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gh-96143: Improve perf profiler docs (#96445)

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Erlend E. Aasland 2022-10-27 15:06:48 +02:00 • committed by GitHub
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Doc/howto/perf_profiling.rst
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@ -8,10 +8,11 @@ Python support for the Linux ``perf`` profiler
:author: Pablo Galindo
The Linux ``perf`` profiler is a very powerful tool that allows you to profile and
obtain information about the performance of your application. ``perf`` also has
a very vibrant ecosystem of tools that aid with the analysis of the data that it
produces.
`The Linux perf profiler <https://perf.wiki.kernel.org>`_
is a very powerful tool that allows you to profile and obtain
information about the performance of your application.
``perf`` also has a very vibrant ecosystem of tools
that aid with the analysis of the data that it produces.
The main problem with using the ``perf`` profiler with Python applications is that
``perf`` only allows to get information about native symbols, this is, the names of
@ -25,7 +26,7 @@ fly before the execution of every Python function and it will teach ``perf`` the
relationship between this piece of code and the associated Python function using
`perf map files`_.
.. warning::
.. note::
Support for the ``perf`` profiler is only currently available for Linux on
selected architectures. Check the output of the configure build step or
@ -51,11 +52,11 @@ For example, consider the following script:
if __name__ == "__main__":
baz(1000000)
We can run perf to sample CPU stack traces at 9999 Hertz:
We can run ``perf`` to sample CPU stack traces at 9999 Hertz::
$ perf record -F 9999 -g -o perf.data python my_script.py
Then we can use perf report to analyze the data:
Then we can use ``perf`` report to analyze the data:
.. code-block:: shell-session
@ -101,7 +102,7 @@ As you can see here, the Python functions are not shown in the output, only ``_P
functions use the same C function to evaluate bytecode so we cannot know which Python function corresponds to which
bytecode-evaluating function.
Instead, if we run the same experiment with perf support activated we get:
Instead, if we run the same experiment with ``perf`` support enabled we get:
.. code-block:: shell-session
@ -147,52 +148,58 @@ Instead, if we run the same experiment with perf support activated we get:
Enabling perf profiling mode
----------------------------
How to enable ``perf`` profiling support
----------------------------------------
There are two main ways to activate the perf profiling mode. If you want it to be
active since the start of the Python interpreter, you can use the ``-Xperf`` option:
``perf`` profiling support can either be enabled from the start using
the environment variable :envvar:`PYTHONPERFSUPPORT` or the
:option:`-X perf <-X>` option,
or dynamically using :func:`sys.activate_stack_trampoline` and
:func:`sys.deactivate_stack_trampoline`.
$ python -Xperf my_script.py
The :mod:`!sys` functions take precedence over the :option:`!-X` option,
the :option:`!-X` option takes precedence over the environment variable.
You can also set the :envvar:`PYTHONPERFSUPPORT` to a nonzero value to actiavate perf
profiling mode globally.
Example, using the environment variable::
There is also support for dynamically activating and deactivating the perf
profiling mode by using the APIs in the :mod:`sys` module:
$ PYTHONPERFSUPPORT=1
$ python script.py
$ perf report -g -i perf.data
Example, using the :option:`!-X` option::
$ python -X perf script.py
$ perf report -g -i perf.data
Example, using the :mod:`sys` APIs in file :file:`example.py`:
.. code-block:: python
import sys
sys.activate_stack_trampoline("perf")
import sys
# Run some code with Perf profiling active
sys.activate_stack_trampoline("perf")
do_profiled_stuff()
sys.deactivate_stack_trampoline()
sys.deactivate_stack_trampoline()
non_profiled_stuff()
# Perf profiling is not active anymore
...then::
These APIs can be handy if you want to activate/deactivate profiling mode in
response to a signal or other communication mechanism with your process.
Now we can analyze the data with ``perf report``:
$ perf report -g -i perf.data
$ python ./example.py
$ perf report -g -i perf.data
How to obtain the best results
-------------------------------
------------------------------
For the best results, Python should be compiled with
``CFLAGS="-fno-omit-frame-pointer -mno-omit-leaf-frame-pointer"`` as this allows
profilers to unwind using only the frame pointer and not on DWARF debug
information. This is because as the code that is interposed to allow perf
information. This is because as the code that is interposed to allow ``perf``
support is dynamically generated it doesn't have any DWARF debugging information
available.
You can check if you system has been compiled with this flag by running:
You can check if your system has been compiled with this flag by running::
$ python -m sysconfig | grep 'no-omit-frame-pointer'