My first instinct was just a hashmap for totals and a linear scan on every query, which works but feels bad when you imagine thousands of queries hitting a large dataset.
Clarify the operation semantics (update vs query) and constraints (number of experiences, update frequency, query frequency). Then propose a data structure that maintains running totals and efficiently retrieves the maximum, such as a hash map for totals plus a heap or balanced BST for ordering, and analyze time/space trade-offs.
Pro tip: Mention that you can optimize by lazily updating the heap or using a bucket approach if earnings are bounded, and always discuss how your solution scales with high-frequency updates and queries.
Ask about operation types, expected scale (number of experiences, updates per second, queries per second), and whether earnings can be negative or only increase.
Use a hash map to store each experience's total earnings for O(1) updates, and a max-heap or balanced BST to track the top earner for O(log n) queries.
On update, modify the total in the hash map and update the heap/BST (or mark stale). On query, return the top element, cleaning stale entries if using lazy deletion.
Compare approaches: heap gives O(log n) update/query with lazy deletion; balanced BST gives O(log n) both; bucket approach gives O(1) if earnings range is small. Discuss memory and concurrency if needed.
Address ties, empty queries, negative updates, and potential need for top-k or real-time streaming with sliding windows.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.