QO-Insight: Inspecting Steered Query Optimizers

Summary: Presents QO-Insight, a visual analytics tool for exploring execution traces of steered query optimizers that accept per-query hints to correct planner mistakes. Enables DBAs to open the black box, qualitatively diagnose steering effects and improve steering strategies. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13417
Venue: VLDB
Year: 2023
Pagerank: 5.2094004e-05
Overall Rank: 9,846 | 32.45%
DOI: 10.14778/3611540.3611586

Incoming Non-self Citations Over Time

Authors

1. Christoph Anneser (Technical University of Munich)
2. Mario Petruccelli (Technical University of Munich)
3. Nesime Tatbul (Intel; Massachusetts Institute of Technology)
4. David Cohen (Intel)
5. Zhenggang Xu (Meta)
6. Prithviraj Pandian (Meta)
7. Nikolay Laptev (Meta)
8. Ryan Marcus (University of Pennsylvania)
9. Alfons Kemper (Technical University of Munich)

BibTeX Citation

@article{anneser_vldb23,
        title = {{QO-Insight: Inspecting Steered Query Optimizers}},
        author = {Anneser, Christoph and Petruccelli, Mario and Tatbul, Nesime and Cohen, David and Xu, Zhenggang and Pandian, Prithviraj and Laptev, Nikolay and Marcus, Ryan and Kemper, Alfons},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {12},
        pages = {3922--3925},
        doi = {10.14778/3611540.3611586},
        url = {https://doi.org/10.14778/3611540.3611586},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,929	AutoSteer: Learned Query Optimization for Any SQL Database	2023	VLDB	6.4423294e-05
10,834	QOVIS: Understanding and Diagnosing Query Optimizer via a Visualization-assisted Approach	2025	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
378	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019638121
1,604	The Picasso Database Query Optimizer Visualizer	2010	VLDB	0.00010230973
3,998	Deploying a Steered Query Optimizer in Production at Microsoft	2022	SIGMOD	6.9676473e-05
4,929	AutoSteer: Learned Query Optimization for Any SQL Database	2023	VLDB	6.4423294e-05
4,961	A SQL Debugger Built from Spare Parts: Turning a SQL:1999 Database System into Its Own Debugger	2015	SIGMOD	6.4276648e-05
5,059	Steering Query Optimizers: A Practical Take on Big Data Workloads	2021	SIGMOD	6.3807509e-05
5,881	MOCHA: A Tool for Visualizing Impact of Operator Choices in Query Execution Plans for Database Education	2022	VLDB	6.0529753e-05
6,056	QE3D: Interactive Visualization and Exploration of Complex, Distributed Query Plans	2015	SIGMOD	5.9925637e-05
6,504	What Makes a Good Physical Plan? — Experiencing Hardware-Conscious Query Optimization with Candomble	2016	SIGMOD	5.8585206e-05
7,448	Interactive Plan Hints for Query Optimization	2009	SIGMOD	5.6147364e-05
7,614	I-Rex: An Interactive Relational Query Explainer for SQL	2020	VLDB	5.5815004e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	5,059	Steering Query Optimizers: A Practical Take on Big Data Workloads	2021	SIGMOD
2	1,073	A General Framework for the Optimization of Object-Oriented Queries	1992	SIGMOD
3	290	An Overview of Query Optimization in Relational Systems	1998	PODS
4	2,944	Query Optimizers: Time to Rethink the Contract?	2009	SIGMOD
5	5,329	SeeDB: Visualizing Database Queries Efficiently	2014	VLDB
6	7,156	The Case for Deep Query Optimisation	2020	CIDR
7	4,929	AutoSteer: Learned Query Optimization for Any SQL Database	2023	VLDB
8	7,448	Interactive Plan Hints for Query Optimization	2009	SIGMOD
9	3,998	Deploying a Steered Query Optimizer in Production at Microsoft	2022	SIGMOD
10	10,834	QOVIS: Understanding and Diagnosing Query Optimizer via a Visualization-assisted Approach	2025	VLDB