Ultron: History-Based Query Optimization at Databricks
Summary: Ultron brings history-based, subplan-level query optimization to Databricks’ massive lakehouse, compensating for sparse statistics by learning from repetitive workloads. Its drift-aware, three-tier architecture safely complements AQE, improving eligible join latency 25% in production. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Supun Nakandala (Databricks)
- 2. RK Korlapati (Databricks)
- 3. Eric Liang (Databricks)
- 4. Yunjia Zhang (Databricks)
- 5. Yuhao Zhang (Databricks)
- 6. Andrey Gubichev (Databricks)
- 7. Kelvin Jiang (Databricks)
- 8. Yannis Sismanis (Databricks)
- 9. Mostafa Mokhtar (Databricks)
- 10. Sid Taneja (Databricks)
- 11. Vuk Ercegovac (Databricks)
- 12. Amir Hormati (Databricks)
- 13. Amit Shukla (Databricks)
- 14. Michalis Petropoulos (Databricks)
- 15. Reynold Xin (Databricks)
- 16. Ryan Marcus (University of Pennsylvania)
BibTeX Citation
@article{nakandala_vldb26,
title = {{Ultron: History-Based Query Optimization at Databricks}},
author = {Nakandala, Supun and Korlapati, RK and Liang, Eric and Zhang, Yunjia and Zhang, Yuhao and Gubichev, Andrey and Jiang, Kelvin and Sismanis, Yannis and Mokhtar, Mostafa and Taneja, Sid and Ercegovac, Vuk and Hormati, Amir and Shukla, Amit and Petropoulos, Michalis and Xin, Reynold and Marcus, Ryan},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4358--4371},
doi = {10.14778/3827998.3828038},
url = {https://doi.org/10.14778/3827998.3828038},
year = {2026}
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