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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
- 6429
- Venue
- SIGMOD
- Year
- 2022
- Pagerank
- 5.9915268e-05
- Overall Rank
- 4,687 | 67.43%
- 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 |
| 4,592 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
6.056004e-05 |
| 5,339 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5596755e-05 |
| 5,654 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3882121e-05 |
| 6,883 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
4.8918682e-05 |
| 8,378 |
Towards Building Autonomous Data Services on Azure |
2023 |
SIGMOD |
4.5275731e-05 |
| 8,579 |
Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All? |
2025 |
CIDR |
4.4877266e-05 |
| 8,660 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
4.4680058e-05 |
| 9,107 |
BASE: Bridging the Gap between Cost and Latency for Query Optimization |
2023 |
VLDB |
4.3907944e-05 |
| 9,581 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
4.3186744e-05 |
| 9,709 |
QO-Insight: Inspecting Steered Query Optimizers |
2023 |
VLDB |
4.2951473e-05 |
| 9,980 |
Survivorship Bias in Industrial Database Workloads |
2026 |
CIDR |
4.1905499e-05 |
| 10,112 |
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,219 |
Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,501 |
Intra-Query Runtime Elasticity for Cloud-Native Data Analysis |
2025 |
SIGMOD |
4.1905499e-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 |
0.00084679526 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 101 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049778866 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036859633 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 371 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.00025382677 |
| 634 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018844568 |
| 662 |
Database Tuning Advisor for Microsoft SQL Server 2005 |
2004 |
VLDB |
0.00018478597 |
| 796 |
SageDB: A Learned Database System |
2019 |
CIDR |
0.00016541749 |
| 1,856 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00010319105 |
| 2,080 |
Towards a Learning Optimizer for Shared Clouds |
2019 |
VLDB |
9.5954034e-05 |
| 2,467 |
CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads |
2011 |
VLDB |
8.7264908e-05 |
| 3,623 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
6.9017341e-05 |
| 4,171 |
Computation Reuse in Analytics Job Service at Microsoft |
2018 |
SIGMOD |
6.3800823e-05 |
| 5,994 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
5.2367998e-05 |
| 6,278 |
The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward |
2021 |
VLDB |
5.1241654e-05 |
| 6,298 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1182917e-05 |
| 7,652 |
Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward |
2021 |
VLDB |
4.6831938e-05 |
| 7,685 |
AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft |
2020 |
VLDB |
4.6753414e-05 |
| 8,196 |
SparkCruise: Workload Optimization in Managed Spark Clusters at Microsoft |
2021 |
VLDB |
4.5568952e-05 |
| 8,219 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
4.551524e-05 |
| 9,138 |
Phoebe: A Learning-based Checkpoint Optimizer |
2021 |
VLDB |
4.3842765e-05 |
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Adaptive Query Processing in the Looking Glass |
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AutoSteer: Learned Query Optimization for Any SQL Database |
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CIDR |
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| 6,471 |
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SIGMOD |
5.0438582e-05 |
| 2,244 |
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9.2095696e-05 |
| 9,709 |
QO-Insight: Inspecting Steered Query Optimizers |
2023 |
VLDB |
4.2951473e-05 |
| 5,994 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
5.2367998e-05 |