Python 分析器?

源代碼： Lib/profile.py and Lib/pstats.py

分析器簡介?

cProfile 和 profile 提供了 Python 程序的 deterministic profiling 。 profile 是一組統計數據，描述程序的各個部分執行的頻率和時間。這些統計數據可以通過 pstats 模塊格式化為報告。

Python 標準庫提供了同一分析接口的兩種不同實現：

  1. 對于大多數用戶，建議使用 cProfile ；這是一個C擴展插件，開銷合理，適合于分析長時間運行的程序。該插件基于 lsprof ，由 Brett Rosen 和 Ted Chaotter 貢獻。

  2. profile 是一個純 Python 模塊（cProfile 就是模仿其接口的 C 實現），但它會顯著增加配置程序的開銷。如果你正在嘗試以某種方式擴展分析器，則使用此模塊可能會更容易完成任務。該模塊最初由Jim Roskind 設計和編寫。

注解

profiler 分析器模塊被設計為給指定的程序提供執行概要文件，而不是用于基準測試目的（ timeit 才是用于此目標的，它能獲得合理準確的結果）。這特別適用于將 Python 代碼與 C 代碼進行基準測試：分析器為Python 代碼引入開銷，但不會為C級函數引入開銷，因此 C 代碼似乎比任何Python 代碼都更快。

即時用戶手冊?

本節是為 “不想閱讀手冊” 的用戶提供的。它提供了非常簡短的概述，并允許用戶快速對現有應用程序執行評測。

要分析采用單個參數的函數，可以執行以下操作：

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

（如果 cProfile 在您的系統上不可用，請使用 profile 。）

上述操作將運行 re.compile() 并打印分析結果，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)

第一行顯示監聽了197個調用。在這些調用中，有192個是 原始的 ，這意味著調用不是通過遞歸引發的。下一行: Ordered by: standard name ，表示最右邊列中的文本字符串用于對輸出進行排序。列標題包括：

ncalls

調用次數

tottime

在指定函數中花費的總時間（不包括調用子函數的時間）

percall

是 tottime 除以 ncalls 的商

cumtime

指定的函數及其所有子函數（從調用到退出）消耗的累積時間。這個數字對于遞歸函數來說是準確的。

percall

是 cumtime 除以原始調用（次數）的商

filename:lineno(function)

提供相應數據的每個函數

如果第一列中有兩個數字（例如3/1），則表示函數遞歸。第二個值是原始調用次數，第一個是調用的總次數。請注意，當函數不遞歸時，這兩個值是相同的，并且只打印單個數字。

profile 運行結束時，打印輸出不是必須的。也可以通過為 run() 函數指定文件名，將結果保存到文件中：

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

pstats.Stats 類從文件中讀取 profile 結果，并以各種方式對其進行格式化。

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] (-m module | myscript.py)

-o 將profile 結果寫入文件而不是標準輸出

-s 指定 sort_stats() 排序值之一以對輸出進行排序。這僅適用于未提供 -o 的情況

-m 指定要分析的是模塊而不是腳本。

3.7 新版功能: Added the -m option.

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.

您也可以嘗試：

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 和 cProfile 模塊參考?

profile 和 cProfile 模塊都提供下列函數：

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
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())
enable()?

Start collecting profiling data.

disable()?

Stop collecting profiling data.

create_stats()?

停止收集分析數據，并在內部將結果記錄為當前 profile。

print_stats(sort=-1)?

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

dump_stats(filename)?

將當前profile 的結果寫入 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.

Stats 類?

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 對象有以下方法:

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 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:

有效字符串參數

有效枚舉參數

含義

'calls'

SortKey.CALLS

調用次數

'cumulative'

SortKey.CUMULATIVE

累積時間

'cumtime'

N/A

累積時間

'file'

N/A

文件名

'filename'

SortKey.FILENAME

文件名

'module'

N/A

文件名

'ncalls'

N/A

調用次數

'pcalls'

SortKey.PCALLS

原始調用計數

'line'

SortKey.LINE

行號

'name'

SortKey.NAME

函數名稱

'nfl'

SortKey.NFL

名稱/文件/行

'stdname'

SortKey.STDNAME

標準名稱

'time'

SortKey.TIME

內部時間

'tottime'

N/A

內部時間

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.

3.7 新版功能: 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 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.

什么是確定性性能分析？?

確定性性能分析 旨在反映這樣一個事實：即所有 函數調用 、 函數返回 和 異常 事件都被監控，并且對這些事件之間的間隔（在此期間用戶的代碼正在執行）進行精確計時。相反，統計分析（不是由該模塊完成）隨機采樣有效指令指針，并推斷時間花費在哪里。后一種技術傳統上涉及較少的開銷（因為代碼不需要檢測），但只提供了時間花在哪里的相對指示。

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.

調用計數統計信息可用于識別代碼中的錯誤（意外計數），并識別可能的內聯擴展點（高頻調用）。內部時間統計可用于識別應仔細優化的 "熱循環" 。累積時間統計可用于識別算法選擇上的高級別錯誤。注意，該分析器中對累積時間的異常處理允許將算法的遞歸實現與迭代實現的統計信息直接進行比較。

局限性?

一個限制是關于時間信息的準確性。確定性性能分析存在一個涉及精度的基本問題。最明顯的限制是，底層的 "時鐘" 只以大約0.001秒的速度（通常）運行。因此，沒有什么測量會比底層時鐘更精確。如果進行了足夠的測量，那么 "誤差" 將趨于平均。不幸的是，刪除第一個錯誤會導致第二個錯誤來源。

第二個問題是，從調度事件到分析器獲取時間的調用實際獲取時鐘狀態，這需要 "一段時間" 。類似地，從獲取時鐘值（然后保存）開始，直到再次執行用戶代碼為止，退出分析器事件句柄時也存在一定的延遲。因此，多次調用單個函數或調用多個函數通常會累積此錯誤。以這種方式累積的誤差通常小于時鐘的精度（小于一個時鐘周期），但它 可以 累積并變得非常客觀。

與開銷較低的 cProfile 相比， profile 的問題更為嚴重。出于這個原因， profile 提供了一種針對指定平臺的自我校準方法，以便可以在很大程度上（平均地）消除此誤差。

準確估量?

profile 模塊的 profiler 會從每個事件處理時間中減去一個常量，以補償調用 time 函數和存儲結果的開銷。默認情況下，常數為0。對于特定的平臺，可用以下程序獲得更好修正常數（ 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.process_time() 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.

當你有一個一致的答案時，有三種方法可以使用：

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.

使用自定義計時器?

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 準確估量). 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().