This is the main question and it sprawls in every direction.
Start by clarifying requirements and scale, then design a high-level architecture that separates read and write paths. Focus on the core challenge of preventing double-bookings under high concurrency, using a combination of distributed locking, idempotency, and transactional consistency. Finally, address availability and caching strategies to handle viral traffic spikes.
Pro tip: Emphasize that correctness (no double-bookings) trumps availability during booking writes, but reads can be eventually consistent. Mention that you'd use a saga pattern or two-phase commit only if necessary, and prefer single-digit millisecond locking with Redis or a database with strong consistency.
Ask about expected traffic (e.g., millions of concurrent users), booking patterns, consistency needs, and geographic distribution. Define functional and non-functional requirements.
Propose a microservices-based architecture with separate services for search, booking, inventory, and payment. Use API gateway, load balancers, and CDN for static content.
Design a schema for hotels, rooms, availability, and bookings. Choose a relational database (e.g., PostgreSQL) for bookings to ensure ACID, and NoSQL or in-memory stores for availability caching.
Detail how to prevent double-bookings: use distributed locks (e.g., Redis Redlock) or optimistic concurrency control with versioning. Ensure idempotent booking requests and handle failures with retries and compensating transactions.
Explain how to handle viral spikes: auto-scaling, read replicas, caching availability data, and rate limiting. Discuss trade-offs between consistency and availability (CAP theorem) and how to achieve eventual consistency for reads.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Availability count updates to the UI are fine to lose since a stale count just gets refreshed on the next poll.
Start by clarifying the system's requirements and the specific flows involved, then compare protocols based on latency, overhead, reliability, and ordering needs. For each flow, decide whether it can tolerate message loss or requires guaranteed delivery, and justify your protocol choice with trade-offs.
Pro tip: Demonstrate maturity by acknowledging that most systems use a mix of protocols and that the choice depends on the specific use case, not a one-size-fits-all solution. Also, mention that you would measure and monitor the impact of protocol choices on key metrics like latency and error rates.
Ask questions to understand the system's scale, latency requirements, and the nature of the data flows (e.g., real-time updates, video streaming, chat).
List the different flows in the system and categorize them by whether they require reliability, ordering, low latency, or can tolerate loss.
For each flow, compare UDP, WebSocket, and HTTP based on overhead, latency, reliability, and complexity. Consider factors like connection setup, head-of-line blocking, and statefulness.
Assign the most suitable protocol to each flow, explaining why it fits the requirements and what trade-offs are acceptable.
For flows that cannot tolerate loss, describe how you would ensure reliability (e.g., application-level acks, retries) and consider fallback mechanisms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
My first instinct was a distributed lock per room-night, which I knew was going to invite follow-up about lock contention.
Start by clarifying the requirements: the system must guarantee that only one booking succeeds for the last room under high concurrency. Then propose a layered solution combining database-level atomic operations (e.g., conditional updates or unique constraints) with application-level locking or optimistic concurrency control, and discuss trade-offs like performance, consistency, and scalability.
Pro tip: Mention that you would use a database transaction with a conditional UPDATE (e.g., UPDATE rooms SET available = available - 1 WHERE room_id = ? AND available > 0) and check the affected row count, as this is simple, atomic, and avoids race conditions without distributed locks. Also highlight the importance of idempotency keys to handle retries safely.
Confirm the expected scale, consistency requirements (strong vs. eventual), and whether the system is single-node or distributed. This sets the context for choosing the right concurrency control mechanism.
Pinpoint the exact operation that must be atomic: checking availability and decrementing inventory for the specific room and dates. This is the race condition hotspot.
Select from options like database transactions with row-level locks, optimistic concurrency control (version numbers), conditional updates, or distributed locks (e.g., Redis). Explain why your choice fits the scenario.
Discuss handling of retries, idempotency, deadlocks, lock timeouts, and network partitions. Explain how to ensure correctness even if a node fails mid-operation.
Compare performance, complexity, and consistency guarantees of your approach. Mention how it scales under high concurrency and whether it introduces bottlenecks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sharding by room type or room ID keeps all writes for a given room on one shard which makes the no-double-booking guarantee easier to enforce.
Start by clarifying the workload characteristics and access patterns for a viral hotel scenario, then compare sharding by room versus bucketing by user ID across dimensions like scalability, hotspot mitigation, and query efficiency. Conclude with a recommendation that balances even load distribution with operational simplicity, possibly using a hybrid approach.
Pro tip: Emphasize that the choice depends on the dominant query pattern: if most queries are by room (e.g., availability, bookings), sharding by room is natural; if user-centric (e.g., recommendations, history), bucketing by user ID is better. Also mention that viral events often cause skewed traffic, so a dynamic sharding strategy may be needed.
Identify the primary access patterns (read/write ratio, queries per second, data size) and the nature of the viral event (e.g., sudden spike in bookings vs. views).
Discuss how partitioning by room ID distributes data and load, and evaluate its pros (locality for room-specific queries) and cons (hotspots if certain rooms are popular).
Explain how bucketing by user ID (e.g., hash of user ID) spreads load across shards, and assess its pros (even distribution for user-centric operations) and cons (cross-shard queries for room data).
Weigh the trade-offs based on the workload, and propose a solution (e.g., shard by room for booking data, bucket by user for session data) or a hybrid approach with consistent hashing.
Discuss how to handle hotspots (e.g., dynamic rebalancing, caching, read replicas) and ensure the system can scale horizontally during viral spikes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Kept it fairly standard: hotels table, room_types table, an inventory table keyed on (hotel_id, room_type_id, date) with a count column, and a bookings table.
Start by clarifying the core entities and their relationships, then propose a normalized relational schema with appropriate indexes for fast availability lookups. Explain how to handle transactional booking writes using ACID transactions and isolation levels, and discuss scaling strategies like sharding or caching for high read throughput.
Pro tip: Mention that availability lookups can be optimized with a materialized view or a separate inventory table that tracks room counts per date, updated transactionally. This shows you understand the trade-off between read performance and write consistency.
Define the main entities: Hotel, RoomType, Room, Inventory, and Booking. Establish relationships: a hotel has many room types, each room type has many rooms, inventory tracks availability per room type per date, and bookings link to specific rooms and dates.
Propose a normalized relational schema with tables for Hotels, RoomTypes, Rooms, Inventory (with columns like room_type_id, date, total_rooms, booked_rooms), and Bookings. Use foreign keys to enforce referential integrity.
Create indexes on Inventory (room_type_id, date) and consider denormalizing availability into a separate table or using a materialized view. Discuss caching strategies (e.g., Redis) for frequently accessed dates.
Use database transactions with appropriate isolation levels (e.g., Serializable or Repeatable Read) to prevent double-booking. Implement optimistic concurrency control with versioning or pessimistic locking on inventory rows.
Discuss sharding by hotel_id or region, read replicas for availability queries, and the trade-offs between consistency and latency. Mention eventual consistency for non-critical data like reviews.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.