使用@functools.lru_decorator in python实现LRU缓存

Question

所以我一直在尝试为我的项目实现 LRU 缓存，使用 python 函数工具 lru_cache。作为参考，我使用了 this。以下是参考中使用的代码。

def timed_lru_cache(maxsize, seconds):
    def wrapper_cache(func):
        func = lru_cache(maxsize=maxsize)(func)
        func.lifetime = timedelta(seconds=seconds)
        func.expiration = datetime.utcnow() + func.lifetime

        @wraps(func)
        def wrapped_func(*args, **kwargs):
            if datetime.utcnow() >= func.expiration:
                func.cache_clear()
                func.expiration = datetime.utcnow() + func.lifetime

            return func(*args, **kwargs)

        return wrapped_func

    return wrapper_cache

    @timed_lru_cache(maxsize=config.cache_size, seconds=config.ttl)
    def load_into_cache(id):
        return object

在包装函数部分，func.cache_clear() 清除整个缓存以及所有项目。我需要帮助才能在插入后仅删除超过其过期时间的元素。有什么解决办法吗？

Answer 1

我认为适应现有的lru_cache并不那么容易，而且我认为链接方法不是很清楚。

相反，我从头开始实现了一个定时 lru 缓存。请参阅顶部的文档字符串以了解用法。

它根据输入的args和kwargs存储一个key，并管理两个结构：

key => (expiry, result)
最近使用的列表，其中第一项是最近最少使用的

每次您尝试获取项目时，都会在“最近使用”列表中查找密钥。如果不存在，则会将其添加到列表和映射中。如果存在，我们检查过期时间是否在过去。如果是，我们重新计算结果，并更新。否则我们可以 return 映射中的任何内容。

from datetime import datetime, timedelta
from functools import wraps
from typing import Any, Dict, List, Optional, Tuple


class TimedLRUCache:
    """ Cache that caches results based on an expiry time, and on least recently used.
    
        Items are eliminated first if they expire, and then if too many "recent" items are being
        stored. 
        
        There are two methods of using this cache, either the `get` method`, or calling this as a
        decorator. The `get` method accepts any arbitrary function, but on the parameters are
        considered in the key, so it is advisable not to mix function.
        
        >>> cache = TimedLRUCache(5)
        >>> def foo(i):
        ...     return i + 1
        
        >>> cache.get(foo, 1)  # runs foo
        >>> cache.get(foo, 1)  # returns the previously calculated result
        
        As a decorator is more familiar:
        
        >>> @TimedLRUCache(5)
        ... def foo(i):
        ...     return i + 1
        
        >>> foo(1)  # runs foo
        >>> foo(1)  # returns the previously calculated result
        
        
        Either method can allow for fine-grained control of the cache:
        
        >>> five_second_cache = TimedLRUCache(5)
        >>> @five_second_cache
        ... def foo(i):
        ...     return i + 1
        
        >>> five_second_cache.clear_cache()  # resets the cache (clear every item)
        >>> five_second_cache.prune()  # clear invalid items
    """
    _items: Dict[int, Tuple[datetime, Any]]
    _recently_added: List[int]

    delta: timedelta
    max_size: int

    def __init__(self, seconds: Optional[int] = None, max_size: Optional[int] = None):
        self.delta = timedelta(seconds=seconds) if seconds else None
        self.max_size = max_size

        self._items = {}
        self._recently_added = []
        
    def __call__(self, func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            return self.get(func, args, kwargs)
        return wrapper

    @staticmethod
    def _get_key(args, kwargs) -> int:
        """ Get the thing we're going to use to lookup items in the cache. """
        key = (args, tuple(sorted(kwargs.items())))
        return hash(key)

    def _update(self, key: int, item: Any) -> None:
        """ Make sure an item is up to date. """
        if key in self._recently_added:
            self._recently_added.remove(key)
        # the first item in the list is the least recently used
        self._recently_added.append(key)
        self._items[key] = (datetime.now() + self.delta, item)

        # when this function is called, something has changed, so we can also sort out the cache
        self.prune()

    def prune(self):
        """ Clear out everything that no longer belongs in the cache

            First delete everything that has expired. Then delete everything that isn't recent (only
            if there is a `max_size`).
        """
        # clear out anything that no longer belongs in the cache.
        current_time = datetime.now()
        # first get rid of things which have expired
        for key, (expiry, item) in self._items.items():
            if expiry < current_time:
                del self._items[key]
                self._recently_added.remove(key)
        # then make sure there aren't too many recent items
        if self.max_size:
            self._recently_added[:-self.max_size] = []

    def clear_cache(self):
        """ Clear everything from the cache """
        self._items = {}
        self._recently_added = []

    def get(self, func, args, kwargs):
        """ Given a function and its arguments, get the result using the cache

            Get the key from the arguments of the function. If the key is in the cache, and the
            expiry time of that key hasn't passed, return the result from the cache.

            If the key *has* expired, or there are too many "recent" items, recalculate the result,
            add it to the cache, and then return the result.
        """
        key = self._get_key(args, kwargs)
        current_time = datetime.now()
        if key in self._recently_added:
            # there is something in the cache
            expiry, item = self._items.get(key)
            if expiry < current_time:
                # the item has expired, so we need to get the new value
                new_item = func(*args, **kwargs)
                self._update(key, new_item)
                return new_item
            else:
                # we can use the existing value
                return item
        else:
            # never seen this before, so add it
            new_item = func(*args, **kwargs)
            self._update(key, new_item)
            return new_item

使用@functools.lru_decorator in python实现LRU缓存

Implement LRU cache with using @functools.lru_decorator in python

python

lru

functools