← Bytedance Interview Insights
I started with entities: events, venues, seats, users, bookings.
Start by clarifying functional and non-functional requirements, then design a normalized relational schema that captures entities like User, Event, Venue, Showtime, Seat, and Booking. Discuss how to handle concurrency and scalability, and consider trade-offs between SQL and NoSQL for different parts of the system.
Pro tip: Demonstrate awareness of real-world constraints like seat locking, payment integration, and high-traffic events (e.g., flash sales) by mentioning optimistic vs. pessimistic locking and idempotency. Also, briefly touch on how Bytedance's scale might influence sharding and caching strategies.
Ask about scale, read/write patterns, consistency needs, and features like seat selection, cancellations, and waitlists. Confirm whether the system handles both concerts and movies with shared or separate models.
List main entities: User, Event (with type: concert/movie), Venue, Hall/Screen, Showtime, Seat, Booking, Payment, and possibly Ticket. Define their attributes and relationships.
Propose tables with primary/foreign keys, indexes, and constraints. For example, Showtime links Event and Venue, Seat is tied to Hall, and Booking references Showtime and User. Discuss normalization vs. denormalization for performance.
Explain how to prevent double-booking using transactions, locking (optimistic/pessimistic), or seat reservation with TTL. Mention idempotent booking APIs and handling payment failures.
Talk about sharding by event or region, caching hot data (e.g., seat maps), and using NoSQL for flexible event metadata. Compare SQL vs. NoSQL for booking transactions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Picked AWS, went with S3 plus Redshift, talked about a star schema.
Start by clarifying requirements (data volume, query patterns, latency, budget) and then propose a layered architecture on one cloud (e.g., AWS) with ingestion, storage, processing, and serving layers. Explain how users query the data via SQL engines, BI tools, and APIs, and discuss trade-offs between batch and real-time, cost, and performance.
Pro tip: Emphasize separation of storage and compute and the importance of a metadata catalog for self-service analytics; this shows you understand modern cloud data warehouse design and user enablement.
Ask about data sources, volume, velocity, variety, query patterns (ad-hoc, dashboards, ML), latency needs, and budget. This ensures your design is fit for purpose.
Pick a cloud (e.g., AWS) and select managed services: S3 for storage, Glue for ETL/catalog, Redshift for warehouse, Kinesis for streaming, and QuickSight for BI. Justify choices based on requirements.
Outline layers: ingestion (batch/stream), storage (raw, curated, modeled), processing (ETL/ELT), and serving. Use columnar formats (Parquet), partitioning, and compression for performance and cost.
Explain how users query: SQL via Redshift, BI tools via JDBC/ODBC, APIs for applications, and ad-hoc queries on S3 using Athena. Discuss concurrency, workload management, and caching.
Discuss trade-offs: cost vs performance, batch vs streaming, normalization vs denormalization. Cover security (IAM, encryption), monitoring, and scaling strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.