Query optimization using restructured views: Theory and experiments

Abstract

We study optimization of relational queries using materialized views, where views may be regular or restructured. In a restructured view, some data from the base table(s) are represented as metadata—that is, schema information, such as table and attribute names—or vice versa. Using restructured views in query optimization opens up a new spectrum of views that were not previously available, and can result in significant additional savings in query-evaluation costs. These savings can be obtained due to a significantly larger set of views to choose from, and may involve reduced table sizes, elimination of self-joins, clustering produced by restructuring, and horizontal partitioning. In this paper we propose a general query-optimization framework that treats regular and restructured views in a uniform manner and is applicable to SQL select-project-join queries and views without or with aggregation. Within the framework we provide (1) algorithms to determine when a view (regular or restructured) is usable in answering a query and (2) algorithms to rewrite queries using usable views. Semantic information, such as knowledge of the key of a view, can be used to further optimize a rewritten query. Within our general query-optimization framework, we develop techniques for determining the key of a (regular or restructured) view, and show how this information can be used to further optimize a rewritten query. It is straightforward to integrate all our algorithms and techniques into standard query-optimization algorithms. Our extensive experimental results illustrate how using restructured views (in addition to regular views) in query optimization can result in a significant reduction in query-processing costs compared to a system that uses only regular views.