LRU算法實現緩存定期處理
LRU定義
LRU是什麼？按照英文的直接原義就是Least Recently Used,最近最久未使用法，它是按照一個非常著名的計算機操作系統基礎理論得來的： 最近使用的頁面數據會在未來一段時期內仍然被使用,已經很久沒有使用的頁面很有可能在未來較長的一段時間內仍然不會被使用。 基於這個思想,會存在一種緩存淘汰機制，每次從內存中找到最久未使用的數據然後置換出來，從而存入新的數據！它的主要衡量指標是使用的時間，附加指標是使用的次數。在計算機中大量使用了這個機制，它的合理性在於優先篩選熱點數據，所謂熱點數據，就是最近最多使用的數據！因爲，利用LRU我們可以解決很多實際開發中的問題，並且很符合業務場景。
python實現
import time
import functools
from collections import OrderedDict
# 1 LRU設計緩存
class LRUCacheDict :
def __init__(self, max_size=1024, expiration=60):
"""最大容量爲1024個key ，每個key的有效期爲60s"""
self .max_size = max_size
self.expiration = expiration
self._cache = {}
self._access_records = OrderedDict() # 記錄訪問時間
self._expire_records = OrderedDict() # 記錄失效時間
def __setitem__(self, key, value):
"""設置緩存"""
now= int(time.time())
self.__delete__(key)
self._cache[key] = value
self._expire_records[key] = now + self.expiration
self._access_records[key] = now
self.clean_up ()
def __getitem__(self, key):
"""獲取緩存"""
now = int(time.time())
del self._access_records[key]
self._access_records[key] = now
self.clean_up()
return self.cache[key]
def __contains__(self, key):
self.clean_up()
return key in self._cache
def __delete__ (self, key) :
if key in self._cache :
del self._cache[key]
del self._expire_records[key]
del self._access_records[key]
def clean_up(self):
"""去掉無效（過期或者超出存儲大小）的生是存"""
if self.expiration is None :
return None
pending_delete_keys = []
now = int(time.time())
# 刪除已經過期的緩存
for k, v in self._expire_records.items():
if v < now:
pending_delete_keys.append(k)
for del_k in pending_delete_keys:
self.__delete__(del_k)
# 如果數據堂大於max_size ， 則刪掉最舊的緩存
while(len(self._cache) > self.max_size):
for k in self._access_records:
self.__delete__(k)
break
# 2 將複雜些的LRU緩存函數轉換成裝飾器
def cache_it(max_size=1024, expiration=60) :
"""可以設置過期時間的緩存器"""
CACHE = LRUCacheDict(max_size=max_size, expiration=expiration)
def wrapper(func):
@functools.wraps(func)
def inner(*args, **kwargs):
key = repr(*args, **kwargs)
try:
result = CACHE[key]
except KeyError:
result = func(*args, ** kwargs)
CACHE[key] = result
return result
return inner
return wrapper
@cache_it(max_size=10, expiration=3)
def query_it(sql):
time.sleep(1)
result = 'execute %s' % sql
print(result)
return result
if __name__ == '__main__':
# 1 直接測試
cache_dict = LRUCacheDict(max_size=2, expiration=10)
cache_dict['name'] = 'achjiang'
cache_dict['age'] = 30
cache_dict['addr'] = 'jiangsu '
print('name' in cache_dict) # 輸出False，因爲容量是2 ，第一個key會被刪掉
print('age' in cache_dict) # 輸出True
time.sleep(11)
print( 'age' in cache_dict) # 輸出False，因爲緩存失效了
# 2 緩存裝飾器方法實現
query_it(100)
輸出
False
True
False
execute 100
附錄：python3中使用了functools.lru_cache函數封裝該功能源碼：
def lru_cache(maxsize=128, typed=False):
"""Least-recently-used cache decorator.
If *maxsize* is set to None, the LRU features are disabled and the cache
can grow without bound.
If *typed* is True, arguments of different types will be cached separately.
For example, f(3.0) and f(3) will be treated as distinct calls with
distinct results.
Arguments to the cached function must be hashable.
