Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All?
Summary: Advocates Query Optimizer as a Service (QOaaS) in unified LakeHouse stacks to centralize workload-level optimization and enable multi-engine federation instead of per-engine or library-shared QOs. Shares experience extending Calcite/Cascades across SQL Server, Fabric DW, SCOPE and Spark prototypes, reporting early wins and substantial engineering and semantic challenges. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Rana Alotaibi (Saudi Data and Artificial Intelligence Authority)
- 2. Yuanyuan Tian (Microsoft)
- 3. Stefan Grafberger (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 4. Jesús Camacho-Rodríguez (Microsoft)
- 5. Nicolas Bruno (Microsoft)
- 6. Brian Kroth (Microsoft)
- 7. Sergiy Matusevych (Microsoft)
- 8. Ashvin Agrawal (Microsoft)
- 9. Mahesh Behera (Microsoft)
- 10. Ashit Gosalia (Microsoft)
- 11. Cesar Galindo-Legaria (Microsoft)
- 12. Milind Joshi (Microsoft)
- 13. Milan Potocnik (Microsoft)
- 14. Beysim Sezgin (Microsoft)
- 15. Xiaoyu Li (Microsoft)
- 16. Carlo Curino (Microsoft)
BibTeX Citation
@inproceedings{alotaibi_cidr25,
address = {Amsterdam, Netherlands},
series = {{CIDR} '25},
title = {{Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All?}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Alotaibi, Rana and Tian, Yuanyuan and Grafberger, Stefan and Camacho-Rodríguez, Jesús and Bruno, Nicolas and Kroth, Brian and Matusevych, Sergiy and Agrawal, Ashvin and Behera, Mahesh and Gosalia, Ashit and Galindo-Legaria, Cesar and Joshi, Milind and Potocnik, Milan and Sezgin, Beysim and Li, Xiaoyu and Curino, Carlo},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,587 | Maximus: A Modular Accelerated Query Engine for Data Analytics on Heterogeneous Systems | 2025 | SIGMOD | 5.7429023e-05 |
| 10,125 | End-to-End Declarative Data Analytics: Co-designing Engines, Interfaces, and Cloud Infrastructure | 2026 | CIDR | 5.0751052e-05 |
| 10,250 | AnyBlox: A Framework for Self-Decoding Datasets | 2025 | VLDB | 5.051964e-05 |
| 10,857 | Interoperable ACID Transactions for Open Table Formats | 2026 | VLDB | 4.9793485e-05 |
| 10,951 | QueryBrew: System-Agnostic SQL-to-SQL Query Optimization | 2026 | VLDB | 4.9793485e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 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 | 5,110 | Steering Query Optimizers: A Practical Take on Big Data Workloads | 2021 | SIGMOD |
| 2 | 10,125 | End-to-End Declarative Data Analytics: Co-designing Engines, Interfaces, and Cloud Infrastructure | 2026 | CIDR |
| 3 | 10,100 | Adaptive data transformations for QaaS | 2025 | CIDR |
| 4 | 379 | Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources | 2018 | SIGMOD |
| 5 | 2,088 | Query Optimization in Microsoft SQL Server PDW | 2012 | SIGMOD |
| 6 | 2,834 | Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings | 2020 | SIGMOD |
| 7 | 5,348 | Adaptive and Robust Query Execution for Lakehouses at Scale | 2024 | VLDB |
| 8 | 4,818 | Continuous Cloud-Scale Query Optimization and Processing | 2013 | VLDB |
| 9 | 3,949 | Deploying a Steered Query Optimizer in Production at Microsoft | 2022 | SIGMOD |
| 10 | 5,891 | Unified Query Optimization in the Fabric Data Warehouse | 2024 | SIGMOD |