C implementation of Python 3 functools.lru_cache
C implementation of Python 3 functools.lru_cache. Provides speedup of 10-30x over standard library. Passes test suite from standard library for lru_cache.
Provides 2 Least Recently Used caching function decorators:
- clru_cache - built-in (faster)
- >>> from fastcache import clru_cache, __version__ >>> __version__ '1.0.2' >>> @clru_cache(maxsize=325, typed=False) ... def fib(n): ... """Terrible Fibonacci number generator.""" ... return n if n < 2 else fib(n-1) + fib(n-2) ... >>> fib(300) 222232244629420445529739893461909967206666939096499764990979600 >>> fib.cache_info() CacheInfo(hits=298, misses=301, maxsize=325, currsize=301) >>> print(fib.__doc__) Terrible Fibonacci number generator. >>> fib.cache_clear() >>> fib.cache_info() CacheInfo(hits=0, misses=0, maxsize=325, currsize=0) >>> fib.__wrapped__(300) 222232244629420445529739893461909967206666939096499764990979600 >>> type(fib) >>> <class 'fastcache.clru_cache'>
- lru_cache - python wrapper around clru_cache
- >>> from fastcache import lru_cache >>> @lru_cache(maxsize=128, typed=False) ... def f(a, b): ... pass ... >>> type(f) >>> <class 'function'>
(c)lru_cache(maxsize=128, typed=False, state=None, unhashable=’error’)
Least-recently-used cache decorator.
If maxsize is set to None, the LRU features are disabled and the cache can grow without bound.
If typed is True, arguments of different types will be cached separately. For example, f(3.0) and f(3) will be treated as distinct calls with distinct results.
If state is a list or dict, the items will be incorporated into the argument hash.
The result of calling the cached function with unhashable (mutable) arguments depends on the value of unhashable:
If unhashable is ‘error’, a TypeError will be raised.
If unhashable is ‘warning’, a UserWarning will be raised, and the wrapped function will be called with the supplied arguments. A miss will be recorded in the cache statistics.
If unhashable is ‘ignore’, the wrapped function will be called with the supplied arguments. A miss will will be recorded in the cache statistics.
View the cache statistics named tuple (hits, misses, maxsize, currsize) with f.cache_info(). Clear the cache and statistics with f.cache_clear(). Access the underlying function with f.__wrapped__.
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