Start by clarifying the problem: what defines 'top earning' (e.g., top N, top 10%, or above a threshold), and what data structures are available (array, SQL table, etc.). Then propose an efficient algorithm, such as using a min-heap of size N for top N, or sorting if N is large, and discuss trade-offs in time/space complexity. Finally, consider edge cases like ties, missing data, and scalability.
Pro tip: Mention that for large datasets, a single-pass heap-based approach is often preferred over full sorting because it runs in O(M log N) time and O(N) space, where M is the number of employees and N is the number of top earners. Also, clarify whether the data fits in memory or requires a distributed approach like MapReduce.
Ask questions to understand what 'top earning' means: top N, top percentile, or threshold? Also confirm the data format (array, SQL table, stream) and constraints (memory, time).
Select an appropriate approach: for top N, use a min-heap of size N; for top percentile, sort or use quickselect; for SQL, use ORDER BY with LIMIT. Discuss trade-offs.
Explain time and space complexity of your chosen method. If data is huge, mention distributed processing (e.g., MapReduce) or external sorting.
Address ties (e.g., multiple employees with same salary), missing or negative salaries, and empty dataset. Decide how to break ties (e.g., by employee ID).
Restate your solution, emphasizing efficiency and correctness. Offer to code it if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.