AutoSteer: Learned Query Optimization for Any SQL Database
Summary: AutoSteer: a portable, learning-based system that steers any SQL optimizer exposing tunable knobs by extending Bao with automated hint-set discovery and low-integration APIs for monolithic and disaggregated engines. Evaluated on PostgreSQL, Presto, Spark, MySQL and DuckDB, it outperforms native optimizers (up to ~40% for Presto), matches Bao while reducing human supervision, and ships open-source with a visual tool. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Christoph Anneser (Technical University of Munich)
- 2. Nesime Tatbul (Intel; Massachusetts Institute of Technology)
- 3. David Cohen (Intel)
- 4. Zhenggang Xu (Meta)
- 5. Prithviraj Pandian (Meta)
- 6. Nikolay Laptev (Meta)
- 7. Ryan Marcus (University of Pennsylvania)
BibTeX Citation
@article{anneser_vldb23,
title = {{AutoSteer: Learned Query Optimization for Any SQL Database}},
author = {Anneser, Christoph and Tatbul, Nesime and Cohen, David and Xu, Zhenggang and Pandian, Prithviraj and Laptev, Nikolay and Marcus, Ryan},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {3515--3527},
doi = {10.14778/3611540.3611544},
url = {https://doi.org/10.14778/3611540.3611544},
year = {2023}
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