Prototype Validation of the Rectangular Attribute Cardinality Map for Query Optimization in Database Systems

Abstract

Current business database systems utilize histograms to approximate frequency distributions of attribute values of relations. These are used to efficiently estimate query result sizes and access plan costs and thus minimize the query response time for business (and non-commercial) database systems. In two recent works [10, 11] we proposed two new forms of histogram-like techniques called the Rectangular and Trapezoidal Attribute Cardinality Maps (ACM) respectively, that give much smaller estimation errors than the traditional equi-width and equi-depth histograms currently being used by many commercial database systems. In [10, 11] we also provided a fairly extensive mathematical analysis for their average and worst case errors for their frequency estimates — which, in turn, were verified for synthetic data.

This paper reports the prototype validation for the Rectangular- ACM (R-ACM) for query optimization in real-world database systems. By using an extensive set of experiments using real-life data [1,2], we demonstrate that the R-ACM scheme is much more accurate than the traditional histograms for query result size estimation. We anticipate that it could become an invaluable tool for query optimization in the future.