{"id":155558,"date":"2000-03-01T00:00:00","date_gmt":"2000-03-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/automating-statistics-management-for-query-optimizers\/"},"modified":"2018-10-16T19:59:33","modified_gmt":"2018-10-17T02:59:33","slug":"automating-statistics-management-for-query-optimizers","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/automating-statistics-management-for-query-optimizers\/","title":{"rendered":"Automating Statistics Management for Query Optimizers"},"content":{"rendered":"<div class=\"asset-content\">\n<p>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.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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. 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Therefore, database management systems are increasingly being used as an important component in applications. Yet, the problem of tuning database management systems for achieving required performance is significant, and results in high total cost of ownership (TCO). The goal of our research in the AutoAdmin project is to make database systems self-tuning and self-administering. 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