Python support for the Linux perf profiler (original) (raw)
author:
Pablo Galindo
The Linux perf profileris 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 thatperf 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 usingperf 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_TRAMPOLINEto 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_MallocHow 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() andsys.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 my_script.py $ perf report -g -i perf.data
Example, using the -X option:
$ perf record -F 9999 -g -o perf.data python -X perf my_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 withCFLAGS="-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 perfsupport 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 variablePYTHON_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 -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.sofiles in the current directory which are ELF images for all the JIT trampolines that were created by Python.
Warning
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 smallperf 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 higher - Adding optimizations (
-O1,-O2, etc.) typically reduces stack size - Frame pointers (
-fno-omit-frame-pointer) generally provide more reliable stack unwinding