asyncio, async-aware
mode will not be able to capture task information. | .. note::
Async-aware profiling requires the target process to have the :mod:`asyncio`
module loaded. If you profile a script before it imports asyncio, async-aware
mode will not be able to capture task information. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6340e7c1-9fde-4a5a-a727-6fa26adc233c | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,894 | supabase-export-v2 | 3ecddbe505dfd356 | 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. | trusted_official_docs | CPython Docs | 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. | 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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
64e2f354-7cb8-4f96-b320-f24c2119ce76 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,983 | supabase-export-v2 | a3bb464717c97738 | 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... | trusted_official_docs | CPython Docs | 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... | 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... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
66f3c5b4-46ec-4011-8f98-4efcfe80add2 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,825 | supabase-export-v2 | b55f91f51c2c4e27 | second, the profiler may not collect enough samples for reliable results. Use :mod:`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
time... | trusted_official_docs | CPython Docs | second, the profiler may not collect enough samples for reliable results. Use :mod:`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
time... | second, the profiler may not collect enough samples for reliable results. Use :mod:`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
time... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
67fa40da-a704-460a-8788-c3ed00447f35 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,081 | supabase-export-v2 | a46587496c2368d1 | 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:: | trusted_official_docs | CPython Docs | 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:: | 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:: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
69a57da6-7283-4cf1-a320-393cd0ae22f7 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,061 | supabase-export-v2 | 1e0aeee0cef3b476 | 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 ... | trusted_official_docs | CPython Docs | 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 ... | 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 ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6b2a0408-d39d-49b0-8474-c49c8a54294b | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,914 | supabase-export-v2 | a339863f77620f13 | 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. | trusted_official_docs | CPython Docs | 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. | 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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6e665882-43ad-4057-995b-6c6323c0656e | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,029 | supabase-export-v2 | d5b1182bcef8f7ce | 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 over... | trusted_official_docs | CPython Docs | 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 over... | 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 over... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
70cc6909-6d05-4090-8b8a-6abd22514bec | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,827 | supabase-export-v2 | 853fd09d299aab1f | comparing two implementations where the difference might be only 1-2%, sampling noise can obscure real differences. Use :mod:`timeit` for micro-benchmarks or :mod:`profiling.tracing` for precise measurements.
The key difference from :mod:`profiling.tracing` is how measurement happens. A tracing profiler instruments you... | trusted_official_docs | CPython Docs | comparing two implementations where the difference might be only 1-2%, sampling noise can obscure real differences. Use :mod:`timeit` for micro-benchmarks or :mod:`profiling.tracing` for precise measurements.
The key difference from :mod:`profiling.tracing` is how measurement happens. A tracing profiler instruments you... | comparing two implementations where the difference might be only 1-2%, sampling noise can obscure real differences. Use :mod:`timeit` for micro-benchmarks or :mod:`profiling.tracing` for precise measurements.
The key difference from :mod:`profiling.tracing` is how measurement happens. A tracing profiler instruments you... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
710a74f1-3d5f-458c-aa8d-e2c7067b60de | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,099 | supabase-export-v2 | 5b67e2c1de48ac2d | 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 :option:`--mode` option is not available with Gecko format;
all relevant data is captured automatically. | trusted_official_docs | CPython Docs | 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 :option:`--mode` option is not available with Gecko format;
all relevant data is captured automatically. | 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 :option:`--mode` option is not available with Gecko format;
all relevant data is captured automatically. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
73c85fb5-dd3f-4c9d-9da0-281bce3ccddf | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,905 | supabase-export-v2 | bc27ab1f47f44839 | 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 | trusted_official_docs | CPython Docs | 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 | 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 | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
75371638-f772-4ab7-8113-88288d8fdf75 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,153 | supabase-export-v2 | c3f686f61ea49c59 | 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.
:kbd:`r`
Reset all statistics and start fresh. This is disabled after profiling
finishes to prevent accidental data loss. | trusted_official_docs | CPython Docs | 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.
:kbd:`r`
Reset all statistics and start fresh. This is disabled after profiling
finishes to prevent accidental data loss. | 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.
