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Success of commercial query optimizers and database management systems (object-oriented or relational) depend on accurate cost estimation of various query reorderings [BGI]. Estimating predicate selectivity, or the fraction of rows in a database that satisfy a selection predicate, is key to determining the optimal join order. Previous work has concentrated on estimating selectivity for numeric fields [ASW, HaSa, IoP, LNS, SAC, WVT]. With the popularity of textual data being stored in databases, it has become important to estimate selectivity accurately for alphanumeric fields. A particularly problematic predicate used against alphanumeric fields is the SQL LIKE predicate [Dat]. Techniques used for estimating numeric selectivity are not suited for estimating alphanumeric selectivity.
In this paper, we study for the first time the problem of estimating alphanumeric selectivity in the presence of wildcards. Based on the intuition that the model built by a data compressor on an input text encapsulates information about common substrings in the text, we develop a technique based on the suffix tree data structure to estimate alphanumeric selectivity. In a statistics generation pass over the database, we construct a compact suffix tree-based structure from the columns of the database. We then look at three families of methods that utilize this structure to estimate selectivity during query plan costing, when a query with predicates on alphanumeric attributes contains wildcards in the predicate.
We evaluate our methods empirically in the context of the TPC-D benchmark. We study our methods experimentally against a variety of query patterns and identify five techniques that hold promise.