测试代码

Question

我做了这个基准测试：

import logging

import copy
import pstats
import cProfile

from io import StringIO

def difference(first_stats, second_stats):
    first_stats = copy.deepcopy( first_stats )
    first_stats.total_calls -= second_stats.total_calls
    first_stats.prim_calls -= second_stats.prim_calls
    first_stats.total_tt -= second_stats.total_tt

    first_set = set( first_stats.stats.keys() )
    second_set = set( second_stats.stats.keys() )
    intersection = first_set.intersection( second_set )

    for func in intersection:
        first_source = first_stats.stats[func]
        second_source = second_stats.stats[func]
        first_cc, first_nc, first_tt, first_ct, callers = first_source
        second_cc, second_nc, second_tt, second_ct, callers = second_source
        first_stats.stats[func] = (first_cc-second_cc, first_nc-second_nc, first_tt-second_tt, first_ct-second_ct, callers)

    for func, source in second_stats.stats.items():

        if func not in first_stats.stats:
            func = list(func)
            func[0] = '* ' + func[0]
            func = tuple(func)
            first_stats.stats[func] = source

    return first_stats

def print_difference(stats):
    output_stream = StringIO()
    stats.stream = output_stream
    stats.sort_stats( "time" )
    stats.print_stats()
    return output_stream.getvalue()

def average(stats, average_count):
    stats.total_calls /= average_count
    stats.prim_calls /= average_count
    stats.total_tt /= average_count

    for func, source in stats.stats.items():
        cc, nc, tt, ct, callers = source
        stats.stats[func] = (cc/average_count, nc/average_count, tt/average_count, ct/average_count, callers)

    return stats

def profile_something(profile_function, average_count, iterations_count):
    output_stream = StringIO()
    profiler_status = pstats.Stats( stream=output_stream )

    for index in range(average_count):
        profiler = cProfile.Profile()
        profiler.enable()

        profile_function( iterations_count )
        profiler.disable()
        profiler_status.add( profiler )

        print( 'Profiled', '{0:7.3f}'.format( profiler_status.total_tt ), 'seconds at', '%3d' % (index + 1),
                'for', profile_function.__name__, flush=True )

    average( profiler_status, average_count )
    profiler_status.sort_stats( "time" )
    profiler_status.print_stats()

    return "\n Profile results for %s\n%s" % ( profile_function.__name__, output_stream.getvalue() ), profiler_status


def run_profiling(first_function, second_function, average_count, iterations_count):
    first_function_results, first_function_stats = profile_something( first_function, average_count, iterations_count )
    second_function_results, second_function_stats = profile_something( second_function, average_count, iterations_count )
    total_difference = print_difference( difference( first_function_stats, second_function_stats ) )
    time_difference = first_function_stats.total_tt - second_function_stats.total_tt

    output = 2500
    output_stream = StringIO()
    print( first_function_results[0:output], file=output_stream )
    print( '\n', second_function_results[0:output], file=output_stream )
    print( '\n\nTotal difference\n', total_difference[0:output], file=output_stream )

    return ( ( time_difference, first_function_stats.total_tt, second_function_stats.total_tt,
                first_function.__name__, second_function.__name__, format(iterations_count, ',d') ),
            output_stream.getvalue() )

def get_loggging_debug():
    logging.Logger.manager.loggerDict.clear()
    log = logging.getLogger( "benchmark" )

    if not log.hasHandlers():
        date_format = "%H:%M:%S"
        string_format = "%(asctime)s:%(msecs)010.6f - %(name)s.%(funcName)s:%(lineno)d - %(message)s"

        stream = logging.StreamHandler()
        formatter = logging.Formatter( string_format, date_format )
        stream.setFormatter( formatter )
        log.addHandler( stream )
    return log

def logging_mod_log_debug_off(iterations_count):
    log = get_loggging_debug()
    log.setLevel( "WARNING" )

    for index in range( iterations_count ):
        log.debug( 'Message' )

def standardlog_fastdebug_off(iterations_count):

    class ClassName(object):

        def __init__(self, is_debug):
            self.is_debug = is_debug

    debugger = ClassName( False )

    for index in range( iterations_count ):

        if debugger.is_debug:
            print( 'Message' )

results= []
results.append( run_profiling( standardlog_fastdebug_off, logging_mod_log_debug_off, 1, 10000000 ) )

print('\n\nResults details:')
for index, result in enumerate( results, start=1 ):
    print( "\n%2d. %s\n" % ( index, result[1] ) )

print('Results resume:')
for index, result in enumerate( results, start=1 ):
    print( '%2d. %10.5f %10.5f %10.5f  %s - %s  = %s iterations' % ( (index,) + result[0]) )

哪个在我的计算机上产生以下结果：

$ python3.7.3 benchmark_tests.py
Profiled   0.910 seconds at   1 for standardlog_fastdebug_off
Profiled  10.450 seconds at   1 for logging_mod_log_debug_off


