The core problem wasn't hard once I stopped overthinking the JSON structure.
Clarify the event types and validity rules, then design a solution that groups events by order ID, sorts by timestamp, and pairs valid start/end events. Compute duration and pay per order, summing to a total, and discuss edge cases like missing pairs or overlapping events.
Pro tip: Mention that you would validate the data and handle invalid or incomplete orders gracefully, as real-world logs often have anomalies. Also, consider using a single pass with a hash map to achieve O(n) time after sorting or if events are already ordered.
Ask about event types (e.g., 'start', 'end'), validity rules (e.g., must start before end, no overlapping), and how to handle incomplete orders. Confirm the output format and whether base rate can vary per event.
Group events by order ID using a hash map, then sort each group by timestamp. Iterate through sorted events to find valid start/end pairs, compute duration, and accumulate pay.
Consider missing start/end events, multiple starts/ends, negative durations, and zero or negative base rates. Decide whether to skip invalid orders or raise errors, and document assumptions.
Discuss time complexity: O(n log n) due to sorting, or O(n) if events are pre-sorted. Space complexity O(n) for the hash map. Mention potential optimizations like streaming if data is large.
Walk through a simple example with a few orders to verify correctness. Test edge cases like an order with only a start event or events out of order.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the input format and edge cases, then propose an algorithm that splits the paid interval at peak boundaries and sums the pay for each sub-interval with the appropriate rate. Discuss time complexity and potential optimizations, and consider how to handle multiple peak windows efficiently.
Pro tip: Mention that you would pre-sort peak windows and use binary search to find overlapping windows, reducing time complexity from O(n) to O(log n) per query. Also, emphasize the importance of handling edge cases like zero-length intervals and adjacent peak windows.
Ask about input format (e.g., list of peak windows, paid interval), whether peak windows can overlap, and how to handle boundaries (inclusive/exclusive). Confirm that pay is prorated per minute/second.
Iterate through all peak windows, compute the overlap with the paid interval, and accumulate pay: base rate for non-overlapping parts and double rate for overlapping parts. Use a sweep-line or interval merging if needed.
If many queries, preprocess peak windows by sorting and merging overlaps, then use binary search to find relevant windows. Compute total pay by summing contributions from each segment.
Discuss time and space complexity of both approaches. For a single query, O(n) is fine; for many queries, O(log n + k) where k is number of overlapping windows. Mention trade-offs between preprocessing and query time.
Walk through a concrete example, such as paid interval [1,5] and peak [3,7], showing split at 3 and 5. Test edge cases: no overlap, full overlap, multiple peaks, zero-length intervals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt like a throwaway at first but the interviewer spent a real amount of time on it.
Start by clarifying the system's requirements and the impact of malformed input on downstream consumers. Then propose a layered defense: validation at ingestion, idempotent processing, and dead-letter queues for unprocessable events. Emphasize trade-offs between strict rejection, graceful degradation, and observability.
Pro tip: Mention that you'd log malformed events with enough context for debugging but avoid logging sensitive data. Also, discuss how you'd monitor and alert on malformed input rates to detect upstream issues early.
Ask about the system's guarantees: is it at-least-once or exactly-once? What are the downstream effects of malformed events? This determines whether to reject, quarantine, or attempt repair.
Implement schema validation and business rule checks (e.g., event order, required fields) as early as possible. Reject or route invalid events to a dead-letter queue with metadata.
Use idempotency keys (e.g., order ID + event type) to deduplicate. For ordering, use sequence numbers or timestamps with buffering/windowing to reorder or detect out-of-order events.
Decide on fallback behavior: skip, retry, or compensate. For missing pairs, consider timeouts or manual intervention. Ensure the system remains available and consistent.
Track metrics on malformed input rates, types, and sources. Alert on anomalies and use insights to improve upstream validation or system resilience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.