Abstract
Steered query optimizers address the planning mistakes of traditional query optimizers by providing them with hints on a per-query basis, thereby guiding them in the right direction. This paper introduces QO-Insight, a visual tool designed for exploring query execution traces of such steered query optimizers. Although steered query optimizers are typically perceived as black boxes, QO-Insight empowers database administrators and experts to gain qualitative insights and enhance their performance through visual inspection and analysis.
Publisher
Association for Computing Machinery (ACM)
Subject
General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development
Reference14 articles.
1. AutoSteer: Learned Query Optimization for Any SQL Database;Anneser Christoph;PVLDB,2023
2. Nicolas Bruno , Surajit Chaudhuri , and Ravishankar Ramamurthy . 2009 . Interactive Plan Hints for Query Optimization. In SIGMOD Conference. ACM, 1043--1046 . Nicolas Bruno, Surajit Chaudhuri, and Ravishankar Ramamurthy. 2009. Interactive Plan Hints for Query Optimization. In SIGMOD Conference. ACM, 1043--1046.
3. A SQL Debugger Built from Spare Parts
4. The Picasso database query optimizer visualizer
5. Query optimization through the looking glass, and what we found running the Join Order Benchmark
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献