lru-cache

本文深入探讨了LRU(Least Recently Used)缓存淘汰策略的工作原理,详细解释了其在内存管理和数据存储中的应用,并提供了相关的实现示例,帮助读者理解如何在实际项目中有效地使用LRU来优化性能。

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class Node(object):

    def __init__(self, results):
        self.results = results
        self.next = next


class LinkedList(object):

    def __init__(self):
        self.head = None
        self.tail = None

    def move_to_front(self, node):
        pass

    def append_to_front(self, node):
        pass

    def remove_from_tail(self):
        pass


class Cache(object):

    def __init__(self, MAX_SIZE):
        self.MAX_SIZE = MAX_SIZE
        self.size = 0
        self.lookup = {}  # key: query, value: node
        self.linked_list = LinkedList()

    def get(self, query):
        """Get the stored query result from the cache.
        Accessing a node updates its position to the front of the LRU list.
        """
        node = self.lookup.get(query)
        if node is None:
            return None
        self.linked_list.move_to_front(node)
        return node.results

    def set(self, results, query):
        """Set the result for the given query key in the cache.
        When updating an entry, updates its position to the front of the LRU list.
        If the entry is new and the cache is at capacity, removes the oldest entry
        before the new entry is added.
        """
        node = self.lookup.get(query)
        if node is not None:
            # Key exists in cache, update the value
            node.results = results
            self.linked_list.move_to_front(node)
        else:
            # Key does not exist in cache
            if self.size == self.MAX_SIZE:
                # Remove the oldest entry from the linked list and lookup
                self.lookup.pop(self.linked_list.tail.query, None)
                self.linked_list.remove_from_tail()
            else:
                self.size += 1
            # Add the new key and value
            new_node = Node(results)
            self.linked_list.append_to_front(new_node)
            self.lookup[query] = new_node
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