The insert operations are straightforward enough but the getLowestK query is where things get interesting.
Start by clarifying the requirements and constraints, then propose a data structure that efficiently supports insertions with referrer updates and threshold-based queries. A balanced BST or a heap combined with a hash map can work, but discuss trade-offs and potential optimizations like bucketing or indexing by revenue.
Pro tip: Demonstrate awareness of real-world constraints: mention that in a production system, you'd consider concurrency, persistence, and scalability, and possibly use a database with appropriate indexes rather than an in-memory structure.
Ask about expected data volume, query frequency, K value, threshold range, and whether updates are frequent. Confirm if customer IDs are unique and if revenue can be negative.
Propose a hash map for O(1) customer lookup and a balanced BST (or skip list) keyed by revenue for ordered queries. Alternatively, consider a min-heap for top-K queries if K is small, but note limitations for threshold queries.
For insert: add customer to hash map and BST; if referrer exists, update referrer's revenue by removing and reinserting in BST. For query: traverse BST in-order starting from threshold, collecting up to K customers.
Insert: O(log n) for BST update, O(1) for hash map. Query: O(log n + K) for BST traversal. Discuss potential optimizations like caching frequent queries or using a Fenwick tree for prefix sums if revenue updates are additive.
Compare with alternative approaches (e.g., sorted array, heap) and mention scalability considerations like sharding, concurrency control, and persistence for a production system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.