:tocdepth: 3 ********************************************************************* :mod:`cachetools` --- Extensible memoizing collections and decorators ********************************************************************* .. module:: cachetools This module provides various memoizing collections and decorators, including variants of the Python Standard Library's `@lru_cache`_ function decorator. For the purpose of this module, a *cache* is a mutable_ mapping_ of a fixed maximum size. When the cache is full, i.e. by adding another item the cache would exceed its maximum size, the cache must choose which item(s) to discard based on a suitable `cache algorithm`_. This module provides multiple cache classes based on different cache algorithms, as well as decorators for easily memoizing function and method calls. .. testsetup:: * from cachetools import cached, cachedmethod, LRUCache, TLRUCache, TTLCache from unittest import mock urllib = mock.MagicMock() import time Cache implementations ===================== This module provides several classes implementing caches using different cache algorithms. All these classes derive from class :class:`Cache`, which in turn derives from :class:`collections.MutableMapping`, and provide :attr:`maxsize` and :attr:`currsize` properties to retrieve the maximum and current size of the cache. When a cache is full, :meth:`Cache.__setitem__()` calls :meth:`self.popitem()` repeatedly until there is enough room for the item to be added. In general, a cache's size is the total size of its item's values. Therefore, :class:`Cache` provides a :meth:`getsizeof` method, which returns the size of a given `value`. The default implementation of :meth:`getsizeof` returns :const:`1` irrespective of its argument, making the cache's size equal to the number of its items, or ``len(cache)``. For convenience, all cache classes accept an optional named constructor parameter `getsizeof`, which may specify a function of one argument used to retrieve the size of an item's value. Note that the values of a :class:`Cache` are mutable by default, as are e.g. the values of a :class:`dict`. It is the user's responsibility to take care that cached values are not accidentally modified. This is especially important when using a custom `getsizeof` function, since the size of an item's value will only be computed when the item is inserted into the cache. .. note:: Please be aware that all these classes are *not* thread-safe. Access to a shared cache from multiple threads must be properly synchronized, e.g. by using one of the memoizing decorators with a suitable `lock` object. .. autoclass:: Cache(maxsize, getsizeof=None) :members: currsize, getsizeof, maxsize This class discards arbitrary items using :meth:`popitem` to make space when necessary. Derived classes may override :meth:`popitem` to implement specific caching strategies. If a subclass has to keep track of item access, insertion or deletion, it may additionally need to override :meth:`__getitem__`, :meth:`__setitem__` and :meth:`__delitem__`. .. autoclass:: FIFOCache(maxsize, getsizeof=None) :members: popitem This class evicts items in the order they were added to make space when necessary. .. autoclass:: LFUCache(maxsize, getsizeof=None) :members: popitem This class counts how often an item is retrieved, and discards the items used least often to make space when necessary. .. autoclass:: LRUCache(maxsize, getsizeof=None) :members: popitem This class discards the least recently used items first to make space when necessary. .. autoclass:: MRUCache(maxsize, getsizeof=None) :members: popitem This class discards the most recently used items first to make space when necessary. .. deprecated:: 5.4 `MRUCache` has been deprecated due to lack of use, to reduce maintenance. Please choose another cache implementation that suits your needs. .. autoclass:: RRCache(maxsize, choice=random.choice, getsizeof=None) :members: choice, popitem This class randomly selects candidate items and discards them to make space when necessary. By default, items are selected from the list of cache keys using :func:`random.choice`. The optional argument `choice` may specify an alternative function that returns an arbitrary element from a non-empty sequence. .. autoclass:: TTLCache(maxsize, ttl, timer=time.monotonic, getsizeof=None) :members: popitem, timer, ttl This class associates a time-to-live value with each item. Items that expire because they have exceeded their time-to-live will be no longer accessible, and will be removed eventually. If no expired items are there to remove, the