On the Calculation of Optimality Ranges for Relational Query Execution Plans

Abstract

Cardinality estimation is a crucial task in query optimization and typically relies on heuristics and basic statistical approximations. At execution time, estimation errors might result in situations where intermediate result sizes may differ from the estimated ones, so that the originally chosen plan is not the optimal plan anymore. In this paper we analyze the deviation from the estimate, and denote the cardinality range of an intermediate result, where the optimal plan remains optimal as the optimality range. While previous work used simple heuristics to calculate similar ranges, we generate the precise bounds for the optimality range considering all relevant plan alternatives. Our experimental results show that the fixed optimality ranges used in previous work fail to characterize the range of cardinalities where a plan is optimal. We derive theoretical worst case bounds for the number of enumerated plans required to compute the precise optimality range, and experimentally show that in real queries this number is significantly smaller. Our experiments also show the benefit for applications like Mid-Query Re-Optimization in terms of significant execution time improvement.