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.
참고
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.
참고
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.
경고
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.