To implement LRU caching, the most common method in Java is to use LinkedHashMap or manually combine hash tables with bidirectional linked lists. 1. When using LinkedHashMap, pass true into the constructor to enable access order, and override the removeEldestEntry method to control capacity; 2. Manual implementation requires defining the bidirectional linked list node class to maintain access order, operations include adding, deleting and moving nodes; 3. If thread safety is required, synchronous blocks or concurrent sets can be used, but pay attention to performance impact. These methods can be selected according to needs, taking into account efficiency and control.
To implement an LRU (Least Recently Used) Cache in Java, you'll typically use a combination of data structures to efficiently track and remove the least recently used items when the cache reaches capacity. The most common approach is to use a double linked list along with a hash map .
Java actually provides a built-in class that makes this easier: LinkedHashMap . It maintains insertion or access order, which allows us to easily evict the least recently used entry.
Here's how to do it step by step:
Using LinkedHashMap for LRU Cache
Java's LinkedHashMap can be extended and customized to behave like an LRU cache by overriding the removeEldestEntry() method. This method controls whether the elderly entry should be removed when a new entry is added.
import java.util.LinkedHashMap;
import java.util.Map;
public class LRUCache extends LinkedHashMap<Integer, Integer> {
private int capacity;
public LRUCache(int capacity) {
// Load factor 0.75f ??is standard; accessOrder true means we want access-order instead of insertion-order
super(capacity, 0.75f, true);
this.capacity = capacity;
}
@Override
protected boolean removeEldestEntry(Map.Entry<Integer, Integer> eldest) {
return size() > capacity;
}
} This is a simple and effective way to build an LRU Cache if thread safety isn't required. Just make sure the constructor passes true as the third argument to enable access order.
Implementing from Scratch with HashMap Double Linked List
If you're implementing it without relying on LinkedHashMap , you'll need:
- A double linked list to keep track of the access order.
- A hash map to store key-node mappings for O(1) access.
Here's a high-level breakdown:
- Each node holds a key-value pair and references to previous and next nodes.
- When a key is accessed (get or put), move its node to the front (most recently used).
- When the cache is full, remove the last node ( least recently used).
You'd define a Node class and manage operations like:
- Adding a node to the front.
- Removing a specific node.
- Moving a node to the front.
This version gives more control but requires careful handling of points and edge cases like removing the head or tail.
Handling Thread Safety
The above implementations are not thread-safe. If your application uses multiple threads, you have two options:
- Wrap access to the cache with
synchronizedblocks. - Use a concurrent data structure or decorate the map with
Collections.synchronizedMap().
For example:
Map<Integer, Integer> synchronizedMap = Collections.synchronizedMap(yourLRUMap);
However, this might affect performance under heavy concurrency. For production systems, consider using libraries or writing fine-grained locking mechanisms.
Implementing an LRU Cache in Java doesn't have to be hard — especially if you take advantage of built-in classes like LinkedHashMap . But understanding the internal mechanics (like double linked lists and hash maps) helps you debug and optimize better when things get more complex.
Basically that's it.
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