Results details:

 1.
 Profile results for standardlog_fastdebug_off
         5.0 function calls in 0.910 seconds

   Ordered by: internal time

      ncalls     tottime     percall     cumtime     percall filename:lineno(function)
         1.0       0.910       0.910       0.910       0.910 benchmark_tests.py:150(standardlog_fastdebug_off)
         1.0       0.000       0.000       0.000       0.000 {built-in method builtins.__build_class__}
         1.0       0.000       0.000       0.000       0.000 benchmark_tests.py:152(ClassName)
         1.0       0.000       0.000       0.000       0.000 benchmark_tests.py:154(__init__)
         1.0       0.000       0.000       0.000       0.000 {method 'disable' of '_lsprof.Profiler' objects}





 Profile results for logging_mod_log_debug_off
         20,000,058.0 function calls in 10.450 seconds

   Ordered by: internal time

      ncalls     tottime     percall     cumtime     percall filename:lineno(function)
         1.0       4.157       4.157      10.450      10.450 benchmark_tests.py:143(logging_mod_log_debug_off)
10,000,000.0       3.968       0.000       6.293       0.000 F:\Python\lib\logging\__init__.py:1362(debug)
10,000,000.0       2.325       0.000       2.325       0.000 F:\Python\lib\logging\__init__.py:1620(isEnabledFor)
         1.0       0.000       0.000       0.000       0.000 benchmark_tests.py:129(get_loggging_debug)
         1.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__.py:1222(getLogger)
         1.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__.py:1342(__init__)
         1.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__.py:1310(_clear_cache)
         1.0       0.000       0.000       0.000       0.000 F:\Python\lib\threading.py:75(RLock)
         5.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__.py:212(_acquireLock)
         3.0       0.000       0.000       0.000       0.000 {method 'clear' of 'dict' objects}
         1.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__.py:794(_addHandlerRef)
         1.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__.py:813(__init__)
         5.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__.py:221(_releaseLock)
         1.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__.py:1355(setLevel)
         1.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__.py:513(__init__)
         3.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__.py:187(_checkLevel)
         1.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__.py:842(createLock)
         1.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__.py:1273(_fixupParents)
         5.0       0.000       0.000       0.000       0.000 {method 'acquire' of '_thread.RLock' objects}
         2.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__.py:727(__init__)
         1.0       0.000       0.000       0.000       0.000 F:\Python\lib\logging\__init__


Total difference
          -20,000,053.0 function calls in -9.540 seconds

   Ordered by: internal time

      ncalls     tottime     percall     cumtime     percall filename:lineno(function)
         1.0       4.157       4.157      10.450      10.450 * benchmark_tests.py:143(logging_mod_log_debug_off)
10,000,000.0       3.968       0.000       6.293       0.000 * F:\Python\lib\logging\__init__.py:1362(debug)
10,000,000.0       2.325       0.000       2.325       0.000 * F:\Python\lib\logging\__init__.py:1620(isEnabledFor)
         1.0       0.910       0.910       0.910       0.910 benchmark_tests.py:150(standardlog_fastdebug_off)
         1.0       0.000       0.000       0.000       0.000 * benchmark_tests.py:129(get_loggging_debug)
         1.0       0.000       0.000       0.000       0.000 {built-in method builtins.__build_class__}
         1.0       0.000       0.000       0.000       0.000 * F:\Python\lib\logging\__init__.py:1222(getLogger)
         1.0       0.000       0.000       0.000       0.000 * F:\Python\lib\logging\__init__.py:1342(__init__)
         1.0       0.000       0.000       0.000       0.000 * F:\Python\lib\logging\__init__.py:1310(_clear_cache)
         1.0       0.000       0.000       0.000       0.000 * F:\Python\lib\threading.py:75(RLock)
         5.0       0.000       0.000       0.000       0.000 * F:\Python\lib\logging\__init__.py:212(_acquireLock)
         3.0       0.000       0.000       0.000       0.000 * ~:0(<method 'clear' of 'dict' objects>)
         1.0       0.000       0.000       0.000       0.000 * F:\Python\lib\logging\__init__.py:794(_addHandlerRef)
         1.0       0.000       0.000       0.000       0.000 * F:\Python\lib\logging\__init__.py:813(__init__)
         5.0       0.000       0.000       0.000       0.000 * F:\Python\lib\logging\__init__.py:221(_releaseLock)
         1.0       0.000       0.000       0.000       0.000 * F:\Python\lib\logging\__init__.py:1355(setLevel)
         1.0       0.000       0.000       0.000       0.000 * F:\Python\lib\logging\__init__.py:513(__init__)
         3.0       0.000       0.000       0.000       0.000 * F:\Python\lib\logging\__init__.py:187(_checkLevel)
         1.0       0.000       0.000       0.000       0.000 * F:\Python\lib\logging\__init__.py:842(createLock)
         1.0       0.000       0.000       0.000       0.000 * F:\Python\lib\logging\__init__.py:1273(_fixupParents)
         5.0       0.000       0.000       0.000       0.000 * ~:0(<method 'acquire' of '_thread.R


