I started with the endpoints and felt okay there.
Start by clarifying requirements and constraints, then design the API endpoints and data model, and finally walk through the booking flow with status codes and indexing. Emphasize idempotency, concurrency control, and payment handling as key aspects.
Pro tip: Discuss how you would handle race conditions during booking (e.g., using optimistic locking or database constraints) and ensure idempotency for payment confirmation to avoid double charges.
Ask questions to understand scope: expected traffic, payment provider integration, cancellation policies, and whether guest authentication is required. This shows you think about real-world constraints.
Define RESTful endpoints for searching availability, creating a booking, retrieving/canceling a booking, and confirming payment. Use appropriate HTTP methods and paths (e.g., GET /availability, POST /bookings).
Specify JSON payloads for each endpoint and map outcomes to HTTP status codes (e.g., 200 OK, 201 Created, 400 Bad Request, 404 Not Found, 409 Conflict). Include error response shapes.
Outline database tables (e.g., Users, Cars, Bookings, Payments) with fields and relationships. Discuss indexes on frequently queried columns (e.g., car_id, start_date, end_date) to optimize availability searches.
Explain how to prevent double bookings (e.g., unique constraints, transactions) and handle payment confirmation idempotently. Mention webhooks or polling for payment status.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview got interesting.
Start by clarifying the use case and access patterns, then compare the two models across storage, query performance, write complexity, and consistency. Conclude with a recommendation that balances trade-offs for Turo's car-sharing marketplace, where availability changes frequently and queries are date-range based.
Pro tip: Mention that the best choice often depends on the ratio of reads to writes and the typical length of availability windows; for Turo, where cars are booked for multi-day trips, range-based storage with efficient overlap queries is usually preferable, but you can also use a hybrid approach with a materialized per-day view for fast lookups.
Ask about query patterns (e.g., find available cars for a date range), write frequency (how often availability changes), and consistency needs. This sets the context for evaluating trade-offs.
Discuss pros: simple queries for a specific date, easy indexing, straightforward updates. Cons: storage overhead for long availability periods, inefficient for range queries (need to scan many rows), and potential write amplification when updating large ranges.
Discuss pros: compact storage, efficient for range queries with interval trees or overlap conditions, fewer rows to update. Cons: more complex queries (overlap logic), harder to enforce uniqueness, and potential for overlapping ranges if not carefully managed.
Evaluate storage cost, read/write performance, query complexity, and consistency. For example, per-date is better for point lookups and simple updates; range is better for long availability windows and range scans.
Suggest a model based on Turo's needs: likely range-based with proper indexing (e.g., using PostgreSQL's daterange and GiST index) or a hybrid approach with a per-day materialized view for fast availability checks. Mention scalability and future needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business rules and constraints around extending a booking, such as availability, pricing, and host approval. Then, walk through the necessary API changes (e.g., new endpoint or updated request/response) and data model modifications (e.g., booking dates, pricing, availability). Finally, discuss trade-offs like concurrency, idempotency, and backward compatibility.
Pro tip: Emphasize the importance of handling edge cases like overlapping bookings and partial availability, and propose a phased rollout with feature flags to mitigate risks.
Ask questions to understand the business rules: Is extension always allowed? Are there fees? Does it require host approval? What about availability and pricing changes?
Propose a new endpoint (e.g., PATCH /bookings/{id}/extend) or modify an existing one. Define request/response schemas, including new checkout date and any additional parameters like approval token.
Identify changes to the booking entity: update checkout date, recalculate total price, adjust availability calendar, and possibly create an audit log or version history.
Discuss locking mechanisms or optimistic concurrency to prevent double-booking. Ensure atomic updates across booking, availability, and payment systems.
Evaluate trade-offs like synchronous vs. asynchronous processing, backward compatibility, and feature flagging. Plan for monitoring and rollback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.