: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:: * import operator from cachetools import cached, cachedmethod, LRUCache, TLRUCache, TTLCache from unittest import mock urllib = mock.MagicMock()
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.
.. 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`.
.. 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::
from datetime import datetime, timedelta
def my_ttu(_key, value, now):
# assume value.ttl contains the item's time-to-live in hours
return now + timedelta(hours=value.ttl)
cache = TLRUCache(maxsize=10, ttu=my_ttu, timer=datetime.now)
The expression `ttu(key, value, timer())` defines the expiration
time of a cache item, and must be comparable against later results
of `timer()`.
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`.
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:
>>> 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 :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:
>>> 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.
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:
>>> @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)
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()
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(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))
.. 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(object):
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 equivalent 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.
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.
.. 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.