LeetCode in Kotlin

146. LRU Cache

Medium

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(capacity: Int) {
    private val nodeMap = HashMap<Int, Node>()
    private val head = Node()
    private val tail = Node()
    private var cacheCapacity = 0

    init {
        head.next = tail
        tail.prev = head
        cacheCapacity = capacity
    }

    fun get(key: Int): Int {
        return if (!nodeMap.containsKey(key)) {
            -1
        } else {
            val requiredNode = nodeMap.getValue(key)
            update(requiredNode)
            requiredNode.value
        }
    }

    fun put(key: Int, value: Int) {
        if (nodeMap.containsKey(key)) {
            val requiredNode = nodeMap.getValue(key)
            requiredNode.value = value
            update(requiredNode)
        } else {
            val newNode = Node(key = key, value = value)
            if (nodeMap.size >= cacheCapacity) {
                val nodeToDelete = tail.prev!!
                remove(nodeToDelete)
                nodeMap.remove(nodeToDelete.key)
            }
            add(newNode)
            nodeMap.put(key, newNode)
        }
    }

    fun add(node: Node) {
        val after = head.next
        head.next = node
        node.prev = head
        node.next = after
        after?.prev = node
    }

    private fun remove(node: Node) {
        val before = node.prev
        val after = node.next

        before?.next = after
        after?.prev = before
    }

    private fun update(node: Node) {
        remove(node)
        add(node)
    }

    data class Node(var key: Int = -1, var value: Int = -1, var next: Node? = null, var prev: Node? = null)
}

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

This site is open source. Improve this page.