On benchmarking

Benchmarking databases is a complex process that requires careful consideration of several factors to ensure accurate and unbiased results. Key principles include understanding the client-server architecture, where latency between the benchmark client and database server can significantly impact results. Resource allocation must be balanced to prevent misleading comparisons, and workload characteristics such as traffic ratios and query patterns need to be carefully configured. The choice between open and closed-loop benchmarks is essential, as each serves different purposes: open loops simulate real-world client demands, while closed loops offer controlled comparisons. Throughput and latency are critical metrics, with percentiles providing a more comprehensive view of latency than averages alone. Proper benchmarking involves warming up caches, aligning database configurations, and accounting for environmental inconsistencies, especially in cloud environments. To ensure meaningful comparisons, benchmarks should compare similar technologies and document setups thoroughly for reproducibility. Common pitfalls include failing to measure at steady state, overlooking client bottlenecks, and reporting only averages without context. By adhering to these practices, benchmarks can provide valuable insights into database performance.