Improved selectivity estimation by combining knowledge from sampling and synopses
Magnus Müller, Guido Moerkotte, Oliver Kolb · Proceedings of the VLDB Endowment · 2018
Estimating selectivities remains a critical task in query processing. Optimizers rely on the accuracy of selectivities when generating execution plans and, in approximate query answering, estimated selectivities affect the quality of the result. Many systems maintain synopses, e.g., histograms, and, in addition, provide sampling facilities. In this paper, we present a novel approach to combine knowledge from synopses and sampling for the purpose of selectivity estimation for conjunctive queries. We first show how to extract information from synopses and sampling such that they are mutually consistent. In a second step, we show how to combine them and decide on an admissible selectivity estimate. We compare our approach to state-of-the-art methods and evaluate the strengths and limitations of each approach.