为什么我的查询速度变慢了？
Why Did My Query Slow Down?
摘要：
许多企业环境的数据库都运行在网络连接的服务器存储基础设施（称为存储区域网络或SAN）上。数据库和SAN都是复杂的系统，需要各自独立的管理团队。本文提出了一个创新的管理框架，以简化需要深入了解数据库和SAN的管理任务。作为一个具体的实例，我们考虑诊断多次执行的数据库查询的性能下降的任务（例如，在周期性报告生成设置中）。此任务非常具有挑战性，因为可能的原因包括特定于数据库的问题、特定于SAN的问题以及由于两个系统之间的交互而出现的问题。此外，这些系统提供的监测数据可能存在噪声。我们描述了DIADS的设计，这是一个集成的诊断工具，数据库和SAN管理员。DIADS生成并使用一个名为注释计划图（Annotated Plan Graphs，APG）的强大抽象，它将数据库和SAN中查询的执行路径联系在一起。DIADS使用将特定领域知识与机器学习技术相结合的创新工作流，成功地应用于诊断PostgreSQL数据库和生产SAN中事件的复杂组合所导致的查询速度降低。
英文摘要：
Many enterprise environments have databases running on network-attached server-storage infrastructure (referred to as Storage Area Networks or SANs). Both the database and the SAN are complex systems that need their own separate administrative teams. This paper puts forth the vision of an innovative management framework to simplify administrative tasks that require an in-depth understanding of both the database and the SAN. As a concrete instance, we consider the task of diagnosing the slowdown in performance of a database query that is executed multiple times (e.g., in a periodic report-generation setting). This task is very challenging because the space of possible causes includes problems specific to the database, problems specific to the SAN, and problems that arise due to interactions between the two systems. In addition, the monitoring data available from these systems can be noisy. We describe the design of DIADS which is an integrated diagnosis tool for database and SAN administrators. DIADS generates and uses a powerful abstraction called Annotated Plan Graphs (APGs) that ties together the execution path of queries in the database and the SAN. Using an innovative workflow that combines domain-specific knowledge with machine-learning techniques, DIADS was applied successfully to diagnose query slowdowns caused by complex combinations of events across a PostgreSQL database and a production SAN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
