Design and implement a data structure for Least Recently Used (LRU) cache. It should support the following operations: get and put.get(key) - Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1.
put(key, value) - Set or insert the value if the key is not already present. When the cache reached its capacity, it should invalidate the least recently used item before inserting a new item.The cache is initialized with a positive capacity.

Follow up:Could you do both operations in O(1) time complexity?

Example:

LRUCache cache = new LRUCache( 2 /* capacity */ );

cache.put(1, 1);
cache.put(2, 2);
cache.get(1);       // returns 1
cache.put(3, 3);    // evicts key 2
cache.get(2);       // returns -1 (not found)
cache.put(4, 4);    // evicts key 1
cache.get(1);       // returns -1 (not found)
cache.get(3);       // returns 3
cache.get(4);       // returns 4

Solution(not optimized yet):

Use hashmap to  cache element to reduce to O(1)👻

import "container/list"

type LRUCache struct {
    Cap int;
    List *list.List;   
}

type Dict struct {
    K int;
    V int;
}


func Constructor(capacity int) LRUCache {
    return LRUCache{Cap: capacity, List: list.New()}
}

func (l *LRUCache) getElement(key int) *list.Element {
    for e := l.List.Front(); e != nil; e = e.Next() {
        if(e.Value.(Dict).K == key) {
            return e
        }
	}
    return nil
}

func (l *LRUCache) Get(key int) int {
    if e := l.getElement(key); e != nil {
        l.List.MoveToFront(e)
        return e.Value.(Dict).V;
    }
    return -1
}


func (l *LRUCache) Put(key int, value int)  {
    // element Exisited
    if e := l.getElement(key); e != nil {
        e.Value = Dict{K:key, V:value}
        l.List.MoveToFront(e)
        return;
    }
    // element not existed
    //len < cap 
    //len == cap
    if l.List.Len() >= l.Cap {
        l.List.Remove(l.List.Back())
    }
    l.List.PushFront(Dict{K:key, V: value})
}