Mastering LRU Cache in Python – With Real-Life Analogies & Clean Code!

📚 Introduction

Imagine you’re a librarian with limited shelf space. Every time a new book comes in, and there’s no space left, you remove the least recently read book to make room.

That’s exactly what an LRU (Least Recently Used) Cache does — and in this blog, you’ll learn how to implement one in Python using just a few lines of code.

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🌀 What is an LRU Cache?

LRU Cache is a data structure that stores a limited number of items. When the cache is full and a new item needs to be added:

• It removes the least recently used item (oldest access).

• It helps save memory while keeping the most relevant data ready.

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🛠️ Use Case in Real Life

Example: Browser Tabs

Think of browser tabs as a cache.

• Your browser keeps a few websites active in memory.

• If memory is full, the browser discards the tab you haven’t visited in the longest time.

This is LRU in action.

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🔧 Let’s Build It in Python

We’ll use Python’s OrderedDict, which keeps track of insertion order and makes LRU logic super clean.

✅ Code:

pythonCopyEditfrom collections import OrderedDict

class LRUCache:
    def __init__(self, capacity: int):
        self.cache = OrderedDict()
        self.capacity = capacity

    def get(self, key: int) -> int:
        if key not in self.cache:
            return -1
        # Move to end to mark as recently used
        self.cache.move_to_end(key)
        return self.cache[key]

    def put(self, key: int, value: int) -> None:
        if key in self.cache:
            # Update existing key
            self.cache.move_to_end(key)
        self.cache[key] = value
        if len(self.cache) > self.capacity:
            # Remove least recently used item (first key)
            self.cache.popitem(last=False)

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👨‍💻 Example Walkthrough

pythonCopyEditlru = LRUCache(2)
lru.put(1, 'A')      # Cache: {1: 'A'}
lru.put(2, 'B')      # Cache: {1: 'A', 2: 'B'}
print(lru.get(1))    # Access 1 → Cache: {2: 'B', 1: 'A'}, Output: A
lru.put(3, 'C')      # Evicts 2 → Cache: {1: 'A', 3: 'C'}
print(lru.get(2))    # 2 was evicted → Output: -1

🧾 Output:

cssCopyEditA
-1

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🎯 How It Works

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✅ Why OrderedDict?

• move_to_end(key): Marks the key as most recently used.

• popitem(last=False): Removes the first inserted key (i.e., the least recently used).

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🧠 Where is LRU Cache Used?

• Web browsers (tab/page caching)

• Database query caches

• File systems

• Image and video processing apps

• Mobile app memory management

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🏁 Conclusion

Building an LRU Cache in Python is surprisingly easy using OrderedDict. It’s a must-know for coding interviews and useful in real-world applications like caching and memory optimization.

Start with simple examples, and try adding delete(), or limit usage by time next.