least recently used items will be discarded first to make space when necessary. By default, the time-to-live is specified in seconds and :func:`time.monotonic` is used to retrieve the current time. .. testcode:: cache = TTLCache(maxsize=10, ttl=60) A custom `timer` function can also be supplied, which does not have to return seconds, or even a numeric value. The expression `timer() + ttl` at the time of insertion defines the expiration time of a cache item and must be comparable against later results of `timer()`, but `ttl` does not necessarily have to be a number, either. .. testcode:: from datetime import datetime, timedelta cache = TTLCache(maxsize=10, ttl=timedelta(hours=12), timer=datetime.now) .. method:: expire(self, time=None) Expired items will be removed from a cache only at the next mutating operation, e.g. :meth:`__setitem__` or :meth:`__delitem__`, and therefore may still claim memory. Calling this method removes all items whose time-to-live would have expired by `time`, so garbage collection is free to reuse their memory. If `time` is :const:`None`, this removes all items that have expired by the current value returned by :attr:`timer`. :returns: An iterable of expired `(key, value)` pairs. .. autoclass:: TLRUCache(maxsize, ttu, timer=time.monotonic, getsizeof=None) :members: popitem, timer, ttu Similar to :class:`TTLCache`, this class also associates an expiration time with each item. However, for :class:`TLRUCache` items, expiration time is calculated by a user-provided time-to-use (`ttu`) function, which is passed three arguments at the time of insertion: the new item's key and value, as well as the current value of `timer()`. .. testcode:: def my_ttu(_key, value, now): # assume value.ttu contains the item's time-to-use in seconds # note that the _key argument is ignored in this example return now + value.ttu cache = TLRUCache(maxsize=10, ttu=my_ttu) The expression `ttu(key, value, timer())` defines the expiration time of a cache item, and must be comparable against later results of `timer()`. As with :class:`TTLCache`, a custom `timer` function can be supplied, which does not have to return a numeric value. .. testcode:: from datetime import datetime, timedelta def datetime_ttu(_key, value, now): # assume now to be of type datetime.datetime, and # value.hours to contain the item's time-to-use in hours return now + timedelta(hours=value.hours) cache = TLRUCache(maxsize=10, ttu=datetime_ttu, timer=datetime.now) Items that expire because they have exceeded their time-to-use will be no longer accessible, and will be removed eventually. If no expired items are there to remove, the least recently used items will be discarded first to make space when necessary. .. method:: expire(self, time=None) Expired items will be removed from a cache only at the next mutating operation, e.g. :meth:`__setitem__` or :meth:`__delitem__`, and therefore may still claim memory. Calling this method removes all items whose time-to-use would have expired by `time`, so garbage collection is free to reuse their memory. If `time` is :const:`None`, this removes all items that have expired by the current value returned by :attr:`timer`. :returns: An iterable of expired `(key, value)` pairs. Extending cache classes ======================= Sometimes it may be desirable to notice when and what cache items are evicted, i.e. removed from a cache to make room for new items. Since all cache implementations call :meth:`popitem` to evict items from the cache, this can be achieved by overriding this method in a subclass: .. doctest:: :pyversion: >= 3 >>> class MyCache(LRUCache): ... def popitem(self): ... key, value = super().popitem() ... print('Key "%s" evicted with value "%s"' % (key, value)) ... return key, value >>> c = MyCache(maxsize=2) >>> c['a'] = 1 >>> c['b'] = 2 >>> c['c'] = 3 Key "a" evicted with value "1" With :class:`TTLCache` and :class:`TLRUCache`, items may also be removed after they expire. In this case, :meth:`popitem` will *not* be called, but :meth:`expire` will be called from the next mutating operation and will return an iterable of the expired `(key, value)` pairs. By overrding :meth:`expire`, a subclass will be able to track expired items: .. doctest:: :pyversion: >= 3 >>> class ExpCache(TTLCache): ... def expire(self, time=None): ... items = super().expire(time) ... print(f"Expired items: {items}") ... return items >>> c = ExpCache(maxsize=10, ttl=1.0) >>> c['a'] = 1 Expired items: [] >>> c['b'] = 2 Expired items: [] >>> time.sleep(1.5) >>> c['c'] = 3 Expired items: [('a', 1), ('b', 2)] Similar to the standard library's :class:`collections.defaultdict`, subclasses of :class:`Cache` may implement a :meth:`__missing__` method which is called by :meth:`Cache.