| .. highlight:: shell-session |
| |
| .. _perf_profiling: |
| |
| ============================================== |
| Python support for the Linux ``perf`` profiler |
| ============================================== |
| |
| :author: Pablo Galindo |
| |
| `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 gets information about native symbols, that is, the names of |
| functions and procedures written in C. This means that the names and file names |
| of Python functions in your code will not appear in the output of ``perf``. |
| |
| Since Python 3.12, the interpreter can run in a special mode that allows Python |
| functions to appear in the output of the ``perf`` profiler. When this mode is |
| enabled, the interpreter will interpose a small piece of code compiled on the |
| 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 |
| :doc:`perf map files <../c-api/perfmaps>`. |
| |
| .. note:: |
| |
| Support for the ``perf`` profiler is currently only available for Linux on |
| select architectures. Check the output of the ``configure`` build step or |
| check the output of ``python -m sysconfig | grep HAVE_PERF_TRAMPOLINE`` |
| to see if your system is supported. |
| |
| For example, consider the following script: |
| |
| .. code-block:: python |
| |
| def foo(n): |
| result = 0 |
| for _ in range(n): |
| result += 1 |
| return result |
| |
| def bar(n): |
| foo(n) |
| |
| def baz(n): |
| bar(n) |
| |
| if __name__ == "__main__": |
| baz(1000000) |
| |
| 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: |
| |
| .. code-block:: shell-session |
| |
| $ perf report --stdio -n -g |
| |
| # Children Self Samples Command Shared Object Symbol |
| # ........ ........ ............ .......... .................. .......................................... |
| # |
| 91.08% 0.00% 0 python.exe python.exe [.] _start |
| | |
| ---_start |
| | |
| --90.71%--__libc_start_main |
| Py_BytesMain |
| | |
| |--56.88%--pymain_run_python.constprop.0 |
| | | |
| | |--56.13%--_PyRun_AnyFileObject |
| | | _PyRun_SimpleFileObject |
| | | | |
| | | |--55.02%--run_mod |
| | | | | |
| | | | --54.65%--PyEval_EvalCode |
| | | | _PyEval_EvalFrameDefault |
| | | | PyObject_Vectorcall |
| | | | _PyEval_Vector |
| | | | _PyEval_EvalFrameDefault |
| | | | PyObject_Vectorcall |
| | | | _PyEval_Vector |
| | | | _PyEval_EvalFrameDefault |
| | | | PyObject_Vectorcall |
| | | | _PyEval_Vector |
| | | | | |
| | | | |--51.67%--_PyEval_EvalFrameDefault |
| | | | | | |
| | | | | |--11.52%--_PyLong_Add |
| | | | | | | |
| | | | | | |--2.97%--_PyObject_Malloc |
| ... |
| |
| As you can see, the Python functions are not shown in the output, only ``_PyEval_EvalFrameDefault`` |
| (the function that evaluates the Python bytecode) shows up. Unfortunately that's not very useful because all Python |
| 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 enabled we get: |
| |
| .. code-block:: shell-session |
| |
| $ perf report --stdio -n -g |
| |
| # Children Self Samples Command Shared Object Symbol |
| # ........ ........ ............ .......... .................. ..................................................................... |
| # |
| 90.58% 0.36% 1 python.exe python.exe [.] _start |
| | |
| ---_start |
| | |
| --89.86%--__libc_start_main |
| Py_BytesMain |
| | |
| |--55.43%--pymain_run_python.constprop.0 |
| | | |
| | |--54.71%--_PyRun_AnyFileObject |
| | | _PyRun_SimpleFileObject |
| | | | |
| | | |--53.62%--run_mod |
| | | | | |
| | | | --53.26%--PyEval_EvalCode |
| | | | py::<module>:/src/script.py |
| | | | _PyEval_EvalFrameDefault |
| | | | PyObject_Vectorcall |
| | | | _PyEval_Vector |
| | | | py::baz:/src/script.py |
| | | | _PyEval_EvalFrameDefault |
| | | | PyObject_Vectorcall |
| | | | _PyEval_Vector |
| | | | py::bar:/src/script.py |
| | | | _PyEval_EvalFrameDefault |
| | | | PyObject_Vectorcall |
| | | | _PyEval_Vector |
| | | | py::foo:/src/script.py |
| | | | | |
| | | | |--51.81%--_PyEval_EvalFrameDefault |
| | | | | | |
| | | | | |--13.77%--_PyLong_Add |
| | | | | | | |
| | | | | | |--3.26%--_PyObject_Malloc |
| |
| |
| |
| How to enable ``perf`` profiling support |
| ---------------------------------------- |
| |
| ``perf`` profiling support can be enabled either 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`. |
| |
