Finished the main problem and all the follow-ups with time to spare, which felt good.
Start by clarifying the problem: define what costs need to be tracked (e.g., per delivery, per driver, or aggregated) and the operations required (e.g., add delivery, compute total cost). Then propose an efficient data structure and algorithm, such as a hash map for driver-specific costs and a running total, ensuring O(1) updates and queries. Finally, discuss how to extend the solution for follow-up requirements like time-based costs or dynamic pricing.
Pro tip: Demonstrate foresight by proactively mentioning potential follow-ups (e.g., handling peak hours or driver bonuses) and how your design can accommodate them without major refactoring. This shows you think beyond the immediate problem and understand real-world scalability.
Ask questions to understand the exact cost model, data inputs, and expected operations (e.g., add delivery, get total cost, get cost per driver). Confirm any constraints like number of drivers or deliveries.
Select appropriate data structures: a hash map to store per-driver costs for O(1) updates and lookups, and a variable for the total cost. Consider if additional structures are needed for follow-ups.
Define methods to add a delivery (updating driver cost and total) and to retrieve costs. Ensure each operation is efficient, ideally O(1) time.
Anticipate extensions like time-based costs, driver-specific rates, or cost aggregation over periods. Explain how to adapt the design, e.g., by adding timestamps or using more complex structures like priority queues.
Discuss time and space complexity of your solution. Mention any trade-offs (e.g., memory vs. speed) and why your approach is optimal for the given scenario.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I structured it by walking through requirements first, then APIs, then the architecture, data model, and finally consistency concerns around double booking.
Start by clarifying requirements and scale (e.g., read/write ratio, search vs booking traffic), then sketch a high-level architecture with separate services for search, booking, and inventory. Focus on the booking flow and concurrency control to prevent double bookings, using database transactions with appropriate isolation or distributed locks.
Pro tip: Emphasize idempotency and exactly-once semantics for booking APIs to handle retries gracefully, and discuss how you'd handle partial failures in a distributed transaction (e.g., using saga pattern).
Ask about expected traffic (e.g., millions of searches, thousands of bookings per second), consistency needs, and global distribution. Define core entities: hotels, rooms, rates, availability, and bookings.
Define RESTful endpoints for search, availability, booking, and cancellation. Model data with tables for hotels, room types, inventory (date-based), and bookings, considering read-heavy search vs write-heavy booking.
Propose a microservices architecture with separate services for search (using Elasticsearch), booking, inventory, and payment. Use a message queue for asynchronous tasks like notifications and a cache (Redis) for hot data.
Use database transactions with SELECT FOR UPDATE or optimistic concurrency control on inventory rows. Alternatively, use a distributed lock (e.g., Redis Redlock) or a reservation service with a two-phase commit. Ensure idempotency keys for booking requests.
Scale reads via caching and read replicas; scale writes via sharding by hotel or region. Implement retries, circuit breakers, and monitoring. Discuss trade-offs between consistency and availability (CAP theorem).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Choose a project where you owned key technical decisions and can clearly articulate the problem, constraints, and outcomes. Structure your answer as a narrative: context, decisions with tradeoffs, results, and a failure with lessons learned. Focus on demonstrating engineering judgment and ownership, not just listing technologies.
Pro tip: Quantify impact and tradeoffs (e.g., 'reduced latency by 40% but increased infra cost by 15%') to show you think in terms of business value, not just code. Also, be honest about failures—interviewers value self-awareness and learning over perfection.
Briefly describe the project's goal, your role, the team size, and the key constraints (time, scale, legacy systems). This helps the interviewer understand the environment for your decisions.
Walk through 2-3 critical decisions, the alternatives considered, and why you chose one over the others. Explicitly state the tradeoffs (e.g., consistency vs. availability, speed vs. maintainability).
Share measurable results: latency, throughput, cost, user impact, etc. Compare before/after and explain how you validated the improvements.
Pick a specific failure (e.g., a bug, outage, or design flaw), explain how you detected it, the root cause, and the steps you took to fix and prevent recurrence.
Summarize what you would do differently and how this experience shaped your engineering approach. Tie it back to the role and company values.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.