ConditionalCache is a set of decorators, that provide conditional function memoization and selective cache clearing.
It works under the same interface that most standard cache decorators like functools.lru_cache or cachetools.ttl_cache, but unlocking a new condition parameter, that will determine if the function result is memoized or not. This feature allows for more granular control over caching behavior, useful for those use cases where we want to store the output only when certain conditions are met. As for example when checking existence in databases.
To install ConditionalCache simply run:
pip install conditional-cacheWorking with ConditionalCache is as simple and straight-forward as using functools.lru_cache, as it works under the same interface.
from conditional_cache import lru_cache
# Memoize the returned element only when it is different than "Not Found"
@lru_cache(maxsize=64, condition=lambda db_value: db_value != "Not Found")
def element_exists_in_db(element_id: int) -> str:
print(f"Asked to DB: {element_id}")
# For the example let's consider that even elements exists.
return "Found" if element_id % 2 == 0 else "Not Found"When we will call this function, it will be execute only once for even numbers, and always for odds.
# Will be executed, and not memoized
print(f"Returned: {element_exists_in_db(element_id=1)}")
# Will be executed again
print(f"Returned: {element_exists_in_db(element_id=1)}\n")
# Will be executed and memoized
print(f"Returned: {element_exists_in_db(element_id=2)}")
# Will return the memoized result without executing again
print(f"Returned: {element_exists_in_db(element_id=2)}")>> Asked to DB: 1
>> Returned: Not Found
>> Asked to DB: 1
>> Returned: Not Found
>> Asked to DB: 2
>> Returned: Found
>> Returned: FoundIf during your execution, you perform an action that invalidate a given function result, you can actively remove that element cache:
# Will return the result that was memoized before
print(f"Returned: {element_exists_in_db(element_id=2)}\n")
# Remove the element from the cache
element_exists_in_db.cache_remove(element_id=2)
# Will be executed again and memoized
print(f"Returned: {element_exists_in_db(element_id=2)}")
# Will return the memoized result
print(f"Returned: {element_exists_in_db(element_id=2)}")>> Returned: Found
>> Asked to DB: 2
>> Returned: Found
>> Returned: Foundconditional_cache.lru_cache(maxsize: int = 128, typed: bool = False, condition: callable = lambda x: True)
An Least Recently Used Cache. It works the same way that functools.lru_cache but accepting conditional storage and selective item removing through <decorated_function>.cache_remove(**args)
maxsize: int. The maximum amount of elements to keep cached. Once the cache is full, new elements will start to override oldest ones.typed: bool. Works the same way that functools.lru_cache. IfTrue, function arguments of different types will be cached separately.condition: callable. It must be a function that receives a single parameter as input (the output of the decorated method) and returns aboolean.Trueif the result should be cached orFalseif it should not.