DOI: 10.66106/skygay.20250206 ISSN: 3105-7500

基于机器学习的数据库查询优化技术研究（Research on Database Query Optimization Technology Based on Machine Learning）

许幸 Xing Xu
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Abstract：As database scale and query complexity continue to grow, traditional query optimization methods based on rules and cost models exhibit significant limitations in adaptability and accuracy. This study focuses on the application of machine learning technologies in database query optimization by constructing a deep learning-based cost prediction model to achieve accurate estimation of query execution costs. Additionally, reinforcement learning algorithms are employed to dynamically optimize query plan selection strategies, thereby enhancing execution efficiency in complex query scenarios. Experimental results demonstrate that, compared to traditional methods, this approach reduces average query response time by 18.7% in the TPC-H benchmark test, significantly improving database system throughput and resource utilization. This provides a new technical pathway for query optimization in large-scale data processing scenarios.

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