从Python 3.2开始，有一个内置的装饰器:

@functools。lru_cache(最大容量= 100,输入= False)

装饰器使用一个可记忆可调用对象来包装函数，该可调用对象最多保存maxsize最近的调用。当使用相同的参数周期性地调用昂贵的或I/O绑定的函数时，它可以节省时间。

用于计算斐波那契数的LRU缓存示例:

from functools import lru_cache

@lru_cache(maxsize=None)
def fib(n):
    if n < 2:
        return n
    return fib(n-1) + fib(n-2)

>>> print([fib(n) for n in range(16)])
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610]

>>> print(fib.cache_info())
CacheInfo(hits=28, misses=16, maxsize=None, currsize=16)

如果你被Python 2困住了。X，这里是其他兼容的内存库列表:

functools32 | PyPI |源代码 repoze。lru | PyPI |源代码 pylru | PyPI |源代码 补丁。functools_lru_cache | PyPI |源代码

Python 3.8 functools。cached_property装饰

https://docs.python.org/dev/library/functools.html#functools.cached_property

来自Werkzeug的cached_property在:https://stackoverflow.com/a/5295190/895245上提到过，但据说派生版本将合并到3.8中，这是非常棒的。

这个装饰器可以被看作是缓存@property，或者是清洁器@functools。Lru_cache，当你没有任何参数时。

医生说:

@functools.cached_property(func) Transform a method of a class into a property whose value is computed once and then cached as a normal attribute for the life of the instance. Similar to property(), with the addition of caching. Useful for expensive computed properties of instances that are otherwise effectively immutable. Example: class DataSet: def __init__(self, sequence_of_numbers): self._data = sequence_of_numbers @cached_property def stdev(self): return statistics.stdev(self._data) @cached_property def variance(self): return statistics.variance(self._data) New in version 3.8. Note This decorator requires that the dict attribute on each instance be a mutable mapping. This means it will not work with some types, such as metaclasses (since the dict attributes on type instances are read-only proxies for the class namespace), and those that specify slots without including dict as one of the defined slots (as such classes don’t provide a dict attribute at all).

从Python 3.2开始，有一个内置的装饰器:

@functools。lru_cache(最大容量= 100,输入= False)

装饰器使用一个可记忆可调用对象来包装函数，该可调用对象最多保存maxsize最近的调用。当使用相同的参数周期性地调用昂贵的或I/O绑定的函数时，它可以节省时间。

用于计算斐波那契数的LRU缓存示例:

from functools import lru_cache

@lru_cache(maxsize=None)
def fib(n):
    if n < 2:
        return n
    return fib(n-1) + fib(n-2)

>>> print([fib(n) for n in range(16)])
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610]

>>> print(fib.cache_info())
CacheInfo(hits=28, misses=16, maxsize=None, currsize=16)

如果你被Python 2困住了。X，这里是其他兼容的内存库列表:

functools32 | PyPI |源代码 repoze。lru | PyPI |源代码 pylru | PyPI |源代码 补丁。functools_lru_cache | PyPI |源代码

尝试joblib https://joblib.readthedocs.io/en/latest/memory.html

from joblib import Memory
memory = Memory(cachedir=cachedir, verbose=0)
@memory.cache
    def f(x):
        print('Running f(%s)' % x)
        return x

class memorize(dict):
    def __init__(self, func):
        self.func = func

    def __call__(self, *args):
        return self[args]

    def __missing__(self, key):
        result = self[key] = self.func(*key)
        return result

示例使用:

>>> @memorize
... def foo(a, b):
...     return a * b
>>> foo(2, 4)
8
>>> foo
{(2, 4): 8}
>>> foo('hi', 3)
'hihihi'
>>> foo
{(2, 4): 8, ('hi', 3): 'hihihi'}

from functools import wraps


def cache(maxsize=128):
    cache = {}

    def decorator(func):
        @wraps(func)
        def inner(*args, no_cache=False, **kwargs):
            if no_cache:
                return func(*args, **kwargs)

            key_base = "_".join(str(x) for x in args)
            key_end = "_".join(f"{k}:{v}" for k, v in kwargs.items())
            key = f"{key_base}-{key_end}"

            if key in cache:
                return cache[key]

            res = func(*args, **kwargs)

            if len(cache) > maxsize:
                del cache[list(cache.keys())[0]]
                cache[key] = res

            return res

        return inner

    return decorator


def async_cache(maxsize=128):
    cache = {}

    def decorator(func):
        @wraps(func)
        async def inner(*args, no_cache=False, **kwargs):
            if no_cache:
                return await func(*args, **kwargs)

            key_base = "_".join(str(x) for x in args)
            key_end = "_".join(f"{k}:{v}" for k, v in kwargs.items())
            key = f"{key_base}-{key_end}"

            if key in cache:
                return cache[key]

            res = await func(*args, **kwargs)

            if len(cache) > maxsize:
                del cache[list(cache.keys())[0]]
                cache[key] = res

            return res

        return inner

    return decorator

示例使用

import asyncio
import aiohttp


# Removes the aiohttp ClientSession instance warning.
class HTTPSession(aiohttp.ClientSession):
    """ Abstract class for aiohttp. """
    
    def __init__(self, loop=None) -> None:
        super().__init__(loop=loop or asyncio.get_event_loop())

    def __del__(self) -> None:
        if not self.closed:
            self.loop.run_until_complete(self.close())
            self.loop.close()
 

        return 
       

            

session = HTTPSession()

@async_cache()
async def query(url, method="get", res_method="text", *args, **kwargs):
    async with getattr(session, method.lower())(url, *args, **kwargs) as res:
        return await getattr(res, res_method)()


async def get(url, *args, **kwargs):
    return await query(url, "get", *args, **kwargs)
 

async def post(url, *args, **kwargs):
    return await query(url, "post", *args, **kwargs)

async def delete(url, *args, **kwargs):
    return await query(url, "delete", *args, **kwargs)