The problem itself isn't algorithmically hard.
Clarify the requirements and edge cases first, then propose a solution that processes events in chronological order while maintaining per-user, per-offer redemption counts and eligibility based on offer time windows. Use a hash map keyed by user and offer to track state, and filter by cutoff date to output eligible offers.
Pro tip: Discuss how to handle out-of-order events and the importance of idempotency in redeem/unredeem operations, as real-world data may have duplicates or late-arriving events. Also, mention the trade-off between memory usage and query speed when pre-aggregating results.
Ask about the definition of 'eligible to have redeemed' (e.g., does it mean the user has redeemed at least once, or is currently eligible to redeem?), how to handle multiple redeems/unredeems, and whether the cutoff date is inclusive. Confirm the output format.
Propose using a hash map to store per-user, per-offer redemption counts and a set to track active redemptions. Also, consider indexing offers by ID for quick lookup of time windows and max counts.
Sort events by timestamp (if not already sorted) and iterate through them up to the cutoff date. For each event, update the redemption count for the user and offer, ensuring not to exceed the max redemption count per user.
After processing events, for each user, check which offers they have redeemed at least once (or are eligible for) based on the final state. Apply any additional constraints such as offer time windows relative to the cutoff date.
Format the output as a mapping from user to a list of offer IDs they are eligible to have redeemed before the cutoff date. Discuss potential optimizations for large datasets, such as streaming processing or batch aggregation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.