I jumped straight into the aggregation logic and completely glossed over the timestamp parsing until the interviewer asked how I'd handle a date like '2026-3-6 9:00' vs '2026-03-06 09:00'.
Start by clarifying requirements: what defines a shift, timezone handling, and whether intervals are fixed or dynamic. Then propose a solution that parses timestamps into a normalized epoch, buckets events by interval (e.g., day or shift), and sums pay values, while filtering out events outside the queried range. Discuss trade-offs between pre-aggregation and on-the-fly computation for scalability.
Pro tip: Mention that you would handle timestamp parsing edge cases (e.g., invalid formats, timezone offsets, DST) by using a robust library and validating inputs early, and that you'd log or skip malformed events to avoid corrupting totals.
Ask about timezone assumptions, definition of shifts, expected volume, and whether intervals are predefined or dynamic. Identify edge cases like invalid timestamps, events at interval boundaries, and events outside the query range.
Decide on a normalized timestamp representation (e.g., epoch milliseconds) and a pay value type (e.g., decimal). Outline parsing logic that handles multiple formats, timezones, and invalid inputs gracefully.
Select between streaming aggregation (e.g., using a hash map keyed by interval) or batch processing. Consider pre-aggregation for performance if queries are frequent, and discuss memory vs. latency trade-offs.
Define how to map a timestamp to an interval (e.g., day, shift) and filter events outside the queried range. Handle boundary conditions (inclusive/exclusive) and ensure correct bucketing across timezones.
Talk about handling large streams, late-arriving events, and potential need for distributed processing. Mention how to extend to other aggregations (e.g., average pay per hour) or real-time dashboards.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.