# The Python Profilers

Source code: Lib/profile.py and Lib/pstats.py

## Introduction to the profilers

`cProfile` and `profile` provide deterministic profiling of Python programs. A profile is a set of statistics that describes how often and for how long various parts of the program executed. These statistics can be formatted into reports via the `pstats` module.

The Python standard library provides two different implementations of the same profiling interface:

1. `cProfile` is recommended for most users; it's a C extension with reasonable overhead that makes it suitable for profiling long-running programs. Based on `lsprof`, contributed by Brett Rosen and Ted Czotter.

2. `profile`, a pure Python module whose interface is imitated by `cProfile`, but which adds significant overhead to profiled programs. If you're trying to extend the profiler in some way, the task might be easier with this module. Originally designed and written by Jim Roskind.

注釈

The profiler modules are designed to provide an execution profile for a given program, not for benchmarking purposes (for that, there is `timeit` for reasonably accurate results). This particularly applies to benchmarking Python code against C code: the profilers introduce overhead for Python code, but not for C-level functions, and so the C code would seem faster than any Python one.

## Instant User's Manual

This section is provided for users that "don't want to read the manual." It provides a very brief overview, and allows a user to rapidly perform profiling on an existing application.

To profile a function that takes a single argument, you can do:

    import cProfile
    import re
    cProfile.run('re.compile("foo|bar")')

(Use `profile` instead of `cProfile` if the latter is not available on your system.)

The above action would run `re.compile()` and print profile results like the following:

          214 function calls (207 primitive calls) in 0.002 seconds

    Ordered by: cumulative time

    ncalls  tottime  percall  cumtime  percall filename:lineno(function)
         1    0.000    0.000    0.002    0.002 {built-in method builtins.exec}
         1    0.000    0.000    0.001    0.001 <string>:1(<module>)
         1    0.000    0.000    0.001    0.001 __init__.py:250(compile)
         1    0.000    0.000    0.001    0.001 __init__.py:289(_compile)
         1    0.000    0.000    0.000    0.000 _compiler.py:759(compile)
         1    0.000    0.000    0.000    0.000 _parser.py:937(parse)
         1    0.000    0.000    0.000    0.000 _compiler.py:598(_code)
         1    0.000    0.000    0.000    0.000 _parser.py:435(_parse_sub)

The first line indicates that 214 calls were monitored. Of those calls, 207 were primitive, meaning that the call was not induced via recursion. The next line: `Ordered by: cumulative time` indicates the output is sorted by the `cumtime` values. The column headings include:

ncalls

  for the number of calls.

tottime

  for the total time spent in the given function (and excluding time made in calls to sub-functions)

percall

  is the quotient of `tottime` divided by `ncalls`

cumtime

  is the cumulative time spent in this and all subfunctions (from invocation till exit). This figure is accurate even for recursive functions.

percall

  is the quotient of `cumtime` divided by primitive calls

filename:lineno(function)

  provides the respective data of each function

When there are two numbers in the first column (for example `3/1`), it means that the function recursed. The second value is the number of primitive calls and the former is the total number of calls. Note that when the function does not recurse, these two values are the same, and only the single figure is printed.

Instead of printing the output at the end of the profile run, you can save the results to a file by specifying a filename to the `run()` function:

    import cProfile
    import re
    cProfile.run('re.compile("foo|bar")', 'restats')

The `pstats.Stats` class reads profile results from a file and formats them in various ways.

The files `cProfile` and `profile` can also be invoked as a script to profile another script. For example:

    python -m cProfile [-o output_file] [-s sort_order] (-m module | myscript.py)

-o <output_file>

  Writes the profile results to a file instead of to stdout.

-s <sort_order>

  Specifies one of the `sort_stats()` sort values to sort the output by. This only applies when `-o` is not supplied.

-m <module>

  Specifies that a module is being profiled instead of a script.

  Added in version 3.7: Added the `-m` option to `cProfile`.

  Added in version 3.8: Added the `-m` option to `profile`.

The `pstats` module's `Stats` class has a variety of methods for manipulating and printing the data saved into a profile results file:

    import pstats
    from pstats import SortKey
    p = pstats.Stats('restats')
    p.strip_dirs().sort_stats(-1).print_stats()

The `strip_dirs()` method removed the extraneous path from all the module names. The `sort_stats()` method sorted all the entries according to the standard module/line/name string that is printed. The `print_stats()` method printed out all the statistics. You might try the following sort calls:

    p.sort_stats(SortKey.NAME)
    p.print_stats()

The first call will actually sort the list by function name, and the second call will print out the statistics. The following are some interesting calls to experiment with:

    p.sort_stats(SortKey.CUMULATIVE).print_stats(10)

This sorts the profile by cumulative time in a function, and then only prints the ten most significant lines. If you want to understand what algorithms are taking time, the above line is what you would use.

