I jumped straight to a queue backed by a list and felt good about it for maybe two minutes.
Start by clarifying requirements and constraints, then propose a data model and algorithm that supports efficient operations. Discuss trade-offs between different data structures and how to handle concurrency and scalability.
Pro tip: Demonstrate awareness of real-world constraints like peak hours and no-shows by suggesting a priority queue with dynamic wait time estimation based on party size and table availability.
Ask questions to understand expected scale, operations, and constraints. Confirm whether wait time should be dynamic and if parties can be prioritized.
Define entities like Party, Waitlist, and Table. Choose appropriate data structures (e.g., priority queue, linked list) to support operations efficiently.
Describe how each operation (add, seat, cancel, estimate wait, list) will be implemented, including time complexity. Consider using a min-heap for wait time or a balanced tree for ordering.
Discuss how to handle multiple concurrent requests, possibly using locks or optimistic concurrency. Consider partitioning by restaurant location or time slots.
Compare alternative approaches (e.g., simple queue vs. priority queue) and mention potential improvements like SMS notifications or integration with table management.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem: define table types, party size compatibility, and how wait times are estimated. Then propose a data model that maps parties to compatible table types and an algorithm that computes wait time based on queue lengths and table turnover rates, considering trade-offs between accuracy and complexity.
Pro tip: Mention that you would use a weighted average of wait times across compatible table types, where weights reflect the likelihood of each table type becoming available, and discuss how to handle real-time updates efficiently.
Ask questions to understand the system: How are tables categorized? What is the party size range? How is wait time currently estimated? Are there priorities or reservations?
Model tables by type (e.g., 2-seat, 4-seat) and parties by size. Create a compatibility mapping (e.g., a party of 3 can use 4-seat or larger tables).
For a given party, identify all compatible table types. For each type, estimate wait time based on queue length and average turnover time. Combine these estimates, e.g., by taking the minimum or a weighted average.
Discuss trade-offs: accuracy vs. computational cost, real-time updates vs. batch processing. Suggest optimizations like caching or incremental updates.
Handle scenarios like large parties, no available tables, or fluctuating demand. Ensure the solution scales with many tables and parties.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: consistency needs, latency tolerance, and failure modes. Then propose a concurrency control strategy, such as optimistic locking with versioning or pessimistic locking, and discuss trade-offs. Finally, address how to handle conflicts and ensure a consistent user experience across devices.
Pro tip: Mention that you would first try to avoid shared mutable state by using an append-only event log or CRDTs, which can simplify concurrency. Also, emphasize the importance of idempotent operations and client-side retries with exponential backoff.
Ask about consistency requirements (strong vs eventual), expected load, and whether offline mode is needed. This shapes the concurrency approach.
Select between optimistic (e.g., version numbers, ETags) and pessimistic (e.g., locks, transactions) concurrency control based on contention and latency needs.
Define how conflicts are detected and resolved, such as last-write-wins, merge policies, or user prompts. Ensure the resolution is deterministic and fair.
Make operations idempotent and include client-side retry logic with backoff to handle transient failures and duplicate requests.
Plan for logging, metrics, and alerts to detect concurrency issues in production. Be ready to adjust the strategy based on real-world contention.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.