How do benchmarks evaluate adaptive query optimization?

Benchmarks evaluate adaptive query optimization (AQQ) by measuring how effectively a database system adjusts query execution plans in response to runtime conditions. They focus on three key aspects: performance improvements, resource efficiency, and the system’s ability to correct suboptimal plans. For example, a benchmark might compare the execution time of a query using static optimization (where the plan is fixed) versus adaptive optimization (where the plan changes based on data statistics or runtime feedback). Metrics like query latency, CPU/memory usage, and the number of plan revisions are tracked to quantify the benefits of AQO. This helps determine whether the overhead of runtime adjustments justifies the performance gains.

A common approach involves simulating real-world scenarios where static plans fail. For instance, benchmarks might use skewed data distributions or unpredictable workload patterns to stress-test the optimizer. Consider a query joining large tables where initial statistics underestimate the size of intermediate results. A static plan might choose a nested loop join, leading to poor performance. An adaptive optimizer could switch to a hash join mid-execution after detecting the miscalculation. Tools like PostgreSQL’s EXPLAIN ANALYZE or Oracle’s SQL Monitoring can capture these plan changes, allowing benchmarks to compare the final execution strategy against the original. Tests also evaluate how quickly the system adapts—for example, whether it corrects a bad plan within the first few iterations of a recurring query.

Benchmarks further assess trade-offs, such as the computational cost of monitoring runtime data versus the benefits of plan adjustments. For example, adaptive optimization in SQL Server’s batch mode processing might reduce query time by 30% but increase memory usage by 15% due to runtime statistics collection. Tests also examine edge cases, like highly volatile data or concurrent workloads, to ensure stability. A well-designed benchmark will isolate variables (e.g., data size, hardware constraints) to pinpoint how AQO contributes to performance. By combining controlled experiments with real-world workload simulations, developers gain actionable insights into when and how to leverage adaptive optimization effectively.