在 python 中使用 @functools.lru_decorator 實現 LRU 緩存

Question

所以我一直在嘗試為我的項目實現 LRU 緩存，使用 python functools lru_cache。 作為參考，我使用了 this 。 以下是參考中使用的代碼。

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

在包裝的 func 部分中， func.cache_clear()清除整個緩存以及所有項目。 我需要幫助才能在插入后僅刪除超過過期時間的元素。 有什么解決辦法嗎？

Answer 1

我不認為改編現有的lru_cache有那么容易，而且我認為鏈接的方法不是很清楚。

相反，我從頭開始實現了一個定時 lru 緩存。 有關用法，請參閱頂部的文檔字符串。

它根據輸入的args和kwargs存儲一個密鑰，並管理兩個結構：

• key => (expiry, result)的映射
• 最近使用的列表，其中第一項是最近最少使用的

每次您嘗試獲取項目時，都會在“最近使用”列表中查找密鑰。 如果它不存在，它將被添加到列表和映射中。 如果它在那里，我們檢查到期是否在過去。 如果是，我們重新計算結果並更新。 否則我們可以只返回映射中的任何內容。

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