III: Small: Novel Paradigms for Automated Index Tuning

索引对数据库系统的性能有至关重要的影响，因此为工作负载创建正确的索引(也称为索引调优)是数据库管理中的一项重要任务。因此，自动索引调优技术已成为管理员不可或缺的工具。然而，现有技术的目标是在单个机器上托管单个数据库的简化场景，这与在实践中观察到的更复杂的系统体系结构不匹配，例如具有虚拟数据库的多租户系统或无共享的并行数据库。这种不匹配会导致未充分利用可用系统资源的次优调优。此外，大多数技术要求管理员详细了解预期的数据库工作负载，当工作负载具有不可预测的特征(例如，特别数据分析中的工作负载)时，这是不可行的。另一种选择是在线分析工作负载的技术，但这种技术承担了对索引维护决策的完全控制，基本上绕过了管理员。拟议的项目开发了新的索引调整技术，以解决上述缺点。第一个贡献是一种新的调优范例，它将在线工作负载分析与管理员的反馈和交互结合在一起，从而结合了以前方法的最佳功能。理论基础是在线优化领域已有结果的新扩展。随后，该范例专门用于计算集群和虚拟数据库上的数据库，这是以前的技术没有涵盖的两种实用体系结构。该项目将影响计算机科学专业学生在数据库系统方面的教育，并将产生更有效的数据库管理工具。欲了解更多信息，请访问http://www.cs.ucsc.edu/~alkis/tuning.的项目页面

Indexes impact crucially the performance of a database system, and hence creating the right indexes for a workload, also known as index tuning, is an important task in database administration. Automated index tuning techniques have thus become an indispensable tool for administrators. However, existing techniques target the simplified scenario of a single database hosted on a single machine, which does not match the more complex system architectures observed in practice, e.g., multi-tenant systems with virtualized databases, or shared-nothing parallel databases. This mismatch results in suboptimal tuning that underutilizes the available system resources. Moreover, the majority of techniques require the administrator to have detailed knowledge of the anticipated database workload, which is not feasible when the workload has unpredictable characteristics (e.g., workload in ad-hoc data analysis). An alternative is techniques that analyze the workload online, but such techniques assume total control of index-maintenance decisions and essentially sidestep the administrator. The proposed project develops novel index tuning techniques that address the aforementioned shortcomings. The first contribution is a new tuning paradigm that couples online workload analysis with feedback and interaction from the administrator, thus combining the best features of previous approaches. The theoretical foundation is a novel extension of previous results from the field of online optimization. Subsequently, the paradigm is specialized for databases on compute clusters and virtualized databases, two practical architectures that are not covered by previous techniques. The project will impact the education of Computer Science students in database systems and will lead to more effective tools for database administration. For further information see the project page at http://www.cs.ucsc.edu/~alkis/tuning.