一句话：提出基于子群发现的 SQL 工作负载分析方法，自动发现导致查询变慢等问题的模式并提供交互可视化。
数据库管理员在分析查询工作负载、识别 schema 问题与性能瓶颈时，往往难以自动找出仅共享某些属性且同时影响执行时间等目标度量的查询子集。本文引入一种基于子群发现（Subgroup Discovery）的原创方法，将该通用数据挖掘框架实例化到 SQL 工作负载分析中，模式定义在查询子句、环境变量、数据库告警和指标的组合上，并开发交互式可视化工具支持知识发现。作者分析公司数百个数据库的一周工作负载，公开数据集与源码，实验表明该方法能发现具有洞察力的假设，有助于定位慢查询、高 I/O 等问题的根因。
Among daily tasks of database administrators (DBAs), the analysis of query workloads to identify schema issues and improving performances is crucial. Although DBAs can easily pinpoint queries repeatedly causing performance issues, it remains challenging to automatically identify subsets of queries that share some properties only (a pattern) and simultaneously foster some target measures, such as execution time. Patterns are defined on combinations of query clauses, environment variables, database alerts and metrics and help answer questions like what makes SQL queries slow? What makes I/O communications high? Automatically discovering these patterns in a huge search space and providing them as hypotheses for helping to localize issues and root-causes is important in the context of explainable AI. To tackle it, we introduce an original approach rooted on Subgroup Discovery. We show how to instantiate and develop this generic data-mining framework to identify potential causes of SQL workloads issues. We believe that such data-mining technique is not trivial to apply for DBAs. As such, we also provide a visualization tool for interactive knowledge discovery. We analyse a one week workload from hundreds of databases from our company, make both the dataset and source code available, and experimentally show that insightful hypotheses can be discovered.
@inproceedings{paperbot6634,
title = {"What makes my queries slow?": Subgroup Discovery for SQL Workload Analysis},
author = {Youcef Remil and Anes Bendimerad and Romain Mathonat and Philippe Chaleat and Mehdi Kaytoue},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678915}
}