This is the kind of question where you think you know the answer until you're actually saying it out loud.
Start by clarifying the data characteristics (volume, velocity, variety, access patterns) and the application requirements (consistency, latency, scalability). Then map each database type to the scenarios where it excels, using concrete examples. Finally, recommend a decision framework based on trade-offs, and mention that polyglot persistence is often the pragmatic choice.
Pro tip: Emphasize that the choice should be driven by access patterns and consistency requirements, not by hype or familiarity. Also, mention that you would prototype with a small dataset to validate assumptions before committing.
Ask about data volume, schema flexibility, read/write patterns, latency and consistency needs, and query complexity. This ensures you understand the problem before prescribing a solution.
Discuss when RDBMS (e.g., PostgreSQL, MySQL) is appropriate: structured data, ACID transactions, complex joins, and moderate scale. Mention that they can scale vertically and with read replicas, but sharding is complex.
Cover key-value, document, and wide-column stores (e.g., MongoDB, Cassandra, DynamoDB). Highlight their strengths: horizontal scalability, flexible schemas, high write throughput, and eventual consistency. Note trade-offs in query flexibility and transactions.
Explain that columnar warehouses (e.g., Redshift, BigQuery, Snowflake) are optimized for analytical queries over large datasets, with columnar storage, compression, and massively parallel processing. They are not ideal for transactional workloads.
Synthesize by matching requirements to the best-fit database, and consider polyglot persistence. Justify your choice with trade-offs and mention potential hybrid architectures (e.g., OLTP in RDBMS, OLAP in warehouse).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Spent probably too long on the entity-relationship part and didn't get to partitioning until they nudged me.
Start by clarifying the access patterns and their priorities, then design the schema to optimize for the most frequent and critical queries. Iterate on the design by considering trade-offs between normalization, denormalization, indexing, and partitioning to meet performance and scalability requirements.
Pro tip: Always tie your design decisions back to the specific access patterns and quantify the impact (e.g., 'This index reduces read latency for the most frequent query by 80%'). Also, mention how you would validate the design with load testing and monitoring.
Ask questions to understand the read/write patterns, query frequency, latency requirements, and data volume. Prioritize patterns based on business impact.
Extract core entities from the access patterns and define their relationships (1:1, 1:N, M:N). Consider whether to embed or reference based on access patterns.
Choose indexes (e.g., primary, secondary, composite) to support the most frequent and critical queries. Consider index overhead on writes and storage.
Select partitioning keys to distribute data evenly and enable efficient queries. Consider access patterns that require cross-partition queries and how to mitigate.
Review the design against all access patterns, identify bottlenecks, and propose optimizations. Discuss how to test and monitor the schema in production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Mixed OLTP and OLAP requirements in one question.
Start by clarifying the workload characteristics (write volume, query patterns, latency SLAs) and then propose a hybrid architecture that separates the write-optimized and read-optimized paths. Discuss trade-offs between normalization for writes and denormalization/columnar storage for analytics, and explain how to keep them in sync. Emphasize that the right balance depends on specific requirements and that you would validate with benchmarks.
Pro tip: Mention that you would avoid premature optimization and instead start with a simple design, measure bottlenecks, and then evolve—this shows pragmatism and aligns with Waymo's iterative engineering culture.
Ask about write throughput, query complexity, data freshness, and latency SLAs to understand the constraints. This ensures your design is grounded in actual needs rather than assumptions.
Suggest using a row-based OLTP database (e.g., PostgreSQL, Spanner) for real-time writes and a columnar OLAP store (e.g., BigQuery, ClickHouse) for analytics. Explain that this separation allows each system to be optimized for its purpose.
Describe how to keep the systems in sync, such as change data capture (CDC) or dual writes with eventual consistency. Discuss trade-offs like latency and complexity.
For writes, use a normalized schema to ensure fast inserts and updates. For analytics, use a denormalized, columnar schema with partitioning and clustering to speed up queries.
Acknowledge that a single system (e.g., HTAP databases like TiDB, SingleStore) might suffice for smaller scale, but for heavy analytics, separation is often better. Mention that you would benchmark and iterate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.