mirror of
https://github.com/python/cpython.git
synced 2025-10-31 13:41:24 +00:00
Remove the lfu_cache. Add more tests.
This commit is contained in:
Raymond Hettinger
parent
0f56e90f05
commit
f309828175
3 changed files with 29 additions and 129 deletions
|
|
@ -66,45 +66,32 @@ Some smaller changes made to the core Python language are:
|
|||
New, Improved, and Deprecated Modules
|
||||
=====================================
|
||||
|
||||
* The functools module now includes two new decorators for caching function
|
||||
calls, :func:`functools.lru_cache` and :func:`functools.lfu_cache`. These can
|
||||
save repeated queries to an external resource whenever the results are
|
||||
expected to be the same.
|
||||
* The functools module now includes a new decorator for caching function calls.
|
||||
:func:`functools.lru_cache` can save repeated queries to an external resource
|
||||
whenever the results are expected to be the same.
|
||||
|
||||
For example, adding a caching decorator to a database query function can save
|
||||
database accesses for popular searches::
|
||||
|
||||
@functools.lfu_cache(maxsize=50)
|
||||
@functools.lru_cache(maxsize=300)
|
||||
def get_phone_number(name):
|
||||
c = conn.cursor()
|
||||
c.execute('SELECT phonenumber FROM phonelist WHERE name=?', (name,))
|
||||
return c.fetchone()[0]
|
||||
|
||||
The caches support two strategies for limiting their size to *maxsize*. The
|
||||
LFU (least-frequently-used) cache works bests when popular queries remain the
|
||||
same over time. In contrast, the LRU (least-recently-used) cache works best
|
||||
query popularity changes over time (for example, the most popular news
|
||||
articles change each day as newer articles are added).
|
||||
To help with choosing an effective cache size, the wrapped function is
|
||||
instrumented with two attributes *hits* and *misses*::
|
||||
|
||||
The two caching decorators can be composed (nested) to handle hybrid cases.
|
||||
For example, music searches can reflect both long-term patterns (popular
|
||||
classics) and short-term trends (new releases)::
|
||||
|
||||
@functools.lfu_cache(maxsize=500)
|
||||
@functools.lru_cache(maxsize=100)
|
||||
def find_lyrics(song):
|
||||
query = 'http://www.example.com/songlist/%s' % urllib.quote(song)
|
||||
page = urllib.urlopen(query).read()
|
||||
return parse_lyrics(page)
|
||||
|
||||
To help with choosing an effective cache size, the wrapped function
|
||||
is instrumented with two attributes *hits* and *misses*::
|
||||
|
||||
>>> for song in user_requests:
|
||||
... find_lyrics(song)
|
||||
>>> print(find_lyrics.hits, find_lyrics.misses)
|
||||
>>> for name in user_requests:
|
||||
... get_phone_number(name)
|
||||
>>> print(get_phone_number.hits, get_phone_number.misses)
|
||||
4805 980
|
||||
|
||||
If the phonelist table gets updated, the outdated contents of the cache can be
|
||||
cleared with::
|
||||
|
||||
>>> get_phone_number.clear()
|
||||
|
||||
(Contributed by Raymond Hettinger)
|
||||
|
||||
* The previously deprecated :func:`contextlib.nested` function has been
|
||||
|
|
|
|||
|
|
@ -110,58 +110,6 @@ def __hash__(self):
|
|||
raise TypeError('hash not implemented')
|
||||
return K
|
||||
|
||||
def lfu_cache(maxsize=100):
|
||||
"""Least-frequently-used cache decorator.
|
||||
|
||||
Arguments to the cached function must be hashable.
|
||||
Cache performance statistics stored in f.hits and f.misses.
|
||||
Clear the cache using f.clear().
