Python Profilers 分析器

源代码：Lib/profile.py 和 Lib/pstats.py

profile分析器简介

cProfile 和 profile 提供了 Python 程序的 确定性性能分析 。 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 模块格式化为报表。

Python 标准库提供了同一分析接口的两种不同实现：

• 对于大多数用户，建议使用 cProfile ；这是一个 C 扩展插件，因为其合理的运行开销，所以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。

• profile 是一个纯 Python 模块（cProfile 就是模拟其接口的 C 语言实现），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。

注解

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级别的函数引入开销，因此 C 代码似乎比任何Python 代码都更快。

实时用户手册

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

• 在指定函数中消耗的总时间（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（即：函数运行一次的平均时间）

• 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。

cProfile 和 profile 文件也可以作为脚本调用，以分析另一个脚本。例如：

python -m cProfile [-o output_file] [-s sort_order] (-m module | 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

-m 指定要分析的是模块而不是脚本。

3.7 新版功能: cProfile 添加 -m 选项

3.8 新版功能: profile 添加 -m 选项

The pstats module's Stats class has a variety of methodsfor 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 allthe module names. The sort_stats() method sorted all theentries according to the standard module/line/name string that is printed. Theprint_stats() method printed out all the statistics. Youmight 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 callwill print out the statistics. The following are some interesting calls toexperiment with:

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

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

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

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

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

你也可以尝试：

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

This will sort all the statistics by file name, and then print out statisticsfor only the class init methods (since they are spelled with init inthem). 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 ofcumulative time, and then prints out some of the statistics. To be specific, thelist is first culled down to 50% (re: .5) of its original size, then onlylines containing init are maintained, and that sub-sub-list is printed.

If you wondered what functions called the above functions, you could now (pis 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, orguess what the following functions do:

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

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

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 ispresent, then this function automatically creates a Statsinstance and prints a simple profiling report. If the sort value is specified,it is passed to this Stats instance to control how theresults are sorted.

• profile.runctx(command, globals, locals, filename=None, sort=-1)
• This function is similar to run(), with added arguments to supply theglobals and locals dictionaries for the command string. This routineexecutes:

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 isneeded than what the cProfile.run() function provides.

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

Directly using the Profile class allows formatting profile resultswithout writing the profile data to a file:

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 (supportedonly in cProfile module. see 上下文管理器类型):

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

在 3.8 版更改: 添加了上下文管理器支持。

• enable()

• 开始收集分析数据。仅在 cProfile 可用。

• disable()

• 停止收集分析数据。仅在 cProfile 可用。

• create_stats()

• 停止收集分析数据，并在内部将结果记录为当前 profile。

• printstats(_sort=-1)

• Create a Stats object based on the currentprofile and print the results to stdout.

• dumpstats(_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 andlocal environment.

• runcall(func, *args, **kwargs)

• Profile func(args, *kwargs)

Note that profiling will only work if the called command/function actuallyreturns. If the interpreter is terminated (e.g. via a sys.exit() callduring the called command/function execution) no profiling results will beprinted.

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 afilename (or list of filenames) or from a Profile instance. Outputwill be printed to the stream specified by stream.

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

Instead of reading the profile data from a file, a cProfile.Profileor 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 pathinformation from file names. It is very useful in reducing the size ofthe printout to fit within (close to) 80 columns. This method modifiesthe object, and the stripped information is lost. After performing astrip 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 beindistinguishable (they are on the same line of the same filename, andhave the same function name), then the statistics for these two entriesare accumulated into a single entry.

• add(*filenames)

• This method of the Stats class accumulates additional profilinginformation into the current profiling object. Its arguments should referto 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 functionstatistics.

• dumpstats(_filename)

• Save the data loaded into the Stats object to a file namedfilename. The file is created if it does not exist, and is overwrittenif it already exists. This is equivalent to the method of the same nameon the profile.Profile and cProfile.Profile classes.

• sortstats(*keys_)

• This method modifies the Stats object by sorting it according tothe supplied criteria. The argument can be either a string or a SortKeyenum identifying the basis of a sort (example: 'time', 'name',SortKey.TIME or SortKey.NAME). The SortKey enums argument haveadvantage over the string argument in that it is more robust and lesserror prone.

When more than one key is provided, then additional keys are used assecondary criteria when there is equality in all keys selected beforethem. For example, sort_stats(SortKey.NAME, SortKey.FILE) will sortall 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, aslong as the abbreviation is unambiguous.

