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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
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}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 18 of 18 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 2,765 | Instance-Optimized Data Layouts for Cloud Analytics Workloads | 2021 | SIGMOD |
| 2 | 10,747 | LakeHelm: Zero-Shot Lakehouse Advisor for Joint Engine-Format Selection and Configuration | 2026 | VLDB |
| 3 | 459 | Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores | 2020 | VLDB |
| 4 | 3,563 | Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift | 2023 | SIGMOD |
| 5 | 10,672 | PTO: A Workload-driven Predictive Table Optimizer for Lakehouse Systems | 2026 | SIGMOD |
| 6 | 6,920 | Petabyte-Scale Row-Level Operations in Data Lakehouses | 2024 | VLDB |
| 7 | 10,941 | Ultron: History-Based Query Optimization at Databricks | 2026 | VLDB |
| 8 | 10,080 | LiquidCache: Efficient Pushdown Caching for Cloud-Native Data Analytics | 2025 | VLDB |
| 9 | 5,348 | Adaptive and Robust Query Execution for Lakehouses at Scale | 2024 | VLDB |
| 10 | 9,021 | AutoComp: Automated Data Compaction for Log-Structured Tables in Data Lakes | 2025 | SIGMOD |