Python support for the perf map compatible profilers¶
- autor:
Pablo Galindo
The Linux perf profiler and samply are powerful tools that allow you to profile and obtain information about the performance of your application. Both tools have vibrant ecosystems that aid with the analysis of the data they produce.
The main problem with using these profilers with Python applications is that they only get 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 profiler output.
Since Python 3.12, the interpreter can run in a special mode that allows Python functions to appear in the output of compatible profilers. 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 the profiler the relationship between this piece of code and the associated Python function using perf map files.
Nota
Support for profiling is available on Linux and macOS on select architectures.
Perf is available on Linux, while samply can be used on both Linux and macOS.
samply support on macOS is available starting from Python 3.15.
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.
Por ejemplo, considere el siguiente 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)
Podemos ejecutar perf para obtener un registro de los seguimientos de la pila de CPU a 9999 hercios:
$ perf record -F 9999 -g -o perf.data python my_script.py
Luego podemos usar perf report para analizar los datos:
$ 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%--_PyCompactLong_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.
En cambio, si ejecutamos el mismo experimento con el soporte perf habilitado obtenemos:
$ 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%--_PyCompactLong_Add
| | | | | |
| | | | | |--3.26%--_PyObject_Malloc
Using the samply profiler¶
samply is a modern profiler that can be used as an alternative to perf. It uses the same perf map files that Python generates, making it compatible with Python’s profiling support. samply is particularly useful on macOS where perf is not available.
To use samply with Python, first install it following the instructions at https://github.com/mstange/samply, then run:
$ samply record PYTHONPERFSUPPORT=1 python my_script.py
This will open a web interface where you can analyze the profiling data interactively. The advantage of samply is that it provides a modern web-based interface for analyzing profiling data and works on both Linux and macOS.
On macOS, samply support requires Python 3.15 or later. Also on macOS, samply
can’t profile signed Python executables due to restrictions by macOS. You can
profile with Python binaries that you’ve compiled yourself, or which are
unsigned or locally-signed (such as anything installed by Homebrew). In
order to attach to running processes on macOS, run samply setup once (and
every time samply is updated) to self-sign the samply binary.
Cómo habilitar el soporte de creación de perfiles perf¶
El soporte de creación de perfiles perf se puede habilitar desde el principio usando la variable de entorno PYTHONPERFSUPPORT o la opción -X perf, o dinámicamente usando sys.activate_stack_trampoline() y sys.deactivate_stack_trampoline().
Las funciones sys tienen prioridad sobre la opción -X, la opción -X tiene prioridad sobre la variable de entorno.
Ejemplo, usando la variable de entorno:
$ PYTHONPERFSUPPORT=1 perf record -F 9999 -g -o perf.data python my_script.py
$ perf report -g -i perf.data
Ejemplo, usando la opción -X:
$ perf record -F 9999 -g -o perf.data python -X perf my_script.py
$ perf report -g -i perf.data
Ejemplo, usando las API sys en el archivo example.py:
import sys
sys.activate_stack_trampoline("perf")
do_profiled_stuff()
sys.deactivate_stack_trampoline()
non_profiled_stuff()
…entonces:
$ perf record -F 9999 -g -o perf.data python ./example.py
$ perf report -g -i perf.data
Cómo obtener los mejores resultados¶
Para obtener mejores resultados, Python debe compilarse con CFLAGS="-fno-omit-frame-pointer -mno-omit-leaf-frame-pointer" ya que esto permite a los perfiladores desenrollarse usando solo el puntero del marco y no en la información de depuración de DWARF. Esto se debe a que como el código que se interpone para permitir el soporte perf se genera dinámicamente, no tiene ninguna información de depuración DWARF disponible.
Puede verificar si su sistema ha sido compilado con este indicador ejecutando:
$ python -m sysconfig | grep 'no-omit-frame-pointer'
Si no ve ningún resultado, significa que su intérprete no ha sido compilado con punteros de marco y, por lo tanto, es posible que no pueda mostrar funciones de Python en el resultado de 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.
Nota
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 -k 1 --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.
Advertencia
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 you can change the size by passing it after
a comma like --call-graph dwarf,16384.
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.
The stack size is particularly important when profiling Python code compiled
with low optimization levels (like -O0), as these builds tend to have
larger stack frames. If you are compiling Python with -O0 and not seeing
Python functions in your profiling output, try increasing the stack dump
size to 65528 bytes (the maximum):
$ perf record -F 9999 -g -k 1 --call-graph dwarf,65528 -o perf.data python -Xperf_jit my_script.py
Different compilation flags can significantly impact stack sizes:
Builds with
-O0typically have much larger stack frames than those with-O1or higherAdding optimizations (
-O1,-O2, etc.) typically reduces stack sizeFrame pointers (
-fno-omit-frame-pointer) generally provide more reliable stack unwinding