mirror of https://github.com/python/cpython
211 lines
9.5 KiB
ReStructuredText
211 lines
9.5 KiB
ReStructuredText
.. highlight:: shell-session
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.. _perf_profiling:
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==============================================
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Python support for the Linux ``perf`` profiler
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==============================================
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:author: Pablo Galindo
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`The Linux perf profiler <https://perf.wiki.kernel.org>`_
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is a very powerful tool that allows you to profile and obtain
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information about the performance of your application.
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``perf`` also has a very vibrant ecosystem of tools
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that aid with the analysis of the data that it produces.
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The main problem with using the ``perf`` profiler with Python applications is that
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``perf`` only allows to get information about native symbols, this is, the names of
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the functions and procedures written in C. This means that the names and file names
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of the Python functions in your code will not appear in the output of the ``perf``.
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Since Python 3.12, the interpreter can run in a special mode that allows Python
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functions to appear in the output of the ``perf`` profiler. When this mode is
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enabled, the interpreter will interpose a small piece of code compiled on the
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fly before the execution of every Python function and it will teach ``perf`` the
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relationship between this piece of code and the associated Python function using
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`perf map files`_.
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.. note::
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Support for the ``perf`` profiler is only currently available for Linux on
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selected architectures. Check the output of the configure build step or
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check the output of ``python -m sysconfig | grep HAVE_PERF_TRAMPOLINE``
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to see if your system is supported.
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For example, consider the following script:
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.. code-block:: python
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def foo(n):
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result = 0
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for _ in range(n):
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result += 1
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return result
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def bar(n):
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foo(n)
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def baz(n):
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bar(n)
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if __name__ == "__main__":
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baz(1000000)
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We can run ``perf`` to sample CPU stack traces at 9999 Hertz::
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$ perf record -F 9999 -g -o perf.data python my_script.py
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Then we can use ``perf`` report to analyze the data:
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.. code-block:: shell-session
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$ perf report --stdio -n -g
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# Children Self Samples Command Shared Object Symbol
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# ........ ........ ............ .......... .................. ..........................................
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#
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91.08% 0.00% 0 python.exe python.exe [.] _start
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---_start
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--90.71%--__libc_start_main
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Py_BytesMain
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|--56.88%--pymain_run_python.constprop.0
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| |
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| |--56.13%--_PyRun_AnyFileObject
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| | _PyRun_SimpleFileObject
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| | |
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| | |--55.02%--run_mod
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| | | --54.65%--PyEval_EvalCode
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| | | _PyEval_EvalFrameDefault
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| | | PyObject_Vectorcall
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| | | _PyEval_Vector
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| | | _PyEval_EvalFrameDefault
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| | | PyObject_Vectorcall
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| | | _PyEval_Vector
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| | | _PyEval_EvalFrameDefault
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| | | PyObject_Vectorcall
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| | | _PyEval_Vector
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| | | |--51.67%--_PyEval_EvalFrameDefault
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| | | | |--11.52%--_PyLong_Add
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| | | | | |--2.97%--_PyObject_Malloc
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...
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As you can see here, the Python functions are not shown in the output, only ``_Py_Eval_EvalFrameDefault`` appears
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(the function that evaluates the Python bytecode) shows up. Unfortunately that's not very useful because all Python
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functions use the same C function to evaluate bytecode so we cannot know which Python function corresponds to which
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bytecode-evaluating function.
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Instead, if we run the same experiment with ``perf`` support enabled we get:
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.. code-block:: shell-session
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$ perf report --stdio -n -g
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# Children Self Samples Command Shared Object Symbol
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# ........ ........ ............ .......... .................. .....................................................................
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#
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90.58% 0.36% 1 python.exe python.exe [.] _start
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---_start
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--89.86%--__libc_start_main
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Py_BytesMain
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|--55.43%--pymain_run_python.constprop.0
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| |--54.71%--_PyRun_AnyFileObject
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| | _PyRun_SimpleFileObject
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| | |--53.62%--run_mod
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| | | --53.26%--PyEval_EvalCode
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| | | py::<module>:/src/script.py
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| | | _PyEval_EvalFrameDefault
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| | | PyObject_Vectorcall
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| | | _PyEval_Vector
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| | | py::baz:/src/script.py
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| | | _PyEval_EvalFrameDefault
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| | | PyObject_Vectorcall
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| | | _PyEval_Vector
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| | | py::bar:/src/script.py
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| | | _PyEval_EvalFrameDefault
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| | | PyObject_Vectorcall
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| | | _PyEval_Vector
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| | | py::foo:/src/script.py
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| | | |--51.81%--_PyEval_EvalFrameDefault
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| | | | |--13.77%--_PyLong_Add
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| | | | | |--3.26%--_PyObject_Malloc
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How to enable ``perf`` profiling support
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----------------------------------------
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``perf`` profiling support can either be enabled from the start using
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the environment variable :envvar:`PYTHONPERFSUPPORT` or the
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:option:`-X perf <-X>` option,
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or dynamically using :func:`sys.activate_stack_trampoline` and
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:func:`sys.deactivate_stack_trampoline`.
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The :mod:`!sys` functions take precedence over the :option:`!-X` option,
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the :option:`!-X` option takes precedence over the environment variable.
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Example, using the environment variable::
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$ PYTHONPERFSUPPORT=1
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$ python script.py
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$ perf report -g -i perf.data
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Example, using the :option:`!-X` option::
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$ python -X perf script.py
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$ perf report -g -i perf.data
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Example, using the :mod:`sys` APIs in file :file:`example.py`:
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.. code-block:: python
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import sys
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sys.activate_stack_trampoline("perf")
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do_profiled_stuff()
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sys.deactivate_stack_trampoline()
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non_profiled_stuff()
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...then::
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$ python ./example.py
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$ perf report -g -i perf.data
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How to obtain the best results
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------------------------------
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For the best results, Python should be compiled with
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``CFLAGS="-fno-omit-frame-pointer -mno-omit-leaf-frame-pointer"`` as this allows
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profilers to unwind using only the frame pointer and not on DWARF debug
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information. This is because as the code that is interposed to allow ``perf``
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support is dynamically generated it doesn't have any DWARF debugging information
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available.
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You can check if your system has been compiled with this flag by running::
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$ python -m sysconfig | grep 'no-omit-frame-pointer'
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If you don't see any output it means that your interpreter has not been compiled with
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frame pointers and therefore it may not be able to show Python functions in the output
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of ``perf``.
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.. _perf map files: https://github.com/torvalds/linux/blob/0513e464f9007b70b96740271a948ca5ab6e7dd7/tools/perf/Documentation/jit-interface.txt
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