LRU Cache

LeetCode 146 | Difficulty: Medium

Medium

Problem Description

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 * 10^5 calls will be made to get and put.

Topics: Hash Table, Linked List, Design, Doubly-Linked List

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Approach

Hash Map

Use a hash map for O(1) average lookups. Store seen values, frequencies, or indices. The key question: what should I store as key, and what as value?

When to use

Need fast lookups, counting frequencies, finding complements/pairs.

Design

Choose the right data structures to meet the required time complexities for each operation. Consider hash maps for O(1) access, doubly-linked lists for O(1) insertion/deletion, and combining structures for complex requirements.

When to use

Implementing a data structure or system with specific operation time requirements.

Linked List

Use pointer manipulation. Common techniques: dummy head node to simplify edge cases, fast/slow pointers for cycle detection and middle finding, prev/curr/next pattern for reversal.

When to use

In-place list manipulation, cycle detection, merging lists, finding the k-th node.

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Solutions

Solution 1: C# (Best: 544 ms)

Metric	Value
Runtime	544 ms
Memory	50.5 MB
Date	2020-01-15

Solution

public class LRUCache
{
	
private Dictionary<int, CacheItem> map = new Dictionary<int, CacheItem>();
private class CacheItem
{
	public int CacheKey { get; set; }
	public int CacheValue { get; set; }
}	
private LinkedList<CacheItem> lru = new LinkedList<CacheItem>();
	private int capacity;
	public LRUCache(int capacity) { this.capacity = capacity; }
	public int Get(int key)
	{
		if (!map.ContainsKey(key)) return -1;
		lru.Remove(map[key]);
		lru.AddLast(map[key]);
		return map[key].CacheValue;
	}
	public void Put(int key, int val)
	{
		if (map.ContainsKey(key))
		{
			map[key].CacheValue = val;
			lru.Remove(map[key]);
			lru.AddLast(map[key]);
			return;
		}
		if (map.Count >= capacity)
		{
			map.Remove(lru.First.Value.CacheKey);
			lru.RemoveFirst();
		}
		map.Add(key, new CacheItem { CacheKey = key, CacheValue = val });
		lru.AddLast(map[key]);
	}
}



/**
 * 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);
 */

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Complexity Analysis

Approach	Time	Space
Hash Map	$O(n)$	$O(n)$
Linked List	$O(n)$	$O(1)$

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Interview Tips

Key Points

• Discuss the brute force approach first, then optimize. Explain your thought process.
• Hash map gives O(1) lookup — think about what to use as key vs value.
• Draw the pointer changes before coding. A dummy head node simplifies edge cases.
• Clarify the expected time complexity for each operation before choosing data structures.