How do benchmarks evaluate database indexing strategies?

Benchmarks evaluate database indexing strategies by measuring performance under controlled workloads, comparing metrics like query speed, write overhead, and storage costs. They simulate real-world scenarios to test how different indexing approaches handle specific operations, such as read-heavy queries, updates, or complex joins. For example, a benchmark might compare a B-tree index’s performance on range queries against a hash index’s speed for exact lookups. By isolating variables like hardware and dataset size, benchmarks reveal how indexing choices impact trade-offs between read efficiency, write latency, and resource usage.

A common approach involves standardized datasets (e.g., TPC-C for transactional workloads or TPC-H for analytics) and predefined queries. For instance, a benchmark might measure the time to execute 10,000 SELECT statements with a WHERE clause using a clustered index versus a non-clustered index. Write performance is tested by timing INSERT/UPDATE operations while maintaining indexes—like observing how a full-text index slows down document ingestion compared to a simpler index. Benchmarks also assess scalability by increasing dataset size to see if an index maintains performance (e.g., a B-tree’s logarithmic scaling vs. a bitmap index’s memory demands).

Finally, benchmarks highlight context-specific trade-offs. For example, a covering index might reduce query latency by including all needed columns, but increase storage and slow down writes. A spatial index like an R-tree could speed up geographic queries but add complexity for non-spatial data. Developers use these insights to choose strategies aligned with their workload patterns—like favoring write-optimized indexes for logging systems or read-optimized ones for reporting. Benchmarks provide concrete data to balance theoretical advantages with practical constraints.