Arnd Christian König, Bolin Ding, Surajit Chaudhuri, and Vivek R. Narasayya
The need for accurate SQL progress estimation in the context of decision support administration has led to a number of techniques proposed for this task. Unfortunately, no single one of these progress estimators behaves robustly across the variety of SQL queries encountered in practice, meaning that each technique performs poorly for a signiﬁcant fraction of queries. This paper proposes a novel estimator selection framework that uses a statistical model to characterize the sets of conditions under which certain estimators outperform others, leading to a signiﬁcant increase in estimation robustness. The generality of this framework also enables us to add a number of novel “special purpose” estimators which increase accuracy further. Most importantly, the resulting model generalizes well to queries very different from the ones used to train it. We validate our ﬁndings using a large number of industrial real-life and benchmark workloads.
In Proceedings of the VLDB Endowment, the 38th International Conference on Very Large Data Bases (VLDB 2012)
Publisher Very Large Data Bases Endowment Inc.