[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123443-en":3,"doc-seo-123443-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123443,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Advances and Challenges in Machine Learning-Based Cardinality Estimation for Database Query Optimization","Accurate cardinality estimation is essential for database query optimization, since it determines how many tuples match predicates and strongly influences the optimizer’s execution plan. Traditional techniques often break down under complex query structures, skewed data distributions, and high-dimensional schemas. This review surveys recent machine learning-based cardinality estimation, grouping methods into query-driven, data-driven, and hybrid models, then analyzes architectures, training strategies, and predictive behavior.","Advances and Challenges in Machine LearningBased Cardinality Estimation for Database Query Optimization  \nZhibin Guan  \nSchool of Software Engineering, Sichuan University, Chengdu, China  \nAbstract. Accurate cardinality estimation is critical for optimizing database queries, yet traditional methods often fail to provide reliable predictions in the face of complex queries, skewed data distributions, and high-dimensional schemas. As data volumes and query complexity grow, more robust and adaptive estimation techniques have become essential for maintaining efficient query performance. This paper surveys recent advancements in machine learning-based cardinality estimation methods, categorizing them into three main types: query-driven, data-driven, and hybrid models. Each approach is analyzed in terms of its model architecture, training strategies, and predictive performance. Notable techniques include PostCENN’s integration into PostgreSQL, FACE’s use of normalizing flows, and UAE’s autoregressive learning across data and query distributions.  \nComparative evaluations highlight how these models address specific challenges in cardinality estimation. The results show that while machine learning methods significantly reduce estimation errors, they also share limitations such as sensitivity to workload drift, scalability challenges with large schemas, and poor generalization in long-tail query regions. To address these, this paper proposed future directions including Bayesian updating, sparse factor graph modeling, and active query synthesis. These innovations hold promise for building more accurate, scalable, and adaptive estimators that can enhance database system performance under diverse and dynamic workloads.  \n1 Introduction  \nDatabase management systems play a critical role in modern information technology, serving as the backbone for data storage, retrieval, and manipulation across countless applications. One of the most important tasks in database query optimization is cardinality estimation, which refers to the process of predicting how many tuples will satisfy a given query predicate. Accurate cardinality estimation is essential because it directly impacts the efficiency of query execution plans selected by the optimizer. When estimations are inaccurate, query processors may choose suboptimal execution plans, resulting in slow query response times and excessive resource consumption. Therefore, exploring more effective methods for cardinality  \n[2022141461143@stu.scu.edu.cn](2022141461143@stu.scu.edu.cn)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nestimation has become an urgent research focus in the database community as data volumes continue to grow exponentially.  \nTraditionally, database systems have relied on Alon, Martias and Szegedy (AMS)-based methods for cardinality estimation. The pioneering work by Flajolet introduced the approach of estimating cardinalities in the range of millions with a comparatively high accuracy using only 􀝈􀝋􀝃2 􀝈􀝋􀝃2 of memory [1] . Subsequently, Flajolet proposed more sophisticated techniques such as SuperLogLog [1], and HyperLogLog [2] . Additionally, Heule developed the HyperLogLog++ algorithm that could decrease the space cost and increase the estimation accuracy of the original algorithm [3] . However, these traditional approaches suffer from several limitations, including the curse of dimensionality, inability to capture complex data correlations, and poor performance on skewed data distributions. Furthermore, their effectiveness deteriorates significantly when dealing with complex queries involving multiple joins and predicates.  \nIn recent years, researchers have made significant progress in applying machine learning to cardinality estimation, advancing the field through three main te","cbCaimihV8Y0Tbrd","https://ap.wps.com/l/cbCaimihV8Y0Tbrd","pdf",299697,1,"English","en",105,"# Introduction\n## Cardinality estimation and its impact on query optimization\n## Traditional estimation methods and limitations\n## Machine learning approaches: query-driven, data-driven, hybrid","[{\"question\":\"What does cardinality estimation mean in database query optimization?\",\"answer\":\"Cardinality estimation predicts how many tuples satisfy a query predicate. Its accuracy directly affects the execution plans chosen by the optimizer.\"},{\"question\":\"Why do traditional cardinality estimation methods struggle?\",\"answer\":\"They have limitations such as inability to capture complex correlations, poor behavior on skewed distributions, and significant degradation for queries with multiple joins and predicates.\"},{\"question\":\"How does the paper categorize machine learning-based cardinality estimation methods?\",\"answer\":\"It groups them into three types: query-driven methods, data-driven approaches, and hybrid frameworks that balance both objectives.\"}]","Advances and Challenges in Machine Learning-Based Cardinality Estimation for Database Query Optimization | PDF",1785816547,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"advances-and-challenges-in-machine-learning-based-cardinality-estimation-for-database-query-optimization","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/advances-and-challenges-in-machine-learning-based-cardinality-estimation-for-database-query-optimization/123443/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What does cardinality estimation mean in database query optimization?","Question",{"text":74,"@type":75},"Cardinality estimation predicts how many tuples satisfy a query predicate. Its accuracy directly affects the execution plans chosen by the optimizer.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Why do traditional cardinality estimation methods struggle?",{"text":79,"@type":75},"They have limitations such as inability to capture complex correlations, poor behavior on skewed distributions, and significant degradation for queries with multiple joins and predicates.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the paper categorize machine learning-based cardinality estimation methods?",{"text":83,"@type":75},"It groups them into three types: query-driven methods, data-driven approaches, and hybrid frameworks that balance both objectives.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]