The following are the valid string and SortKey:

有效字符串参数

有效枚举参数

含义

'calls'

SortKey.CALLS

调用次数

'cumulative'

SortKey.CUMULATIVE

累积时间

'cumtime'

N/A

累积时间

'file'

N/A

文件名

'filename'

SortKey.FILENAME

文件名

'module'

N/A

文件名

'ncalls'

N/A

调用次数

'pcalls'

SortKey.PCALLS

原始调用计数

'line'

SortKey.LINE

行号

'name'

SortKey.NAME

函数名称

'nfl'

SortKey.NFL

名称/文件/行

'stdname'

SortKey.STDNAME

标准名称

'time'

SortKey.TIME

内部时间

'tottime'

N/A

内部时间

Note that all sorts on statistics are in descending order (placing mosttime consuming items first), where as name, file, and line number searchesare in ascending order (alphabetical). The subtle distinction betweenSortKey.NFL and SortKey.STDNAME is that the standard name is asort of the name as printed, which means that the embedded line numbersget compared in an odd way. For example, lines 3, 20, and 40 would (ifthe 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 assort_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 oldstyle format (numeric) is used, only one sort key (the numeric key) willbe used, and additional arguments will be silently ignored.

3.7 新版功能: Added the SortKey enum.

• reverse_order()

• This method for the Stats class reverses the ordering of thebasic list within the object. Note that by default ascending vsdescending order is properly selected based on the sort key of choice.

• printstats(*restrictions_)

• This method for the Stats class prints out a report as describedin the profile.run() definition.

The order of the printing is based on the lastsort_stats() operation done on the object (subject tocaveats in add() andstrip_dirs()).

The arguments provided (if any) can be used to limit the list down to thesignificant entries. Initially, the list is taken to be the complete setof profiled functions. Each restriction is either an integer (to select acount of lines), or a decimal fraction between 0.0 and 1.0 inclusive (toselect a percentage of lines), or a string that will interpreted as aregular 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 printfunctions that were part of filename .*foo:. In contrast, thecommand:

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.

• printcallers(*restrictions_)

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

  • With profile, a number is shown in parentheses after each callerto show how many times this specific call was made. For convenience, asecond non-parenthesized number repeats the cumulative time spent in thefunction at the right.

  • With cProfile, each caller is preceded by three numbers: thenumber of times this specific call was made, and the total andcumulative times spent in the current function while it was invoked bythis specific caller.

• printcallees(*restrictions_)

• This method for the Stats class prints a list of all functionthat were called by the indicated function. Aside from this reversal ofdirection of calls (re: called vs was called by), the arguments andordering are identical to the print_callers() method.

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。

在Python中，由于在执行过程中总有一个活动的解释器，因此执行确定性评测不需要插入指令的代码。Python 自动为每个事件提供一个:dfn:钩子 （可选回调）。此外，Python 的解释特性往往会给执行增加太多开销，以至于在典型的应用程序中，确定性分析往往只会增加很小的处理开销。结果是，确定性分析并没有那么代价高昂，但是它提供了有关 Python 程序执行的大量运行时统计信息。

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。请注意，该分析器中对累积时间的异常处理，允许直接比较算法的递归实现与迭代实现的统计信息。

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" 周期大约为0.001秒（通常）。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调用获取时间函数实际 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。尽管这种方式的误差通常小于时钟的精度（小于一个时钟周期），但它 可以 累积并变得非常可观。

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。 校准后，结果将更准确（在最小二乘意义上），但它有时会产生负数（当调用计数异常低，且概率之神对您不利时：-）。因此 不要 对产生的负数感到惊慌。它们应该只在你手工校准分析器的情况下才会出现，实际上结果比没有校准的情况要好。

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, directlyand again under the profiler, measuring the time for both. It then computes thehidden 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.process_time() asthe timer, the magical number is about 4.04e-6.

The object of this exercise is to get a fairly consistent result. If yourcomputer is very fast, or your timer function has poor resolution, you mighthave 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 thenyour results will "less often" show up as negative in profile statistics.

使用自定义计时器

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

pr = profile.Profile(your_time_func)

The resulting profiler will then call your_time_func. Depending on whetheryou 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 whosesum is the current time (like what os.times() returns). If thefunction returns a single time number, or the list of returned numbers haslength 2, then you will get an especially fast version of the dispatchroutine.

Be warned that you should calibrate the profiler class for the timer functionthat you choose (see 准确估量). For most machines, a timerthat returns a lone integer value will provide the best results in terms oflow overhead during profiling. (os.times() is pretty bad, as itreturns a tuple of floating point values). If you want to substitute abetter timer in the cleanest fashion, derive a class and hardwire areplacement dispatch method that best handles your timer call, along with theappropriate 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 specifyingthe real duration of one unit of time. For example, ifyour_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 timerfunctions should be used with care and should be as fast as possible. Forthe best results with a custom timer, it might be necessary to hard-code itin the C source of the internal _lsprof module.

Python 3.3 adds several new functions in time that can be used to makeprecise measurements of process or wall-clock time. For example, seetime.perf_counter().