Python 對 Linux "perf" 分析器的支援
***********************************

作者:
   Pablo Galindo

Linux 性能分析器 (Linux perf profiler) 是一個非常強大的工具，可讓你分
析並取得有關應用程式的性能資訊。"perf" 還擁有一個非常活躍的工具生態系
統，有助於分析其生成的資料。

在 Python 應用程式中使用 "perf" 分析器的主要問題是 "perf" 僅取得有關原
生符號的資訊，即用 C 編寫的函式和程式的名稱。這表示程式碼中的 Python
函式名稱和檔案名稱不會出現在 "perf" 的輸出中。

從 Python 3.12 開始，直譯器可以在特殊模式下執行，該模式允許 Python 函
式出現在 "perf" 分析器的輸出中。啟用此模式後，直譯器將在執行每個
Python 函式之前插入 (interpose) 一小段動態編譯的程式碼，並使用 perf
map 檔案來告訴 "perf" 這段程式碼與相關聯的 Python 函式間的關係。

備註:

  目前對 "perf" 分析器的支援僅適用於 Linux 的特定架構上。檢查
  "configure" 建構步驟的輸出或檢查 "python -m sysconfig | grep
  HAVE_PERF_TRAMPOLINE" 的輸出來查看你的系統是否支援。

例如，參考以下腳本：

   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)

我們可以執行 "perf" 以 9999 赫茲取樣 CPU 堆疊追蹤 (stack trace)：

   $ perf record -F 9999 -g -o perf.data python my_script.py

然後我們可以使用 "perf report" 來分析資料：

   $ 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
   ...

如你所見，Python 函式未顯示在輸出中，僅顯示
"_Py_Eval_EvalFrameDefault" （為 Python 位元組碼 (bytecode) 求值的函式
）。不幸的是，這不是很有用，因為所有 Python 函式都使用相同的 C 函式來
替位元組碼求值，因此我們無法知道哪個 Python 函式是對應於哪個位元組碼計
算函式。

作為替代，如果我們在啟用 "perf" 支援的情況下執行相同的實驗，我們會得到
：

   $ 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


如何啟用 "perf" 分析支援
========================

要啟用 "perf" 分析支援，可以在一開始就使用環境變數 "PYTHONPERFSUPPORT"
或使用 "-X perf" 選項，也可以使用 "sys.activate_stack_trampoline()" 和
"sys.deactivate_stack_trampoline()" 來動態啟用。

"sys" 函式優先於 "-X" 選項、"-X" 選項優先於環境變數。

例如，使用環境變數：

   $ PYTHONPERFSUPPORT=1 perf record -F 9999 -g -o perf.data python my_script.py
   $ perf report -g -i perf.data

例如，使用 "-X" 選項：

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

例如，在 "example.py" 檔案中使用 "sys" API：

   import sys

   sys.activate_stack_trampoline("perf")
   do_profiled_stuff()
   sys.deactivate_stack_trampoline()

   non_profiled_stuff()

...然後：

   $ perf record -F 9999 -g -o perf.data python ./example.py
   $ perf report -g -i perf.data


如何獲得最佳結果
================

為了獲得最佳結果，應使用 "CFLAGS="-fno-omit-frame-pointer -mno-omit-
leaf-frame-pointer"" 來進行 Python 編譯，因為這能允許分析器僅使用
frame 指標而不是 DWARF 除錯資訊來解析 (unwind)。這是因為，由於插入以允
許 "perf" 支援的程式碼是動態生成的，因此它沒有任何可用的 DWARF 除錯資
訊。

你可以透過執行以下指令來檢查你的系統是否已使用此旗標進行編譯：

   $ python -m sysconfig | grep 'no-omit-frame-pointer'

如果你沒有看到任何輸出，則表示你的直譯器尚未使用 frame 指標進行編譯，
因此它可能無法在 "perf" 的輸出中顯示 Python 函式。


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.

備註:

  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

或使用環境變數：

   $ 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.

警告:

  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 "-O0" typically have much larger stack frames than
    those with "-O1" or higher

  * Adding optimizations ("-O1", "-O2", etc.) typically reduces stack
    size

  * Frame pointers ("-fno-omit-frame-pointer") generally provide more
    reliable stack unwinding
