{"id":154459,"date":"2007-01-01T00:00:00","date_gmt":"2007-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/online-autoadmin-physical-design-tuning\/"},"modified":"2024-02-28T13:18:09","modified_gmt":"2024-02-28T21:18:09","slug":"online-autoadmin-physical-design-tuning","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/online-autoadmin-physical-design-tuning\/","title":{"rendered":"Online autoadmin: (physical design tuning)"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Existing solutions for the automated physical design problem require explicit invocations of tuning tools and critically depend on DBAs gathering representative workloads manually. In this demonstration, we show an alternative approach to the physical design problem. Specifically, we demonstrate a novel monitoring\/tuning DBMS component that we prototyped in Microsoft SQL Server 2005 as a server-side extension. This component is always-on and continuously modifies the current physical design reacting to varying workload or data characteristics. Our solution imposes low overhead and takes into account storage constraints, update statements, and the cost to create physical structures.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Existing solutions for the automated physical design problem require explicit invocations of tuning tools and critically depend on DBAs gathering representative workloads manually. In this demonstration, we show an alternative approach to the physical design problem. Specifically, we demonstrate a novel monitoring\/tuning DBMS component that we prototyped in Microsoft SQL Server 2005 as a server-side [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13563],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-154459","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"Association for Computing Machinery, 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