Efficient AI-Driven Query Optimization in Large-Scale Databases: A Reinforcement Learning and Graph-Based Approach
As data-centric applications become increasingly complex, understanding effective query optimization in large-scale relational databases is crucial for managing this complexity. Yet, traditional cost-based and heuristic approaches simply do not scale, adapt, or remain accurate in highly dynamic mult...
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MDPI AG
2025-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/11/1700 |
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| author | Najla Sassi Wassim Jaziri |
| author_facet | Najla Sassi Wassim Jaziri |
| author_sort | Najla Sassi |
| collection | DOAJ |
| description | As data-centric applications become increasingly complex, understanding effective query optimization in large-scale relational databases is crucial for managing this complexity. Yet, traditional cost-based and heuristic approaches simply do not scale, adapt, or remain accurate in highly dynamic multi-join queries. This research work proposes the reinforcement learning and graph-based hybrid query optimizer (GRQO), the first ever to apply reinforcement learning and graph theory for optimizing query execution plans, specifically in join order selection and cardinality estimation. By employing proximal policy optimization for adaptive policy learning and using graph-based schema representations for relational modeling, GRQO effectively traverses the combinatorial optimization space. Based on TPC-H (1 TB) and IMDB (500 GB) workloads, GRQO runs 25% faster in query execution time, scales 30% better, reduces CPU and memory use by 20–25%, and reduces the cardinality estimation error by 47% compared to traditional cost-based optimizers and machine learning-based optimizers. These findings highlight the ability of GRQO to optimize performance and resource efficiency in database management in cloud computing, data warehousing, and real-time analytics. |
| format | Article |
| id | doaj-art-c0ecd0c0096a48e2a4c0cc772dd02e23 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-c0ecd0c0096a48e2a4c0cc772dd02e232025-08-20T03:46:46ZengMDPI AGMathematics2227-73902025-05-011311170010.3390/math13111700Efficient AI-Driven Query Optimization in Large-Scale Databases: A Reinforcement Learning and Graph-Based ApproachNajla Sassi0Wassim Jaziri1Department of Management Information Systems, School of Business, King Faisal University, Hofuf 31982, Saudi ArabiaDepartment of Management Information Systems, School of Business, King Faisal University, Hofuf 31982, Saudi ArabiaAs data-centric applications become increasingly complex, understanding effective query optimization in large-scale relational databases is crucial for managing this complexity. Yet, traditional cost-based and heuristic approaches simply do not scale, adapt, or remain accurate in highly dynamic multi-join queries. This research work proposes the reinforcement learning and graph-based hybrid query optimizer (GRQO), the first ever to apply reinforcement learning and graph theory for optimizing query execution plans, specifically in join order selection and cardinality estimation. By employing proximal policy optimization for adaptive policy learning and using graph-based schema representations for relational modeling, GRQO effectively traverses the combinatorial optimization space. Based on TPC-H (1 TB) and IMDB (500 GB) workloads, GRQO runs 25% faster in query execution time, scales 30% better, reduces CPU and memory use by 20–25%, and reduces the cardinality estimation error by 47% compared to traditional cost-based optimizers and machine learning-based optimizers. These findings highlight the ability of GRQO to optimize performance and resource efficiency in database management in cloud computing, data warehousing, and real-time analytics.https://www.mdpi.com/2227-7390/13/11/1700query optimizationreinforcement learninggraph neural networksjoin order selectionlarge-scale databasesresource efficiency |
| spellingShingle | Najla Sassi Wassim Jaziri Efficient AI-Driven Query Optimization in Large-Scale Databases: A Reinforcement Learning and Graph-Based Approach Mathematics query optimization reinforcement learning graph neural networks join order selection large-scale databases resource efficiency |
| title | Efficient AI-Driven Query Optimization in Large-Scale Databases: A Reinforcement Learning and Graph-Based Approach |
| title_full | Efficient AI-Driven Query Optimization in Large-Scale Databases: A Reinforcement Learning and Graph-Based Approach |
| title_fullStr | Efficient AI-Driven Query Optimization in Large-Scale Databases: A Reinforcement Learning and Graph-Based Approach |
| title_full_unstemmed | Efficient AI-Driven Query Optimization in Large-Scale Databases: A Reinforcement Learning and Graph-Based Approach |
| title_short | Efficient AI-Driven Query Optimization in Large-Scale Databases: A Reinforcement Learning and Graph-Based Approach |
| title_sort | efficient ai driven query optimization in large scale databases a reinforcement learning and graph based approach |
| topic | query optimization reinforcement learning graph neural networks join order selection large-scale databases resource efficiency |
| url | https://www.mdpi.com/2227-7390/13/11/1700 |
| work_keys_str_mv | AT najlasassi efficientaidrivenqueryoptimizationinlargescaledatabasesareinforcementlearningandgraphbasedapproach AT wassimjaziri efficientaidrivenqueryoptimizationinlargescaledatabasesareinforcementlearningandgraphbasedapproach |