`cachetools` — Extensible memoizing collections and decorators¶

This module provides various memoizing collections and decorators, including variants of the Python 3 Standard Library @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. In general, a cache’s size is the total size of its items, and an item’s size is a property or function of its value, e.g. the result of sys.getsizeof(value). For the trivial but common case that each item counts as 1, a cache’s size is equal to the number of its items, or len(cache).

Multiple cache classes based on different caching algorithms are implemented, and decorators for easily memoizing function and method calls are provided, too.

Cache implementations¶

This module provides several classes implementing caches using different cache algorithms. All these classes derive from class Cache, which in turn derives from collections.MutableMapping, and provide maxsize and currsize properties to retrieve the maximum and current size of the cache. When a cache is full, Cache.__setitem__() calls self.popitem() repeatedly until there is enough room for the item to be added.

Cache also features a getsizeof() method, which returns the size of a given value. The default implementation of getsizeof() returns 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.

class cachetools.Cache(maxsize, missing=None, getsizeof=None)¶

Mutable mapping to serve as a simple cache or cache base class.

This class discards arbitrary items using popitem() to make space when necessary. Derived classes may override 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 __getitem__(), __setitem__() and __delitem__().

Deprecated since version 2.1: The missing argument. Override __missing__() in a subclass instead.

currsize¶: The current size of the cache.

static getsizeof(value)¶: Return the size of a cache element’s value.

maxsize¶: The maximum size of the cache.

class cachetools.LFUCache(maxsize, missing=None, getsizeof=None)¶

Least Frequently Used (LFU) cache implementation.

This class counts how often an item is retrieved, and discards the items used least often to make space when necessary.

Deprecated since version 2.1: The missing argument. Override __missing__() in a subclass instead.

popitem()¶: Remove and return the (key, value) pair least frequently used.

class cachetools.LRUCache(maxsize, missing=None, getsizeof=None)¶

Least Recently Used (LRU) cache implementation.

This class discards the least recently used items first to make space when necessary.

Deprecated since version 2.1: The missing argument. Override __missing__() in a subclass instead.

popitem()¶: Remove and return the (key, value) pair least recently used.

class cachetools.RRCache(maxsize, choice=random.choice, missing=None, getsizeof=None)¶

Random Replacement (RR) cache implementation.

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 random.choice(). The optional argument choice may specify an alternative function that returns an arbitrary element from a non-empty sequence.

Deprecated since version 2.1: The missing argument. Override __missing__() in a subclass instead.

choice¶: The choice function used by the cache.

popitem()¶: Remove and return a random (key, value) pair.

class cachetools.TTLCache(maxsize, ttl, timer=time.time, missing=None, getsizeof=None)¶

LRU Cache implementation with per-item time-to-live (TTL) value.

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 the time.time() function is used to retrieve the current time. A custom timer function can be supplied if needed.

Deprecated since version 2.1: The missing argument. Override __missing__() in a subclass instead.

expire(self, time=None)¶: Expired items will be removed from a cache only at the next mutating operation, e.g. __setitem__() or __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 None, this removes all items that have expired by the current value returned by timer.

popitem()¶: Remove and return the (key, value) pair least recently used that has not already expired.

timer¶: The timer function used by the cache.

ttl¶: The time-to-live value of the cache’s items.

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 popitem() to evict items from the cache, this can be achieved by overriding this method in a subclass:

>>> from cachetools import LRUCache
>>> 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"

Similar to the standard library’s collections.defaultdict, subclasses of Cache may implement a __missing__() method which is called by Cache.__getitem__() if the requested key is not found:

