Processing Star Queries on Hierarchically-Clustered Fact Tables

摘要

Star queries are the most prevalent kind of queries in data warehousing, OLAP and business intelligence applications. Thus, there is an imperative need for efficiently processing star queries. To this end, a new class of fact table organizations has emerged that exploits path-based surrogate keys in order to hierarchically cluster the fact table data of a star schema [DRSN98, MRB99, KS01]. In the context of these new organizations, star query processing changes radically. In this paper, we present a complete abstract processing plan that captures all the necessary steps in evaluating such queries over hierarchically clustered fact tables. Furthermore, we present optimizations for surrogate key processing and a novel early grouping transformation for grouping on the dimension hierarchies. Our algorithms have been already implemented in a commercial relational database management system (RDBMS) and the experimental evaluation, as well as customer feedback, indicates speed-ups of orders of magnitude for typical star queries in real world applications.