I started with the obvious stuff: search by city and dates, view availability, book and cancel.
Start by clarifying the scope and assumptions (e.g., single hotel vs. chain, B2C vs. B2B) to show you don't jump into solutions prematurely. Then, structure your answer by walking through the core user journeys (search, book, manage) and derive functional requirements from each, while briefly noting non-functional aspects like scalability and consistency.
Pro tip: Explicitly call out the double-booking problem and how you'd handle it (e.g., locking, idempotency, or optimistic concurrency) — this shows you understand the hardest part of booking systems and can connect requirements to real engineering challenges.
Ask questions to narrow down the system: Is it for a single hotel or a chain? Is it B2C or B2B? What scale? This prevents over-engineering and ensures you focus on relevant requirements.
Map out the primary flows: searching for rooms, making a booking, managing reservations, and handling payments. This helps organize functional requirements around real user needs.
For each journey, list specific features: search filters, availability check, booking creation, cancellation, etc. Prioritize them (must-have vs. nice-to-have) to show product sense.
Discuss key technical challenges like concurrency (double-booking), consistency, and idempotency. This demonstrates you can connect requirements to system design constraints.
Briefly recap the core requirements and suggest next steps (e.g., diving into data model or API design) to show you can drive the interview forward.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scope and requirements of the system, then identify the core entities and their attributes, and finally describe the relationships between them using an entity-relationship diagram or textual description. Emphasize how the data model supports the key use cases and scalability needs of the system.
Pro tip: Demonstrate awareness of trade-offs by discussing normalization vs. denormalization and how the data model might evolve over time. Also, relate the data model to real-world constraints like data access patterns and consistency requirements.
Ask questions to understand the system's purpose, key features, and expected scale. This ensures the data model aligns with business needs.
List the main objects or concepts in the domain, such as User, Company, Employee, Payroll, etc., and define their key attributes.
Describe how entities relate to each other (one-to-one, one-to-many, many-to-many) and specify cardinality and optionality.
Discuss unique constraints, foreign keys, and indexes needed for efficient queries and data integrity.
Walk through common queries or operations to ensure the model supports them efficiently and identify potential bottlenecks.
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Probably the hardest part of the whole interview.
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 and optimistic or pessimistic locking, and discuss trade-offs and failure handling.
Pro tip: Mention that double-booking prevention often requires a combination of database constraints and application-level checks, and that idempotency keys can help handle retries safely.
Ask about expected traffic, consistency requirements, and whether the system can tolerate temporary inconsistencies. This shows you understand the problem context.
Discuss optimistic locking (version checks) versus pessimistic locking (SELECT FOR UPDATE) and when each is appropriate based on contention levels.
Propose using unique constraints or exclusion constraints to enforce booking rules at the database level, ensuring atomicity.
Explain how to handle conflicts gracefully, such as returning a clear error or retrying with backoff, and using idempotency keys to avoid duplicate bookings.
Compare approaches in terms of performance, scalability, and complexity, and suggest monitoring and metrics to detect issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by explaining the concept of idempotency and how it applies to payment processing. Then describe a concrete implementation using idempotency keys, database constraints, and proper transaction handling. Finally, discuss how to handle edge cases like concurrent requests and network failures.
Pro tip: Mention that idempotency keys should be generated by the client and stored server-side with a unique constraint, and that you should return the same response for duplicate requests to ensure consistency.
Explain that an operation is idempotent if performing it multiple times has the same effect as performing it once. This is crucial for payment processing to avoid double charges.
Describe how the client generates a unique idempotency key for each payment request and sends it with the request. The server uses this key to detect and handle retries.
Explain that the server stores the idempotency key along with the payment result in a database with a unique constraint. On a retry, the server checks if the key exists and returns the stored result instead of reprocessing.
Discuss using database transactions or locks to handle concurrent requests with the same idempotency key, ensuring only one request processes the payment.
Mention handling scenarios like network failures, timeouts, and key expiration. Ensure that even if the client doesn't receive a response, the payment is not duplicated.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the part I felt least confident in.
Start by clarifying the functional requirements and scale expectations, then derive quantitative constraints like QPS, peak load, and storage needs using assumptions and back-of-the-envelope calculations. Finally, discuss how these constraints influence design decisions such as database sharding, caching, and autoscaling.
Pro tip: Always state your assumptions explicitly and validate them with the interviewer; this shows you understand that constraints are often negotiated and not given. Also, relate constraints to business impact (e.g., cost, user experience) to demonstrate maturity.
Ask questions to understand the expected user base, usage patterns, and growth projections. Identify read vs. write ratios and data retention policies.
Calculate average QPS from daily active users and actions per user, then apply a peak factor (e.g., 2-5x) to account for traffic spikes. Consider diurnal patterns and special events.
Estimate storage requirements based on data volume per user/action, retention period, and replication. Determine IOPS and throughput needs based on query patterns.
Apply growth projections (e.g., 2x in 6 months) and safety margins (e.g., 30% headroom) to ensure the system can handle future load without immediate re-architecture.
Explain how the derived constraints drive choices like sharding, caching, read replicas, and autoscaling policies. Discuss trade-offs between consistency, availability, and cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about read replicas for search, caching availability with a short TTL, and async updates for pricing.
Start by clarifying the scale, read/write ratio, and freshness requirements, then propose a multi-layered architecture that separates read and write paths. Focus on caching, replication, and asynchronous indexing to handle read-heavy traffic while ensuring availability data is updated with acceptable latency. Discuss trade-offs between consistency, latency, and cost.
Pro tip: Emphasize that freshness requirements drive the design: if near-real-time freshness is needed, consider change data capture (CDC) and streaming updates to caches; if eventual consistency is acceptable, batch updates can reduce complexity. Always tie your choices back to business impact, like user experience and operational cost.
Ask about expected read QPS, write QPS, data size, freshness SLA, and consistency needs. This shapes the entire design.
Introduce caching layers (CDN, application cache, distributed cache like Redis) and read replicas to offload the primary database. Consider search-specific optimizations like inverted indexes or Elasticsearch.
Use asynchronous processing (message queues, CDC) to update caches and indexes without blocking writes. Choose between push (invalidate/update on write) and pull (periodic refresh) based on freshness needs.
Make components stateless and horizontally scalable. Use replication, sharding, and failover strategies. Monitor and auto-scale based on load.
Compare consistency vs. latency, cost vs. performance, and complexity vs. maintainability. Justify your choices based on the requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.