If you were looking to see what functions were looping a lot, and taking a lot of time, you would do:

    p.sort_stats(SortKey.TIME).print_stats(10)

to sort according to time spent within each function, and then print the statistics for the top ten functions.

You might also try:

    p.sort_stats(SortKey.FILENAME).print_stats('__init__')

This will sort all the statistics by file name, and then print out statistics for only the class init methods (since they are spelled with `__init__` in them). As one final example, you could try:

    p.sort_stats(SortKey.TIME, SortKey.CUMULATIVE).print_stats(.5, 'init')

This line sorts statistics with a primary key of time, and a secondary key of cumulative time, and then prints out some of the statistics. To be specific, the list is first culled down to 50% (re: `.5`) of its original size, then only lines containing `init` are maintained, and that sub-sub-list is printed.

If you wondered what functions called the above functions, you could now (`p` is still sorted according to the last criteria) do:

    p.print_callers(.5, 'init')

and you would get a list of callers for each of the listed functions.

If you want more functionality, you're going to have to read the manual, or guess what the following functions do:

    p.print_callees()
    p.add('restats')

Invoked as a script, the `pstats` module is a statistics browser for reading and examining profile dumps. It has a simple line-oriented interface (implemented using `cmd`) and interactive help.

## `profile` and `cProfile` Module Reference

Both the `profile` and `cProfile` modules provide the following functions:

profile.run(command, filename=None, sort=-1)

  この関数は `exec()` 関数に渡せる一つの引数と、オプション引数としてファイル名を指定できます。全ての場合でこのルーチンは以下を実行します:

      exec(command, __main__.__dict__, __main__.__dict__)

  そして実行結果からプロファイル情報を収集します。 ファイル名が指定されていない場合は、 `Stats` のインスタンスが自動的に作られ、簡単なプロファイルレポートが表示されます。 sort が指定されている場合は、 `Stats` インスタンスに渡され、結果をどのように並び替えるかを制御します。

profile.runctx(command, globals, locals, filename=None, sort=-1)

  This function is similar to `run()`, with added arguments to supply the globals and locals mappings for the command string. This routine executes:

      exec(command, globals, locals)

  そしてプロファイル統計情報を `run()` 同様に収集します。

class profile.Profile(timer=None, timeunit=0.0, subcalls=True, builtins=True)

  This class is normally only used if more precise control over profiling is needed than what the `cProfile.run()` function provides.

  timer 引数に、コードの実行時間を計測するためのカスタムな時刻取得用関数を渡せます。これは現在時刻を表す単一の数値を返す関数でなければなりません。もしこれが整数を返す関数ならば、 timeunit には単位時間当たりの実際の持続時間を指定します。たとえば関数が 1000 分の 1 秒単位で計測した時間を返すとすると、 timeunit は `.001` でしょう。

  `Profile` クラスを直接使うと、プロファイルデータをファイルに書き出すことなしに結果をフォーマット出来ます:

      import cProfile, pstats, io
      from pstats import SortKey
      pr = cProfile.Profile()
      pr.enable()
      # ... do something ...
      pr.disable()
      s = io.StringIO()
      sortby = SortKey.CUMULATIVE
      ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
      ps.print_stats()
      print(s.getvalue())

  The `Profile` class can also be used as a context manager (supported only in `cProfile` module. see コンテキストマネージャ型):

      import cProfile

      with cProfile.Profile() as pr:
          # ... do something ...

          pr.print_stats()

  バージョン 3.8 で変更: コンテキストマネージャーサポートが追加されました。

  enable()

    Start collecting profiling data. Only in `cProfile`.

  disable()

    Stop collecting profiling data. Only in `cProfile`.

  create_stats()

    プロファイリングデータの収集を停止し、現在のプロファイルとして結果を内部で記録します。

  print_stats(sort=-1)

    現在のプロファイルに基づいて `Stats` オブジェクトを作成し、結果を標準出力に出力します。

    The sort parameter specifies the sorting order of the displayed statistics. It accepts a single key or a tuple of keys to enable multi-level sorting, as in `Stats.sort_stats`.

    Added in version 3.13: `print_stats()` now accepts a tuple of keys.

  dump_stats(filename)

    現在のプロファイルの結果を filename に書き出します。

  run(cmd)

    `exec()` を用いて cmd をプロファイルします。

  runctx(cmd, globals, locals)

    `exec()` を用いて指定されたグローバルおよびローカルな環境で cmd をプロファイルします。

  runcall(func, /, *args, **kwargs)

    `func(*args, **kwargs)` をプロファイルします。

Note that profiling will only work if the called command/function actually returns. If the interpreter is terminated (e.g. via a `sys.exit()` call during the called command/function execution) no profiling results will be printed.