:kbd:`r`
Reset all statistics and start fresh. This is disabled after profiling
finishes to prevent accidental data loss. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
77ff1607-0fd5-4bc6-a6a3-afc9b24139b4 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,135 | supabase-export-v2 | e9e830420d1c2548 | # 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 --... | trusted_official_docs | CPython Docs | # 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 --... | # 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 --... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
78ff0c43-850f-405a-bd8f-fbb1cd05ec22 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,040 | supabase-export-v2 | 07aa02b50a304646 | 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: | trusted_official_docs | CPython Docs | 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: | 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: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
79a16b27-f42e-42fc-84ea-3c53e14bffb7 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,015 | supabase-export-v2 | 70f2093c7d8527d6 | 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)) | trusted_official_docs | CPython Docs | 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)) | 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)) | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
79d19338-1265-4dff-9d14-0fd06163ec3d | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,096 | supabase-export-v2 | b3d5db66d964a96f | features beyond basic flame graphs, including a timeline view, call tree exploration, and marker visualization. See the `Firefox Profiler documentation <https://profiler.firefox.com/docs/#/>`__ for detailed usage instructions.
To use the output, open the Firefox Profiler in your browser and load the
JSON file. The prof... | trusted_official_docs | CPython Docs | features beyond basic flame graphs, including a timeline view, call tree exploration, and marker visualization. See the `Firefox Profiler documentation <https://profiler.firefox.com/docs/#/>`__ for detailed usage instructions.
To use the output, open the Firefox Profiler in your browser and load the
JSON file. The prof... | features beyond basic flame graphs, including a timeline view, call tree exploration, and marker visualization. See the `Firefox Profiler documentation <https://profiler.firefox.com/docs/#/>`__ for detailed usage instructions.
To use the output, open the Firefox Profiler in your browser and load the
JSON file. The prof... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
7c15bbd8-ed48-4ce1-b650-a038ba39c3bd | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,875 | supabase-export-v2 | 3457588a63723b63 | ``-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. | trusted_official_docs | CPython Docs | ``-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. | ``-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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
7dc36e47-3fea-4c3d-9fad-fc7796252086 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,949 | supabase-export-v2 | c5250b2016c04a61 | 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. | trusted_official_docs | CPython Docs | 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. | 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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
7dc7f9e7-963c-4827-9470-9a307e8aa66f | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,042 | supabase-export-v2 | 697280c6b9694b6d | 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. | trusted_official_docs | CPython Docs | 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. | 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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
7df3b3e6-ace9-4207-b12f-503aecc1bf1f | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,044 | supabase-export-v2 | 7281cbb93783f012 | 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: | trusted_official_docs | CPython Docs | 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: | 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: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
851e20da-71ff-40f7-bb79-be7d124c4bde | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,817 | supabase-export-v2 | 2fc62d85a5c84927 | 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. | trusted_official_docs | CPython Docs | 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. | 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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
854c8bf9-67e5-42d1-a3a1-9c01cc3cb99c | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,179 | supabase-export-v2 | ea8b16286bca2e31 | 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 :option:`--async-mode`\ ``=all``, tasks that are suspended (awaiting I/O, locks, or
other tasks) are also included. This mode is useful for understanding what... | trusted_official_docs | CPython Docs | 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 :option:`--async-mode`\ ``=all``, tasks that are suspended (awaiting I/O, locks, or
other tasks) are also included. This mode is useful for understanding what... | 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 :option:`--async-mode`\ ``=all``, tasks that are suspended (awaiting I/O, locks, or
other tasks) are also included. This mode is useful for understanding what... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
869a8b88-b8dd-4e09-a309-b8812a08fac1 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,927 | supabase-export-v2 | 17e10b0523dd91d7 | 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. | trusted_official_docs | CPython Docs | 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. | 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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
86a70c59-fd59-4a1a-9ff1-a2d010512d04 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,158 | supabase-export-v2 | f7495bfed18878e2 | :kbd:`←` :kbd:`→` or :kbd:`↑` :kbd:`↓` In per-thread view, navigate between threads. Navigation wraps around from the last thread to the first and vice versa.
:kbd:`+` / :kbd:`-`
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). Fas... | trusted_official_docs | CPython Docs | :kbd:`←` :kbd:`→` or :kbd:`↑` :kbd:`↓` In per-thread view, navigate between threads. Navigation wraps around from the last thread to the first and vice versa.
