{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T09:00:04Z","timestamp":1775638804950,"version":"3.50.1"},"reference-count":75,"publisher":"Association for Computing Machinery (ACM)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:p>\n            Cardinality estimation (CardEst) plays a significant role in generating high-quality query plans for a query optimizer in DBMS. In the last decade, an increasing number of advanced CardEst methods (especially ML-based) have been proposed with outstanding estimation accuracy and inference latency. However, there exists no study that systematically evaluates the quality of these methods and answer the fundamental problem:\n            <jats:italic>to what extent can these methods improve the performance of query optimizer in real-world settings, which is the ultimate goal of a CardEst method.<\/jats:italic>\n          <\/jats:p>\n          <jats:p>In this paper, we comprehensively and systematically compare the effectiveness of CardEst methods in a real DBMS. We establish a new benchmark for CardEst, which contains a new complex real-world dataset STATS and a diverse query workload STATS-CEB. We integrate multiple most representative CardEst methods into an open-source DBMS PostgreSQL, and comprehensively evaluate their true effectiveness in improving query plan quality, and other important aspects affecting their applicability. We obtain a number of key findings under different data and query settings. Furthermore, we find that the widely used estimation accuracy metric (Q-Error) cannot distinguish the importance of different sub-plan queries during query optimization and thus cannot truly reflect the generated query plan quality. Therefore, we propose a new metric P-Error to evaluate the performance of CardEst methods, which overcomes the limitation of Q-Error and is able to reflect the overall end-to-end performance of CardEst methods. It could serve as a better optimization objective for future CardEst methods.<\/jats:p>","DOI":"10.14778\/3503585.3503586","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T22:18:07Z","timestamp":1649974687000},"page":"752-765","source":"Crossref","is-referenced-by-count":94,"title":["Cardinality estimation in DBMS"],"prefix":"10.14778","volume":"15","author":[{"given":"Yuxing","family":"Han","sequence":"first","affiliation":[{"name":"Alibaba Group"}]},{"given":"Ziniu","family":"Wu","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Peizhi","family":"Wu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}]},{"given":"Rong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Jingyi","family":"Yang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}]},{"given":"Liang Wei","family":"Tan","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}]},{"given":"Kai","family":"Zeng","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Gao","family":"Cong","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}]},{"given":"Yanzhao","family":"Qin","sequence":"additional","affiliation":[{"name":"Alibaba Group and Peking University"}]},{"given":"Andreas","family":"Pfadler","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Zhengping","family":"Qian","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Jingren","family":"Zhou","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Jiangneng","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Bin","family":"Cui","sequence":"additional","affiliation":[{"name":"Peking University"}]}],"member":"320","published-online":{"date-parts":[[2022,4,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Nicolas Bruno Surajit Chaudhuri and Luis Gravano. 2001. 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