VLDB2022
AutoDI: Towards an Automatic Plan Regression Analysis
Hai Lan, Yuanjia Zhang, Zhifeng Bao, Yu Dong, Dongxu Huang, Liu Tang, Jian Zhang
Abstract
Manual analysis on plan regression is both labor-intensive and inefficient for a large query plan and numerous queries. In this paper, we demonstrate AutoDI, an automatic detection and inference tool that has been developed to investigate why a sub-optimal plan is obtained by analyzing two different plans of the same query. AutoDI consists of two main modules, Difference Finder and Inference. The former aims to find where the two plans are different, and the latter tries to obtain the reasons why the differences come out. In our demonstration, we use a real plan regression in TiDB to show how AutoDI works.