:kbd:`+` / :kbd:`-`
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). Fas... | :kbd:`←` :kbd:`→` or :kbd:`↑` :kbd:`↓` In per-thread view, navigate between threads. Navigation wraps around from the last thread to the first and vice versa.
:kbd:`+` / :kbd:`-`
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). Fas... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
88b3dd72-5687-49b6-9837-8a4d049f1288 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,932 | supabase-export-v2 | 38b586473ded0c56 | 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 invest... | trusted_official_docs | CPython Docs | 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 invest... | 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 invest... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
88b4b389-6542-4bcb-ab6e-6cc51d88e9db | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,116 | supabase-export-v2 | eb2152bde1a3f280 | When opcode-level profiling is enabled with :option:`--opcodes`, each hot line can be expanded to show which bytecode instructions consumed time:
.. figure:: tachyon-heatmap-with-opcodes.png
:alt: Heatmap with expanded bytecode panel
:align: center
:width: 100% | trusted_official_docs | CPython Docs | When opcode-level profiling is enabled with :option:`--opcodes`, each hot line can be expanded to show which bytecode instructions consumed time:
.. figure:: tachyon-heatmap-with-opcodes.png
:alt: Heatmap with expanded bytecode panel
:align: center
:width: 100% | When opcode-level profiling is enabled with :option:`--opcodes`, each hot line can be expanded to show which bytecode instructions consumed time:
.. figure:: tachyon-heatmap-with-opcodes.png
:alt: Heatmap with expanded bytecode panel
:align: center
:width: 100% | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
893bb82e-8185-4684-873c-b8efa9985d24 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,257 | supabase-export-v2 | 63373ed8a669b793 | .. option:: -o <path>, --output <path>
Output file or directory path. Default behavior varies by format:
:option:`--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``). :option:`--heatma... | trusted_official_docs | CPython Docs | .. option:: -o <path>, --output <path>
Output file or directory path. Default behavior varies by format:
:option:`--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``). :option:`--heatma... | .. option:: -o <path>, --output <path>
Output file or directory path. Default behavior varies by format:
:option:`--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``). :option:`--heatma... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
8e3c9e91-9a4c-48c1-a178-b8cfc056648f | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,105 | supabase-export-v2 | 15ce4cbabdec37ba | .. figure:: tachyon-gecko-opcodes.png :alt: Firefox Profiler Marker Chart with opcodes :align: center :width: 100%
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
``CAL... | trusted_official_docs | CPython Docs | .. figure:: tachyon-gecko-opcodes.png :alt: Firefox Profiler Marker Chart with opcodes :align: center :width: 100%
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
``CAL... | .. figure:: tachyon-gecko-opcodes.png :alt: Firefox Profiler Marker Chart with opcodes :align: center :width: 100%
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
``CAL... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
8edb3999-8738-43aa-8b35-e12ba350712d | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,180 | supabase-export-v2 | 0501f7b592a652a1 | 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
------------------------------------- | trusted_official_docs | CPython Docs | 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
------------------------------------- | 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
------------------------------------- | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
918c75d9-f4f4-4caf-8485-e6389f418245 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,903 | supabase-export-v2 | e7ff517b9cff6d16 | 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`` | trusted_official_docs | CPython Docs | 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`` | 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`` | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
91f60e1d-7134-4cb6-a139-03307500d4a0 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,157 | supabase-export-v2 | 0ad239f97bbe0e5c | 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.
:kbd:`←` :kbd:`→` or :kbd:`↑` :kbd:`↓`
In per-thread view, navigate between threads. Navigation wraps around
from the last thread to the... | trusted_official_docs | CPython Docs | 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.
:kbd:`←` :kbd:`→` or :kbd:`↑` :kbd:`↓`
In per-thread view, navigate between threads. Navigation wraps around
from the last thread to the... | 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.
