LRU cache

Problem

Design a data structure that follows the constraints of a Least Recently Used (LRU) cache.

Implement the LRUCache class:

• LRUCache(int capacity) Initialize the LRU cache with positive size capacity.
• int get(int key) Return the value of the key if the key exists, otherwise return -1.
• void put(int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity from this operation, evict the least recently used key.

The functions get and put must each run in O(1) average time complexity.

Example 1:

Input
["LRUCache", "put", "put", "get", "put", "get", "put", "get", "get", "get"]
[[2], [1, 1], [2, 2], [1], [3, 3], [2], [4, 4], [1], [3], [4]]
Output
[null, null, null, 1, null, -1, null, -1, 3, 4]

Explanation
LRUCache lRUCache = new LRUCache(2);
lRUCache.put(1, 1); // cache is {1=1}
lRUCache.put(2, 2); // cache is {1=1, 2=2}
lRUCache.get(1);    // return 1
lRUCache.put(3, 3); // LRU key was 2, evicts key 2, cache is {1=1, 3=3}
lRUCache.get(2);    // returns -1 (not found)
lRUCache.put(4, 4); // LRU key was 1, evicts key 1, cache is {4=4, 3=3}
lRUCache.get(1);    // return -1 (not found)
lRUCache.get(3);    // return 3
lRUCache.get(4);    // return 4

Constraints:

• 1 <= capacity <= 3000
• 0 <= key <= 104
• 0 <= value <= 105
• At most 2 * 105 calls will be made to get and put.

Solution

class LRUCache {
    static class Node {
        int key;
        int val;
        Node next;
        Node prev;
        Node(int key, int val) {
            this.val = val;
            this.key = key;
        }
    }

    int size;
    Node head;
    Node tail;

    // mapping of key: node
    // allows O(1) delete
    Map<Integer, Node> m = new HashMap<>();

    public LRUCache(int capacity) {
        this.size = capacity;
        this.head = new Node(-1, -1);
        this.tail = new Node(-2, -2);
        head.next = tail;
        tail.prev = head;
    }

    void add(int key, int value) {
        var n = new Node(key ,value);
        m.put(key, n);
        n.prev = tail.prev;
        n.next = tail;
        n.prev.next = n;
        n.next.prev = n;
    }

    void remove(int key) {
        var n = m.get(key);
        m.remove(key);
        n.prev.next = n.next;
        n.next.prev = n.prev;
    }

    public int get(int key) {
        if (m.containsKey(key)) {
            var value = m.get(key).val;
            remove(key);
            add(key, value);
            return value;
        } else {
            return -1;
        }
    }

    public void put(int key, int value) {
        if (m.containsKey(key)) {
            remove(key);
        }
        add(key, value);
        if (m.size() > size) {
            remove(head.next.key);
        }
    }
}

/**
 * Your LRUCache object will be instantiated and called as such:
 * LRUCache obj = new LRUCache(capacity);
 * int param_1 = obj.get(key);
 * obj.put(key,value);
 */