__getitem__` if the requested key is not found: .. doctest:: :pyversion: >= 3 >>> class PepStore(LRUCache): ... def __missing__(self, key): ... """Retrieve text of a Python Enhancement Proposal""" ... url = 'http://www.python.org/dev/peps/pep-%04d/' % key ... with urllib.request.urlopen(url) as s: ... pep = s.read() ... self[key] = pep # store text in cache ... return pep >>> peps = PepStore(maxsize=4) >>> for n in 8, 9, 290, 308, 320, 8, 218, 320, 279, 289, 320: ... pep = peps[n] >>> print(sorted(peps.keys())) [218, 279, 289, 320] Note, though, that such a class does not really behave like a *cache* any more, and will lead to surprising results when used with any of the memoizing decorators described below. However, it may be useful in its own right. Memoizing decorators ==================== The :mod:`cachetools` module provides decorators for memoizing function and method calls. This can save time when a function is often called with the same arguments: .. doctest:: >>> @cached(cache={}) ... def fib(n): ... 'Compute the nth number in the Fibonacci sequence' ... return n if n < 2 else fib(n - 1) + fib(n - 2) >>> fib(42) 267914296 .. decorator:: cached(cache, key=cachetools.keys.hashkey, lock=None, info=False) Decorator to wrap a function with a memoizing callable that saves results in a cache. The `cache` argument specifies a cache object to store previous function arguments and return values. Note that `cache` need not be an instance of the cache implementations provided by the :mod:`cachetools` module. :func:`cached` will work with any mutable mapping type, including plain :class:`dict` and :class:`weakref.WeakValueDictionary`. `key` specifies a function that will be called with the same positional and keyword arguments as the wrapped function itself, and which has to return a suitable cache key. Since caches are mappings, the object returned by `key` must be hashable. The default is to call :func:`cachetools.keys.hashkey`. If `lock` is not :const:`None`, it must specify an object implementing the `context manager`_ protocol. Any access to the cache will then be nested in a ``with lock:`` statement. This can be used for synchronizing thread access to the cache by providing a :class:`threading.Lock` instance, for example. .. note:: The `lock` context manager is used only to guard access to the cache object. The underlying wrapped function will be called outside the `with` statement, and must be thread-safe by itself. The decorator's `cache`, `key` and `lock` parameters are also available as :attr:`cache`, :attr:`cache_key` and :attr:`cache_lock` attributes of the memoizing wrapper function. These can be used for clearing the cache or invalidating individual cache items, for example. .. testcode:: from threading import Lock # 640K should be enough for anyone... @cached(cache=LRUCache(maxsize=640*1024, getsizeof=len), lock=Lock()) def get_pep(num): 'Retrieve text of a Python Enhancement Proposal' url = 'http://www.python.org/dev/peps/pep-%04d/' % num with urllib.request.urlopen(url) as s: return s.read() # make sure access to cache is synchronized with get_pep.cache_lock: get_pep.cache.clear() # always use the key function for accessing cache items with get_pep.cache_lock: get_pep.cache.pop(get_pep.cache_key(42), None) For the common use case of clearing or invalidating the cache, the decorator also provides a :func:`cache_clear()` function which takes care of locking automatically, if needed: .. testcode:: # no need for get_pep.cache_lock here get_pep.cache_clear() If `info` is set to :const:`True`, the wrapped function is instrumented with a :func:`cache_info()` function that returns a named tuple showing `hits`, `misses`, `maxsize` and `currsize`, to help measure the effectiveness of the cache. .. note:: Note that this will inflict a - probably minor - performance penalty, so it has to be explicitly enabled. .. doctest:: :pyversion: >= 3 >>> @cached(cache=LRUCache(maxsize=32), info=True) ... def get_pep(num): ... url = 'http://www.python.org/dev/peps/pep-%04d/' % num ... with urllib.request.urlopen(url) as s: ... return s.read() >>> for n in 8, 290, 308, 320, 8, 218, 320, 279, 289, 320, 9991: ... pep = get_pep(n) >>> get_pep.cache_info() CacheInfo(hits=3, misses=8, maxsize=32, currsize=8) The original underlying function is accessible through the :attr:`__wrapped__` attribute. This can be used for introspection or for bypassing the cache. It is also possible