{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adaptive-cardinality-estimation","title":"Adaptive Cardinality Estimation","arxiv_id":"1711.08330","date":"2017-11-22","proceeding":null,"authors":["Oleg Ivanov","Sergey Bartunov"],"abstract":"In this paper we address cardinality estimation problem which is an important\nsubproblem in query optimization. Query optimization is a part of every\nrelational DBMS responsible for finding the best way of the execution for the\ngiven query. These ways are called plans. The execution time of different plans\nmay differ by several orders, so query optimizer has a great influence on the\nwhole DBMS performance. We consider cost-based query optimization approach as\nthe most popular one. It was observed that cost-based optimization quality\ndepends much on cardinality estimation quality. Cardinality of the plan node is\nthe number of tuples returned by it.\n  In the paper we propose a novel cardinality estimation approach with the use\nof machine learning methods. The main point of the approach is using query\nexecution statistics of the previously executed queries to improve cardinality\nestimations. We called this approach adaptive cardinality estimation to reflect\nthis point. The approach is general, flexible, and easy to implement. The\nexperimental evaluation shows that this approach significantly increases the\nquality of cardinality estimation, and therefore increases the DBMS performance\nfor some queries by several times or even by several dozens of times.","url_abs":"http://arxiv.org/abs/1711.08330v1","url_pdf":"http://arxiv.org/pdf/1711.08330v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adaptive-cardinality-estimation","repo_url":"https://github.com/tigvarts/aqo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"adaptive-cardinality-estimation","repo_url":"https://github.com/postgrespro/aqo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}