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

职业：从文本文档中挖掘提示来指导自动数据库性能调优

• 批准号: 2239326
• 负责人: Immanuel Trummer
• 金额: $ 59.49万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239326&HistoricalAwards=false
• 关键词: CAREER; Mining; Hints; Text; Documents

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.

数据库管理系统；也就是说，处理和管理大型数据集的系统在几乎所有工业部门中得到广泛使用。它们的性能取决于各种调整决策，决定系统如何在内部处理数据。对于非专业用户来说，很难找到优化性能的设置。这促使人们创建自动数据库调整工具，试图为其找到最佳设置。然而，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互联网论坛上的讨论。目前，自动化工具无法从此类文本中受益，从而导致效率低下。该项目旨在创建自动化数据库调整工具，从各种文本文档中提取有用的调整信息。通过提高自动调优工具的质量，该项目为非专业用户提供了支持，并减少了行业对高度专业化工人的需求，这些需求目前导致了人员短缺并阻碍了新技术的采用。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员。该项目分为两个主要研究方向，致力于对数据库最有用的两类文本文档系统调优：有关数据集的文本和有关数据库管理系统的文本。基于 Transformer 的语言模型将用于从此类文本文档中提取相关信息。由此产生的见解可以通过多种方式用于数据库调优：在调优之前指导数据分析操作、细化用于调优的成本模型或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源相结合（例如，针对特定调整选择进行性能测量的试运行）。该项目将考虑一组具有代表性的经典数据库调优问题，包括选择辅助索引数据结构以最佳支持数据处理的问题，以及寻找数据库系统配置参数的最佳值的问题。所有项目成果都将集成到一个软件包中，以使用文本文档作为输入进行自动数据库调整。该软件将向公众发布。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

DB-BERT: making database tuning tools “read” the manual

• DOI: 10.1007/s00778-023-00831-y
• 发表时间: 2023-12
• 期刊: The VLDB Journal
• 作者: Immanuel Trummer