基于机器学习的数据库查询优化技术研究
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许幸
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眉山药科职业学院，四川眉山，620000
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摘要：随着数据库规模与查询复杂度的持续增长，传统基于规则与代价模型的查询优化方法在适应性与精准度上存在明显局限。该研究聚焦机器学习技术在数据库查询优化中的应用，通过构建基于深度学习的代价预测模型，实现对查询执行代价的精准预估；同时，利用强化学习算法动态优化查询计划选择策略，提升复杂查询场景下的执行效率。实验结果表明，相较于传统方法，该技术在TPC-H 基准测试中可将查询响应时间平均缩短18.7%，显著提升了数据库系统的吞吐量与资源利用率，为大规模数据处理场景下的查询优化提供了新的技术路径。
关健词：机器学习；数据库查询优化；代价预测；强化学习；查询计划；深度学习 |
Research on Database Query Optimization Technology Based on Machine Learning
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Xing Xu
Meishan Pharmaceutical College, Meishan Sichuan 620000 , China
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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.
Keywords : Machine Learning; Database Query Optimization; Cost Prediction; Reinforcement Learning; Query Plan; Deep Learning
参考文献
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