{"id":154366,"date":"2006-01-01T00:00:00","date_gmt":"2006-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/scalable-exploration-of-physical-database-design\/"},"modified":"2018-10-16T20:38:25","modified_gmt":"2018-10-17T03:38:25","slug":"scalable-exploration-of-physical-database-design","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/scalable-exploration-of-physical-database-design\/","title":{"rendered":"Scalable Exploration of Physical Database Design"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Physical database design is critical to the performance of a large-scale DBMS. The corresponding automated design tuning tools need to select the best physical design from a large set of candidate designs quickly. However, for large workloads, evaluating the cost of each query in the workload for every candidate does not scale. To overcome this, we present a novel comparison primitive that only evaluates a fraction of the workload and provides an accurate estimate of the likelihood of selecting correctly. We show how to use this primitive to construct accurate and scalable selection procedures. Furthermore, we address the issue of ensuring that the estimates are conservative, even for highly skewed cost distributions. The proposed techniques are evaluated through a prototype implementation inside a commercial physical design tool.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Physical database design is critical to the performance of a large-scale DBMS. The corresponding automated design tuning tools need to select the best physical design from a large set of candidate designs quickly. However, for large workloads, evaluating the cost of each query in the workload for every candidate does not scale. To overcome this, [&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":[13555],"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-154366","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"IEEE Computer Society","msr_edition":"22nd International Conference on Data Engineering","msr_affiliation":"","msr_published_date":"2006-01-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"209248","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"ICDE06-final.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/ICDE06-final.pdf","id":209248,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":209248,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/ICDE06-final.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"chrisko","user_id":31427,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=chrisko"},{"type":"text","value":"Shubha U. Nabar","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[957177],"msr_project":[169456],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"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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