AutoLiquid: Autonomic Data Layout Optimization for the Databricks Lakehouse
Summary: AutoLiquid autonomously selects and applies Liquid Clustering keys from scan telemetry, using sampled shadow verification to prevent regressions. Deployed at Databricks, it manages millions of tables and matches or improves customer-chosen layouts on 95%+ of tables. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Supun Nakandala (Databricks)
- 2. Naga Raju Bhanoori (Databricks)
- 3. Yunjia Zhang (Databricks)
- 4. Parimarjan Negi (Databricks)
- 5. Ankur Sharma (Databricks)
- 6. Eric Liang (Databricks)
- 7. Cindy Jiang (Databricks)
- 8. Sirui Sun (Databricks)
- 9. Terry Kim (Databricks)
- 10. Mostafa Mokhtar (Databricks)
- 11. Vijayan Prabhakaran (Databricks)
- 12. Bart Samwel (Databricks)
- 13. Sunitha Beeram (Databricks)
- 14. Siddharth Taneja (Databricks)
- 15. Amir Hormati (Databricks)
- 16. Amit Shukla (Databricks)
- 17. Michalis Petropoulos (Databricks)
- 18. Reynold Xin (Databricks)
BibTeX Citation
@article{nakandala_vldb26,
title = {{AutoLiquid: Autonomic Data Layout Optimization for the Databricks Lakehouse}},
author = {Nakandala, Supun and Bhanoori, Naga Raju and Zhang, Yunjia and Negi, Parimarjan and Sharma, Ankur and Liang, Eric and Jiang, Cindy and Sun, Sirui and Kim, Terry and Mokhtar, Mostafa and Prabhakaran, Vijayan and Samwel, Bart and Beeram, Sunitha and Taneja, Siddharth and Hormati, Amir and Shukla, Amit and Petropoulos, Michalis and Xin, Reynold},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4023--4035},
doi = {10.14778/3827998.3828013},
url = {https://doi.org/10.14778/3827998.3828013},
year = {2026}
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