我看到python 3.2在functools库中具有作为装饰器的备注。 http://docs.python.org/py3k/library/functools.html#functools.lru_cache
不幸的是,它还没有反向移植到2.7。有什么特定的原因,为什么它在2.7中不可用?是否有任何第三方库提供相同的功能,或者我应该编写自己的库?
我看到python 3.2在functools库中具有作为装饰器的备注。 http://docs.python.org/py3k/library/functools.html#functools.lru_cache
不幸的是,它还没有反向移植到2.7。有什么特定的原因,为什么它在2.7中不可用?是否有任何第三方库提供相同的功能,或者我应该编写自己的库?
Answers:
有什么特定的原因,为什么它在2.7中不可用?
@Nirk已经提供了原因:不幸的是,2.x行仅收到错误修复,并且仅针对3.x开发了新功能。
是否有任何第三方库提供相同的功能?
repoze.lru
是适用于Python 2.6,Python 2.7和Python 3.2的LRU缓存实现。
文档和源代码可在GitHub上获得。
简单用法:
from repoze.lru import lru_cache
@lru_cache(maxsize=500)
def fib(n):
if n < 2:
return n
return fib(n-1) + fib(n-2)
functools32
还是repoze.lru
?
repoze.lru
而且functools32
都慢。编写自己的装饰器更快。
Python 3.2.3中有一个用于Python 2.7和PyPy的functools
模块的反向端口:functools32。
它包括lru_cache
装饰器。
functools32
还是repoze.lru
?
pylru
不做锁。因此,这不是一个公平的比较。
我当时处在同样的情况下,自己被迫实施。python 3.x实现还存在其他一些问题:
import time
import functools
import collections
def lru_cache(maxsize = 255, timeout = None):
"""lru_cache(maxsize = 255, timeout = None) --> returns a decorator which returns an instance (a descriptor).
Purpose - This decorator factory will wrap a function / instance method and will supply a caching mechanism to the function.
For every given input params it will store the result in a queue of maxsize size, and will return a cached ret_val
if the same parameters are passed.
Params - maxsize - int, the cache size limit, anything added above that will delete the first values enterred (FIFO).
This size is per instance, thus 1000 instances with maxsize of 255, will contain at max 255K elements.
- timeout - int / float / None, every n seconds the cache is deleted, regardless of usage. If None - cache will never be refreshed.
Notes - If an instance method is wrapped, each instance will have it's own cache and it's own timeout.
- The wrapped function will have a cache_clear variable inserted into it and may be called to clear it's specific cache.
- The wrapped function will maintain the original function's docstring and name (wraps)
- The type of the wrapped function will no longer be that of a function but either an instance of _LRU_Cache_class or a functool.partial type.
On Error - No error handling is done, in case an exception is raised - it will permeate up.
"""
class _LRU_Cache_class(object):
def __init__(self, input_func, max_size, timeout):
self._input_func = input_func
self._max_size = max_size
self._timeout = timeout
# This will store the cache for this function, format - {caller1 : [OrderedDict1, last_refresh_time1], caller2 : [OrderedDict2, last_refresh_time2]}.
# In case of an instance method - the caller is the instance, in case called from a regular function - the caller is None.
self._caches_dict = {}
def cache_clear(self, caller = None):
# Remove the cache for the caller, only if exists:
if caller in self._caches_dict:
del self._caches_dict[caller]
self._caches_dict[caller] = [collections.OrderedDict(), time.time()]
def __get__(self, obj, objtype):
""" Called for instance methods """
return_func = functools.partial(self._cache_wrapper, obj)
return_func.cache_clear = functools.partial(self.cache_clear, obj)
# Return the wrapped function and wraps it to maintain the docstring and the name of the original function:
return functools.wraps(self._input_func)(return_func)
def __call__(self, *args, **kwargs):
""" Called for regular functions """
return self._cache_wrapper(None, *args, **kwargs)
# Set the cache_clear function in the __call__ operator:
__call__.cache_clear = cache_clear
def _cache_wrapper(self, caller, *args, **kwargs):
# Create a unique key including the types (in order to differentiate between 1 and '1'):
kwargs_key = "".join(map(lambda x : str(x) + str(type(kwargs[x])) + str(kwargs[x]), sorted(kwargs)))
key = "".join(map(lambda x : str(type(x)) + str(x) , args)) + kwargs_key
# Check if caller exists, if not create one:
if caller not in self._caches_dict:
self._caches_dict[caller] = [collections.OrderedDict(), time.time()]
else:
# Validate in case the refresh time has passed:
if self._timeout != None:
if time.time() - self._caches_dict[caller][1] > self._timeout:
self.cache_clear(caller)
# Check if the key exists, if so - return it:
cur_caller_cache_dict = self._caches_dict[caller][0]
if key in cur_caller_cache_dict:
return cur_caller_cache_dict[key]
# Validate we didn't exceed the max_size:
if len(cur_caller_cache_dict) >= self._max_size:
# Delete the first item in the dict:
cur_caller_cache_dict.popitem(False)
# Call the function and store the data in the cache (call it with the caller in case it's an instance function - Ternary condition):
cur_caller_cache_dict[key] = self._input_func(caller, *args, **kwargs) if caller != None else self._input_func(*args, **kwargs)
