# 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:

    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:

    $ 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:

    $ 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 `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 `sys` module:

    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`.