Exploiting hardware performance counters with flow and context sensitive profiling
Proceedings of the ACM SIGPLAN 1997 conference on Programming language design and implementation
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
Diagnosing Estimation Errors in Page Counts Using Execution Feedback
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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The query optimizer models data distribution and access paths to make the optimal plan choice for a given query. Sometimes the plan selection is poor because of modeling limitations, outdated statistics, incorrect optimization heuristics, etc. Hence it is useful to examine the plan choice made by the optimizer from an execution perspective and to impose validation rules on the actual execution plan to evaluate plan suitability. This approach treats the optimizer as a black box. The plan validation is based on the queries and data instead of the optimizer implementation details. This paper describes {XPC}, a rule-based tool for Microsoft SQL Server [1] that helps users and developers achieve a better understanding of plan performance. We apply ideas similar to code profilers [2] to examine plan execution performance along with heuristic rules to the actual execution profile and probe for inefficiencies. This paper describes the overview and implementation of {XPC} and presents rules showing how {XPC} is useful in targeting plan performance issues.