| .. highlight:: shell-session |
| |
| .. _perf_profiling: |
| |
| ============================================== |
| 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 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 |
| the functions and procedures written in C. This means that the names and file names |
| of the Python functions in your code will not appear in the output of the ``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 |
| `perf map files`_. |
| |
| .. warning:: |
| |
| Support for the ``perf`` profiler is only currently available for Linux on |
| selected 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 here, the Python functions are not shown in the output, only ``_Py_Eval_EvalFrameDefault`` appears |
| (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 activated 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 |
| |
| |
| |
| Enabling perf profiling mode |
| ---------------------------- |
| |
| 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: |
| |
| $ python -Xperf my_script.py |
| |
| You can also set the :envvar:`PYTHONPERFSUPPORT` to a nonzero value to actiavate perf |
| profiling mode globally. |
| |
| There is also support for dynamically activating and deactivating the perf |
| profiling mode by using the APIs in the :mod:`sys` module: |
| |
| .. code-block:: python |
| |
| import sys |
| sys.activate_stack_trampoline("perf") |
| |
| # Run some code with Perf profiling active |
| |
| sys.deactivate_stack_trampoline() |
| |
| # Perf profiling is not active anymore |
| |
| 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 |
| |
| |
| 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 |
| 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: |
| |
| $ 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``. |
| |
| .. _perf map files: https://github.com/torvalds/linux/blob/0513e464f9007b70b96740271a948ca5ab6e7dd7/tools/perf/Documentation/jit-interface.txt |
