可以使用[@cache](https://docs.python.org/3/library/functools.htmlfunctools.cache)装饰器(以前称为@lru_cache)作为“简单的轻量级无界函数缓存”。 典型的例子是计算斐波那契数列,其中缓存中间结果,显着加快计算速度: from functoolsimportcache @cache def fibonacci(n:
key=compute_key(function,args,kw)#if has it?if(keyincacheandnotis_obsolete(cache[key],duration)):print'we got a winner'returncache['key']['value']#calcresult = function(*args,**kw) cache[key]= {'value':result,'time':time.time()}returnresultreturn__memoizereturn_memoize sha hash 键...
:param cache_time 缓存的时间 :type cache_time : float"""def_cached_function_result_for_a_time(fun): @wraps(fun)def__cached_function_result_for_a_time(*args, **kwargs):iflen(cls.func_result_dict) > 1024: cls.func_result_dict.clear() key=cls._make_arguments_to_key(args, kwargs)...
@lru_cache(key=custom_key_function) def function_with_custom_key(arg): # 使用自定义键进行缓存 return result 缓存的元数据 lru_cache对象还具有一些有用的元数据,如hits(缓存命中次数)和misses(缓存未命中次数): result = expensive_function(arg) print(expensive_function.cache_info()) # 输出缓存信息,...
@lru_cache(key=custom_key_function) def function_with_custom_key(arg): # 使用自定义键进行缓存 return result 1. 2. 3. 4. 5. 6. 7. 缓存的元数据 lru_cache对象还具有一些有用的元数据,如hits(缓存命中次数)和misses(缓存未命中次数): ...
# Using Python's functools' lru_cache function import functools @functools.lru_cache() def fibonacci_v2(n): if n == 0: return 0 elif n == 1: return 1 return fibonacci_v2(n - 1) + fibonacci_v2(n-2) def _test_10_v1(numbers): ...
@st.cache(suppress_st_warning=True)defexpensive_computation(a,b):st.write("Cache miss: expensive_computation(",a,",",b,") ran")time.sleep(2)# This makes thefunctiontake 2s to runreturn{"output":a*b}# 👈 Mutable object a=2b=21res=expensive_computation(a,b)st.write("Result:",res...
decode_responses=True) result = r.ping() print("Ping returned : "+ str(result)) result = r.set("Message","Hello!, The cache is working with Python!") print("SET Message returned : "+ str(result)) result = r.get("Message") print("GET Message returned : "+ result) result = r...
importfunctoolsimporttime#cachingupto12differentresults@functools.lru_cache(maxsize=12)defslow_func(x):time.sleep(2)#Simulatelongcomputationreturnxslow_func(1)#...waitingfor2secbeforegettingresultslow_func(1)#alreadycached-resultreturnedinstantaneously!slow_func(3)#...waitingfor2secbeforegettingresult ...
from functools import lru_cache @lru_cache(maxsize=None) # 缓存所有结果,可以根据实际情况设置缓存大小 def expensive_computation(x): # 假设这是一个计算成本很高的函数 print("Computing...") return x ** x # 第一次调用时会执行计算 result1 = expensive_computation(5) ...