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Deploying a Steered Query Optimizer in Production at Microsoft
Summary: Steers a query optimizer toward workload-specific plans by pushing exploration offline in QO-Advisor, deployed in production at Microsoft. Externalizes planning to an offline pipeline, budgets steering actions, avoids regressions, and enables default use on SCOPE workloads.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6428
- Venue
- SIGMOD
- Year
- 2022
- Pagerank
- 5.997226e-05
- Overall Rank
- 4,690 | 67.38%
- DOI
-
10.1145/3514221.3526052
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 14 of 14 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 4,593 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
6.0606891e-05 |
| 5,334 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5649836e-05 |
| 5,640 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3933314e-05 |
| 6,885 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
4.895386e-05 |
| 8,416 |
Towards Building Autonomous Data Services on Azure |
2023 |
SIGMOD |
4.5196199e-05 |
| 8,582 |
Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All? |
2025 |
CIDR |
4.492033e-05 |
| 8,659 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
4.4722928e-05 |
| 9,108 |
BASE: Bridging the Gap between Cost and Latency for Query Optimization |
2023 |
VLDB |
4.3950066e-05 |
| 9,587 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
4.3215645e-05 |
| 9,710 |
QO-Insight: Inspecting Steered Query Optimizers |
2023 |
VLDB |
4.299267e-05 |
| 9,981 |
Survivorship Bias in Industrial Database Workloads |
2026 |
CIDR |
4.1945683e-05 |
| 10,112 |
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,219 |
Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,491 |
Intra-Query Runtime Elasticity for Cloud-Native Data Analysis |
2025 |
SIGMOD |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 22 |
SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets |
2008 |
VLDB |
0.0008456613 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059038975 |
| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036721403 |
| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 359 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.0002592783 |
| 640 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018759152 |
| 661 |
Database Tuning Advisor for Microsoft SQL Server 2005 |
2004 |
VLDB |
0.00018481174 |
| 801 |
SageDB: A Learned Database System |
2019 |
CIDR |
0.00016505496 |
| 1,855 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00010315245 |
| 2,083 |
Towards a Learning Optimizer for Shared Clouds |
2019 |
VLDB |
9.5834572e-05 |
| 2,470 |
CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads |
2011 |
VLDB |
8.7333019e-05 |
| 3,625 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
6.9055212e-05 |
| 4,174 |
Computation Reuse in Analytics Job Service at Microsoft |
2018 |
SIGMOD |
6.3856219e-05 |
| 6,040 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
5.2412035e-05 |
| 6,261 |
The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward |
2021 |
VLDB |
5.1350714e-05 |
| 6,297 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1227886e-05 |
| 7,655 |
Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward |
2021 |
VLDB |
4.6872456e-05 |
| 7,684 |
AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft |
2020 |
VLDB |
4.6796855e-05 |
| 8,197 |
SparkCruise: Workload Optimization in Managed Spark Clusters at Microsoft |
2021 |
VLDB |
4.5607121e-05 |
| 8,220 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
4.5557328e-05 |
| 9,194 |
Phoebe: A Learning-based Checkpoint Optimizer |
2021 |
VLDB |
4.3761777e-05 |
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SIGMOD |
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