Design and implement a data structure forLeast Recently Used (LRU) cache. It should support the following operations:getandput.

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.

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

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

分析：

使用双向链表可以保证插入和替换的时间复杂度是O(1)O(1)，但查询的时间复杂度是O(n)O(n)，因为需要对双链表进行遍历。为了让查找效率也达到O(1)O(1)，很自然的会想到使用 hash table

实现：

1，直接使用java 的doublelinkedhashmap,如果面试官同意的话。

//LinkedHashMap的一个构造函数，当参数accessOrder为true时，即会按照访问顺序排序，最近访问的放在最前，最早访问的放在后面
public LinkedHashMap(int initialCapacity, float loadFactor, boolean accessOrder) {
        super(initialCapacity, loadFactor);
        this.accessOrder = accessOrder;
}

//LinkedHashMap自带的判断是否删除最老的元素方法，默认返回false，即不删除老数据
//我们要做的就是重写这个方法，当满足一定条件时删除老数据
protected boolean removeEldestEntry(Map.Entry<K,V> eldest) {
        return false;
}

实现

import java.util.LinkedHashMap;
import java.util.Map;


public class LRUCache2<K, V> extends LinkedHashMap<K, V> {
    private final int MAX_CACHE_SIZE;

    public LRUCache2(int cacheSize) {
        super((int) Math.ceil(cacheSize / 0.75) + 1, 0.75f, true);
        MAX_CACHE_SIZE = cacheSize;
    }

    @Override
    protected boolean removeEldestEntry(Map.Entry eldest) {
        return size() > MAX_CACHE_SIZE;
    }

    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        for (Map.Entry<K, V> entry : entrySet()) {
            sb.append(String.format("%s:%s ", entry.getKey(), entry.getValue()));
        }
        return sb.toString();
    }
}

2，自己构建数据结构hashmap+doublelinkedlist来实现

public class LRUCache {
    private class Node{
        Node prev;
        Node next;
        int key;
        int value;

        public Node(int key, int value) {
            this.key = key;
            this.value = value;
            this.prev = null;
            this.next = null;
        }
    }

    private int capacity;
    private HashMap<Integer, Node> hs = new HashMap<Integer, Node>();
    private Node head = new Node(-1, -1);
    private Node tail = new Node(-1, -1);

    public LRUCache(int capacity) {
        this.capacity = capacity;
        tail.prev = head;
        head.next = tail;
    }

    public int get(int key) {
        if( !hs.containsKey(key)) {
            return -1;
        }

        // remove current
        Node current = hs.get(key);
        current.prev.next = current.next;
        current.next.prev = current.prev;

        // move current to tail
        move_to_tail(current);

        return hs.get(key).value;
    }

    public void set(int key, int value) {
        // this internal `get` method will update the key's position in the linked list.
        if (get(key) != -1) {
            hs.get(key).value = value;
            return;
        }

        if (hs.size() == capacity) {
            hs.remove(head.next.key);
            head.next = head.next.next;
            head.next.prev = head;
        }

        Node insert = new Node(key, value);
        hs.put(key, insert);
        move_to_tail(insert);
    }

    private void move_to_tail(Node current) {
        current.prev = tail.prev;
        tail.prev = current;
        current.prev.next = current;
        current.next = tail;
    }
}

3,线程安全的调用linkedhashmap

import java.util.LinkedHashMap;
import java.util.concurrent.locks.Lock;
import java.util.concurrent.locks.ReentrantLock;

public class LRULinkedHashMap<K, V> extends LinkedHashMap<K, V> {
    /**
     * 
     */
    private static final long serialVersionUID = -952299094512767664L;
    private final int maxCapacity;
    private static final float DEFAULT_LOAD_FACTOR = 0.75f;
    private final Lock lock = new ReentrantLock();

    public LRULinkedHashMap(int maxCapacity) {
        super(maxCapacity, DEFAULT_LOAD_FACTOR, true);
        this.maxCapacity = maxCapacity;
    }

    @Override
    protected boolean removeEldestEntry(java.util.Map.Entry<K, V> eldest) {
        return size() > maxCapacity;
    }

    @Override
    public V get(Object key) {
        try {
            lock.lock();
            return super.get(key);
        } finally {
            lock.unlock();
        }
    }
//可以根据实际情况，考虑对不同的操作加锁
    @Override
    public V put(K key, V value) {
        try {
                        lock.lock();
            return super.put(key, value);
        } finally {
                        lock.unlock();
        }
    }

}