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.