:kbd:`←` :kbd:`→` or :kbd:`↑` :kbd:`↓`
In per-thread view, navigate between threads. Navigation wraps around
from the last thread to the... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
9285f0d9-7b22-42a9-859e-b5a87eb28f5b | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,131 | supabase-export-v2 | d377da42060c3f09 | 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: | trusted_official_docs | CPython Docs | 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: | 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: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
92d58072-e1af-4453-95e9-06a371673438 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,953 | supabase-export-v2 | 636ffef250c2f367 | 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``, t... | trusted_official_docs | CPython Docs | 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``, t... | 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``, t... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
95e65368-869a-4fbe-ba84-27ae3f62835c | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,938 | supabase-export-v2 | 4c336bd93949a373 | 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 do... | trusted_official_docs | CPython Docs | 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 do... | 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 do... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
960bc5aa-fd9f-4091-adf4-377d2ecbd407 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,073 | supabase-export-v2 | a254649d70190676 | 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 ... | trusted_official_docs | CPython Docs | 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 ... | 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 ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
97e81269-28ed-445a-ba63-da317a165137 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,078 | supabase-export-v2 | 76f828450590fa7d | 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. | trusted_official_docs | CPython Docs | 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. | 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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
9ad3f00b-c9f5-4c5d-83d0-bd8a207cd9e2 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,090 | supabase-export-v2 | a6e35443d9f7cc95 | - **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 percent... | trusted_official_docs | CPython Docs | - **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 percent... | - **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 percent... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a03aad32-3ab1-4420-bca5-8f5e08556392 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,064 | supabase-export-v2 | a475e0c764a0599f | 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. | trusted_official_docs | CPython Docs | 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. | 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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a10810ae-6894-4004-9fac-f0840eb7501e | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,155 | supabase-export-v2 | 748e252c365611e2 | 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.
:kbd:`c`
Clear the current filter and show all functions again. | trusted_official_docs | CPython Docs | 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.
:kbd:`c`
Clear the current filter and show all functions again. | 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.
:kbd:`c`
Clear the current filter and show all functions again. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a2c0958a-4d67-4dae-a789-3a171137fd1e | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,178 | supabase-export-v2 | 9ff33cfa3eb82442 | 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 :option:`--async-mode`\ ``=running`` (the default), only the task currently executing
on the CPU is profiled. This shows where your program is actively... | trusted_official_docs | CPython Docs | 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 :option:`--async-mode`\ ``=running`` (the default), only the task currently executing
on the CPU is profiled. This shows where your program is actively... | 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 :option:`--async-mode`\ ``=running`` (the default), only the task currently executing
on the CPU is profiled. This shows where your program is actively... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a3846ec0-ae91-4da3-9557-dd99989e4594 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,114 | supabase-export-v2 | 92f620d8b63791c3 | 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... | trusted_official_docs | CPython Docs | 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... | 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... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a3f9b35e-ec60-4510-ab65-861b65ff1573 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,878 | supabase-export-v2 | 95df8e9ea153aec6 | 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 ... | trusted_official_docs | CPython Docs | 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 ... | 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 ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a4485bef-7470-4b92-81ec-19bf645bcda9 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,917 | supabase-export-v2 | 2f72043dfb627359 | 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: | trusted_official_docs | CPython Docs | 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: | 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: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a496747b-8c49-4930-a2e4-f68fd581da9d | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,076 | supabase-export-v2 | 0b6319abd36a5d16 | 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: | trusted_official_docs | CPython Docs | 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: | 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: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a4fdd1db-b275-4232-82fc-362c0a5fad64 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,868 | supabase-export-v2 | c8c6cae6828b91f0 | 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,... | trusted_official_docs | CPython Docs | 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,... | 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,... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a518ac50-ecd0-4d11-a63c-8b2229b826bc | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,826 | supabase-export-v2 | afb48de4f86a4fe0 | sampling cannot provide them. Sampling estimates frequency from snapshots, so if you need to know precisely how many times a function was called, use :mod:`profiling.tracing`.
When comparing two implementations where the difference might be only 1-2%,
sampling noise can obscure real differences. Use :mod:`timeit` for
m... | trusted_official_docs | CPython Docs | sampling cannot provide them. Sampling estimates frequency from snapshots, so if you need to know precisely how many times a function was called, use :mod:`profiling.tracing`.