## The `Stats` Class

Analysis of the profiler data is done using the `Stats` class.

class pstats.Stats(*filenames or profile, stream=sys.stdout)

  This class constructor creates an instance of a "statistics object" from a filename (or list of filenames) or from a `Profile` instance. Output will be printed to the stream specified by stream.

  The file selected by the above constructor must have been created by the corresponding version of `profile` or `cProfile`. To be specific, there is no file compatibility guaranteed with future versions of this profiler, and there is no compatibility with files produced by other profilers, or the same profiler run on a different operating system. If several files are provided, all the statistics for identical functions will be coalesced, so that an overall view of several processes can be considered in a single report. If additional files need to be combined with data in an existing `Stats` object, the `add()` method can be used.

  Instead of reading the profile data from a file, a `cProfile.Profile` or `profile.Profile` object can be used as the profile data source.

  `Stats` objects have the following methods:

  strip_dirs()

    This method for the `Stats` class removes all leading path information from file names. It is very useful in reducing the size of the printout to fit within (close to) 80 columns. This method modifies the object, and the stripped information is lost. After performing a strip operation, the object is considered to have its entries in a "random" order, as it was just after object initialization and loading. If `strip_dirs()` causes two function names to be indistinguishable (they are on the same line of the same filename, and have the same function name), then the statistics for these two entries are accumulated into a single entry.

  add(*filenames)

    This method of the `Stats` class accumulates additional profiling information into the current profiling object. Its arguments should refer to filenames created by the corresponding version of `profile.run()` or `cProfile.run()`. Statistics for identically named (re: file, line, name) functions are automatically accumulated into single function statistics.

  dump_stats(filename)

    Save the data loaded into the `Stats` object to a file named filename. The file is created if it does not exist, and is overwritten if it already exists. This is equivalent to the method of the same name on the `profile.Profile` and `cProfile.Profile` classes.

  sort_stats(*keys)

    This method modifies the `Stats` object by sorting it according to the supplied criteria. The argument can be either a string or a SortKey enum identifying the basis of a sort (example: `'time'`, `'name'`, `SortKey.TIME` or `SortKey.NAME`). The SortKey enums argument have advantage over the string argument in that it is more robust and less error prone.

    When more than one key is provided, then additional keys are used as secondary criteria when there is equality in all keys selected before them. For example, `sort_stats(SortKey.NAME, SortKey.FILE)` will sort all the entries according to their function name, and resolve all ties (identical function names) by sorting by file name.

    For the string argument, abbreviations can be used for any key names, as long as the abbreviation is unambiguous.

    The following are the valid string and SortKey:

    Valid String Arg | Valid enum Arg | Meaning
    --- | --- | ---
    `'calls'` | SortKey.CALLS | call count
    `'cumulative'` | SortKey.CUMULATIVE | cumulative time
    `'cumtime'` | N/A | cumulative time
    `'file'` | N/A | file name
    `'filename'` | SortKey.FILENAME | file name
    `'module'` | N/A | file name
    `'ncalls'` | N/A | call count
    `'pcalls'` | SortKey.PCALLS | primitive call count
    `'line'` | SortKey.LINE | line number
    `'name'` | SortKey.NAME | function name
    `'nfl'` | SortKey.NFL | name/file/line
    `'stdname'` | SortKey.STDNAME | standard name
    `'time'` | SortKey.TIME | internal time
    `'tottime'` | N/A | internal time

    Note that all sorts on statistics are in descending order (placing most time consuming items first), where as name, file, and line number searches are in ascending order (alphabetical). The subtle distinction between `SortKey.NFL` and `SortKey.STDNAME` is that the standard name is a sort of the name as printed, which means that the embedded line numbers get compared in an odd way. For example, lines 3, 20, and 40 would (if the file names were the same) appear in the string order 20, 3 and 40. In contrast, `SortKey.NFL` does a numeric compare of the line numbers. In fact, `sort_stats(SortKey.NFL)` is the same as `sort_stats(SortKey.NAME, SortKey.FILENAME, SortKey.LINE)`.

    For backward-compatibility reasons, the numeric arguments `-1`, `0`, `1`, and `2` are permitted. They are interpreted as `'stdname'`, `'calls'`, `'time'`, and `'cumulative'` respectively. If this old style format (numeric) is used, only one sort key (the numeric key) will be used, and additional arguments will be silently ignored.

