How can I implement LRU caching in my application?

How can I implement LRU caching in my application?

To implement LRU (Least Recently Used) caching, you can use a combination of a hash map and a doubly linked list. The hash map allows for O(1) access to cache items, while the linked list maintains the order of usage.

Can you provide a code example for that?

Sure! Here's a simple Python implementation:

class Node:
    def __init__(self, key, value):
        self.key = key
        self.value = value
        self.prev = None
        self.next = None

class LRUCache:
    def __init__(self, capacity):
        self.capacity = capacity
        self.cache = {}  # key -> Node
        self.head = Node(0, 0)  # dummy head
        self.tail = Node(0, 0)  # dummy tail
        self.head.next = self.tail
        self.tail.prev = self.head

    def _remove(self, node):
        prev_node = node.prev
        next_node = node.next
        prev_node.next = next_node
        next_node.prev = prev_node

    def _add(self, node):
        prev_node = self.tail.prev
        prev_node.next = node
        node.prev = prev_node
        node.next = self.tail
        self.tail.prev = node

    def get(self, key):
        if key in self.cache:
            node = self.cache[key]
            self._remove(node)
            self._add(node)
            return node.value
        return -1

    def put(self, key, value):
        if key in self.cache:
            self._remove(self.cache[key])
        node = Node(key, value)
        self.cache[key] = node
        self._add(node)
        if len(self.cache) > self.capacity:
            lru = self.head.next
            self._remove(lru)
            del self.cache[lru.key]

What are the advantages of using LRU caching?

The advantages of LRU caching include improved performance by reducing the time to access frequently used data, efficient memory usage by discarding the least recently used items, and adaptability to changing access patterns.