Robustness metrics for relational query execution plans

Abstract

The quality of query execution plans in database systems determines how fast a query can be executed. It has been shown that conventional query optimization still selects suboptimal or even bad execution plans, due to errors in the cardinality estimation. Although cardinality estimation errors are an evident problem, they are in general not considered in the selection of query execution plans. In this paper, we present three novel metrics for the robustness of relational query execution plans w.r.t. cardinality estimation errors. We also present a novel plan selection strategy that takes both, estimated cost and estimated robustness into account, when choosing a plan for execution. Finally, we share the results of our experimental comparison between robust and conventional plan selection on real world and synthetic benchmarks, showing a speedup of at most factor 3.49.

Robust query processing for linked data fragments

“…where the size of the intervals indicate the uncertainty of the optimizer [8]. Wolf et al [30] propose cardinality-based and selectivity-based robustness metrics for query plans. The core idea is computing the cost of a query plan as a function of the cardinality and selectivity estimations at all edges in the plan.…”

“…In contrast to [7] and [8], CROP has to rely on coarse grained statistics that do not allow for computing selectivity or cardinality estimation probabilities. Similar to [30], our measure computes the robustness of query plans and selects a robust plan from a selection of the cheapest plans.…”