III: Small: Novel Paradigms for Automated Index Tuning

III：小：自动索引调整的新颖范式

• 批准号: 1018914
• 负责人: Neoklis Polyzotis
• 金额: $ 49.97万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018914&HistoricalAwards=false
• 关键词: III; Small; Novel; Paradigms; Automated

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.

索引对数据库系统的性能有着至关重要的影响，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，现有技术的目标是托管在单个机器上的单个数据库的简化场景，这与实践中观察到的更复杂的系统架构不匹配，例如，具有虚拟化数据库或无共享并行数据库的多租户系统。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，这在工作负载具有不可预测的特性（例如，ad-hoc数据分析中的工作负载）。另一种方法是在线分析工作负载的技术，但这种技术假定对索引维护决策具有完全控制权，并且基本上避开了管理员。拟议的项目开发新的索引调优技术，解决上述缺点。第一个贡献是一个新的调优范例，它将在线工作负载分析与来自管理员的反馈和交互耦合在一起，从而结合了以前方法的最佳特性。理论基础是在线优化领域先前成果的新颖扩展。随后，该范例专门用于计算集群上的数据库和虚拟化数据库，这两种实用的架构没有被以前的技术所覆盖。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，并将导致更有效的数据库管理工具。欲了解更多信息，请访问项目页面http://www.cs.ucsc.edu/~alkis/tuning。