🔖 Description:
An efficient Least Recently Used (LRU) Cache implemented in Python using 🗂️ doubly linked list and 🔑 hashmap for O(1) operations.
✅ Supports get(key) and put(key, value) in O(1) time
🔁 Automatically evicts the least recently used item when capacity is exceeded
🧩 Uses:
- 🗃️ Doubly Linked List to maintain usage order
- ⚡ Hash Map (dict) for quick key-node lookup
lru_cache.py– main implementation and example usage
lru = LRUCache(3)
lru.put(1, 1)
lru.put(2, 2)
lru.put(3, 3)
print(lru.get(1)) # Output: 1
lru.put(4, 4) # Evicts key 2
print(lru.get(2)) # Output: -1
print(lru.get(3)) # Output: 3
print(lru.get(4)) # Output: 4
🎯 Why This Project?
💡 For CS (Computer Science) :
Demonstrates proficiency in classic data structures 🏗️ and algorithm design ⚙️
Shows understanding of system performance optimization 🚀 via caching strategies
📊 For DS (Data Science) :
Highlights coding 💻 and logical thinking 🧠 beyond library usage
LRU cache logic is commonly used in feature stores, database query optimization, and large-scale data pipelines
🔮 Future Work
🌟 Implement LFU Cache for frequency-based eviction
🌐 Build a RESTful API integrating this cache with a database
⚖️ Compare with Python’s built-in OrderedDict LRU implementations
🙋♂️ Author
Shuai Qian （Alex Quinn）