Selectivity Estimation for Range Predicates using Lightweight Models - Dutt, Anshuman; Wang, Chi; Nazi, Azade; Kandula, Srikanth; Narasayya, Vivek; Chaudhuri, Surajit (45th International Conference on Very Large Data Bases (VLDB 2019), 2019)
Query optimizers depend on selectivity estimates of query predicates to produce a good execution plan. When a query contains multiple predicates, today's optimizers use a variety of assumptions, such as independence between predicates, to estimate selectivity. While such techniques have the benefit of fast estimation and small memory footprint, they often incur large selectivity estimation errors. In this work, we reconsider selectivity estimation as a regression problem. We explore application of neural networks and tree-based ensembles to the important problem of selectivity estimation of multi-dimensional range predicates. While a straightforward solution does not outperform baseline, we propose two simple yet effective design choices, i.e., regression label transformation and feature engineering, motivated by the selectivity estimation context. Through extensive empirical evaluation across a variety of datasets, we show that the proposed models deliver both highly accurate estimates as well as fast estimation.