Adaptive and Robust Query Processing with SHARP
Pedro Bizarro, David J. DeWitt · 2006
Database catalogs often do not contain enough statistical information to correctly cost all possible physical plans. In their absence, the optimizer can produce incorrect estimates and select sub-optimal plans for execution. To address this problem for a sub-class of queries, we propose SHARP, a new multi-join, adaptive, relational operator that joins three or more relations of a star-join. SHARP reduces the possible impact of optimizer mistakes by determining which plan to execute independently of optimization estimates. During normal query processing, SHARP collects statistics, and by using a combination of latebinding plan decisions and tuple routing strategies, it is able to change join order and table access methods. However, unlike previous tuple routing operators used for in-memory stream processing, SHARP was designed to process local relations with sizes much larger than available memory. We have implemented SHARP in the open-source DBMS Predator, and we present an extensive experimental evaluation showing the significant performance benefits of SHARP.