15. LRU Cache
mediumAsked at ChimeDesign a Least Recently Used cache with O(1) get and put operations.
By Alex Chen, Founder, InterviewChamp.AI · Last verified
Problem
Design a data structure that supports get(key) returning the value if the key exists or -1 otherwise, and put(key, value) inserting or updating the value. When the cache reaches its capacity, evict the least recently used key. Both operations must run in O(1) average time.
Constraints
1 <= capacity <= 30000 <= key <= 10^40 <= value <= 10^5At most 2 * 10^5 calls will be made to get and put.
Examples
Example 1
LRUCache(2); put(1,1); put(2,2); get(1); put(3,3); get(2)[null,null,null,1,null,-1]Example 2
capacity=1; put(2,1); get(2); put(3,2); get(2); get(3)[null,null,1,null,-1,2]Approaches
1. Array of pairs
Store entries in an array and scan on every get; reorder on access.
- Time
- O(n)
- Space
- O(n)
// get: linear scan, splice to front
// put: linear scan; if full, pop tail; unshift newTradeoff:
2. Hash map + doubly linked list
Use a hash map of key to list-node for O(1) lookup and a doubly linked list ordered by recency. Move-to-front on access, evict tail on overflow.
- Time
- O(1)
- Space
- O(capacity)
class LRUCache {
constructor(cap) {
this.cap = cap;
this.map = new Map();
}
get(k) {
if (!this.map.has(k)) return -1;
const v = this.map.get(k);
this.map.delete(k);
this.map.set(k, v);
return v;
}
put(k, v) {
if (this.map.has(k)) this.map.delete(k);
else if (this.map.size >= this.cap) {
this.map.delete(this.map.keys().next().value);
}
this.map.set(k, v);
}
}Tradeoff:
Chime-specific tips
Chime runs LRU layers in front of their balance-projection service, so interviewers reward candidates who can explain eviction semantics under cache stampede.
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