概述

# mem.py
import functools
import time


def memoize(fn):
    """
    Decorate a function so that it results are cached in memory.

    >>> import random
    >>> random.seed(0)
    >>> f = lambda x: random.randint(0,10)
    >>> [f(1) for _ in range(10)]
    [9,8,4,2,5,3,6]
    >>> [f(2) for _ in range(10)]
    [9,6,10,9]
    >>> g = memoize(f)
    >>> [g(1) for _ in range(10)]
    [3,3]
    >>> [g(2) for _ in range(10)]
    [8,8]
    """
    cache = {}

    @functools.wraps(fn)
    def wrapped(*args,**kwargs):
        key = args,tuple(sorted(kwargs))
        try:
            return cache[key]
        except KeyError:
            cache[key] = fn(*args,**kwargs)
            return cache[key]
    return wrapped


def network_call(user_id):
    time.sleep(1)
    return 1


@memoize
def search(user_id):
    response = network_call(user_id)
    # do stuff to response
    return response

我对这段代码进行了测试,在这里我模拟了network_call()的不同返回值,以确保我在search()中做的一些修改按预期工作.

import mock

import mem


@mock.patch('mem.network_call')
def test_search(mock_network_call):
    mock_network_call.return_value = 2
    assert mem.search(1) == 2


@mock.patch('mem.network_call')
def test_search_2(mock_network_call):
    mock_network_call.return_value = 3
    assert mem.search(1) == 3

但是,当我运行这些测试时,我得到了一个失败,因为search()返回一个缓存的结果.

CAESAR-BAUTISTA:~ caesarbautista$py.test test_mem.py
============================= test session starts ==============================
platform darwin -- Python 2.7.8 -- py-1.4.26 -- pytest-2.6.4
collected 2 items

test_mem.py .F

=================================== FAILURES ===================================
________________________________ test_search_2 _________________________________

args = (<MagicMock name='network_call' id='4438999312'>,),keywargs = {}
extra_args = [<MagicMock name='network_call' id='4438999312'>]
entered_patchers = [<mock._patch object at 0x108913dd0>]
exc_info = (<class '_pytest.assertion.reinterpret.AssertionError'>,AssertionError(u'assert 2 == 3\n +  where 2 = <function search at 0x10893f848>(1)\n +    where <function search at 0x10893f848> = mem.search',<traceback object at 0x1089502d8>)
patching = <mock._patch object at 0x108913dd0>
arg = <MagicMock name='network_call' id='4438999312'>

    @wraps(func)
    def patched(*args,**keywargs):
        # don't use a with here (backwards compatability with Python 2.4)
        extra_args = []
        entered_patchers = []

        # can't use try...except...finally because of Python 2.4
        # compatibility
        exc_info = tuple()
        try:
            try:
                for patching in patched.patchings:
                    arg = patching.__enter__()
                    entered_patchers.append(patching)
                    if patching.attribute_name is not None:
                        keywargs.update(arg)
                    elif patching.new is DEFAULT:
                        extra_args.append(arg)

                args += tuple(extra_args)
>               return func(*args,**keywargs)

/opt/Boxen/homebrew/lib/python2.7/site-packages/mock.py:1201:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

mock_network_call = <MagicMock name='network_call' id='4438999312'>

    @mock.patch('mem.network_call')
    def test_search_2(mock_network_call):
        mock_network_call.return_value = 3
>       assert mem.search(1) == 3
E       assert 2 == 3
E        +  where 2 = <function search at 0x10893f848>(1)
E        +    where <function search at 0x10893f848> = mem.search

test_mem.py:15: AssertionError
====================== 1 Failed,1 passed in 0.03 seconds ======================

有没有办法测试记忆功能？我考虑了一些替代方案,但它们都有缺点.

一种解决方案是模拟memoize().我不愿意这样做,因为它泄漏了测试的实现细节.从理论上讲,我应该能够在没有系统其他部分的情况下记忆和取消默认功能,包括测试,从功能角度注意.

另一种解决方案是重写代码以公开修饰函数.也就是说,我可以这样做：

def _search(user_id):
    return network_call(user_id)
search = memoize(_search)

然而,这遇到了与上面相同的问题,尽管它可能更糟,因为它不适用于递归函数.

这有效地使得memoized数据成为一个全局变量(就像函数一样,它的共享范围).

顺便说一下,这就是你在测试时遇到困难的原因！

那么,如何将它包装成一个对象呢？

import functools
import time

def memoize(meth):
    @functools.wraps(meth)
    def wrapped(self,*args,**kwargs):

        # Prepare and get reference to cache
        attr = "_memo_{0}".format(meth.__name__)
        if not hasattr(self,attr):
            setattr(self,attr,{})
        cache = getattr(self,attr)

        # Actual caching
        key = args,tuple(sorted(kwargs))
        try:
            return cache[key]
        except KeyError:
            cache[key] = meth(self,**kwargs)
            return cache[key]

    return wrapped

def network_call(user_id):
    print "Was called with: %s" % user_id
    return 1

class NetworkEngine(object):

    @memoize
    def search(self,user_id):
        return network_call(user_id)


if __name__ == "__main__":
    e = NetworkEngine()
    for v in [1,1,2]:
        e.search(v)
    NetworkEngine().search(1)

产量：

Was called with: 1
Was called with: 2
Was called with: 1

换句话说,NetworkEngine的每个实例都有自己的缓存.只需重用相同的一个来共享一个缓存,或者实例化一个新缓存以获得一个新的缓存.

在您的测试代码中,您将使用：

@mock.patch('mem.network_call')
def test_search(mock_network_call):
    mock_network_call.return_value = 2
    assert mem.NetworkEngine().search(1) == 2

总结

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