When comparing two implementations where the difference might be only 1-2%,
sampling noise can obscure real differences. Use :mod:`timeit` for
m... | sampling cannot provide them. Sampling estimates frequency from snapshots, so if you need to know precisely how many times a function was called, use :mod:`profiling.tracing`.
When comparing two implementations where the difference might be only 1-2%,
sampling noise can obscure real differences. Use :mod:`timeit` for
m... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a67befe4-332e-44b9-b07f-5ede635d7afe | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,022 | supabase-export-v2 | c4a2af8f234cce3e | 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. | trusted_official_docs | CPython Docs | 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. | 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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a87d5adf-0645-44a9-9c3c-567c8a14b99f | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,967 | supabase-export-v2 | f064f44bd69624cd | profiler instance is automatically spawned to profile it. This is useful for applications that use :mod:`multiprocessing`, :mod:`subprocess`, :mod:`concurrent.futures` with :class:`~concurrent.futures.ProcessPoolExecutor`, or other process spawning mechanisms.
.. code-block:: python
:caption: worker_pool.py | trusted_official_docs | CPython Docs | profiler instance is automatically spawned to profile it. This is useful for applications that use :mod:`multiprocessing`, :mod:`subprocess`, :mod:`concurrent.futures` with :class:`~concurrent.futures.ProcessPoolExecutor`, or other process spawning mechanisms.
.. code-block:: python
:caption: worker_pool.py | profiler instance is automatically spawned to profile it. This is useful for applications that use :mod:`multiprocessing`, :mod:`subprocess`, :mod:`concurrent.futures` with :class:`~concurrent.futures.ProcessPoolExecutor`, or other process spawning mechanisms.
.. code-block:: python
:caption: worker_pool.py | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a883a0a0-f7b9-4dca-afb5-622acfe3882b | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,822 | supabase-export-v2 | 0023fd7773240c98 | 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
-------------------------------- | trusted_official_docs | CPython Docs | 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
-------------------------------- | 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
-------------------------------- | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a9f8d748-3c74-4f2c-8388-a7a00d80d889 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,859 | supabase-export-v2 | c3e7b3bcf0b9fe9c | 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. | trusted_official_docs | CPython Docs | 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 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, official-docs, cpython, P0 | Local_Trusted_Corpus | |
aab65976-dd9a-4863-9bc2-35083fe08f22 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,943 | supabase-export-v2 | 2f9f5d59681532f4 | 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. | trusted_official_docs | CPython Docs | 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. | 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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
ab940050-5580-440d-bddb-d4870ddf8a5b | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,026 | supabase-export-v2 | 229a2e2e455a3cd7 | 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 propag... | trusted_official_docs | CPython Docs | 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 propag... | 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 propag... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
aedf5e9a-43fb-47b3-9d8e-835780d756a7 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,820 | supabase-export-v2 | 1819e99fb98f1942 | 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 set... | trusted_official_docs | CPython Docs | 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 set... | 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 set... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
afbc501a-09fd-477a-bf66-da1e685e06ab | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,957 | supabase-export-v2 | d9e4cfe2a8d01728 | 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 o... | trusted_official_docs | CPython Docs | 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 o... | 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 o... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b002832a-eb81-479b-a20d-3bf4b66942a4 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,046 | supabase-export-v2 | 9508ad9d4efd6be2 | 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 funct... | trusted_official_docs | CPython Docs | 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 funct... | 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 funct... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b0615a7a-5b23-4479-8702-068cfde2cfa9 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,828 | supabase-export-v2 | d8690d161a198cee | 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
instrumen... | trusted_official_docs | CPython Docs | 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
instrumen... | 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
