Went with Flight, Passenger, and Flight_ticket tables which felt pretty natural.
Start by clarifying the core requirements and scope, then identify the main entities and their relationships. Propose a normalized schema with key tables, and discuss trade-offs and scalability considerations.
Pro tip: Mention how you would handle concurrency and seat locking during booking to prevent double-booking, as this is a critical real-world issue in flight reservation systems.
Ask about expected scale, read/write patterns, and specific features like multi-leg flights, seat selection, and payment integration to tailor the schema.
List the main entities such as Flight, Airport, Aircraft, Seat, Passenger, Booking, and Payment, and define their attributes.
Establish relationships between entities, e.g., a Flight has many Seats, a Booking has many Passengers, and a Flight connects two Airports.
Create normalized tables with primary and foreign keys, and consider junction tables for many-to-many relationships like Booking_Passenger.
Address normalization vs. denormalization, indexing strategies, and how to handle high concurrency and large data volumes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about indexing flight_id and passenger_id as foreign keys, and putting an index on departure time for search queries.
Start by clarifying the workload and query patterns before proposing any indexes, since indexes must serve actual queries. Then walk through each table's primary access paths (joins, filters, sorts) and propose indexes that cover those patterns, explaining the trade-offs in write performance and storage. Finally, mention how you would validate the choices with EXPLAIN plans and monitor index usage.
Pro tip: Always tie every index to a specific query or workload requirement, and explicitly state the cost: each index slows down writes and consumes storage. This shows you understand that indexing is a trade-off, not a checklist.
Ask about the most frequent and critical queries, expected read/write ratio, and data volume. This ensures your index recommendations are grounded in real usage rather than assumptions.
Look at columns used in WHERE clauses, JOIN conditions, ORDER BY, and GROUP BY. Prioritize columns with high selectivity and frequent use.
For queries filtering on multiple columns, propose composite indexes with the most selective column first, considering equality before range conditions. Mention covering indexes to avoid table lookups.
Discuss the impact on write performance, storage, and maintenance. Consider whether a covering index, index-only scan, or partitioning might be better than multiple single-column indexes.
Explain how you would test the indexes using EXPLAIN plans, measure query performance, and monitor index usage to remove unused indexes. Emphasize that indexing is an iterative process.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the exact branching factor mechanics.
Start by defining a B-tree index as a balanced tree data structure optimized for disk-based storage, then explain its internal node structure and search/insert operations. Emphasize how its design minimizes disk I/O and maintains balance, making it ideal for database indexing.
Pro tip: Relate the B-tree's high fanout and balanced height to real-world database performance, showing you understand the trade-offs between read/write efficiency and storage overhead.
Explain that a B-tree index is a self-balancing tree data structure that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time.
Detail that each node contains multiple keys and child pointers, with all leaves at the same depth, and that nodes are sized to match disk blocks to minimize I/O.
Walk through how a search traverses from root to leaf, comparing keys at each node to choose the correct child, resulting in O(log n) disk accesses.
Describe how insertions may cause node splits, propagating upward and possibly increasing tree height, but always maintaining balance and the B-tree properties.
Conclude with why B-trees are used in databases: efficient range queries, high fanout reducing tree height, and optimized for disk-based systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the end-to-end flow from the user's perspective, then dive into the backend components and queries that support each step. Emphasize how the system ensures data consistency, scalability, and a smooth user experience, especially under high traffic. Conclude by discussing trade-offs and potential optimizations.
Pro tip: Show that you consider failure scenarios and idempotency—mention how you'd handle payment failures, duplicate bookings, and race conditions. This demonstrates production maturity beyond just happy-path design.
Describe the user journey: search flights, select flight, enter passenger details, review and pay, receive confirmation. Highlight key UX considerations like loading states, error handling, and responsiveness.
Explain how the frontend communicates with backend APIs (e.g., REST or GraphQL) for each step, including request/response payloads and authentication/authorization.
Detail the backend services involved (flight search, booking, payment, inventory) and the database queries they execute, such as SELECT for availability, INSERT for booking, and UPDATE for seat inventory.
Discuss how you ensure atomicity across services, e.g., using distributed transactions, sagas, or two-phase commit, and how you handle concurrent bookings and inventory locking.
Cover confirmation emails, updating loyalty programs, and analytics. Mention caching, read replicas, and sharding to handle scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints, such as expected concurrency and consistency needs. Then propose a layered solution using database transactions with appropriate isolation levels, optimistic or pessimistic locking, and possibly a distributed lock for scale. Finally, discuss trade-offs and how you would handle failures and retries.
Pro tip: Mention that you would first try to solve it at the database level with unique constraints or conditional updates, as they are simple and reliable, before introducing distributed locks which add complexity. Also, highlight the importance of idempotency keys to handle retries safely.
Ask about the scale, consistency requirements, and whether the system is distributed. This determines the appropriate solution.
Propose using transactions with SELECT ... FOR UPDATE or unique constraints on seat and time slot to prevent double booking.
Discuss trade-offs: optimistic locking (version checks) for low contention, pessimistic locking for high contention but with performance impact.
If the system is distributed, consider using a distributed lock (e.g., Redis, ZooKeeper) but note the added complexity and potential for failures.
Ensure idempotency and proper error handling so that retries don't cause double bookings. Discuss compensation or rollback strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.