    Added in version 3.7: Added the SortKey enum.

  reverse_order()

    This method for the `Stats` class reverses the ordering of the basic list within the object. Note that by default ascending vs descending order is properly selected based on the sort key of choice.

  print_stats(*restrictions)

    This method for the `Stats` class prints out a report as described in the `profile.run()` definition.

    The order of the printing is based on the last `sort_stats()` operation done on the object (subject to caveats in `add()` and `strip_dirs()`).

    The arguments provided (if any) can be used to limit the list down to the significant entries. Initially, the list is taken to be the complete set of profiled functions. Each restriction is either an integer (to select a count of lines), or a decimal fraction between 0.0 and 1.0 inclusive (to select a percentage of lines), or a string that will be interpreted as a regular expression (to pattern match the standard name that is printed). If several restrictions are provided, then they are applied sequentially. For example:

        print_stats(.1, 'foo:')

    would first limit the printing to first 10% of list, and then only print functions that were part of filename `.*foo:`. In contrast, the command:

        print_stats('foo:', .1)

    would limit the list to all functions having file names `.*foo:`, and then proceed to only print the first 10% of them.

  print_callers(*restrictions)

    This method for the `Stats` class prints a list of all functions that called each function in the profiled database. The ordering is identical to that provided by `print_stats()`, and the definition of the restricting argument is also identical. Each caller is reported on its own line. The format differs slightly depending on the profiler that produced the stats:

    - With `profile`, a number is shown in parentheses after each caller to show how many times this specific call was made. For convenience, a second non-parenthesized number repeats the cumulative time spent in the function at the right.

    - With `cProfile`, each caller is preceded by three numbers: the number of times this specific call was made, and the total and cumulative times spent in the current function while it was invoked by this specific caller.

  print_callees(*restrictions)

    This method for the `Stats` class prints a list of all function that were called by the indicated function. Aside from this reversal of direction of calls (re: called vs was called by), the arguments and ordering are identical to the `print_callers()` method.

  get_stats_profile()

    This method returns an instance of StatsProfile, which contains a mapping of function names to instances of FunctionProfile. Each FunctionProfile instance holds information related to the function's profile such as how long the function took to run, how many times it was called, etc...

    Added in version 3.9: Added the following dataclasses: StatsProfile, FunctionProfile. Added the following function: get_stats_profile.

## What Is Deterministic Profiling?

Deterministic profiling is meant to reflect the fact that all function call, function return, and exception events are monitored, and precise timings are made for the intervals between these events (during which time the user's code is executing). In contrast, statistical profiling (which is not done by this module) randomly samples the effective instruction pointer, and deduces where time is being spent. The latter technique traditionally involves less overhead (as the code does not need to be instrumented), but provides only relative indications of where time is being spent.

Python の場合、実行中は必ずインタプリタが動作しているため、決定論的プロファイリングを行うために計測用のコードを追加する必要はありません。 Python は自動的に各イベントに フック (オプションのコールバック) を提供します。加えて Python のインタプリタという性質によって、実行時に大きなオーバーヘッドを伴う傾向がありますが、それに比べると一般的なアプリケーションでは決定論的プロファイリングで追加される処理のオーバーヘッドは少ない傾向にあります。結果的に、決定論的プロファイリングは少ないコストで Python プログラムの実行時間に関する詳細な統計を得られる方法となっているのです。

呼び出し回数はコード中のバグ発見にも使用できます (とんでもない数の呼び出しが行われている部分)。インライン拡張の対象とすべき部分を見つけるためにも使えます (呼び出し頻度の高い部分)。内部時間の統計は、注意深く最適化すべき"ホットループ"の発見にも役立ちます。累積時間の統計は、アルゴリズム選択に関連した高レベルのエラー検知に役立ちます。なお、このプロファイラは再帰的なアルゴリズム実装の累計時間を計ることが可能で、通常のループを使った実装と直接比較することもできるようになっています。

## 制限事項

一つの制限はタイミング情報の正確さに関するものです。決定論的プロファイラには正確さに関する根本的問題があります。最も明白な制限は、 (一般に) "クロック"は .001 秒の精度しかないということです。これ以上の精度で計測することはできません。仮に充分な精度が得られたとしても、"誤差"が計測の平均値に影響を及ぼすことがあります。この最初の誤差を取り除いたとしても、それがまた別の誤差を引き起こす原因となります。

