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 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 perf map files.

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:

   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, 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:

   $ 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 "PYTHONPERFSUPPORT" or the "-X perf" option,
or dynamically using "sys.activate_stack_trampoline()" and
"sys.deactivate_stack_trampoline()".

The "sys" functions take precedence over the "-X" option, the "-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 "-X" option:

   $ perf record -F 9999 -g -o perf.data python -X perf script.py
   $ perf report -g -i perf.data

Example, using the "sys" APIs in file "example.py":

   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
"PYTHON_PERF_JIT_SUPPORT" or the "-X perf_jit" 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.
