snapcache 1.0.0

Snap-in memory caching for Python, no persistence, just pure function result caching.

SnapCache

Snap-in memory caching for Python, no persistence, just pure function result caching.

Motivation

Python’s built-in functools.lru_cache offers an easy way to cache function results, but it has limitations. For example, attempting to cache complex objects like NumPy arrays results in a TypeError: unhashable type: 'numpy.ndarray'.

SnapCache addresses this issue by offering a similar simple decorator interface while supporting caching for any data structure ✨.

Installation

To install SnapCache, use pip:

pip install snapcache

Usage

Function-level Caching

You can cache function results using the WithCache decorator. Just apply it to any function you want to memoize.

from snapcache import WithCache

@WithCache(maxsize=3)
def add(a, b):
    return a + b

# Call the function multiple times
print(add(1, 2))  # First call, result is computed and cached
print(add(1, 2))  # Second call, result is retrieved from cache

Method-level Caching

You can also apply the decorator to class methods:

class Foo:
    @WithCache(maxsize=3)
    def bar(self, a, b):
        return a - b

foo = Foo()
print(foo.bar(10, 5))  # First call, result is computed and cached
print(foo.bar(10, 5))  # Second call, result is retrieved from cache

License

This project is licensed under the MIT License - see the LICENSE file for details.