instrumen... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b46ec8ed-87ac-474e-bed7-2bd585a1c144 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,867 | supabase-export-v2 | 0dba41c1fa6b2caa | 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?". | trusted_official_docs | CPython Docs | 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?". | 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?". | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b4d10061-62dd-4cac-9cac-8205438d0e92 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,001 | supabase-export-v2 | 930a72da08317d10 | 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 ti... | trusted_official_docs | CPython Docs | 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 ti... | 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 ti... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b61092ab-8083-49d3-8371-3d62afa69b09 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,854 | supabase-export-v2 | 5b4359164f23fc00 | 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... | trusted_official_docs | CPython Docs | 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... | 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... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b6b53d05-1ee0-48a4-91ee-90157eb35e94 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,144 | supabase-export-v2 | 87aed1d6fba33c43 | 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 :option:`--opcodes` is enabled, an additional opcode panel appears below the
main table, showing instruction-level statistics for the currently selected
... | trusted_official_docs | CPython Docs | 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 :option:`--opcodes` is enabled, an additional opcode panel appears below the
main table, showing instruction-level statistics for the currently selected
... | 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 :option:`--opcodes` is enabled, an additional opcode panel appears below the
main table, showing instruction-level statistics for the currently selected
... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b80dad37-c7bd-49c5-9597-4266a61528dd | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,141 | supabase-export-v2 | beb524f8fe528117 | .. figure:: tachyon-live-mode-2.gif :alt: Tachyon live mode showing all threads :align: center :width: 100%
Live mode displays real-time profiling statistics, showing combined
data from multiple threads in a multi-threaded application. | trusted_official_docs | CPython Docs | .. figure:: tachyon-live-mode-2.gif :alt: Tachyon live mode showing all threads :align: center :width: 100%
Live mode displays real-time profiling statistics, showing combined
data from multiple threads in a multi-threaded application. | .. figure:: tachyon-live-mode-2.gif :alt: Tachyon live mode showing all threads :align: center :width: 100%
Live mode displays real-time profiling statistics, showing combined
data from multiple threads in a multi-threaded application. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b8d7eaab-83e4-4320-ad88-4dde399b4198 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,120 | supabase-export-v2 | 66c56b845afc5c17 | `Try the interactive example <../_static/tachyon-example-heatmap.html>`__!
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. ... | trusted_official_docs | CPython Docs | `Try the interactive example <../_static/tachyon-example-heatmap.html>`__!
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. ... | `Try the interactive example <../_static/tachyon-example-heatmap.html>`__!
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. ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b9667281-7c43-4e89-a6fd-885a10e405cb | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,028 | supabase-export-v2 | 05187341b46203e8 | 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 ``... | trusted_official_docs | CPython Docs | 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 ``... | 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 ``... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
bafd227c-26b1-42ff-a176-2e1eadb91228 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,974 | supabase-export-v2 | e3ff62198e413a1a | 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: | trusted_official_docs | CPython Docs | 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: | 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: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
bba23c05-b684-4767-b295-cf0b95730c2d | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,892 | supabase-export-v2 | 39dbbe72f1ada484 | 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. | trusted_official_docs | CPython Docs | 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. | 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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c0a3cb07-80fb-43ef-bf25-32921382e204 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,077 | supabase-export-v2 | 6bbb66dc2b11396f | 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 t... | trusted_official_docs | CPython Docs | 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 t... | 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 t... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c2428a73-2770-4ce3-9063-6f087e9cc543 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,855 | supabase-export-v2 | d3bc697ec762e744 | 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 | trusted_official_docs | CPython Docs | 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 | 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 | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c24cb771-b512-4394-8695-5b26bf68c2aa | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,108 | supabase-export-v2 | 17f177f241e012ca | Heatmap format (:option:`--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 | trusted_official_docs | CPython Docs | Heatmap format (:option:`--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 | Heatmap format (:option:`--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 | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c2f3faca-171c-4ed5-9ce3-83c4005ab59e | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,161 | supabase-export-v2 | 2dc776dc663e27b5 | :kbd:`h` or :kbd:`?` Show the help screen with all available commands.
:kbd:`j` / :kbd:`k` (or :kbd:`Up` / :kbd:`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. | trusted_official_docs | CPython Docs | :kbd:`h` or :kbd:`?` Show the help screen with all available commands.
:kbd:`j` / :kbd:`k` (or :kbd:`Up` / :kbd:`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. | :kbd:`h` or :kbd:`?` Show the help screen with all available commands.
