I started with a naive per-row array scan and the interviewer immediately pushed on what happens with a 10,000-seat venue under heavy load.
Start by clarifying requirements (e.g., seat layout, middle aisle, concurrency needs) and then propose a data structure like a segment tree or bitset to efficiently find and book adjacent seats. Discuss trade-offs between simplicity and performance, and outline a concurrency strategy such as optimistic locking or partitioning.
Pro tip: Demonstrate awareness of real-world constraints: mention that in high-concurrency scenarios, you might need to handle race conditions with distributed locks or transactional isolation, and that the choice of data structure depends on read/write patterns.
Ask about seat layout (rows, columns, middle aisle), booking constraints (group size, adjacency), and expected scale (concurrent users, number of seats).
Propose a data structure to represent seat availability, such as a bitset per row or a segment tree, and explain how to efficiently find adjacent seats.
Describe algorithms for booking (find and reserve adjacent seats), cancelling (free seats), and handling the middle aisle by splitting rows into segments.
Discuss time and space complexity for each operation, comparing alternatives (e.g., linear scan vs. segment tree) and justifying your choice.
Explain how to handle concurrent bookings at scale, using techniques like optimistic locking, partitioning by row, or distributed transactions, and discuss trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.