# 27.4. Python 分析器

源代码： Lib/profile.py and Lib/pstats.py

## 27.4.1. 分析器简介

`cProfile` 和 `profile` 提供了 Python 程序的 deterministic profiling 。 profile 是一组统计数据，描述程序的各个部分执行的频率和时间。这些统计数据可以通过 `pstats` 模块格式化为报告。

Python 标准库提供了同一分析接口的两种不同实现：

1. 对于大多数用户，建议使用 `cProfile` ；这是一个C扩展插件，开销合理，适合于分析长时间运行的程序。该插件基于 `lsprof` ，由 Brett Rosen 和 Ted Chaotter 贡献。

2. `profile` 是一个纯 Python 模块（`cProfile` 就是模仿其接口的 C 实现），但它会显著增加配置程序的开销。如果你正在尝试以某种方式扩展分析器，则使用此模块可能会更容易完成任务。该模块最初由Jim Roskind 设计和编写。

備註

profiler 分析器模块被设计为给指定的程序提供执行概要文件，而不是用于基准测试目的（ `timeit` 才是用于此目标的，它能获得合理准确的结果）。这特别适用于将 Python 代码与 C 代码进行基准测试：分析器为Python 代码引入开销，但不会为C级函数引入开销，因此 C 代码似乎比任何Python 代码都更快。

## 27.4.2. 即时用户手册

本节是为 “不想阅读手册” 的用户提供的。它提供了非常简短的概述，并允许用户快速对现有应用程序执行评测。

要分析采用单个参数的函数，可以执行以下操作：

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

（如果 `cProfile` 在您的系统上不可用，请使用 `profile` 。）

上述操作将运行 `re.compile()` 并打印分析结果，如下所示：

          197 function calls (192 primitive calls) in 0.002 seconds

    Ordered by: standard name

    ncalls  tottime  percall  cumtime  percall filename:lineno(function)
         1    0.000    0.000    0.001    0.001 <string>:1(<module>)
         1    0.000    0.000    0.001    0.001 re.py:212(compile)
         1    0.000    0.000    0.001    0.001 re.py:268(_compile)
         1    0.000    0.000    0.000    0.000 sre_compile.py:172(_compile_charset)
         1    0.000    0.000    0.000    0.000 sre_compile.py:201(_optimize_charset)
         4    0.000    0.000    0.000    0.000 sre_compile.py:25(_identityfunction)
       3/1    0.000    0.000    0.000    0.000 sre_compile.py:33(_compile)

第一行显示监听了197个调用。在这些调用中，有192个是 原始的 ，这意味着调用不是通过递归引发的。下一行: `Ordered by: standard name` ，表示最右边列中的文本字符串用于对输出进行排序。列标题包括：

ncalls

  调用次数

tottime

  在指定函数中花费的总时间（不包括调用子函数的时间）

percall

  是 `tottime` 除以 `ncalls` 的商

cumtime

  指定的函数及其所有子函数（从调用到退出）消耗的累积时间。这个数字对于递归函数来说是准确的。

percall

  是 `cumtime` 除以原始调用（次数）的商

filename:lineno(function)

  提供相应数据的每个函数

如果第一列中有两个数字（例如3/1），则表示函数递归。第二个值是原始调用次数，第一个是调用的总次数。请注意，当函数不递归时，这两个值是相同的，并且只打印单个数字。

profile 运行结束时，打印输出不是必须的。也可以通过为 `run()` 函数指定文件名，将结果保存到文件中：

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

`pstats.Stats` 类从文件中读取 profile 结果，并以各种方式对其进行格式化。

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

    python -m cProfile [-o output_file] [-s sort_order] myscript.py

`-o` 将profile 结果写入文件而不是标准输出

`-s` 指定 `sort_stats()` 排序值之一以对输出进行排序。这仅适用于未提供 `-o` 的情况

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
    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('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('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('time').print_stats(10)

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

您也可以尝试：

    p.sort_stats('file').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('time', '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.

## 27.4.3. `profile` 和 `cProfile` 模块参考

`profile` 和 `cProfile` 模块都提供下列函数：

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

  This function takes a single argument that can be passed to the `exec()` function, and an optional file name. In all cases this routine executes:

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

  and gathers profiling statistics from the execution. If no file name is present, then this function automatically creates a `Stats` instance and prints a simple profiling report. If the sort value is specified, it is passed to this `Stats` instance to control how the results are sorted.

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

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

      exec(command, globals, locals)

  and gathers profiling statistics as in the `run()` function above.

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.

  A custom timer can be supplied for measuring how long code takes to run via the timer argument. This must be a function that returns a single number representing the current time. If the number is an integer, the timeunit specifies a multiplier that specifies the duration of each unit of time. For example, if the timer returns times measured in thousands of seconds, the time unit would be `.001`.

  Directly using the `Profile` class allows formatting profile results without writing the profile data to a file:

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

  `enable`()

    Start collecting profiling data.

  `disable`()

    Stop collecting profiling data.

  `create_stats`()

    停止收集分析数据，并在内部将结果记录为当前 profile。

  `print_stats`(sort=-1)

    Create a `Stats` object based on the current profile and print the results to stdout.

  `dump_stats`(filename)

    将当前profile 的结果写入 filename 。

  `run`(cmd)

    Profile the cmd via `exec()`.

