Benchmarks assess query planning efficiency by measuring how effectively a database system generates and executes optimal query execution plans. They do this by running standardized tests that simulate real-world workloads, then analyzing metrics like execution time, resource usage, and plan quality. For example, benchmarks such as TPC-H or TPC-DS use complex queries with joins, aggregations, and subqueries to stress-test the planner’s ability to select efficient access paths (e.g., index scans vs. full table scans) and join orders. These tests reveal how well the planner balances speed, accuracy, and resource constraints when generating plans.
Key metrics include query execution time, plan generation time, and the difference between estimated and actual resource usage (e.g., rows processed, memory consumption). Benchmarks also evaluate whether the planner adapts to data distribution changes, like skewed datasets or updated statistics. For instance, if a planner consistently underestimates the size of intermediate results, it might choose nested loop joins instead of hash joins, leading to slower execution. Tools like PostgreSQL’s EXPLAIN ANALYZE help compare the planner’s predictions against real-world performance, exposing inefficiencies in cost modeling or statistics usage. Additionally, benchmarks measure scalability by increasing query complexity or concurrency to see if planning time grows linearly or becomes a bottleneck.
A practical example is testing how a database handles a multi-table join with varying filter conditions. A benchmark might run this query with different data volumes and index configurations to see if the planner selects the optimal join order and access methods. For example, a good planner might switch from a merge join to a hash join when data exceeds memory limits. Another test could involve parameterized queries to check if the planner caches and reuses efficient plans appropriately. By isolating planning time from execution time, benchmarks also highlight scenarios where overly complex optimization strategies (e.g., exhaustive join order exploration) slow down overall performance, guiding developers to tune planning heuristics or caching mechanisms.