The output format question tripped me up more than the logic itself.
Start by clarifying the output format and constraints, then propose an efficient solution using a hash set of booking IDs from payments. Iterate through bookings to check membership, and discuss trade-offs like time/space complexity and potential optimizations for large datasets.
Pro tip: Mention that if the lists are huge, you might avoid loading all payment IDs into memory by sorting both lists and using a merge-like approach, but acknowledge the hash set is simpler and usually faster for in-memory data.
Ask if the output should be a list of booking IDs with a boolean, or just the bookings that have payments. Confirm if bookings without payments should be included.
Propose using a hash set to store booking IDs from payments for O(1) lookups. Discuss alternatives like sorting and binary search or merge join.
Iterate through payments to build the set, then iterate through bookings to check membership and produce output. Mention handling duplicates and missing IDs.
State time complexity O(n + m) and space O(m) for the set, where n is bookings and m is payments. Compare with sorting approach O(n log n + m log m) time and O(1) extra space.
Address empty lists, duplicate payments, payments referencing non-existent bookings, and memory constraints for large data. Suggest streaming or external sorting if needed.
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Didn't change much from my original approach since I was already using a set, so duplicates were handled implicitly.
Start by clarifying the current data model and assumptions, then systematically explore the impact on data structures, algorithms, and system design. Focus on how to handle one-to-many relationships, aggregation, and potential performance implications, while discussing trade-offs and scalability.
Pro tip: Emphasize that you would first confirm the business requirements and access patterns before jumping to a solution, as premature optimization or over-engineering can lead to unnecessary complexity. Also, mention that you would consider both read and write paths, as they often have different optimization strategies.
Ask questions to understand the context: Are multiple payments partial or full? Can they be refunded? What are the query patterns (e.g., get total paid, list payments)? This ensures you address the right problem.
Change from a one-to-one to a one-to-many relationship. Introduce a separate Payment entity with a foreign key to Booking, or embed payments as a list if using a document store. Discuss normalization vs. denormalization.
Update operations like calculating total paid, checking payment status, or processing refunds to iterate over multiple payments. Consider aggregation functions (SUM, COUNT) and how to efficiently retrieve payments (e.g., pagination, indexing).
Analyze the impact on database performance: indexing foreign keys, potential N+1 query problems, and caching strategies. Discuss how to handle large numbers of payments per booking.
Ensure atomicity when updating booking status based on payments. Discuss transaction boundaries, locking, and idempotency for payment operations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.