Benchmarks evaluate adaptive query optimization by systematically testing how well a database management system (DBMS) can adjust its query execution strategies based on changing conditions and workloads. Adaptive query optimization refers to the system's ability to modify its approach to executing a query in real-time, improving performance as new data becomes available or as conditions change. Benchmarks typically involve predefined workloads that simulate various usage scenarios, allowing developers to gauge the effectiveness and efficiency of adaptive strategies in the DBMS.
To set up a benchmark for adaptive query optimization, developers often use a mix of static and dynamic query workloads. For example, a static workload might consist of a fixed set of queries run against a stable dataset, allowing for a baseline measurement of how the system performs without adaptations. In contrast, a dynamic workload could introduce variations in data distribution or query complexity during runtime, testing the system’s ability to adapt. Metrics such as response time, resource utilization (like CPU and memory), and overall throughput are carefully monitored to assess how well the adaptive strategies perform in changing environments.
Specific benchmarks, such as TPC-H or TPC-DS, often include scenarios that require the DBMS to adapt on-the-fly. These scenarios may involve joining large tables or performing aggregations under different data distributions. By observing how effectively a system can pivot its execution plans in response to real-time data changes, developers can evaluate the strengths and weaknesses of its adaptive query optimization capabilities. This practical evaluation helps developers choose the right DBMS for their needs, ensuring optimal performance for their applications.