`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.

Note

Several features are now marked as deprecated and will be removed in the next major release, cachetools version 2.0. If you happen to rely on any of these features, it is highly recommended to specify your module dependencies accordingly, for example cachetools ~= 1.1 when using setuptools.

Changed in version 1.1: Moved functools.lru_cache() compatible decorators to the cachetools.func module. For backwards compatibility, they continue to be visible in this module as well.

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, setitem() calls popitem() repeatedly until there is enough room for the item to be added.

All cache classes accept an optional missing keyword argument in their constructor, which can be used to provide a default factory function. If the key key is not present, the cache[key] operation calls Cache.__missing__(), which in turn calls missing with key as its sole argument. The cache will then store the object returned from missing(key) as the new cache value for key, possibly discarding other items if the cache is full. This may be used to provide memoization for existing single-argument functions:

from cachetools import LRUCache
import urllib.request

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()

cache = LRUCache(maxsize=4, missing=get_pep)

for n in 8, 9, 290, 308, 320, 8, 218, 320, 279, 289, 320, 9991:
    try:
        print(n, len(cache[n]))
    except urllib.error.HTTPError:
        print(n, 'Not Found')
print(sorted(cache.keys()))

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__(). If a subclass wants to store meta data with its values, i.e. the value argument passed to Cache.__setitem__() is different from what the derived class’s __setitem__() received, it will probably need to override getsizeof(), too.

currsize¶: The current size of the cache.

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.

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.

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.

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 removed automatically. If no expired items are there to remove, the least recently used items will be discarded first to make space when necessary. Trying to access an expired item will raise a KeyError.

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.

expire(self, time=None)¶

Remove expired items from the cache.

Since expired items will be “physically” removed from a cache only at the next mutating operation, e.g. __setitem__() or __delitem__(), to avoid changing the underlying dictionary while iterating over it, expired items may still claim memory although they are no longer accessible. 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.

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=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 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, hashkey, LRUCache

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)

New in version 1.1.

@cachetools.cachedmethod(cache, key=hashkey, lock=None, typed=False)¶

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.

If key or the optional typed keyword argument are set to True, the typedkey() function is used for generating hash keys. This has been deprecated in favor of specifying key=typedkey explicitly.

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))

For backwards compatibility, the default key function used by cachedmethod() will generate distinct keys for different methods to ease using a shared cache with multiple methods. This has been deprecated, and relying on this feature is strongly discouraged. When using a shared cache, distinct key functions should be used, as with the cached() decorator.

New in version 1.1: The optional key and lock parameters.

Changed in version 1.1: The __wrapped__ attribute is now set when running Python 2.7, too.

Deprecated since version 1.1: The typed argument. Use key=typedkey instead.

Deprecated since version 1.1: When using a shared cached for multiple methods, distinct key function should be used.

Deprecated since version 1.1: The wrapper function’s cache attribute. Use the original function passed as the decorator’s cache argument to access the cache object.

Key functions¶

The following functions can be used as key functions with the cached() and cachedmethod() decorators:

cachetools.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.

New in version 1.1.

cachetools.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.

New in version 1.1.

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.

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.

In addition to maxsize, all decorators accept the following optional keyword arguments:

typed, if is set to True, will cause function arguments of different types to be cached separately. For example, f(3) and f(3.0) will be treated as distinct calls with distinct results.
getsizeof specifies a function of one argument that will be applied to each cache value to determine its size. The default value is None, which will assign each item an equal size of 1. This has been deprecated in favor of the new cachetools.cached() decorator, which allows passing fully customized cache objects.
lock specifies a function of zero arguments that returns a context manager to lock the cache when necessary. If not specified, threading.RLock will be used to synchronize access from multiple threads. The use of lock is discouraged, and the lock argument has been deprecated.

New in version 1.1: Formerly, the decorators provided by cachetools.func were part of the cachetools module.

@cachetools.func.lfu_cache(maxsize=128, typed=False, getsizeof=None, lock=threading.RLock)¶: Decorator that wraps a function with a memoizing callable that saves up to maxsize results based on a Least Frequently Used (LFU) algorithm.

Deprecated since version 1.1: The getsizeof and lock arguments.

@cachetools.func.lru_cache(maxsize=128, typed=False, getsizeof=None, lock=threading.RLock)¶: Decorator that wraps a function with a memoizing callable that saves up to maxsize results based on a Least Recently Used (LRU) algorithm.

Deprecated since version 1.1: The getsizeof and lock arguments.

@cachetools.func.rr_cache(maxsize=128, choice=random.choice, typed=False, getsizeof=None, lock=threading.RLock)¶: Decorator that wraps a function with a memoizing callable that saves up to maxsize results based on a Random Replacement (RR) algorithm.

Deprecated since version 1.1: The getsizeof and lock arguments.

@cachetools.func.ttl_cache(maxsize=128, ttl=600, timer=time.time, typed=False, getsizeof=None, lock=threading.RLock)¶: 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.

Deprecated since version 1.1: The getsizeof and lock arguments.

cachetools — Extensible memoizing collections and decorators¶

Cache implementations¶

Decorators¶

Key functions¶

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

`cachetools` — Extensible memoizing collections and decorators¶

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