论文标题
使用无模型增强学习的自动化数据库索引
Automated Database Indexing using Model-free Reinforcement Learning
论文作者
论文摘要
为有效查询配置数据库是一项复杂的任务，通常由数据库管理员执行。解决真正优化数据库访问的构建索引的问题需要大量的数据库和域知识，缺乏通常会导致浪费空间和内存的无关索引，这可能会损害查询数据库性能以及肯定会降级性能以进行更新。我们开发了一个架构，以通过使用强化学习来自动索引数据库的问题来通过在数据库的整个生命周期中索引数据来优化查询。在我们的实验评估中，与增强学习和遗传算法相关的工作相比，我们的体系结构表现出卓越的性能，保持近乎最佳的索引配置并有效地扩展到大型数据库。
Configuring databases for efficient querying is a complex task, often carried out by a database administrator. Solving the problem of building indexes that truly optimize database access requires a substantial amount of database and domain knowledge, the lack of which often results in wasted space and memory for irrelevant indexes, possibly jeopardizing database performance for querying and certainly degrading performance for updating. We develop an architecture to solve the problem of automatically indexing a database by using reinforcement learning to optimize queries by indexing data throughout the lifetime of a database. In our experimental evaluation, our architecture shows superior performance compared to related work on reinforcement learning and genetic algorithms, maintaining near-optimal index configurations and efficiently scaling to large databases.
