Improved histograms for selectivity estimation of range predicates

TLDR

Commercial database systems use histograms to summarize relation contents for efficient query size and cost estimation, yet a systematic comparison of histogram types and their design choices has never been performed. This work presents a comprehensive taxonomy of histogram types, introduces novel design choices, and derives new histogram variants. The authors construct the taxonomy, propose new dimension choices, combine them to create new histogram types, and demonstrate that sampling can lower construction cost. Empirical evaluation shows that certain newly defined histogram types achieve the best overall performance in range‑predicate selectivity estimation.

Abstract

Many commercial database systems maintain histograms to summarize the contents of relations and permit efficient estimation of query result sizes and access plan costs. Although several types of histograms have been proposed in the past, there has never been a systematic study of all histogram aspects, the available choices for each aspect, and the impact of such choices on histogram effectiveness. In this paper, we provide a taxonomy of histograms that captures all previously proposed histogram types and indicates many new possibilities. We introduce novel choices for several of the taxonomy dimensions, and derive new histogram types by combining choices in effective ways. We also show how sampling techniques can be used to reduce the cost of histogram construction. Finally, we present results from an empirical study of the proposed histogram types used in selectivity estimation of range predicates and identify the histogram types that have the best overall performance.