What is an LRU cache and how can you implement it in O(1)?

Deep dive

Expanding on the short answer — what usually matters in practice:

• Context (tags): lru, cache, hashmap, linked-list, big-o
• Complexity: compare typical operations (average vs worst-case).
• Invariants: what must always hold for correctness.
• When the choice is wrong: production symptoms (latency, GC, cache misses).
• Explain the "why", not just the "what" (intuition + consequences).
• Trade-offs: what you gain/lose (time, memory, complexity, risk).
• Edge cases: empty inputs, large inputs, invalid inputs, concurrency.

Examples

Here’s an additional example (building on the short answer):

type Node = { key: string; prev?: Node; next?: Node }

// idea:
// map: key -> node
// list: head <-> ... <-> tail
// get(key): move node to head (O(1))
// put(key): insert/move to head; if over capacity, evict tail (O(1))

Common pitfalls

• Too generic: no concrete trade-offs or examples.
• Mixing average-case and worst-case (e.g., complexity).
• Ignoring constraints: memory, concurrency, network/disk costs.

Interview follow-ups

• When would you choose an alternative and why?
• What production issues show up and how do you diagnose them?
• How would you test edge cases?