18. LRU Cache
mediumAsked at FreshworksDesign an O(1) least-recently-used cache — Freshworks asks this verbatim because their multi-tenant ticket cache is exactly this data structure.
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Problem
Design a data structure that supports get(key) and put(key, value) in O(1) average time, evicting the least-recently-used key when capacity is exceeded.
Constraints
1 <= capacity <= 30000 <= key, value <= 10^4Up to 2 * 10^5 calls
Examples
Example 1
capacity=2; put(1,1), put(2,2), get(1), put(3,3), get(2), put(4,4), get(1), get(3), get(4)1, -1, -1, 3, 4Example 2
capacity=1; put(1,1), put(2,2), get(1)-1Approaches
1. Brute force (array scan)
Store entries in an array; on access, move to end. Eviction = shift from front. O(n) per op.
- Time
- O(n) per op
- Space
- O(n)
class LRUCache { constructor(cap){this.cap=cap;this.arr=[]} /* scan for key, splice, push */ }Tradeoff:
2. Map preserves insertion order
JS Map keeps insertion order. On get, delete + reinsert so the key is freshest. On put past capacity, evict map.keys().next().value (the oldest).
- Time
- O(1) average per op
- Space
- O(capacity)
class LRUCache {
constructor(capacity) { this.capacity = capacity; this.map = new Map(); }
get(key) {
if (!this.map.has(key)) return -1;
const v = this.map.get(key);
this.map.delete(key); this.map.set(key, v);
return v;
}
put(key, value) {
if (this.map.has(key)) this.map.delete(key);
else if (this.map.size >= this.capacity) this.map.delete(this.map.keys().next().value);
this.map.set(key, value);
}
}Tradeoff:
Freshworks-specific tips
Freshworks will probe whether you know the canonical doubly-linked-list + hash-map design — even if you ship the Map shortcut, sketch the DLL diagram so they see you understand the underlying primitive.
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