return cur_caller_cache_dict[key]
# Return the decorator wrapping the class (also wraps the instance to maintain the docstring and the name of the original function):
return (lambda input_func : functools.wraps(input_func)(_LRU_Cache_class(input_func, maxsize, timeout)))
#!/usr/bin/python
# -*- coding: utf-8 -*-
import time
import random
import unittest
import lru_cache
class Test_Decorators(unittest.TestCase):
def test_decorator_lru_cache(self):
class LRU_Test(object):
"""class"""
def __init__(self):
self.num = 0
@lru_cache.lru_cache(maxsize = 10, timeout = 3)
def test_method(self, num):
"""test_method_doc"""
self.num += num
return self.num
@lru_cache.lru_cache(maxsize = 10, timeout = 3)
def test_func(num):
"""test_func_doc"""
return num
@lru_cache.lru_cache(maxsize = 10, timeout = 3)
def test_func_time(num):
"""test_func_time_doc"""
return time.time()
@lru_cache.lru_cache(maxsize = 10, timeout = None)
def test_func_args(*args, **kwargs):
return random.randint(1,10000000)
# Init vars:
c1 = LRU_Test()
c2 = LRU_Test()
m1 = c1.test_method
m2 = c2.test_method
f1 = test_func
# Test basic caching functionality:
self.assertEqual(m1(1), m1(1))
self.assertEqual(c1.num, 1) # c1.num now equals 1 - once cached, once real
self.assertEqual(f1(1), f1(1))
# Test caching is different between instances - once cached, once not cached:
self.assertNotEqual(m1(2), m2(2))
self.assertNotEqual(m1(2), m2(2))
# Validate the cache_clear funcionality only on one instance:
prev1 = m1(1)
prev2 = m2(1)
prev3 = f1(1)
m1.cache_clear()
self.assertNotEqual(m1(1), prev1)
self.assertEqual(m2(1), prev2)
self.assertEqual(f1(1), prev3)
# Validate the docstring and the name are set correctly:
self.assertEqual(m1.__doc__, "test_method_doc")
self.assertEqual(f1.__doc__, "test_func_doc")
self.assertEqual(m1.__name__, "test_method")
self.assertEqual(f1.__name__, "test_func")
# Test the limit of the cache, cache size is 10, fill 15 vars, the first 5 will be overwritten for each and the other 5 are untouched. Test that:
c1.num = 0
c2.num = 10
m1.cache_clear()
m2.cache_clear()
f1.cache_clear()
temp_list = map(lambda i : (test_func_time(i), m1(i), m2(i)), range(15))
for i in range(5, 10):
self.assertEqual(temp_list[i], (test_func_time(i), m1(i), m2(i)))
for i in range(0, 5):
self.assertNotEqual(temp_list[i], (test_func_time(i), m1(i), m2(i)))
# With the last run the next 5 vars were overwritten, now it should have only 0..4 and 10..14:
for i in range(5, 10):
self.assertNotEqual(temp_list[i], (test_func_time(i), m1(i), m2(i)))
# Test different vars don't collide:
self.assertNotEqual(test_func_args(1), test_func_args('1'))
self.assertNotEqual(test_func_args(1.0), test_func_args('1.0'))
self.assertNotEqual(test_func_args(1.0), test_func_args(1))
self.assertNotEqual(test_func_args(None), test_func_args('None'))
self.assertEqual(test_func_args(test_func), test_func_args(test_func))
self.assertEqual(test_func_args(LRU_Test), test_func_args(LRU_Test))
self.assertEqual(test_func_args(object), test_func_args(object))
self.assertNotEqual(test_func_args(1, num = 1), test_func_args(1, num = '1'))
# Test the sorting of kwargs:
self.assertEqual(test_func_args(1, aaa = 1, bbb = 2), test_func_args(1, bbb = 2, aaa = 1))
self.assertNotEqual(test_func_args(1, aaa = '1', bbb = 2), test_func_args(1, bbb = 2, aaa = 1))
# Sanity validation of values
c1.num = 0
c2.num = 10
m1.cache_clear()
m2.cache_clear()
f1.cache_clear()
self.assertEqual((f1(0), m1(0), m2(0)), (0, 0, 10))
self.assertEqual((f1(0), m1(0), m2(0)), (0, 0, 10))
self.assertEqual((f1(1), m1(1), m2(1)), (1, 1, 11))
self.assertEqual((f1(2), m1(2), m2(2)), (2, 3, 13))
self.assertEqual((f1(2), m1(2), m2(2)), (2, 3, 13))
self.assertEqual((f1(3), m1(3), m2(3)), (3, 6, 16))
self.assertEqual((f1(3), m1(3), m2(3)), (3, 6, 16))
self.assertEqual((f1(4), m1(4), m2(4)), (4, 10, 20))
self.assertEqual((f1(4), m1(4), m2(4)), (4, 10, 20))
# Test timeout - sleep, it should refresh cache, and then check it was cleared:
prev_time = test_func_time(0)
self.assertEqual(test_func_time(0), prev_time)
self.assertEqual(m1(4), 10)
self.assertEqual(m2(4), 20)
time.sleep(3.5)
self.assertNotEqual(test_func_time(0), prev_time)
self.assertNotEqual(m1(4), 10)
self.assertNotEqual(m2(4), 20)
if __name__ == '__main__':
unittest.main()
http://www.python.org/download/releases/3.2.3/
从Python 2.7的最终版本开始,2.x版本将仅收到错误修复,并且仅针对3.x开发新功能。
Python 2.7从3.1开始具有一些功能,但是在3.2中添加了lru_cache
如评论中所述,http://code.activestate.com/recipes/578078-py26-and-py30-backport-of-python-33s-lru-cache/是一个潜在的解决方案