preface

recently, I saw the interview questions about redis when I visited the blog. It was mentioned that redis will use LRU and other elimination mechanisms when its memory reaches the maximum limit. Then I found some information about this to share with you. LRU is generally like this: the most recently used ones are put in the front, and the most recently unused ones are put in the back. If a new number comes and the memory is full at this time, the old number needs to be eliminated. In order to move data conveniently, you must use a data structure similar to linked list. In addition, to judge whether the data is the latest or the oldest, you should also use keys such as HashMap -Data structure in the form of value.

Implementation of the first method using HashMap

public class LRUCache {

    int capacity;
    Map<Integer,Integer> map;

    public LRUCache(int capacity){
        this.capacity = capacity;
        map = new LinkedHashMap<>();
    }

    public int get(int key){
        //If not found
        if (!map.containsKey(key)){
            return -1;
        }
        //refresh data if found
        Integer value = map.remove(key);
        map.put(key,value);
        return value;
    }

    public void put(int key,int value){
        if (map.containsKey(key)){
            map.remove(key);
            map.put(key,value);
            return;
        }
        map.put(key,value);
        //exceeds the capacity, delete the longest useless that is the first, or you can override the removeEldestEntry method
        if (map.size() > capacity){
            map.remove(map.entrySet().iterator().next().getKey());
        }
    }

    public static void main(String[] args) {
        LRUCache lruCache = new LRUCache(10);
        for (int i = 0; i < 10; i++) {
            lruCache.map.put(i,i);
            System.out.println(lruCache.map.size());
        }
        System.out.println(lruCache.map);
        lruCache.put(10,200);
        System.out.println(lruCache.map);
    }

The second implementation (double linked list + HashMap)

public class LRUCache {

    private int capacity;
    private Map<Integer,ListNode>map;
    private ListNode head;
    private ListNode tail;

    public LRUCache2(int capacity){
        this.capacity = capacity;
        map = new HashMap<>();
        head = new ListNode(-1,-1);
        tail = new ListNode(-1,-1);
        head.next = tail;
        tail.pre = head;
    }

    public int get(int key){
        if (!map.containsKey(key)){
            return -1;
        }
        ListNode node = map.get(key);
        node.pre.next = node.next;
        node.next.pre = node.pre;
        return node.val;
    }

    public void put(int key,int value){
        if (get(key)!=-1){
            map.get(key).val = value;
            return;
        }
        ListNode node = new ListNode(key,value);
        map.put(key,node);
        moveToTail(node);

        if (map.size() > capacity){
            map.remove(head.next.key);
            head.next = head.next.next;
            head.next.pre = head;
        }
    }

    //Move the node to the tail
    private void moveToTail(ListNode node) {
        node.pre = tail.pre;
        tail.pre = node;
        node.pre.next = node;
        node.next = tail;
    }

    //Define bidirectional linked table nodes
    private class ListNode{
        int key;
        int val;
        ListNode pre;
        ListNode next;

        //Initializing a two-way linked table
        public ListNode(int key,int val){
            this.key = key;
            this.val = val;
            pre = null;
            next = null;
        }
    }
}

like the first method, it will be easier to copy removeeldestentry. Here is a brief demonstration

public class LRUCache extends LinkedHashMap<Integer,Integer> {


    private int capacity;
    
    @Override
    protected boolean removeEldestEntry(Map.Entry<Integer, Integer> eldest) {
        return size() > capacity;
    }
}