  `runctx`(cmd, globals, locals)

    Profile the cmd via `exec()` with the specified global and local environment.

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

    Profile `func(*args, **kwargs)`

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.

## 27.4.4. `Stats` 类

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` 对象有以下方法:

  `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 is typically a string identifying the basis of a sort (example: `'time'` or `'name'`).

    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('name', 'file')` will sort all the entries according to their function name, and resolve all ties (identical function names) by sorting by file name.

    Abbreviations can be used for any key names, as long as the abbreviation is unambiguous. The following are the keys currently defined:

    Valid Arg | 含义
    --- | ---
    `'calls'` | 调用次数
    `'cumulative'` | 累积时间
    `'cumtime'` | 累积时间
    `'file'` | 文件名
    `'filename'` | 文件名
    `'module'` | 文件名
    `'ncalls'` | 调用次数
    `'pcalls'` | 原始调用计数
    `'line'` | 行号
    `'name'` | 函数名称
    `'nfl'` | 名称/文件/行
    `'stdname'` | 标准名称
    `'time'` | 内部时间
    `'tottime'` | 内部时间

    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 `'nfl'` and `'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, `'nfl'` does a numeric compare of the line numbers. In fact, `sort_stats('nfl')` is the same as `sort_stats('name', 'file', '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.

  `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 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.

## 27.4.5. 什么是确定性性能分析？

确定性性能分析 旨在反映这样一个事实：即所有 函数调用 、 函数返回 和 异常 事件都被监控，并且对这些事件之间的间隔（在此期间用户的代码正在执行）进行精确计时。相反，统计分析（不是由该模块完成）随机采样有效指令指针，并推断时间花费在哪里。后一种技术传统上涉及较少的开销（因为代码不需要检测），但只提供了时间花在哪里的相对指示。

In Python, since there is an interpreter active during execution, the presence of instrumented code is not required to do deterministic profiling. Python automatically provides a hook (optional callback) for each event. In addition, the interpreted nature of Python tends to add so much overhead to execution, that deterministic profiling tends to only add small processing overhead in typical applications. The result is that deterministic profiling is not that expensive, yet provides extensive run time statistics about the execution of a Python program.

调用计数统计信息可用于识别代码中的错误（意外计数），并识别可能的内联扩展点（高频调用）。内部时间统计可用于识别应仔细优化的 「热循环」 。累积时间统计可用于识别算法选择上的高级别错误。注意，该分析器中对累积时间的异常处理允许将算法的递归实现与迭代实现的统计信息直接进行比较。

## 27.4.6. 局限性

一个限制是关于时间信息的准确性。确定性性能分析存在一个涉及精度的基本问题。最明显的限制是，底层的 「时钟」 只以大约0.001秒的速度（通常）运行。因此，没有什么测量会比底层时钟更精确。如果进行了足够的测量，那么 「误差」 将趋于平均。不幸的是，删除第一个错误会导致第二个错误来源。

第二个问题是，从调度事件到分析器获取时间的调用实际获取时钟状态，这需要 「一段时间」 。类似地，从获取时钟值（然后保存）开始，直到再次执行用户代码为止，退出分析器事件句柄时也存在一定的延迟。因此，多次调用单个函数或调用多个函数通常会累积此错误。以这种方式累积的误差通常小于时钟的精度（小于一个时钟周期），但它 可以 累积并变得非常客观。

与开销较低的 `cProfile` 相比， `profile` 的问题更为严重。出于这个原因， `profile` 提供了一种针对指定平台的自我校准方法，以便可以在很大程度上（平均地）消除此误差。

## 27.4.7. 准确估量

`profile` 模块的 profiler 会从每个事件处理时间中减去一个常量，以补偿调用 time 函数和存储结果的开销。默认情况下，常数为0。对于特定的平台，可用以下程序获得更好修正常数（ 局限性 ）。

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

The method executes the number of Python calls given by the argument, directly and again under the profiler, measuring the time for both. It then computes the hidden overhead per profiler event, and returns that as a float. For example, on a 1.8Ghz Intel Core i5 running Mac OS X, and using Python’s time.clock() as the timer, the magical number is about 4.04e-6.

The object of this exercise is to get a fairly consistent result. If your computer is very fast, or your timer function has poor resolution, you might have to pass 100000, or even 1000000, to get consistent results.

当你有一个一致的答案时，有三种方法可以使用：

    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)

If you have a choice, you are better off choosing a smaller constant, and then your results will 「less often」 show up as negative in profile statistics.

## 27.4.8. 使用自定义计时器

If you want to change how current time is determined (for example, to force use of wall-clock time or elapsed process time), pass the timing function you want to the `Profile` class constructor:

    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` should return a single number, or a list of numbers whose sum is the current time (like what `os.times()` returns). If the function returns a single time number, or the list of returned numbers has length 2, then you will get an especially fast version of the dispatch routine.

  Be warned that you should calibrate the profiler class for the timer function that you choose (see 准确估量). 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` should return a single number. If it returns integers, you can also invoke the class constructor with a second argument specifying the real duration of one unit of time. For example, if `your_integer_time_func` returns times measured in thousands of seconds, you would construct the `Profile` instance as follows:

      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 adds several new functions in `time` that can be used to make precise measurements of process or wall-clock time. For example, see `time.perf_counter()`.