Implement LRU cache algorithm in Java

In this article, we will discuss how to design and implement an LRU cache algorithm in Java to get fast fetching and updating items. This Least Recently Used (LRU) discards the least recently used items first when the cache is full and a new item is added which is not there in cache.

Designing LRU strategies

First, we have to focus on some problems associated with the LRU implementation before going to designing.

LRU storage size

Fix the size of the cache to avoid memory limit exceeding. The size should be bounded to take care of memory usages.

Eviction Policy

As the name suggested, we have to evict least recently used item first from the cache when the cache is full.

Fast Fetching & Updation

We have to choose such data structure that can provide fast fetching and updating and also which supports get and put.

Implement LRU cache algorithm

Deciding a base data structure storage for this algorithm is much important, so as per our need we have to go with hashing based lookup by using the HashMap. As we know that HashMap will make get operation in constant time that is O(1) time. The HashMap is our choice to store the key of the LRU cache.

Also, we want to find a data structure which can remove/add in O(1) time, and we can traverse each node based on the recency by using each node references. We can use a double linked list for this purpose. As we know that if already know about the node then LinkedList removal/addition in O(1) time.

So, finally, we have decided two data structure HashMap and Double LinkedList to implement LRU cache with efficient performance. Let’s create the create classes to design and implement LRU cache.

package com.dineshonjava.algo.lru;

/**
 * @author Dinesh.Rajput
 *
 */
public class Node {
	
	long key;
        long value;
	Node prev;
	Node next;
 
    public Node(long key, long value){
        this.key   = key;
        this.value = value;
    }
}

Let’s see the following code for LRUCache class like the following:

/**
 * 
 */
package com.dineshonjava.algo.lru;

import java.util.HashMap;
import java.util.Map;

/**
 * @author Dinesh.Rajput
 *
 */
public class LRUCache {
	
    Node head;
    Node tail;
    Map<Long, Node> map = null;
    int capacity = 0;
    
    public LRUCache(int capacity) {
        this.capacity = capacity;
        this.map = new HashMap<>();
    }
    
    public long get(long key) {
    	
    	if(map.get(key) == null){
            return -1;
        }
 
        Node item = map.get(key);
        //move to tail
        removeNode(item);
        addToTail(item);
 
        return item.value;
    }
 
    public void put(Long key, int value) {
    	
    	if(map.containsKey(key)){
            Node item = map.get(key);
            item.value = value;
 
            //move to tail
            removeNode(item);
            addToTail(item);
        }else{
            if(map.size() >= capacity){
                //delete head
                map.remove(head.key);
                removeNode(head);
            }
 
            //add to tail
            Node node = new Node(key, value);
            addToTail(node);
            map.put(key, node);
        }
    }
 
    private void removeNode(Node node){
    	
        if(node.prev != null){
        	node.prev.next = node.next;
        }else{
            head = node.next;
        }
 
        if(node.next != null){
        	node.next.prev = node.prev;
        }else{
            tail = node.prev;
        }
    }
 
    private void addToTail(Node node){
    	
        if(tail != null){
            tail.next = node;
        }
 
        node.prev = tail;
        node.next = null;
        tail = node;
 
        if(head == null){
            head = tail;   
        }
    }
}

Let’s test this LRU cache algorithm like the following test class:

/**
 * 
 */
package com.dineshonjava.algo.lru;

/**
 * @author Dinesh.Rajput
 *
 */
public class TestLRUCache {

	/**
	 * @param args
	 */
	public static void main(String[] args) {
		
		System.out.println("Going to test the LRU Cache Implementation"); 
		
		LRUCache cache = new LRUCache(5);
		
		//Storing first value 10 with key (1) in the cache. 
        cache.put(1l, 10);  
        
      //Storing second value 10 with key (2) in the cache. 
        cache.put(2l, 20);
        
      //Storing third value 10 with key (3) in the cache. 
        cache.put(3l, 30);
        
      //Storing fourth value 10 with key (4) in the cache. 
        cache.put(4l, 40);
        
      //Storing fifth value 10 with key (5) in the cache. 
        cache.put(5l, 50);
        
        
        System.out.println("Value for the key: 1 is " +  
                           cache.get(1)); // returns 10 
  
      // evicts key 2 and store a key (6) with value 60 in the cache. 
        cache.put(6l, 60);  
  
        System.out.println("Value for the key: 2 is " +  
                cache.get(2)); // returns -1 (not found) 
  
        //evicts key 3 and store a key (7) with value 70 in the cache. 
        cache.put(7l, 70); 
        
        System.out.println("Value for the key: 3 is " + 
               cache.get(3)); // returns -1 (not found) 
        
        System.out.println("Value for the key: 4 is " +  
                           cache.get(4)); // returns 40 
        
        System.out.println("Value for the key: 5 is " + 
                           cache.get(5)); // return 50 
		
	}

}

Now run this code and see the following output:

Going to test the LRU Cache Implementation
Value for the key: 1 is 10
Value for the key: 2 is -1
Value for the key: 3 is -1
Value for the key: 4 is 40
Value for the key: 5 is 50