{"id":155390,"date":"2008-08-01T00:00:00","date_gmt":"2008-08-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-pay-as-you-go-framework-for-query-execution-feedback\/"},"modified":"2018-10-16T19:56:59","modified_gmt":"2018-10-17T02:56:59","slug":"a-pay-as-you-go-framework-for-query-execution-feedback","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-pay-as-you-go-framework-for-query-execution-feedback\/","title":{"rendered":"A Pay-As-You-Go Framework for Query Execution Feedback"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Past work has suggested that query execution feedback can be useful in improving the quality of plans by correcting cardinality estimation errors in the query optimizer. The state-of-the-art approach for obtaining execution feedback is \u201cpassive\u201d monitoring which records the cardinality of each operator in the execution plan. We observe that there are many cases where even after repeated executions of the same query with use of feedback from passive monitoring, suboptimal choices in the execution plan cannot be corrected. We present a novel \u201cpay-as-you-go\u201d framework in which a query potentially incurs a small overhead on each execution but obtains cardinality information that is not available with passive monitoring alone. Such a framework can significantly extend the reach of query execution feedback in obtaining better plans. We have implemented our techniques in Microsoft SQL Server, and our evaluation on real world and synthetic queries suggests that plan quality can improve significantly compared to passive monitoring even at low overheads.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Past work has suggested that query execution feedback can be useful in improving the quality of plans by correcting cardinality estimation errors in the query optimizer. The state-of-the-art approach for obtaining execution feedback is \u201cpassive\u201d monitoring which records the cardinality of each operator in the execution plan. We observe that there are many cases where [&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":"","msr-author-ordering":[{"type":"user_nicename","value":"surajitc"},{"type":"user_nicename","value":"viveknar"},{"type":"user_nicename","value":"ravirama"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"VLDB","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"All articles published in this journal are protected by copyright, which covers the exclusive rights to reproduce and distribute the article (e.g., as offprints), as well as all translation rights. 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For cloud database service providers, the importance of query optimization is amplified due to the scale (e.g., millions of databases hosted) and variety of different workloads for which the query optimizer is expected to work well \"out-of-the-box\". Query optimization is challenging due to the richness of SQL queries that contain&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/967236"}]}},{"ID":169456,"post_title":"AutoAdmin","post_name":"autoadmin","post_type":"msr-project","post_date":"2001-11-02 14:41:11","post_modified":"2019-02-05 12:04:17","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/autoadmin\/","post_excerpt":"Database management systems provide functionality that is central to developing business applications. 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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