to use a single shared cache object with multiple functions. However, care must be taken that different cache keys are generated for each function, even for identical function arguments: .. doctest:: :options: +ELLIPSIS >>> from cachetools.keys import hashkey >>> from functools import partial >>> # shared cache for integer sequences >>> numcache = {} >>> # compute Fibonacci numbers >>> @cached(numcache, key=partial(hashkey, 'fib')) ... def fib(n): ... return n if n < 2 else fib(n - 1) + fib(n - 2) >>> # compute Lucas numbers >>> @cached(numcache, key=partial(hashkey, 'luc')) ... def luc(n): ... return 2 - n if n < 2 else luc(n - 1) + luc(n - 2) >>> fib(42) 267914296 >>> luc(42) 599074578 >>> list(sorted(numcache.items())) [..., (('fib', 42), 267914296), ..., (('luc', 42), 599074578)] .. decorator:: cachedmethod(cache, key=cachetools.keys.methodkey, lock=None) Decorator to wrap a class or instance method with a memoizing callable that saves results in a (possibly shared) cache. The main difference between this and the :func:`cached` function decorator is that `cache` and `lock` are not passed objects, but functions. Both will be called with :const:`self` (or :const:`cls` for class methods) as their sole argument to retrieve the cache or lock object for the method's respective instance or class. .. note:: As with :func:`cached`, the context manager obtained by calling ``lock(self)`` will only guard access to the cache itself. It is the user's responsibility to handle concurrent calls to the underlying wrapped method in a multithreaded environment. The `key` function will be called as `key(self, *args, **kwargs)` to retrieve a suitable cache key. Note that the default `key` function, :func:`cachetools.keys.methodkey`, ignores its first argument, i.e. :const:`self`. This has mostly historical reasons, but also ensures that :const:`self` does not have to be hashable. You may provide a different `key` function, e.g. :func:`cachetools.keys.hashkey`, if you need :const:`self` to be part of the cache key. One advantage of :func:`cachedmethod` over the :func:`cached` function decorator is that cache properties such as `maxsize` can be set at runtime: .. testcode:: class CachedPEPs: def __init__(self, cachesize): self.cache = LRUCache(maxsize=cachesize) @cachedmethod(lambda self: self.cache) def get(self, num): """Retrieve text of a Python Enhancement Proposal""" url = 'http://www.python.org/dev/peps/pep-%04d/' % num with urllib.request.urlopen(url) as s: return s.read() peps = CachedPEPs(cachesize=10) print("PEP #1: %s" % peps.get(1)) .. testoutput:: :hide: :options: +ELLIPSIS PEP #1: ... When using a shared cache for multiple methods, be aware that different cache keys must be created for each method even when function arguments are the same, just as with the `@cached` decorator: .. testcode:: class CachedReferences: def __init__(self, cachesize): self.cache = LRUCache(maxsize=cachesize) @cachedmethod(lambda self: self.cache, key=partial(hashkey, 'pep')) def get_pep(self, num): """Retrieve text of a Python Enhancement Proposal""" url = 'http://www.python.org/dev/peps/pep-%04d/' % num with urllib.request.urlopen(url) as s: return s.read() @cachedmethod(lambda self: self.cache, key=partial(hashkey, 'rfc')) def get_rfc(self, num): """Retrieve text of an IETF Request for Comments""" url = 'https://tools.ietf.org/rfc/rfc%d.txt' % num with urllib.request.urlopen(url) as s: return s.read() docs = CachedReferences(cachesize=100) print("PEP #1: %s" % docs.get_pep(1)) print("RFC #1: %s" % docs.get_rfc(1)) .. testoutput:: :hide: :options: +ELLIPSIS PEP #1: ... RFC #1: ... ***************************************************************** :mod:`cachetools.keys` --- Key functions for memoizing decorators ***************************************************************** .. module:: cachetools.keys This module provides several functions that can be used as key functions with the :func:`cached` and :func:`cachedmethod` decorators: .. autofunction:: hashkey This function returns a :class:`tuple` instance suitable as a cache key, provided the positional and keywords arguments are hashable. .. autofunction:: methodkey This function is similar to :func:`hashkey`, but ignores its first positional argument, i.e. `self` when used with the :func:`cachedmethod` decorator. .. autofunction:: typedkey This function is similar to :func:`hashkey`, but arguments of different types will yield distinct cache keys. For example, ``typedkey(3)`` and ``typedkey(3.0)`` will return different results. .. autofunction:: typedmethodkey This function is