🎯 CODING CHALLENGE #9 - LRU Cache
Difficulty: Medium-Hard | Asked at: Amazon, Google, Meta

Design a Least Recently Used (LRU) cache with O(1) get and put operations.

cache = LRUCache(2)
cache.put(1, 1)
cache.put(2, 2)
cache.get(1)       # returns 1, and marks 1 as recently used
cache.put(3, 3)    # evicts key 2 (least recently used)
cache.get(2)       # returns -1 (not found)

💡 Hint: This is the perfect combo problem - you need O(1) lookup (hash map) AND O(1) reordering/eviction (doubly linked list). Neither alone gets you there.

Solution:

python
class Node:
    def __init__(self, key, val):
        self.key = key
        self.val = val
        self.prev = None
        self.next = None

class LRUCache:
    def __init__(self, capacity):
        self.capacity = capacity
        self.cache = {}
        self.head = Node(0, 0)
        self.tail = Node(0, 0)
        self.head.next = self.tail
        self.tail.prev = self.head

    def _remove(self, node):
        node.prev.next = node.next
        node.next.prev = node.prev

    def _add_to_front(self, node):
        node.next = self.head.next
        node.prev = self.head
        self.head.next.prev = node
        self.head.next = node

    def get(self, key):
        if key not in self.cache:
            return -1
        node = self.cache[key]
        self._remove(node)
        self._add_to_front(node)
        return node.val

    def put(self, key, value):
        if key in self.cache:
            self._remove(self.cache[key])
        node = Node(key, value)
        self.cache[key] = node
        self._add_to_front(node)

        if len(self.cache) > self.capacity:
            lru = self.tail.prev
            self._remove(lru)
            del self.cache[lru.key]

Complexity: O(1) time for both get and put, O(capacity) space.

Common mistake: Trying to use Python's built-in list to track recency order - list.remove() and re-inserting are O(n), which defeats the entire point. The doubly linked list is what makes removal from the middle O(1), because you don't need to search for the node - you already have a direct reference to it.

💡 Pro tip: in a real interview, mentioning OrderedDict in Python (which has built-in move_to_end()) is a great way to show you know the language deeply - but building it from scratch with a hash map + linked list is what interviewers actually want to see, since it proves you understand WHY it's O(1), not just that a library exists.

This is consistently rated one of the best "combines two data structures" interview questions. Have you built this before, or is this your first time seeing it? 👇