Answering complex SQL queries using automatic summary tables

TLDR

Modern decision‑support queries involve complex joins, arithmetic, user‑defined functions, multidimensional aggregation, and nested subqueries, and their large data volumes and interactive response requirements make materialized views essential for acceptable performance. This study investigates how materialized views can be used to answer SQL queries. We propose a novel algorithm that rewrites a user query to access one or more existing materialized views, extending prior work by handling complex expressions, multidimensional aggregation, and nested subqueries through a graphical representation and bottom‑up pair‑wise node matching between query and view graphs. The approach delivers modularity and extensibility, enabling rewriting of a broad class of queries.

Abstract

We investigate the problem of using materialized views to answer SQL queries. We focus on modern decision-support queries, which involve joins, arithmetic operations and other (possibly user-defined) functions, aggregation (often along multiple dimensions), and nested subqueries. Given the complexity of such queries, the vast amounts of data upon which they operate, and the requirement for interactive response times, the use of materialized views (MVs) of similar complexity is often mandatory for acceptable performance. We present a novel algorithm that is able to rewrite a user query so that it will access one or more of the available MVs instead of the base tables. The algorithm extends prior work by addressing the new sources of complexity mentioned above, that is, complex expressions, multidimensional aggregation, and nested subqueries. It does so by relying on a graphical representation of queries and a bottom-up, pair-wise matching of nodes from the query and MV graphs. This approach offers great modularity and extensibility, allowing for the rewriting of a large class of queries.