>>> from cachetools import LRUCache
>>> import urllib.request
>>> class PepStore(LRUCache):
...     def __missing__(self, key):
...         """Retrieve text of a Python Enhancement Proposal"""
...         url = 'http://www.python.org/dev/peps/pep-%04d/' % key
...         try:
...             with urllib.request.urlopen(url) as s:
...                 pep = s.read()
...                 self[key] = pep  # store text in cache
...                 return pep
...         except urllib.error.HTTPError:
...             return 'Not Found'  # do not store in cache
>>> peps = PepStore(maxsize=4)
>>> for n in 8, 9, 290, 308, 320, 8, 218, 320, 279, 289, 320, 9991:
...     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 cachetools module provides decorators for memoizing function and method calls. This can save time when a function is often called with the same arguments:

from cachetools import cached

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

for i in range(100):
    print('fib(%d) = %d' % (i, fib(i)))

@cachetools.cached(cache, key=cachetools.keys.hashkey, lock=None)¶

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 cachetools module. cached() will work with any mutable mapping type, including plain dict and 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 cachetools.keys.hashkey().

If lock is not 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 threading.RLock 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 original underlying function is accessible through the __wrapped__ attribute of the memoizing wrapper function. This can be used for introspection or for bypassing the cache.

To perform operations on the cache object, for example to clear the cache during runtime, the cache should be assigned to a variable. When a lock object is used, any access to the cache from outside the function wrapper should also be performed within an appropriate with statement:

from threading import RLock
from cachetools import cached, LRUCache

cache = LRUCache(maxsize=100)
lock = RLock()

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

# make sure access to cache is synchronized
with lock:
    cache.clear()

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:

from functools import partial
from cachetools import cached, LRUCache
from cachetools.keys import hashkey

cache = LRUCache(maxsize=100)

@cached(cache, key=partial(hashkey, 'fib'))
def fib(n):
    return n if n < 2 else fib(n - 1) + fib(n - 2)

@cached(cache, key=partial(hashkey, 'fac'))
def fac(n):
    return 1 if n == 0 else n * fac(n - 1)

print(fib(42))
print(fac(42))
print(cache)

@cachetools.cachedmethod(cache, key=cachetools.keys.hashkey, 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 cached() function decorator is that cache and lock are not passed objects, but functions. Both will be called with self (or 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 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.

One advantage of cachedmethod() over the cached() function decorator is that cache properties such as maxsize can be set at runtime:

import operator
import urllib.request

from cachetools import LRUCache, cachedmethod

class CachedPEPs(object):

    def __init__(self, cachesize):
        self.cache = LRUCache(maxsize=cachesize)

    @cachedmethod(operator.attrgetter('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))

`cachetools.keys` — Key functions for memoizing decorators¶

This module provides several functions that can be used as key functions with the cached() and cachedmethod() decorators:

cachetools.keys.hashkey(*args, **kwargs)¶

Return a cache key for the specified hashable arguments.

This function returns a tuple instance suitable as a cache key, provided the positional and keywords arguments are hashable.

cachetools.keys.typedkey(*args, **kwargs)¶

Return a typed cache key for the specified hashable arguments.

This function is similar to hashkey(), but arguments of different types will yield distinct cache keys. For example, typedkey(3) and typedkey(3.0) will return different results.

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 TypeError, since 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(env.items())
    return key

The envkey() function can then be used in decorator declarations like this:

@cached(LRUCache(maxsize=128), key=envkey)

`cachetools.func` — `functools.lru_cache()` compatible decorators¶

To ease migration from (or to) Python 3’s 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. Note that unlike functools.lru_cache(), setting maxsize to None is not supported.

If the optional argument typed is set to 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.

The wrapped function is instrumented with cache_info() and cache_clear() functions to provide information about cache performance and clear the cache. See the functools.lru_cache() documentation for details.

@cachetools.func.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.

@cachetools.func.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.

@cachetools.func.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.

@cachetools.func.ttl_cache(maxsize=128, ttl=600, timer=time.time, 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.

cachetools — Extensible memoizing collections and decorators¶

Cache implementations¶

Extending cache classes¶

Memoizing decorators¶

cachetools.keys — Key functions for memoizing decorators¶

cachetools.func — functools.lru_cache() compatible decorators¶

`cachetools` — Extensible memoizing collections and decorators¶

`cachetools.keys` — Key functions for memoizing decorators¶

`cachetools.func` — `functools.lru_cache()` compatible decorators¶