一种基于深度强化学习的索引选择方法

Index Selection Method Based on Deep Reinforcement Learning

数据库索引是提高数据库的查询性能的重要方式之一.文中提出一种基于深度强化学习的索引选择方法,能够实现单列索引和多列索引的选择.该方法将索引选择问题和深度强化学习相结合,将索引选择过程建模为马尔科夫决策过程,首先利用索引评价规则生成候选索引,从而降低神经网络的维度,并且能生成多列索引,接着通过定义深度强化学习过程中数据库环境的状态表示、智能体的动作和奖励函数,从而充分考虑了索引之间可能的交互,实现在给定工作负载下选择满足限制条件下的最优的索引组合.实验结果表明,相较于当前经典的索引选择方法选择的索引组合,本文提出的索引选择方法选择出的索引组合能显著地提升数据库系统的查询性能.

Database indexing is one of the important ways to improve the query performance of database.In this paper,an index selection method based on deep reinforcement learning is proposed,which can realize the selection of single-column index and multi-column index.The method combines the index selection problem and deep reinforcement learning by modeling the index selection process as a Markovian decision process,firstly,using index evaluation rules to generate candidate indexes,thus reducing the dimensionality of the neural network,and being able to generate multi-column indexes.By defining the state representation of the database environment,the actions of the agent and the reward function during the deep reinforcement learning process,the possible interactions between the indexes are fully considered,and the optimal indexes under the given workload is selected.The experimental results show that the indexes selected by the proposed index selection method can significantly improve the query performance of the database system compared with the indexes selected by the current classical index selection method.