118 lines
3.5 KiB
Python
118 lines
3.5 KiB
Python
"""`functools.lru_cache` compatible memoizing function decorators."""
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__all__ = ("fifo_cache", "lfu_cache", "lru_cache", "mru_cache", "rr_cache", "ttl_cache")
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import math
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import random
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import time
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try:
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from threading import RLock
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except ImportError: # pragma: no cover
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from dummy_threading import RLock
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from . import FIFOCache, LFUCache, LRUCache, MRUCache, RRCache, TTLCache
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from . import cached
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from . import keys
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class _UnboundTTLCache(TTLCache):
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def __init__(self, ttl, timer):
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TTLCache.__init__(self, math.inf, ttl, timer)
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@property
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def maxsize(self):
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return None
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def _cache(cache, maxsize, typed):
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def decorator(func):
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key = keys.typedkey if typed else keys.hashkey
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wrapper = cached(cache=cache, key=key, lock=RLock(), info=True)(func)
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wrapper.cache_parameters = lambda: {"maxsize": maxsize, "typed": typed}
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return wrapper
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return decorator
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def fifo_cache(maxsize=128, typed=False):
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"""Decorator to wrap a function with a memoizing callable that saves
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up to `maxsize` results based on a First In First Out (FIFO)
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algorithm.
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"""
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if maxsize is None:
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return _cache({}, None, typed)
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elif callable(maxsize):
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return _cache(FIFOCache(128), 128, typed)(maxsize)
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else:
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return _cache(FIFOCache(maxsize), maxsize, typed)
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def lfu_cache(maxsize=128, typed=False):
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"""Decorator to wrap a function with a memoizing callable that saves
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up to `maxsize` results based on a Least Frequently Used (LFU)
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algorithm.
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"""
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if maxsize is None:
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return _cache({}, None, typed)
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elif callable(maxsize):
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return _cache(LFUCache(128), 128, typed)(maxsize)
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else:
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return _cache(LFUCache(maxsize), maxsize, typed)
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def lru_cache(maxsize=128, typed=False):
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"""Decorator to wrap a function with a memoizing callable that saves
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up to `maxsize` results based on a Least Recently Used (LRU)
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algorithm.
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"""
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if maxsize is None:
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return _cache({}, None, typed)
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elif callable(maxsize):
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return _cache(LRUCache(128), 128, typed)(maxsize)
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else:
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return _cache(LRUCache(maxsize), maxsize, typed)
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def mru_cache(maxsize=128, typed=False):
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"""Decorator to wrap a function with a memoizing callable that saves
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up to `maxsize` results based on a Most Recently Used (MRU)
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algorithm.
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"""
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if maxsize is None:
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return _cache({}, None, typed)
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elif callable(maxsize):
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return _cache(MRUCache(128), 128, typed)(maxsize)
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else:
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return _cache(MRUCache(maxsize), maxsize, typed)
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def rr_cache(maxsize=128, choice=random.choice, typed=False):
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"""Decorator to wrap a function with a memoizing callable that saves
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up to `maxsize` results based on a Random Replacement (RR)
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algorithm.
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"""
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if maxsize is None:
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return _cache({}, None, typed)
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elif callable(maxsize):
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return _cache(RRCache(128, choice), 128, typed)(maxsize)
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else:
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return _cache(RRCache(maxsize, choice), maxsize, typed)
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def ttl_cache(maxsize=128, ttl=600, timer=time.monotonic, typed=False):
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"""Decorator to wrap a function with a memoizing callable that saves
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up to `maxsize` results based on a Least Recently Used (LRU)
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algorithm with a per-item time-to-live (TTL) value.
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"""
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if maxsize is None:
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return _cache(_UnboundTTLCache(ttl, timer), None, typed)
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elif callable(maxsize):
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return _cache(TTLCache(128, ttl, timer), 128, typed)(maxsize)
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else:
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return _cache(TTLCache(maxsize, ttl, timer), maxsize, typed)
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