.. versionadded:: 3.2
-.. decorator:: lru_cache(maxsize)
+.. decorator:: lru_cache(maxsize=100)
Decorator to wrap a function with a memoizing callable that saves up to the
*maxsize* most recent calls. It can save time when an expensive or I/O bound
function is periodically called with the same arguments.
- The *maxsize* parameter defaults to 100. Since a dictionary is used to cache
- results, the positional and keyword arguments to the function must be
- hashable.
+ Since a dictionary is used to cache results, the positional and keyword
+ arguments to the function must be hashable.
- The wrapped function is instrumented with a :attr:`cache_info` attribute that
- can be called to retrieve a named tuple with the following fields:
+ To help measure the effectiveness of the cache and tune the *maxsize*
+ parameter, the wrapped function is instrumented with a :func:`cache_info`
+ function that returns a :term:`named tuple` showing *hits*, *misses*,
+ *maxsize* and *currsize*.
- - :attr:`maxsize`: maximum cache size (as set by the *maxsize* parameter)
- - :attr:`size`: current number of entries in the cache
- - :attr:`hits`: number of successful cache lookups
- - :attr:`misses`: number of unsuccessful cache lookups.
-
- These statistics are helpful for tuning the *maxsize* parameter and for measuring
- the effectiveness of the cache.
-
- The wrapped function also has a :attr:`cache_clear` attribute which can be
- called (with no arguments) to clear the cache.
+ The decorator also provides a :func:`cache_clear` function for clearing or
+ invalidating the cache.
The original underlying function is accessible through the
- :attr:`__wrapped__` attribute. This allows introspection, bypassing
- the cache, or rewrapping the function with a different caching tool.
+ :attr:`__wrapped__` attribute. This is useful for introspection, for
+ bypassing the cache, or for rewrapping the function with a different cache.
A `LRU (least recently used) cache
- <http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used>`_
- works best when more recent calls are the best predictors of upcoming calls
- (for example, the most popular articles on a news server tend to
- change each day). The cache's size limit assurs that caching does not
- grow without bound on long-running processes such as web servers.
+ <http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used>`_ works
+ best when more recent calls are the best predictors of upcoming calls (for
+ example, the most popular articles on a news server tend to change daily).
+ The cache's size limit assures that the cache does not grow without bound on
+ long-running processes such as web servers.
+
+ Example -- Caching static web content::
+
+ @functools.lru_cache(maxsize=20)
+ def get_pep(num):
+ 'Retrieve text of a Python Enhancement Proposal'
+ resource = 'http://www.python.org/dev/peps/pep-%04d/' % num
+ try:
+ with urllib.request.urlopen(resource) as s:
+ return s.read()
+ except urllib.error.HTTPError:
+ return 'Not Found'
+
+ >>> for n in 8, 290, 308, 320, 8, 218, 320, 279, 289, 320, 9991:
+ ... pep = get_pep(n)
+ ... print(n, len(pep))
+
+ >>> print(get_pep.cache_info())
+ CacheInfo(hits=3, misses=8, maxsize=20, currsize=8)
.. versionadded:: 3.2
c.execute('SELECT phonenumber FROM phonelist WHERE name=?', (name,))
return c.fetchone()[0]
+ >>> for name in user_requests:
+ ... get_phone_number(name) # cached lookup
+
To help with choosing an effective cache size, the wrapped function is
- instrumented with info function:
+ instrumented for tracking cache statistics:
- >>> for name in user_requests:
- ... get_phone_number(name)
>>> get_phone_number.cache_info()
- CacheInfo(maxsize=300, size=300, hits=4805, misses=980)
+ CacheInfo(hits=4805, misses=980, maxsize=300, currsize=300)
If the phonelist table gets updated, the outdated contents of the cache can be
cleared with:
raise TypeError('hash not implemented')
return K
-_CacheInfo = namedtuple("CacheInfo", "maxsize, size, hits, misses")
+_CacheInfo = namedtuple("CacheInfo", "hits misses maxsize currsize")
def lru_cache(maxsize=100):
"""Least-recently-used cache decorator.
def cache_info():
"""Report cache statistics"""
with lock:
- return _CacheInfo(maxsize, len(cache), hits, misses)
+ return _CacheInfo(hits, misses, maxsize, len(cache))
def cache_clear():
"""Clear the cache and cache statistics"""
def orig(x, y):
return 3*x+y
f = functools.lru_cache(maxsize=20)(orig)
- maxsize, currsize, hits, misses = f.cache_info()
+ hits, misses, maxsize, currsize = f.cache_info()
self.assertEqual(maxsize, 20)
self.assertEqual(currsize, 0)
self.assertEqual(hits, 0)
actual = f(x, y)
expected = orig(x, y)
self.assertEqual(actual, expected)
- maxsize, currsize, hits, misses = f.cache_info()
+ hits, misses, maxsize, currsize = f.cache_info()
self.assertTrue(hits > misses)
self.assertEqual(hits + misses, 1000)
self.assertEqual(currsize, 20)
f.cache_clear() # test clearing
- maxsize, currsize, hits, misses = f.cache_info()
+ hits, misses, maxsize, currsize = f.cache_info()
self.assertEqual(hits, 0)
self.assertEqual(misses, 0)
self.assertEqual(currsize, 0)
f(x, y)
- maxsize, currsize, hits, misses = f.cache_info()
+ hits, misses, maxsize, currsize = f.cache_info()
self.assertEqual(hits, 0)
self.assertEqual(misses, 1)
self.assertEqual(currsize, 1)
# Test bypassing the cache
self.assertIs(f.__wrapped__, orig)
f.__wrapped__(x, y)
- maxsize, currsize, hits, misses = f.cache_info()
+ hits, misses, maxsize, currsize = f.cache_info()
self.assertEqual(hits, 0)
self.assertEqual(misses, 1)
self.assertEqual(currsize, 1)
for i in range(5):
self.assertEqual(f(), 20)
self.assertEqual(f_cnt, 5)
- maxsize, currsize, hits, misses = f.cache_info()
+ hits, misses, maxsize, currsize = f.cache_info()
self.assertEqual(hits, 0)
self.assertEqual(misses, 5)
self.assertEqual(currsize, 0)
for i in range(5):
self.assertEqual(f(), 20)
self.assertEqual(f_cnt, 1)
- maxsize, currsize, hits, misses = f.cache_info()
+ hits, misses, maxsize, currsize = f.cache_info()
self.assertEqual(hits, 4)
self.assertEqual(misses, 1)
self.assertEqual(currsize, 1)
# * * * *
self.assertEqual(f(x), x*10)
self.assertEqual(f_cnt, 4)
- maxsize, currsize, hits, misses = f.cache_info()
+ hits, misses, maxsize, currsize = f.cache_info()
self.assertEqual(hits, 12)
self.assertEqual(misses, 4)
self.assertEqual(currsize, 2)
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