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
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
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
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。
我编写了这个简单的装饰器类来缓存函数响应。我发现它对我的项目非常有用:
from datetime import datetime, timedelta
class cached(object):
def __init__(self, *args, **kwargs):
self.cached_function_responses = {}
self.default_max_age = kwargs.get("default_cache_max_age", timedelta(seconds=0))
def __call__(self, func):
def inner(*args, **kwargs):
max_age = kwargs.get('max_age', self.default_max_age)
if not max_age or func not in self.cached_function_responses or (datetime.now() - self.cached_function_responses[func]['fetch_time'] > max_age):
if 'max_age' in kwargs: del kwargs['max_age']
res = func(*args, **kwargs)
self.cached_function_responses[func] = {'data': res, 'fetch_time': datetime.now()}
return self.cached_function_responses[func]['data']
return inner
用法很简单:
import time
@cached
def myfunc(a):
print "in func"
return (a, datetime.now())
@cached(default_max_age = timedelta(seconds=6))
def cacheable_test(a):
print "in cacheable test: "
return (a, datetime.now())
print cacheable_test(1,max_age=timedelta(seconds=5))
print cacheable_test(2,max_age=timedelta(seconds=5))
time.sleep(7)
print cacheable_test(3,max_age=timedelta(seconds=5))
@lru_cache不适合默认attrs
我的@mem装饰:
import inspect
from copy import deepcopy
from functools import lru_cache, wraps
from typing import Any, Callable, Dict, Iterable
# helper
def get_all_kwargs_values(f: Callable, kwargs: Dict[str, Any]) -> Iterable[Any]:
default_kwargs = {
k: v.default
for k, v in inspect.signature(f).parameters.items()
if v.default is not inspect.Parameter.empty
}
all_kwargs = deepcopy(default_kwargs)
all_kwargs.update(kwargs)
for key in sorted(all_kwargs.keys()):
yield all_kwargs[key]
# the best decorator
def mem(func: Callable) -> Callable:
cache = dict()
@wraps(func)
def wrapper(*args, **kwargs) -> Any:
all_kwargs_values = get_all_kwargs_values(func, kwargs)
params = (*args, *all_kwargs_values)
_hash = hash(params)
if _hash not in cache:
cache[_hash] = func(*args, **kwargs)
return cache[_hash]
return wrapper
# some logic
def counter(*args) -> int:
print(f'* not_cached:', end='\t')
return sum(args)
@mem
def check_mem(a, *args, z=10) -> int:
return counter(a, *args, z)
@lru_cache
def check_lru(a, *args, z=10) -> int:
return counter(a, *args, z)
def test(func) -> None:
print(f'\nTest {func.__name__}:')
print('*', func(1, 2, 3, 4, 5))
print('*', func(1, 2, 3, 4, 5))
print('*', func(1, 2, 3, 4, 5, z=6))
print('*', func(1, 2, 3, 4, 5, z=6))
print('*', func(1))
print('*', func(1, z=10))
def main():
test(check_mem)
test(check_lru)
if __name__ == '__main__':
main()
输出:
Test check_mem:
* not_cached: * 25
* 25
* not_cached: * 21
* 21
* not_cached: * 11
* 11
Test check_lru:
* not_cached: * 25
* 25
* not_cached: * 21
* 21
* not_cached: * 11
* not_cached: * 11