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    Implementing LRU Cache using LinkedHashMap in Java
    Published on: 10th April 2016

    This tutorial will introduce you to characteristics of LRU Cache and explain how to implement it using LinkedHashMap in Java


    Caching is often leveraged to maintain the data in form of key-value pair in main memory. This results into better performance in comparison to other options such as database storage and file storage. However since main memory is always limited, we need to define the eviction policy to ensure that cache does not grow beyond a certain size.

    There are different caching algorithms based on the eviction policies that they offer. Some of these caching algorithms are LRU (Least Recently Used), LFU (Least Frequently Used) and MRU (Most Recently Used). In this tutorial, we will be focusing on LRU cache and see how we can implement it using LinkedHashMap class in Java.

    Characteristics of LRU Cache

    Here are the characteristics of LRU cache that distinguishes it from other types of caches -

    1. Maintains fixed number of elements.
    2. Orders the elements based on their usage pattern. Most recently used element always remains on head while least recently used element is stored at tail end.
    3. Removes the element that was least recently used when cache gets full or crosses a threshold ratio (called load factor).
    4. Updates the element's value if element to be inserted already exists.
    LRU Cache Implementation in Java

    We will be extending LinkedHashMap class provided by Java to implement our LRUCache. LinkedHashMap can order the elements in Insertion order as well as Access order. By default, LinkedHashMap maintains the data in Insertion order. However in this case, we will be configuring LinkedHashMap to maintain the data in Access order by setting the accessOrder flag to true in its three argument copy constructor.

    Additionally, we will override method removeEldestEntry that LinkedHashMap calls after every put method call to check whether it should remove the eldest element. In our implementation, we will return true when size becomes greater than the capacity to let LinkedHashMap remove the least recently accessed element.

    Here is the implementation of LRU Cache using LinkedHashMap in Java.

    package com.saintech.allprogtutorials.caches;
    import java.util.LinkedHashMap;
     * This code is distributed under Apache License 2.0.
     * @author Sain Technology Solutions
    public class LRUCacheusingLinkedHashMap<K, V> extends LinkedHashMap<K, V> {
    	private static final long serialVersionUID = 1L;
    	private final int capacity;
    	protected boolean removeEldestEntry(java.util.Map.Entry<K, V> eldest) {
    		// Remove the eldest element whenever size of cache exceeds the capacity
    		return (size() > this.capacity);
    	public LRUCacheusingLinkedHashMap(int capacity) {
    		// Call constructor of LinkedHashMap with accessOrder set to true to
    		// achieve LRU Cache behavior
    		super(capacity + 1, 1.0f, true);
    		this.capacity = capacity;
    	 * Returns the value corresponding to input key from Cache Map.
    	 * @param key
    	 *            Key for the element whose value needs to be returned
    	 * @return Value of the element with input key or null if no such element
    	 *         exists
    	public V find(K key) {
    		return super.get(key);
    	 * Set the element with input key and value in the cache. If the element
    	 * already exits, it updates its value.
    	 * @param key
    	 *            Key of the element
    	 * @param value
    	 *            Value of the element
    	public void set(K key, V value) {
    		super.put(key, value);
    	 * Entry point for testing LRU Cache.
    	public static void main(String[] args) {
    		// Create the cache with capacity 2
    		LRUCacheusingLinkedHashMap<Integer, Integer> cache = new LRUCacheusingLinkedHashMap<>(
    		cache.set(2, 1); // Will add an element with key as 2 and value as 1
    		cache.set(3, 2); // Will add an element with key as 3 and value as 2
    		// Will add an element with key as 4 and value as 3. Also will remove
    		// the element with key 2 as it was added least recently and cache can
    		// just have two elements at a time
    		cache.set(4, 3);
    		// Since element with key 2 was removed, it will return null
    		// It will return value 2 and move the element with key 3 to the head.
    		// After this point, element with key 4 will be least recently accessed
    		// Will add an element with key as 5 and value as 4. Also will remove
    		// the element with key 4 as it was accessed least recently and cache
    		// can just have two elements at a time
    		cache.set(5, 4);
    		// Since element with key 2 was removed, it will return null

    As we can see, this is easiest and reliable way to implement LRU in Java.

    Usage of LRU Cache

    Some of the use cases of LRU cache are as follows –

    1. General Caching - LRU Cache can be used to cache the objects where we want to avoid the calls to database. Using LRU, we always ensure that only the objects that we have used recently remain in cache while getting rid of objects those were not used recently.
    2. Caching Bitmaps in Android - Rendering bitmaps images in Android views can be slow if we load bitmap every time we want to use it. On the other hand, we can’t maintain all the images in memory if there are too many images to render in different views. Therefore we need to keep removing the bitmaps out of memory to avoid out of memory crashes. LRU Cache works very well in this case as it automatically removes the bitmaps that were used least recently.

    Thank you for reading through the tutorial. In case of any feedback/questions/concerns, you can communicate same to us through your comments and we shall get back to you as soon as possible.

    Published on: 10th April 2016