cachetools: Extensible Memoizing Collections and Decorators

Warnings

• breaking Support for cache(self) returning None to suppress any caching has been deprecated. cache(self) should always return a valid cache object.
  Fix: Ensure that the cache function always returns a valid cache object.
• deprecated The typed argument in the cached decorator is deprecated. Use key=typedkey instead.
  Fix: Replace 'typed=True' with 'key=typedkey' in the cached decorator.
• gotcha Access to a shared cache from multiple threads must be properly synchronized to avoid concurrency issues.
  Fix: Use a suitable lock object when accessing the cache from multiple threads.

Install

• pip install cachetools Install cachetools

Imports

• Cache

  from cachetools import Cache

  Cache is the base class for all cache implementations.
• LRUCache

  from cachetools import LRUCache

  LRUCache is a cache implementation that discards the least recently used items.
• cached

  from cachetools import cached

  cached is a decorator for memoizing function calls.

Quickstart

A simple example demonstrating the use of cachetools to memoize Fibonacci number calculations.

import os
from cachetools import cached, LRUCache

# Initialize a cache with a maximum size of 128
cache = LRUCache(maxsize=128)

@cached(cache)
def fib(n):
    if n < 2:
        return n
    return fib(n - 1) + fib(n - 2)

# Compute the 42nd Fibonacci number
print(fib(42))