The O(1) lookup part is what trips you up if you just reach for a sorted structure.
Start by clarifying requirements: O(1) lookup by player ID and maintaining sorted order for leaderboard queries. Propose a hybrid data structure: a hash map for O(1) ID lookup and a balanced BST or skip list for ordered scores, discussing trade-offs. Then explain how updates (score changes) are handled in O(log n) time, and how to retrieve top players efficiently.
Pro tip: Mention that real-world leaderboards often use a combination of in-memory structures and persistent storage, and discuss how to handle ties and concurrent updates—showing you think beyond the basic algorithm.
Ask about expected operations: insert/update score, get player by ID, get top K players, and whether scores can change. Confirm O(1) lookup and sorted order are the main constraints.
Suggest a hash map for O(1) player lookup and a balanced BST (e.g., Red-Black Tree) or skip list for maintaining sorted order by score. Explain how they work together.
Detail time complexities: O(1) for lookup, O(log n) for insert/update/delete in the ordered structure, and O(log n + K) for retrieving top K. Discuss how to handle score updates (remove and re-insert).
Discuss ties (e.g., use player ID as tiebreaker), concurrency (locking or optimistic concurrency), and memory vs. speed trade-offs. Mention alternatives like heaps or sorted arrays and why they fall short.
Talk about sharding, caching, or using a database with indexes for persistence. Suggest how to handle millions of players and frequent updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.