cachetools
— Extensible memoizing collections and decorators¶
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. 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.
Note that the values of a Cache
are mutable by default, as
are e.g. the values of a 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.
-
class
cachetools.
Cache
(maxsize, 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 overridepopitem()
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__()
.-
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.
FIFOCache
(maxsize, getsizeof=None)¶ First In First Out (FIFO) cache implementation.
This class evicts items in the order they were added to make space when necessary.
-
popitem
()¶ Remove and return the (key, value) pair first inserted.
-
-
class
cachetools.
LFUCache
(maxsize, 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, 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.
MRUCache
(maxsize, getsizeof=None)¶ Most Recently Used (MRU) cache implementation.
This class discards the most recently used items first to make space when necessary.
-
popitem
()¶ Remove and return the (key, value) pair most recently used.
-
-
class
cachetools.
RRCache
(maxsize, choice=random.choice, 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.monotonic, 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
time.monotonic()
is used to retrieve the current time. A custom timer function can also be supplied:from datetime import datetime, timedelta cache = TTLCache(maxsize=10, ttl=timedelta(hours=12), timer=datetime.now)
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().
-
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 isNone
, this removes all items that have expired by the current value returned bytimer
.
-
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:
>>> 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:
>>> 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 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
-
@
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 plaindict
andweakref.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 awith lock:
statement. This can be used for synchronizing thread access to the cache by providing athreading.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 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 cachetools.keys import hashkey from threading import Lock cache = LRUCache(maxsize=32) lock = Lock() @cached(cache, key=hashkey, 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 lock: cache.clear() # always use the key function for accessing cache items with lock: cache.pop(hashkey(42), None)
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 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)]
-
@
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 withself
(orcls
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 callinglock(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 thecached()
function decorator is that cache properties such as maxsize can be set at runtime: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))
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:
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))
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)
andtypedkey(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(sorted(env.items()))
return key
The 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'))
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. If maxsize is set to None
,
the caching strategy is effectively disabled and the cache can grow
without bound.
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.
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 cache_parameters()
function that returns a new 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 cache_info()
and
cache_clear()
functions to provide information about cache
performance and clear the cache. Please see the
functools.lru_cache()
documentation for details. Also note that
all the decorators in this module are thread-safe by default.
-
@
cachetools.func.
fifo_cache
(user_function)¶ -
@
cachetools.func.
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.
-
@
cachetools.func.
lfu_cache
(user_function)¶ -
@
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
(user_function)¶ -
@
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.
mru_cache
(user_function)¶ -
@
cachetools.func.
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.
-
@
cachetools.func.
rr_cache
(user_function)¶ -
@
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
(user_function)¶ -
@
cachetools.func.
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.