View the cache statistics named tuple (hits, misses, maxsize, currsize)
with f.cache_info(). Clear the cache and statistics with f.cache_clear().
Access the underlying function with f.__wrapped__.
See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
"""
# Users should only access the lru_cache through its public API:
# cache_info, cache_clear, and f.__wrapped__
# The internals of the lru_cache are encapsulated for thread safety and
# to allow the implementation to change (including a possible C version).
# Early detection of an erroneous call to @lru_cache without any arguments
# resulting in the inner function being passed to maxsize instead of an
# integer or None.
if maxsize is not None and not isinstance(maxsize, int):
raise TypeError('Expected maxsize to be an integer or None')
def decorating_function(user_function):
wrapper = _lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo)
return update_wrapper(wrapper, user_function)
return decorating_function
def _lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo):
# Constants shared by all lru cache instances:
sentinel = object() # unique object used to signal cache misses
make_key = _make_key # build a key from the function arguments
PREV, NEXT, KEY, RESULT = 0, 1, 2, 3 # names for the link fields
cache = {}
hits = misses = 0
full = False
cache_get = cache.get # bound method to lookup a key or return None
cache_len = cache.__len__ # get cache size without calling len()
lock = RLock() # because linkedlist updates aren't threadsafe
root = [] # root of the circular doubly linked list
root[:] = [root, root, None, None] # initialize by pointing to self
if maxsize == 0:
def wrapper(*args, **kwds):
# No caching -- just a statistics update after a successful call
nonlocal misses
result = user_function(*args, **kwds)
misses += 1
return result
elif maxsize is None:
def wrapper(*args, **kwds):
# Simple caching without ordering or size limit
nonlocal hits, misses
key = make_key(args, kwds, typed)
result = cache_get(key, sentinel)
if result is not sentinel:
hits += 1
return result
result = user_function(*args, **kwds)
cache[key] = result
misses += 1
return result
else:
def wrapper(*args, **kwds):
# Size limited caching that tracks accesses by recency
nonlocal root, hits, misses, full
key = make_key(args, kwds, typed)
with lock:
link = cache_get(key)
if link is not None:
# Move the link to the front of the circular queue
link_prev, link_next, _key, result = link
link_prev[NEXT] = link_next
link_next[PREV] = link_prev
last = root[PREV]
last[NEXT] = root[PREV] = link
link[PREV] = last
link[NEXT] = root
hits += 1
return result
result = user_function(*args, **kwds)
with lock:
if key in cache:
# Getting here means that this same key was added to the
# cache while the lock was released. Since the link
# update is already done, we need only return the
# computed result and update the count of misses.
pass
elif full:
# Use the old root to store the new key and result.
oldroot = root
oldroot[KEY] = key
oldroot[RESULT] = result
# Empty the oldest link and make it the new root.
# Keep a reference to the old key and old result to
# prevent their ref counts from going to zero during the
# update. That will prevent potentially arbitrary object
# clean-up code (i.e. __del__) from running while we're
# still adjusting the links.
root = oldroot[NEXT]
oldkey = root[KEY]
oldresult = root[RESULT]
root[KEY] = root[RESULT] = None
# Now update the cache dictionary.
del cache[oldkey]
# Save the potentially reentrant cache[key] assignment
# for last, after the root and links have been put in
# a consistent state.
cache[key] = oldroot
else:
# Put result in a new link at the front of the queue.
last = root[PREV]
link = [last, root, key, result]
last[NEXT] = root[PREV] = cache[key] = link
# Use the cache_len bound method instead of the len() function
# which could potentially be wrapped in an lru_cache itself.
full = (cache_len() >= maxsize)
misses += 1
return result
def cache_info():
"""Report cache statistics"""
with lock:
return _CacheInfo(hits, misses, maxsize, cache_len())
def cache_clear():
"""Clear the cache and cache statistics"""
nonlocal hits, misses, full
with lock:
cache.clear()
root[:] = [root, root, None, None]
hits = misses = 0
full = False
wrapper.cache_info = cache_info
wrapper.cache_clear = cache_clear
return wrapper
try:
from _functools import _lru_cache_wrapper
except ImportError:
pass