Data Modeling for Query Performance Questions

Focuses on schema and data modeling choices that enable efficient querying at scale. Topics include normalization and denormalization trade offs, analytical schemas such as star schema and snowflake schema, the roles of fact tables and dimension tables, modeling for common query patterns and aggregations, and how model choices impact indexing, join costs, and storage. Candidates should be able to justify schema decisions based on query workload, discuss partitioning and sharding implications for model design, and propose modeling adjustments that improve query latency and maintainability.

EasyTechnical

0 practiced

What is partition pruning and how does partitioning a large fact table by date (or other keys) improve query performance? Describe common partitioning schemes (daily, monthly, by hash), how predicate patterns enable pruning, and pitfalls such as too many tiny partitions, non-sargable predicates, and partition maintenance overhead.

EasyTechnical

0 practiced

Given a single large table events(event_id, user_id, event_time, event_type, payload) that is frequently queried by date ranges and event_type, recommend an indexing and physical layout strategy to improve query performance. Consider both row-oriented databases and columnar warehouses: composite/covering indexes, clustering/sort keys, partitioning, and bitmap/applicable indexes. Explain trade-offs for write throughput and storage.

HardTechnical

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Write pseudo-SQL (MERGE-style) or stored-procedure logic to maintain a denormalized daily_summary table from a raw fact_events table using incremental upserts. Include deduplication logic, handling late-arriving events (adjusting previous days), and strategies to minimize locks and contention on the summary table. Target a cloud warehouse that supports MERGE and partitioned tables.

HardTechnical

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A dashboard query performs many multi-way joins across large dimension tables and is slow. Propose a modeling and query-rewrite plan to optimize the query: consider star-schema flattening, creating materialized join tables or denormalized aggregates, using late-binding dimensions, improving statistics, and rewriting joins to reduce intermediate cardinality. Explain how each choice impacts freshness and maintainability.

EasyTechnical

0 practiced

Discuss surrogate keys versus natural keys for dimension tables in an analytical warehouse. Cover their effects on join performance, storage size, ability to implement SCD (Type 2), referential stability when natural attributes change, and how surrogate keys influence ETL design and downstream consumers.

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