9. LRU Cache

Description

LRU Cache
Design a data structure that follows the constraints of a Least Recently Used (LRU) cache.

Implement the LRUCache class:

LRUCache(int capacity) Initialize the LRU cache with positive size capacity.
int get(int key) Return the value of the key if the key exists, otherwise return -1.
void put(int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity from this operation, evict the least recently used key.

The functions get and put must each run in O(1) average time complexity.

Example 1:
Input
["LRUCache", "put", "put", "get", "put", "get", "put", "get", "get", "get"]
[[2], [1, 1], [2, 2], [1], [3, 3], [2], [4, 4], [1], [3], [4]]
Output
[null, null, null, 1, null, -1, null, -1, 3, 4]

Explanation
LRUCache lRUCache = new LRUCache(2);
lRUCache.put(1, 1); // cache is {1=1}
lRUCache.put(2, 2); // cache is {1=1, 2=2}
lRUCache.get(1); // return 1
lRUCache.put(3, 3); // LRU key was 2, evicts key 2, cache is {1=1, 3=3}
lRUCache.get(2); // returns -1 (not found)
lRUCache.put(4, 4); // LRU key was 1, evicts key 1, cache is {4=4, 3=3}
lRUCache.get(1); // return -1 (not found)
lRUCache.get(3); // return 3
lRUCache.get(4); // return 4

Constraints:

1 <= capacity <= 3000
0 <= key <= 10^4
0 <= value <= 10^5
At most 2 * 105 calls will be made to get and put.

Approach

LinkedHashMap has a method which returns false by default meaning do not remove any element now we have made it conditional so we will override this method to check if current capacity of Map is greater than allowed capacity or not
Capacity argument is initial capacity means this is the initial capacity which is by default 16 meaning we kind of find remainder by dividing by 16 and store the value in this array
In case of collision at the same index we store like a linked list
load factor means how much capacity to cross here 75% then double the capacity and last argument is to make sure whether you want the order to be based on access or based on insertion (access order true means least accessed towards end I guess)

protected boolean removeEldestEntry(Map.Entry<K, V> eldest) {
    return false;  // Default behavior: do not remove any entry
}

class LRUCache {
    private final Map<Integer,Integer> cache;
    private final int capacity;
 
    public LRUCache(int capacity) {
        this.capacity = capacity;
        this.cache = new LinkedHashMap<>(capacity, 0.75f, true) {
            protected boolean removeEldestEntry (Map.Entry<Integer, Integer> oldest) {
                return size() > LRUCache.this.capacity;
            }
        };
    }
    
    public int get(int key) {
        return cache.getOrDefault(key,-1);
    }
    
    public void put(int key, int value) {
        cache.put(key, value);
    }
}
 
/**
 * Your LRUCache object will be instantiated and called as such:
 * LRUCache obj = new LRUCache(capacity);
 * int param_1 = obj.get(key);
 * obj.put(key,value);
 */

Solution: `LinkedHashMap` (JDK Built-in)

Intuition

Java’s standard library provides a built-in data structure that combines a Hash Table with a Doubly Linked List: LinkedHashMap.

By default, LinkedHashMap maintains entries in insertion order. However, by passing true for the accessOrder constructor argument, the map automatically rearranges entries in access order (from least-recently used at the head to most-recently used at the tail).

To complete the LRU Cache, we override the protected method removeEldestEntry(). When this method returns true (which happens when size() > capacity), LinkedHashMap automatically evicts the least recently used element at the head.

import java.util.LinkedHashMap;
import java.util.Map;
 
class LRUCache {
    private final Map<Integer, Integer> cache;
    private final int capacity;
 
    public LRUCache(int capacity) {
        this.capacity = capacity;
        // Arguments: (initialCapacity, loadFactor, accessOrder)
        this.cache = new LinkedHashMap<Integer, Integer>(capacity, 0.75f, true) {
            @Override
            protected boolean removeEldestEntry(Map.Entry<Integer, Integer> eldest) {
                return size() > LRUCache.this.capacity;
            }
        };
    }
 
    public int get(int key) {
        return cache.getOrDefault(key, -1);
    }
 
    public void put(int key, int value) {
        cache.put(key, value);
    }
}

0.75f (Load Factor):
The hash table’s resize threshold. When the table becomes 75% full, Java doubles the internal array size to prevent hash collisions and maintain $O (1)$ performance. 0.75f is Java’s default standard value.
true (Access-Order Flag):
Sets accessOrder = true. By default (false), LinkedHashMap orders items by insertion time. Setting it to true switches it to order items by most recent access. Every get() or put() call automatically shifts that entry to the tail (MRU), leaving the Least Recently Used (LRU) entry sitting at the head.
How overriding a method inside the constructor works:
This syntax creates an Anonymous Inner Class. It creates an unnamed, temporary subclass of LinkedHashMap on the fly, allowing you to override removeEldestEntry() directly without having to write a separate .java class file.
What happens when removeEldestEntry returns a boolean:
Java automatically invokes removeEldestEntry() inside every put() operation right after inserting a new item:
Returns true (size() > capacity): LinkedHashMap immediately deletes the eldest entry (the LRU entry at the head) from both the doubly linked list and the hash map.
Returns false: LinkedHashMap does nothing and keeps all entries intact.

