LRU Cache Implementation Using a Doubly Linked List and HashMap

Every high-traffic system — Redis, CPU L1/L2 caches, database buffer pools, CDN edge nodes — relies on the same core idea: memory is finite, so you need a smart eviction strategy. LRU (Least Recently Used) is the industry default because it matches real-world access patterns almost perfectly. When a system is under load, the data you fetched a millisecond ago is far more likely to be needed again than data you fetched an hour ago.

The challenge is the constraint: both get and put must run in O(1) time. A plain array or a single linked list can't do this — finding and moving an element takes O(n). The elegant solution is pairing a HashMap (for O(1) lookup) with a Doubly Linked List (for O(1) insertion and deletion at arbitrary positions). Neither structure alone gets you there. Together, they're unstoppable.

By the end of this article you'll be able to implement a fully functional LRU cache from scratch in Java without relying on LinkedHashMap, explain exactly why each design decision was made, handle all edge cases (capacity 1, duplicate puts, eviction boundary), and walk an interviewer through the internals with confidence. Let's build it piece by piece.

What is LRU Cache Implementation?

LRU Cache Implementation is a core concept in DSA. Rather than starting with a dry definition, let's see it in action and understand why it exists.

// TheCodeForge — LRU Cache Implementation example
// Always use meaningful names, not x or n
public class ForgeExample {
    public static void main(String[] args) {
        String topic = "LRU Cache Implementation";
        System.out.println("Learning: " + topic + " 🔥");
    }
}

Frequently Asked Questions