similar to :func:`typedkey`, but ignores its first positional argument, i.e. `self` when used with the :func:`cachedmethod` decorator. These functions can also be helpful when implementing custom key functions for handling some non-hashable arguments. For example, calling the following function with a dictionary as its `env` argument will raise a :class:`TypeError`, since :class:`dict` is not hashable:: @cached(LRUCache(maxsize=128)) def foo(x, y, z, env={}): pass However, if `env` always holds only hashable values itself, a custom key function can be written that handles the `env` keyword argument specially:: def envkey(*args, env={}, **kwargs): key = hashkey(*args, **kwargs) key += tuple(sorted(env.items())) return key The :func:`envkey` function can then be used in decorator declarations like this:: @cached(LRUCache(maxsize=128), key=envkey) def foo(x, y, z, env={}): pass foo(1, 2, 3, env=dict(a='a', b='b')) **************************************************************************** :mod:`cachetools.func` --- :func:`functools.lru_cache` compatible decorators **************************************************************************** .. module:: cachetools.func To ease migration from (or to) Python 3's :func:`functools.lru_cache`, this module provides several memoizing function decorators with a similar API. All these decorators wrap a function with a memoizing callable that saves up to the `maxsize` most recent calls, using different caching strategies. If `maxsize` is set to :const:`None`, the caching strategy is effectively disabled and the cache can grow without bound. If the optional argument `typed` is set to :const:`True`, function arguments of different types will be cached separately. For example, ``f(3)`` and ``f(3.0)`` will be treated as distinct calls with distinct results. If a `user_function` is specified instead, it must be a callable. This allows the decorator to be applied directly to a user function, leaving the `maxsize` at its default value of 128:: @cachetools.func.lru_cache def count_vowels(sentence): sentence = sentence.casefold() return sum(sentence.count(vowel) for vowel in 'aeiou') The wrapped function is instrumented with a :func:`cache_parameters` function that returns a new :class:`dict` showing the values for `maxsize` and `typed`. This is for information purposes only. Mutating the values has no effect. The wrapped function is also instrumented with :func:`cache_info` and :func:`cache_clear` functions to provide information about cache performance and clear the cache. Please see the :func:`functools.lru_cache` documentation for details. Also note that all the decorators in this module are thread-safe by default. .. decorator:: fifo_cache(user_function) fifo_cache(maxsize=128, typed=False) Decorator that wraps a function with a memoizing callable that saves up to `maxsize` results based on a First In First Out (FIFO) algorithm. .. decorator:: lfu_cache(user_function) lfu_cache(maxsize=128, typed=False) Decorator that wraps a function with a memoizing callable that saves up to `maxsize` results based on a Least Frequently Used (LFU) algorithm. .. decorator:: lru_cache(user_function) lru_cache(maxsize=128, typed=False) Decorator that wraps a function with a memoizing callable that saves up to `maxsize` results based on a Least Recently Used (LRU) algorithm. .. decorator:: mru_cache(user_function) mru_cache(maxsize=128, typed=False) Decorator that wraps a function with a memoizing callable that saves up to `maxsize` results based on a Most Recently Used (MRU) algorithm. .. deprecated:: 5.4 The `mru_cache` decorator has been deprecated due to lack of use. Please choose a decorator based on some other algorithm. .. decorator:: rr_cache(user_function) rr_cache(maxsize=128, choice=random.choice, typed=False) Decorator that wraps a function with a memoizing callable that saves up to `maxsize` results based on a Random Replacement (RR) algorithm. .. decorator:: ttl_cache(user_function) ttl_cache(maxsize=128, ttl=600, timer=time.monotonic, typed=False) Decorator to wrap a function with a memoizing callable that saves up to `maxsize` results based on a Least Recently Used (LRU) algorithm with a per-item time-to-live (TTL) value. .. _@lru_cache: http://docs.python.org/3/library/functools.html#functools.lru_cache .. _cache algorithm: http://en.wikipedia.org/wiki/Cache_algorithms .. _context manager: http://docs.python.org/dev/glossary.html#term-context-manager .. _mapping: http://docs.python.org/dev/glossary.html#term-mapping .. _mutable: http://docs.python.org/dev/glossary.html#term-mutable