Algorithms for index-assisted selectivity estimation

Abstract

The standard mechanisms for query selectivity estimation used in relational database systems rely on properties that are specific to the attribute types. The query optimizer in an extensible database system is, in general, unable to exploit these mechanisms for user-defined types, forcing the database extender to invent new estimation mechanisms. In this paper, we discuss extensions to the generalized search tree (GiST) that simplify the creation of user-defined selectivity estimation methods. An experimental comparison of such methods with multidimensional estimators from the literature has demonstrated very competitive results.