Data Model Design and Access Patterns Questions
Discuss how you'd design data models based on access patterns. Understand relational vs. NoSQL trade-offs. Know when to denormalize, how to handle distributed transactions, and strategies for scaling databases (sharding, partitioning). Discuss read vs. write optimization.
MediumTechnical
0 practiced
Compare storing high-cardinality time-series in wide relational tables, specialized time-series DBs (e.g., TimescaleDB, InfluxDB), and columnar/OLAP stores. Discuss ingestion rates, compression, query patterns for aggregations, downsampling, and operational cost trade-offs for each choice.
EasyTechnical
0 practiced
Describe how to model one-to-many and many-to-many relationships in a relational database and in a document NoSQL store such as MongoDB. Provide schema snippets and explain how read and write access patterns influence whether you embed related data or reference it.
HardTechnical
0 practiced
Your system uses distributed two-phase commit (2PC) and suffers high latency and blocking during failures. Propose an alternative design using Sagas. Compare choreography vs orchestration styles, design the saga tracking data model, show how to implement compensating transactions and idempotency, and outline how partial failures are recovered.
MediumSystem Design
0 practiced
Design an event-sourced model for orders with states such as created, paid, shipped, and returned. Describe the event store schema, how to build and maintain projections/read-models efficiently (snapshots, incremental projection), and best practices for idempotency and event ordering guarantees.
EasyTechnical
0 practiced
Describe denormalization: typical patterns (duplicated columns, precomputed aggregates, embedding joined data), reasons to denormalize for read-heavy workloads, and the trade-offs this introduces for write amplification, storage, and correctness. When would you avoid denormalization?
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