I had a couple of interviews long ago which asked me to implemented a  cache. A cache itself can simply be implemented using a hash table, however adding a size limit gives an interesting twist on the question. Let’s take a look at how we can do this.

Least Recently Used Cache Eviction

To accomplish cache eviction we need to be easily able to:

• query the last recently used item

• mark an item as the most recently used item

A linked list allows for both operations. Checking for the least recently used item can just return the tail. Marking an item as recently used can be simply removing it from its current position and moving it to the head. The missing puzzle piece is finding this item in the linked list quickly.

Hash tables to the rescue

Looking into our data structure toolbox, hash tables allow us to easily index an object in (amortized) constant time. If we create a hash table from key -> list node, we can find the most recently used node in constant time. The converse is true in that we can also still check for the existence (or lack-there-of) in constant time as well.

After looking up the node we can then move it to the front of the linked list to mark it as the most recently used item.

The Java shortcut

Sometimes knowing less common data structures from the standard library of various programming languages can prove to be of help. Given this hybrid data structure we would have to implement a hash table on top of a linked list. However Java already provides this for us in the form of a LinkedHashMap! It even provides an overridable eviction policy method (removeEldestEntry ). The only catch is that by default the linked list order is the insertion order, not access. However one of the constructor exposes an option use the access order instead ().

Without further ado:

import java.util.LinkedHashMap;import java.util.Map; public class LRUCache
    
      extends LinkedHashMap
     
       {  private int cacheSize;   public LRUCache(int cacheSize) {    super(16,  0.75f, true);    this.cacheSize = cacheSize;  }   protected boolean removeEldestEntry(Map.Entry
      
        eldest) {    return size() >= cacheSize;  }}