| The :mod:`!sys` functions take precedence over the :option:`!-X` option, |
| the :option:`!-X` option takes precedence over the environment variable. |
| |
| Example, using the environment variable:: |
| |
| $ PYTHONPERFSUPPORT=1 perf record -F 9999 -g -o perf.data python script.py |
| $ perf report -g -i perf.data |
| |
| Example, using the :option:`!-X` option:: |
| |
| $ perf record -F 9999 -g -o perf.data 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") |
| do_profiled_stuff() |
| sys.deactivate_stack_trampoline() |
| |
| non_profiled_stuff() |
| |
| ...then:: |
| |
| $ perf record -F 9999 -g -o perf.data python ./example.py |
| $ perf report -g -i perf.data |
| |
| |
| How to obtain the best results |
| ------------------------------ |
| |
| For 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`` |
| support is dynamically generated it doesn't have any DWARF debugging information |
| available. |
| |
| You can check if your system has been compiled with this flag by running:: |
| |
| $ python -m sysconfig | grep 'no-omit-frame-pointer' |
| |
| If you don't see any output it means that your interpreter has not been compiled with |
| frame pointers and therefore it may not be able to show Python functions in the output |
| of ``perf``. |
| |
| |
| How to work without frame pointers |
| ---------------------------------- |
| |
| If you are working with a Python interpreter that has been compiled without |
| frame pointers, you can still use the ``perf`` profiler, but the overhead will be |
| a bit higher because Python needs to generate unwinding information for every |
| Python function call on the fly. Additionally, ``perf`` will take more time to |
| process the data because it will need to use the DWARF debugging information to |
| unwind the stack and this is a slow process. |
| |
| To enable this mode, you can use the environment variable |
| :envvar:`PYTHON_PERF_JIT_SUPPORT` or the :option:`-X perf_jit <-X>` option, |
| which will enable the JIT mode for the ``perf`` profiler. |
| |
| .. note:: |
| |
| Due to a bug in the ``perf`` tool, only ``perf`` versions higher than v6.8 |
| will work with the JIT mode. The fix was also backported to the v6.7.2 |
| version of the tool. |
| |
| Note that when checking the version of the ``perf`` tool (which can be done |
| by running ``perf version``) you must take into account that some distros |
| add some custom version numbers including a ``-`` character. This means |
| that ``perf 6.7-3`` is not necessarily ``perf 6.7.3``. |
| |
| When using the perf JIT mode, you need an extra step before you can run ``perf |
| report``. You need to call the ``perf inject`` command to inject the JIT |
| information into the ``perf.data`` file.:: |
| |
| $ perf record -F 9999 -g --call-graph dwarf -o perf.data python -Xperf_jit my_script.py |
| $ perf inject -i perf.data --jit --output perf.jit.data |
| $ perf report -g -i perf.jit.data |
| |
| or using the environment variable:: |
| |
| $ PYTHON_PERF_JIT_SUPPORT=1 perf record -F 9999 -g --call-graph dwarf -o perf.data python my_script.py |
| $ perf inject -i perf.data --jit --output perf.jit.data |
| $ perf report -g -i perf.jit.data |
| |
| ``perf inject --jit`` command will read ``perf.data``, |
| automatically pick up the perf dump file that Python creates (in |
| ``/tmp/perf-$PID.dump``), and then create ``perf.jit.data`` which merges all the |
| JIT information together. It should also create a lot of ``jitted-XXXX-N.so`` |
| files in the current directory which are ELF images for all the JIT trampolines |
| that were created by Python. |
| |
| .. warning:: |
| Notice that when using ``--call-graph dwarf`` the ``perf`` tool will take |
| snapshots of the stack of the process being profiled and save the |
| information in the ``perf.data`` file. By default the size of the stack dump |
| is 8192 bytes but the user can change the size by passing the size after |
| comma like ``--call-graph dwarf,4096``. The size of the stack dump is |
| important because if the size is too small ``perf`` will not be able to |
| unwind the stack and the output will be incomplete. On the other hand, if |
| the size is too big, then ``perf`` won't be able to sample the process as |
| frequently as it would like as the overhead will be higher. |
| |