Complexity

Time Complexity: $O (1)$ for both get and put operations.
Space Complexity: $O (capacity)$ to store up to capacity key-value pairs in memory.

How the LRU Mechanism Works Under the Hood

Under the hood, LinkedHashMap maintains a doubly linked list running through all of its hash table entries (before and after node pointers):

[ Head / Eldest (LRU) ] <-> Node_A <-> Node_B <-> Node_C <-> [ Tail / Youngest (MRU) ]

Access Movement (get / put):

When get(key) or put(key, value) is called, LinkedHashMap looks up the entry in $O (1)$ time using the hash table array.
Because accessOrder is set to true, LinkedHashMap immediately unlinks that entry from its current position in the doubly linked list and reconnects it at the tail (Most Recently Used position).

Automatic Eviction (removeEldestEntry):

Every time put() adds a new entry, it appends it to the tail and invokes removeEldestEntry().
When size() > capacity evaluates to true, LinkedHashMap removes the entry at the head (Least Recently Used position) from both the doubly linked list and the underlying hash table in $O (1)$ time.

How to Make It Thread-Safe

LinkedHashMap is not thread-safe. Because reading from the map (get) modifies the internal doubly linked list pointers to track access order, concurrent access from multiple threads can corrupt the linked list pointers.

Here are two common ways to make this solution thread-safe:

Option 1: Synchronized Wrapper (`Collections.synchronizedMap`)

Wraps every method in a synchronized block using the map’s intrinsic monitor lock:

import java.util.Collections;
import java.util.LinkedHashMap;
import java.util.Map;
 
class LRUCache {
    private final Map<Integer, Integer> cache;
    private final int capacity;
 
    public LRUCache(int capacity) {
        this.capacity = capacity;
        Map<Integer, Integer> map = new LinkedHashMap<Integer, Integer>(capacity, 0.75f, true) {
            @Override
            protected boolean removeEldestEntry(Map.Entry<Integer, Integer> eldest) {
                return size() > LRUCache.this.capacity;
            }
        };
        this.cache = Collections.synchronizedMap(map);
    }
 
    public int get(int key) {
        return cache.getOrDefault(key, -1);
    }
 
    public void put(int key, int value) {
        cache.put(key, value);
    }
}

Option 2: Explicit Lock (`ReentrantLock`)

Offers finer control over lock acquisition and scope:

import java.util.LinkedHashMap;
import java.util.Map;
import java.util.concurrent.locks.ReentrantLock;
 
class LRUCache {
    private final Map<Integer, Integer> cache;
    private final int capacity;
    private final ReentrantLock lock = new ReentrantLock();
 
    public LRUCache(int capacity) {
        this.capacity = capacity;
        this.cache = new LinkedHashMap<Integer, Integer>(capacity, 0.75f, true) {
            @Override
            protected boolean removeEldestEntry(Map.Entry<Integer, Integer> eldest) {
                return size() > LRUCache.this.capacity;
            }
        };
    }
 
    public int get(int key) {
        lock.lock();
        try {
            return cache.getOrDefault(key, -1);
        } finally {
            lock.unlock();
        }
    }
 
    public void put(int key, int value) {
        lock.lock();
        try {
            cache.put(key, value);
        } finally {
            lock.unlock();
        }
    }
}

Easy Memory Rule

“LinkedHashMap(capacity, 0.75f, true) sets access-order mode $\to$ Override removeEldestEntry(size() > capacity) to evict LRU automatically.”

Obsidian Notes

Explorer

9. LRU Cache

Description

Approach

Solution: `LinkedHashMap` (JDK Built-in)

Intuition

Complexity

How the LRU Mechanism Works Under the Hood

How to Make It Thread-Safe

Option 1: Synchronized Wrapper (`Collections.synchronizedMap`)

Option 2: Explicit Lock (`ReentrantLock`)

Easy Memory Rule

Table of Contents

Backlinks

Obsidian Notes

Explorer

9. LRU Cache

Description

Approach

Solution: LinkedHashMap (JDK Built-in)

Intuition

Complexity

How the LRU Mechanism Works Under the Hood

How to Make It Thread-Safe

Option 1: Synchronized Wrapper (Collections.synchronizedMap)

Option 2: Explicit Lock (ReentrantLock)

Easy Memory Rule

Table of Contents

Backlinks

Solution: `LinkedHashMap` (JDK Built-in)

Option 1: Synchronized Wrapper (`Collections.synchronizedMap`)

Option 2: Explicit Lock (`ReentrantLock`)