:kbd:`j` / :kbd:`k` (or :kbd:`Up` / :kbd:`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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c641e4c2-7609-4670-b1a9-bae35d1d8b91 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,940 | supabase-export-v2 | 996f7fc47973aa3f | .. warning::
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... | trusted_official_docs | CPython Docs | .. warning::
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... | .. warning::
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... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c7608b47-7621-403e-bbae-6acbccf36713 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,095 | supabase-export-v2 | f1562d5c1ceb93aa | python -m profiling.sampling run --gecko script.py python -m profiling.sampling run --gecko -o profile.json script.py
The `Firefox Profiler <https://profiler.firefox.com>`__ is a sophisticated
web-based tool originally built for profiling Firefox itself. It provides
features beyond basic flame graphs, including a timel... | trusted_official_docs | CPython Docs | python -m profiling.sampling run --gecko script.py python -m profiling.sampling run --gecko -o profile.json script.py
The `Firefox Profiler <https://profiler.firefox.com>`__ is a sophisticated
web-based tool originally built for profiling Firefox itself. It provides
features beyond basic flame graphs, including a timel... | python -m profiling.sampling run --gecko script.py python -m profiling.sampling run --gecko -o profile.json script.py
The `Firefox Profiler <https://profiler.firefox.com>`__ is a sophisticated
web-based tool originally built for profiling Firefox itself. It provides
features beyond basic flame graphs, including a timel... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c7bd3a9b-992d-4bcb-b16c-cc9f459e5ec8 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,167 | supabase-export-v2 | 776221f7862a9ddb | 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 ... | trusted_official_docs | CPython Docs | 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 ... | 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 ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
cb80b22b-fcb3-4c81-b88a-05b1c5f09629 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,045 | supabase-export-v2 | 74ac697f6350c1f7 | 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 ... | trusted_official_docs | CPython Docs | 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 ... | 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 ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
cbcd00ef-7b4f-4f28-ae66-ea81c8065718 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,152 | supabase-export-v2 | 1e7e56b36a04edbb | :kbd:`s` / :kbd:`S` Cycle through sort orders forward/backward (sample count, percentage, total time, cumulative percentage, cumulative time).
:kbd:`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 dat... | trusted_official_docs | CPython Docs | :kbd:`s` / :kbd:`S` Cycle through sort orders forward/backward (sample count, percentage, total time, cumulative percentage, cumulative time).
:kbd:`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 dat... | :kbd:`s` / :kbd:`S` Cycle through sort orders forward/backward (sample count, percentage, total time, cumulative percentage, cumulative time).
:kbd:`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 dat... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
ce1d38a8-b086-4ec3-9ae7-92cd7767223a | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,147 | supabase-export-v2 | 8e4dd462902e9fc5 | .. figure:: tachyon-live-mode-1.gif :alt: Tachyon live mode with opcode panel :align: center :width: 100%
Live mode with ``--opcodes`` enabled shows an opcode panel with a bytecode
instruction breakdown for the selected function. | trusted_official_docs | CPython Docs | .. figure:: tachyon-live-mode-1.gif :alt: Tachyon live mode with opcode panel :align: center :width: 100%
Live mode with ``--opcodes`` enabled shows an opcode panel with a bytecode
instruction breakdown for the selected function. | .. figure:: tachyon-live-mode-1.gif :alt: Tachyon live mode with opcode panel :align: center :width: 100%
Live mode with ``--opcodes`` enabled shows an opcode panel with a bytecode
instruction breakdown for the selected function. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
cf55f339-0321-4357-afc1-5cc455da0459 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,112 | supabase-export-v2 | 1d12da0bd7b660e1 | 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... | trusted_official_docs | CPython Docs | 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... | 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... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
d09e2e81-6888-40ad-8dcf-d019bb2d7e05 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 35,808 | supabase-export-v2 | 032c583a9590be79 | 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? ============================== | trusted_official_docs | CPython Docs | 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? ============================== | 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? ============================== | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
d0c0bc30-b77f-4451-a6da-0b4196427529 | CPython Docs | file://datasets/cpython/Doc/library/profiling.sampling.rst | unknown | 6cd88c34-20e3-456f-85bb-73037efbca1d | 36,052 | supabase-export-v2 | 6dfcf7867211723b | The pstats format supports several options for controlling the display. The :option:`--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 scr... | trusted_official_docs | CPython Docs | The pstats format supports several options for controlling the display. The :option:`--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 scr... | The pstats format supports several options for controlling the display. The :option:`--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 scr... | python, official-docs, cpython, P0 | Local_Trusted_Corpus |
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