cache, lru-cache, singledispatch

#python #efficiency #decorator #designpattern #programming #overriding

Cache

• Save call results in a dict structure, therefore make the process more fast reusing last results
• It for fast commands, because it can fill all the memory because it is unlimited. Another option is lru_cache with max_size
• They are good options for remote API
• All the arguments of the functions need to be hashables (Hashable) because they will be save in a dict

import functools

from clockdeco import clock

@functools.cache # stacked decorators works like that cache(clock(fibonnaci))
@clock
def fibonacci(n):
	if n < 2:
			return n
		return fibonacci(n - 2) + fibonacci(n - 1)

if __name__ == '__main__':
    print(fibonacci(6))

lru-cache

• Similar to cache but has typed and max_size
• least-recently-used: The old ones that weren’t read are eliminated
  • max_size: limit the amount of entries
  • typed: typed=true means that f(1) and f(1.0) are stored separately

singledispatch

• Imitate the overloading from Java
  • A function implementation can vary, and the called version depends on only from the first type parameter
• Use collections.abc and protocols for the types because they are better for future modification and virtual classes. In other words, avoid concrete types
• singledispatch implementation

Reference

• Ramalho, 2022, p320-329