Automating physical database design has remained a long-term interest in database research due to substantial performance gains afforded by optimised structures. Despite significant progress, a majority of today's commercial solutions are highly manual, requiring offline invocation by database administrators (DBAs) who are expected to identify and supply representative training workloads. Even the latest advancements like query stores provide only limited support for dynamic environments. This status quo is untenable: identifying representative static workloads is no longer realistic; and physical design tools remain susceptible to the query optimiser's cost misestimates. Furthermore, modern application environments such as hybrid transactional and analytical processing (HTAP) systems render analytical modelling next to impossible. We propose a self-driving approach to online index selection that eschews the DBA and query optimiser, and instead learns the benefits of viable structures through strategic exploration and direct performance observation. We view the problem as one of sequential decision making under uncertainty, specifically within the bandit learning setting. Multi-armed bandits balance exploration and exploitation to provably guarantee average performance that converges to policies that are optimal with perfect hindsight. Our comprehensive empirical evaluation against a state-of-the-art commercial tuning tool demonstrates up to 75% speed-up on shifting and ad-hoc workloads and up to 28% speed-up on static workloads in analytical processing environments. In HTAP environments, our solution provides up to 59% speed-up on shifting and 51% speed-up on static workloads. Furthermore, our bandit framework outperforms deep reinforcement learning (RL) in terms of convergence speed and performance volatility (providing up to 58% speed-up).

中文翻译：

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没有 DBA？没有遗憾！多臂老虎机，用于对具有可证明保证的分析和 HTAP 工作负载进行索引调整

由于优化结构提供了显着的性能提升，自动化物理数据库设计一直是数据库研究的长期兴趣。尽管取得了重大进展，但当今的大多数商业解决方案都是高度手动的，需要数据库管理员 (DBA) 进行离线调用，他们希望识别和提供有代表性的培训工作负载。即使像查询存储这样的最新进展也只能为动态环境提供有限的支持。这种现状是站不住脚的：识别有代表性的静态工作负载不再现实；和物理设计工具仍然容易受到查询优化器成本错误估计的影响。此外，诸如混合事务和分析处理 (HTAP) 系统之类的现代应用程序环境使分析建模几乎不可能实现。我们提出了一种自动驱动的在线索引选择方法，它避开了 DBA 和查询优化器，而是通过战略探索和直接性能观察来了解可行结构的好处。我们将此问题视为不确定性下的顺序决策之一，特别是在强盗学习环境中。多臂老虎机平衡探索和开发，以可证明地保证平均性能收敛到完美的事后最佳策略。我们针对最先进的商业调整工具进行的综合实证评估表明，在分析处理环境中，转移和临时工作负载的速度提高了 75%，静态工作负载的速度提高了 28%。在 HTAP 环境中，我们的解决方案提供高达 59% 的转换加速和 51% 的静态工作负载加速。此外，我们的老虎机框架在收敛速度和性能波动方面优于深度强化学习 (RL)（提供高达 58% 的加速）。