考虑以下几点:
@property
def name(self):
if not hasattr(self, '_name'):
# expensive calculation
self._name = 1 + 1
return self._name
我是新来的,但我认为缓存可以分解成一个装饰器。只是我没有找到一个这样的;)
PS,真正的计算不依赖于可变值
考虑以下几点:
@property
def name(self):
if not hasattr(self, '_name'):
# expensive calculation
self._name = 1 + 1
return self._name
我是新来的,但我认为缓存可以分解成一个装饰器。只是我没有找到一个这样的;)
PS,真正的计算不依赖于可变值
当前回答
如果你正在使用Django框架,它有这样一个属性来缓存API的视图或响应 使用@cache_page(time),也可以有其他选项。
例子:
@cache_page(60 * 15, cache="special_cache")
def my_view(request):
...
更多细节可以在这里找到。
其他回答
Werkzeug有一个cached_property装饰器(docs, source)
创建自己的装饰器并使用它
from django.core.cache import cache
import functools
def cache_returned_values(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
key = "choose a unique key here"
results = cache.get(key)
if not results:
results = func(*args, **kwargs)
cache.set(key, results)
return results
return wrapper
现在看函数
@cache_returned_values
def get_some_values(args):
return x
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).
fastcache,这是Python 3 functools.lru_cache的C实现。提供10-30倍于标准库的加速。”
和选择的答案一样,只是导入不同:
from fastcache import lru_cache
@lru_cache(maxsize=128, typed=False)
def f(a, b):
pass
此外,它安装在Anaconda中,不像functools需要安装。
在Python Wiki中还有另一个备忘录装饰器的例子:
http://wiki.python.org/moin/PythonDecoratorLibrary#Memoize
这个例子有点聪明,因为如果参数是可变的,它不会缓存结果。(检查代码,它非常简单和有趣!)