Results resume:
 1.   -9.53998    0.91016   10.45015  standardlog_fastdebug_off - logging_mod_log_debug_off  = 10,000,000 iterations

最重要的是基准报告最后一行中突出显示的总体时间。它说，在10和logging都被禁用的self.is_debug百万次呼叫之后，0.91016秒的print日志花费了{{1} }}秒。慢了十倍。

在启用了10.45015模块和logging语句的情况下，我不打算将print模块与logging语句进行比较，因为print模块提供了大量功能，那么我可以花更多钱时间比简单的logging要短。

但是，禁用了日志记录模块，我希望它不会花费太多时间。还是我计算错误，让10.45015模块花费0.91016秒而不是每个10百万{{1}}秒才花费{{1}}秒？

Answer 1

我想这不是关于logging模块，而是关于Python。考虑以下代码。四个简单的功能。它们之间的唯一区别是执行属性访问的次数。请参见下面的结果。

class A:
    def __init__(self):
        self.a = False
        self.a_b = B()

class B:
    def __init__(self):
        self.b = False
        self.b_c = C()

class C:
    def __init__(self):
        self.c = False

import time
N = 10_000_000
a = A()

def f_0(N):
    start = time.time()
    for dummy in range(N):
        if False:
            print(111)  # never printed
    stop = time.time()
    return stop - start

def f_1(N):
    start = time.time()
    for dummy in range(N):
        if a.a:
            print(111)
    stop = time.time()
    return stop - start

def f_2(N):
    start = time.time()
    for dummy in range(N):
        if a.a_b.b:
            print(111)
    stop = time.time()
    return stop - start

def f_3(N):
    start = time.time()
    for dummy in range(N):
        if a.a_b.b_c.c:
            print(111)
    stop = time.time()
    return stop - start

result = {'x': list(), 'y': list()}
for run in range(10):
    for x, f in enumerate([f_0, f_1, f_2, f_3]):
        result['x'].append(str(x))
        result['y'].append(f(N))
    print(run)

import matplotlib.pyplot as plt
plt.figure()
plt.plot(result['x'], result['y'], '.')
plt.xlabel('argument access chain length')
plt.ylabel('time, seconds')
plt.show()

结果是（Python 3.6.8 / CPython / Win10 / CPU i5-7600）：

原因是众所周知的-当cpu限制问题时，纯python速度很慢。例如，这就是为什么python不太适合用作数字磨床的原因。从我的经验来看，从自定义类和pandas切换到内置字典使我的python代码（大量计算工作）几乎快了10倍。

此外，您还可以查看https://hackernoon.com/yes-python-is-slow-and-i-dont-care-13763980b5a1-适用于代码的日志记录部分。

Answer 2

我真的无法复制您的结果。

如果绝对未配置日志记录模块，则它的速度非常快。

测试代码

import logging
import argparse
import time
import sys


log = logging.getLogger("blep")


def prepare_off():
    pass


def prepare_on():
    logging.basicConfig(level=logging.DEBUG, stream=sys.stdout)


def step(x):
    log.info("now running step %s", x)


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("-n", type=int, default=500_000)
    ap.add_argument(
        "--mode", required=True, choices=("off", "on")
    )
    args = ap.parse_args()

    prepare = globals()[f"prepare_{args.mode}"]

    prepare()
    t0 = time.perf_counter_ns()
    for x in range(args.n):
        step(x)
    t1 = time.perf_counter_ns()
    print(
        vars(args),
        (t1 - t0) / args.n,
        "ns/iter",
        file=sys.stderr,
    )


if __name__ == "__main__":
    main()

成绩单

$ time python3 so54470102.py --mode off >/dev/null
{'n': 500000, 'mode': 'off'} 541.53571 ns/iter
        0.32 real         0.30 user         0.01 sys
$ time python3 so54470102.py --mode on >/dev/null
{'n': 500000, 'mode': 'on'} 26932.005352 ns/iter
       13.52 real        13.01 user         0.47 sys

记住，在探查器下运行会产生很大的开销（当涉及更多调用时，例如在日志记录模块执行操作时，开销会更大）：

$ time python3 -m cProfile -o /dev/null so54470102.py --mode on >/dev/null
{'n': 500000, 'mode': 'on'} 45733.711544 ns/iter
       22.93 real        22.23 user         0.57 sys
$ time python3 -m cProfile -o /dev/null so54470102.py --mode off >/dev/null
{'n': 500000, 'mode': 'off'} 962.122468 ns/iter
        0.54 real         0.52 user         0.01 sys

为什么禁用时标准日志记录模块如此缓慢？

2 个答案:

测试代码

成绩单