methodic_cache

functools.cache() for methods, done correctly.

Features

• Simple to use
• Extendable with custom cache backends (e.g. LRUCache, LFUCache, etc.)
• Works with non-hashable objects
• Works with frozen/slotted classes
• No memory leaks
• 100% test coverage

Installation

pip install methodic_cache

Usage

from methodic_cache import cached_method


class MyClass:
    @cached_method
    def my_method(self, arg1, arg2):
        return arg1 + arg2


my_obj = MyClass()
my_obj.my_method(1, 2)  # returns 3
my_obj.my_method(1, 2)  # returns 3 from the cache

Using classes with __slots__

Classes that define __slots__ need to have a __weakref__ slot to be able to be weakly referenced:

from methodic_cache import cached_method


class MyClass:
    __slots__ = ("my_attr", "__weakref__")  # <-- __weakref__ is required

    def __init__(self, my_attr):
        self.my_attr = my_attr

    @cached_method
    def my_method(self, arg1, arg2):
        print(f"Computing {self.my_attr} + {arg1} + {arg2}...")
        return self.my_attr + arg1 + arg2

my_obj = MyClass(1)
my_obj.my_method(2, 3)
# prints "Computing 1 + 2 + 3..."
# returns 6
my_obj.my_method(2, 3)
# returns 6

Custom cache backends

You can use any cache backend that implements the MutableMapping interface (e.g. dict, lru_cache, functools.lru_cache, etc.). The default cache backend is cachetools.Cache(maxsize=math.inf), which will keep the cache bounded to the lifetime of the self object.

You can use a different cache backend by passing it as the cache_factory argument to cached_method:

from methodic_cache import cached_method
from cachetools import LRUCache


class MyClass:
    @cached_method(cache_factory=lambda: LRUCache(maxsize=1))
    def my_method(self, arg1, arg2):
        print(f"Computing {arg1} + {arg2}...")
        return arg1 + arg2


my_obj = MyClass()
my_obj.my_method(1, 1)
# prints Computing 1 + 1...
# returns 2
my_obj.my_method(1, 1)
# returns 2
my_obj.my_method(2, 2)
# prints Computing 2 + 2...
# returns 4
my_obj.my_method(1, 1)  # <-- this will be recomputed because the cache is full
# prints Computing 1 + 1...
# returns 2