もう一つの問題として、イベントを検知してからプロファイラがその時刻を実際に 取得 するまでに "いくらかの時間がかかる" ことです。同様に、イベントハンドラが終了する時にも、時刻を取得して (そしてその値を保存して) から、ユーザコードが処理を再開するまでの間に遅延が発生します。結果的に多く呼び出される関数または多数の関数から呼び出される関数の情報にはこの種の誤差が蓄積する傾向にあります。このようにして蓄積される誤差は、典型的にはクロックの精度を下回ります (1クロック以下) が、一方でこの時間が累計して非常に大きな値になることも あり得ます 。

The problem is more important with `profile` than with the lower-overhead `cProfile`. For this reason, `profile` provides a means of calibrating itself for a given platform so that this error can be probabilistically (on the average) removed. After the profiler is calibrated, it will be more accurate (in a least square sense), but it will sometimes produce negative numbers (when call counts are exceptionally low, and the gods of probability work against you :-). ) Do not be alarmed by negative numbers in the profile. They should only appear if you have calibrated your profiler, and the results are actually better than without calibration.

## キャリブレーション (補正)

The profiler of the `profile` module subtracts a constant from each event handling time to compensate for the overhead of calling the time function, and socking away the results. By default, the constant is 0. The following procedure can be used to obtain a better constant for a given platform (see 制限事項).

    import profile
    pr = profile.Profile()
    for i in range(5):
        print(pr.calibrate(10000))

calibrate メソッドは引数として与えられた数だけ Python の呼び出しを行います。直接呼び出す場合と、プロファイラを使って呼び出す場合の両方が実施され、それぞれの時間が計測されます。その結果、プロファイラのイベントに隠されたオーバーヘッドが計算され、その値は浮動小数として返されます。たとえば、 1.8 GHz の Intel Core i5 で macOS を使用、 Python の time.process_time() をタイマとして使った場合、値はおよそ 4.04e-6 となります。

この手順で使用しているオブジェクトはほぼ一定の結果を返します。 非常に 早いコンピュータを使う場合、もしくはタイマの性能が貧弱な場合は、一定の結果を得るために引数に 100000 や 1000000 といった大きな値を指定する必要があるかもしれません。

一定の結果が得られたら、それを使う方法には3通りあります:

    import profile

    # 1. Apply computed bias to all Profile instances created hereafter.
    profile.Profile.bias = your_computed_bias

    # 2. Apply computed bias to a specific Profile instance.
    pr = profile.Profile()
    pr.bias = your_computed_bias

    # 3. Specify computed bias in instance constructor.
    pr = profile.Profile(bias=your_computed_bias)

選択肢がある場合は、補正値は小さめに設定した方が良いでしょう。プロファイルの結果に負の値が表われる"頻度が低く"なるはずです。

## カスタムな時刻取得用関数を使う

プロファイラが時刻を取得する方法を変更したいなら (たとえば、実測時間やプロセスの経過時間を使いたい場合)、時刻取得用の関数を `Profile` クラスのコンストラクタに指定することができます:

    pr = profile.Profile(your_time_func)

The resulting profiler will then call `your_time_func`. Depending on whether you are using `profile.Profile` or `cProfile.Profile`, `your_time_func`'s return value will be interpreted differently:

`profile.Profile`

  `your_time_func()` は単一の数値、あるいは (`os.times()` と同じように) その合計が累計時間を示すリストを返すようになっていなければなりません。関数が1つの数値、あるいは長さ2の数値のリストを返すようになっていれば、ディスパッチルーチンには特別な高速化バージョンが使われます。

  Be warned that you should calibrate the profiler class for the timer function that you choose (see キャリブレーション (補正)). For most machines, a timer that returns a lone integer value will provide the best results in terms of low overhead during profiling. (`os.times()` is pretty bad, as it returns a tuple of floating-point values). If you want to substitute a better timer in the cleanest fashion, derive a class and hardwire a replacement dispatch method that best handles your timer call, along with the appropriate calibration constant.

`cProfile.Profile`

  `your_time_func()` は単一の数値を返すようになっていなければなりません。単一の整数を返すのであれば、クラスコンストラクタの第二引数に単位時間当たりの実際の持続時間を渡せます。例えば `your_integer_time_func` が 1000 分の 1 秒単位で計測した時間を返すとすると、 `Profile` インスタンスを以下のように構築出来ます:

      pr = cProfile.Profile(your_integer_time_func, 0.001)

  As the `cProfile.Profile` class cannot be calibrated, custom timer functions should be used with care and should be as fast as possible. For the best results with a custom timer, it might be necessary to hard-code it in the C source of the internal `_lsprof` module.

Python 3.3 で `time` に、プロセス時間や実時間の精密な計測に使える新しい関数が追加されました。 例えば、 `time.perf_counter()` を参照してください。