|
||||
http://en.wikipedia.org/wiki/Cache_algorithms#Least-Frequently_Used
|
||||
|
||||
"""
|
||||
def decorating_function(user_function, tuple=tuple, sorted=sorted,
|
||||
len=len, KeyError=KeyError):
|
||||
cache = {} # mapping of args to results
|
||||
use_count = Counter() # times each key has been accessed
|
||||
kwd_mark = object() # separate positional and keyword args
|
||||
lock = Lock()
|
||||
|
||||
@wraps(user_function)
|
||||
def wrapper(*args, **kwds):
|
||||
key = args
|
||||
if kwds:
|
||||
key += (kwd_mark,) + tuple(sorted(kwds.items()))
|
||||
try:
|
||||
with lock:
|
||||
use_count[key] += 1 # count a use of this key
|
||||
result = cache[key]
|
||||
wrapper.hits += 1
|
||||
except KeyError:
|
||||
result = user_function(*args, **kwds)
|
||||
with lock:
|
||||
use_count[key] += 1 # count a use of this key
|
||||
cache[key] = result
|
||||
wrapper.misses += 1
|
||||
if len(cache) > maxsize:
|
||||
# purge the 10% least frequently used entries
|
||||
for key, _ in nsmallest(maxsize // 10 or 1,
|
||||
use_count.items(),
|
||||
key=itemgetter(1)):
|
||||
del cache[key], use_count[key]
|
||||
return result
|
||||
|
||||
def clear():
|
||||
"""Clear the cache and cache statistics"""
|
||||
with lock:
|
||||
cache.clear()
|
||||
use_count.clear()
|
||||
wrapper.hits = wrapper.misses = 0
|
||||
|
||||
wrapper.hits = wrapper.misses = 0
|
||||
wrapper.clear = clear
|
||||
return wrapper
|
||||
return decorating_function
|
||||
|
||||
def lru_cache(maxsize=100):
|
||||
"""Least-recently-used cache decorator.
|
||||
|
||||
|
|
|
|||
|
|
@ -483,73 +483,38 @@ def orig(x, y):
|
|||
self.assertEqual(f.misses, 1)
|
||||
|
||||
# test size zero (which means "never-cache")
|
||||
f_cnt = 0
|
||||
@functools.lru_cache(0)
|
||||
def f():
|
||||
nonlocal f_cnt
|
||||
f_cnt += 1
|
||||
return 20
|
||||
self.assertEqual(f(), 20)
|
||||
self.assertEqual(f(), 20)
|
||||
self.assertEqual(f(), 20)
|
||||
self.assertEqual(f_cnt, 3)
|
||||
f_cnt = 0
|
||||
for i in range(5):
|
||||
self.assertEqual(f(), 20)
|
||||
self.assertEqual(f_cnt, 5)
|
||||
|
||||
# test size one
|
||||
f_cnt = 0
|
||||
@functools.lru_cache(1)
|
||||
def f():
|
||||
nonlocal f_cnt
|
||||
f_cnt += 1
|
||||
return 20
|
||||
self.assertEqual(f(), 20)
|
||||
self.assertEqual(f(), 20)
|
||||
self.assertEqual(f(), 20)
|
||||
f_cnt = 0
|
||||
for i in range(5):
|
||||
self.assertEqual(f(), 20)
|
||||
self.assertEqual(f_cnt, 1)
|
||||
|
||||
def test_lfu(self):
|
||||
def orig(x, y):
|
||||
return 3*x+y
|
||||
f = functools.lfu_cache(maxsize=20)(orig)
|
||||
|
||||
domain = range(5)
|
||||
for i in range(1000):
|
||||
x, y = choice(domain), choice(domain)
|
||||
actual = f(x, y)
|
||||
expected = orig(x, y)
|
||||
self.assertEquals(actual, expected)
|
||||
self.assert_(f.hits > f.misses)
|
||||
self.assertEquals(f.hits + f.misses, 1000)
|
||||
|
||||
f.clear() # test clearing
|
||||
self.assertEqual(f.hits, 0)
|
||||
self.assertEqual(f.misses, 0)
|
||||
f(x, y)
|
||||
self.assertEqual(f.hits, 0)
|
||||
self.assertEqual(f.misses, 1)
|
||||
|
||||
# test size zero (which means "never-cache")
|
||||
f_cnt = 0
|
||||
@functools.lfu_cache(0)
|
||||
def f():
|
||||
# test size two
|
||||
@functools.lru_cache(2)
|
||||
def f(x):
|
||||
nonlocal f_cnt
|
||||
f_cnt += 1
|
||||
return 20
|
||||
self.assertEqual(f(), 20)
|
||||
self.assertEqual(f(), 20)
|
||||
self.assertEqual(f(), 20)
|
||||
self.assertEqual(f_cnt, 3)
|
||||
|
||||
# test size one
|
||||
return x*10
|
||||
f_cnt = 0
|
||||
@functools.lfu_cache(1)
|
||||
def f():
|
||||
nonlocal f_cnt
|
||||
f_cnt += 1
|
||||
return 20
|
||||
self.assertEqual(f(), 20)
|
||||
self.assertEqual(f(), 20)
|
||||
self.assertEqual(f(), 20)
|
||||
self.assertEqual(f_cnt, 1)
|
||||
for x in 7, 9, 7, 9, 7, 9, 8, 8, 8, 9, 9, 9, 8, 8, 8, 7:
|
||||
# * * * *
|
||||
self.assertEqual(f(x), x*10)
|
||||
self.assertEqual(f_cnt, 4)
|
||||
|
||||
def test_main(verbose=None):
|
||||
test_classes = (
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue