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.