CAREER: Mining Hints from Text Documents to Guide Automated Database Performance Tuning

数据库管理系统；也就是说，处理和管理大型数据集的系统被广泛应用于几乎所有工业部门。它们的性能取决于各种调优决策，这些决策决定了系统如何在内部处理数据。对于外行用户来说，很难找到优化性能的设置。这激发了自动数据库调优工具的创建，这些工具试图为它们找到最佳设置。但是，数据库调优的关键信息通常以自然语言文本的形式提供，包括数据库手册、描述数据集的文本文档，以及以数据库为中心的Internet论坛上的讨论。目前，自动化工具无法从这样的文本中获益，使它们效率低下。这个项目旨在创建自动数据库调优工具，从各种文本文档中提取有用的调优信息。通过提高自动化调优工具的质量，该项目增强了外行用户的能力，减少了对行业中高度专业化工人的需求，目前导致员工短缺，阻碍了新技术的采用。与此同时，该项目旨在创建新的教学产品，帮助教育下一代数据专业人士。该项目分为两个主要研究重点，致力于对数据库系统调优最有用的两类文本文档：关于数据集的文本和关于数据库管理系统的文本。基于转换器的语言模型将用于从这些文本文档中提取相关信息。由此产生的见解可以以多种方式用于数据库调优：在调优之前指导数据分析操作，改进用于调优的成本模型，或者限制调优选择的搜索空间。该项目将探索所有这些选项，将从文本中获得的见解与其他信息来源（例如，对特定调优选择进行性能测量的试运行）结合起来。该项目将考虑一组典型的数据库调优问题，包括，例如，选择辅助索引数据结构以最佳支持数据处理的问题，以及为数据库系统配置参数找到最优值的问题。所有项目成果将集成到一个软件包中，用于自动数据库调优，使用文本文档作为输入。这个软件将向公众发布。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Database management systems; that is, systems that process and manage large data sets, are used widely, across virtually all sectors of industry. Their performance depends on a variety of tuning decisions, determining how the system processes data internally. For lay users, it is very hard to find settings that optimize performance. This has motivated the creation of automated database tuning tools that try to find optimal settings for them. However, crucial information for database tuning is often available in the form of natural language text, including, for instance, the database manual, text documents describing data sets, as well as discussions on database-centric Internet forums. Currently, automated tools are unable to benefit from such text, making them inefficient. This project aims at creating automated database tuning tools that extract useful information for tuning from a variety of text documents. By increasing the quality of automated tuning tools, the project empowers lay users and reduces the need for highly specialized workers in industry, currently causing staff shortages and hampering the adoption of new technology. At the same time, the project aims at the creation of new teaching offerings, helping to educate the next generation of data professionals.The project is divided into two primary research thrusts, dedicated to the two categories of text documents that are most useful for database system tuning: text about data sets and text about database management systems. Transformer-based language models will be used to extract relevant information from such text documents. The resulting insights can be used in multiple ways for database tuning: to guide data profiling operations prior to tuning, to refine cost models used for tuning, or to restrict the search space of tuning choices. The project will explore all of those options, combining insights gained from text with other sources of information (e.g., trial runs that result in performance measurements for specific tuning choices). The project will consider a representative set of classical database tuning problems, including, for instance, the problem of selecting auxiliary index data structures to optimally support data processing, as well as the problem of finding optimal values for database system configuration parameters. All project outcomes will be integrated into a software package for automated database tuning, using text documents as input. This software will be released to the public.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.