Python 3 中 len(set) 与 set.__len__() 的性能分析 [英] Profiled performance of len(set) vs. set.__len__() in Python 3

问题描述

在分析我的 Python 应用程序时，我发现 len() 在使用集合时似乎是一个非常昂贵的应用程序.看下面的代码:

While profiling my Python's application, I've discovered that len() seems to be a very expensive one when using sets. See the below code:

import cProfile

def lenA(s):
    for i in range(1000000):
        len(s);

def lenB(s):
    for i in range(1000000):
        s.__len__();

def main():
    s = set();
    lenA(s);
    lenB(s);

if __name__ == "__main__":
    cProfile.run("main()","stats");

根据下面分析器的统计数据，lenA() 似乎比 lenB() 慢 14 倍:

According to profiler's stats below, lenA() seems to be 14 times slower than lenB():

 ncalls  tottime  percall  cumtime  percall  filename:lineno(function)
      1    1.986    1.986    3.830    3.830  .../lentest.py:5(lenA)
1000000    1.845    0.000    1.845    0.000  {built-in method len}
      1    0.273    0.273    0.273    0.273  .../lentest.py:9(lenB)

我错过了什么吗?目前我使用 __len__() 而不是 len()，但代码看起来很脏:(

Am I missing something? Currently I use __len__() instead of len(), but the code looks dirty :(

推荐答案

显然，len 有一些开销，因为它执行函数调用并将 AttributeError 转换为 类型错误.此外，set.__len__ 是一个如此简单的操作，与几乎任何东西相比，它肯定会非常快，但是在使用 timeit<时，我仍然没有发现任何类似 14 倍差异的东西/代码>:

Obviously, len has some overhead, since it does a function call and translates AttributeError to TypeError. Also, set.__len__ is such a simple operation that it's bound to be very fast in comparison to just about anything, but I still don't find anything like the 14x difference when using timeit:

In [1]: s = set()

In [2]: %timeit s.__len__()
1000000 loops, best of 3: 197 ns per loop

In [3]: %timeit len(s)
10000000 loops, best of 3: 130 ns per loop

你应该总是只调用 len，而不是 __len__.如果对 len 的调用是你程序的瓶颈，你应该重新考虑它的设计，例如在某处缓存大小或在不调用 len 的情况下计算它们.

You should always just call len, not __len__. If the call to len is the bottleneck in your program, you should rethink its design, e.g. cache sizes somewhere or calculate them without calling len.

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