Surajit Chaudhuri and Vivek Narasayya
Statistics play a key role in influencing the quality of plans chosen by a database query optimizer. In this paper, we identify the statistics that are essential for an optimizer. We introduce novel techniques that help significantly reduce the set of statistics that need to be created without sacrificing the quality of query plans generated. We discuss how these techniques can be leveraged to automate statistics management in databases. We have implemented and experimentally evaluated our approach on Microsoft SQL Server 7.0.
|Published in||16th International Conference on Data Engineering|
|Publisher||IEEE Computer Society|
Copyright © 2007 IEEE. Reprinted from IEEE Computer Society. This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to email@example.com. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.