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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
ha1b18f269314e3d0
Venue
SIGMOD
Year
2022
Pagerank
6.9051584e-05
Overall Rank
3,948 | 73.47%
DOI
10.1145/3514221.3526052

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zhang_sigmod22,
        title = {{Deploying a Steered Query Optimizer in Production at Microsoft}},
        author = {Zhang, Wangda and Interlandi, Matteo and Mineiro, Paul and Qiao, Shi and Ghazanfari, Nasim and Lie, Karlen and Friedman, Marc and Hosn, Rafah and Patel, Hiren and Jindal, Alekh},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3526052},
        url = {https://dl.acm.org/doi/10.1145/3514221.3526052},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 16 of 16 citing papers.

Rank Citing Paper Year Venue Pagerank
3,565 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.200937e-05
4,240 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.7064546e-05
4,677 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4721041e-05
5,700 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 6.028998e-05
6,579 Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All? 2025 CIDR 5.7421583e-05
7,546 Learned Offline Query Planning via Bayesian Optimization 2025 SIGMOD 5.4966669e-05
8,357 Towards Building Autonomous Data Services on Azure 2023 SIGMOD 5.3463255e-05
9,214 BASE: Bridging the Gap between Cost and Latency for Query Optimization 2023 VLDB 5.2054849e-05
9,635 Low Rank Learning for Offline Query Optimization 2025 SIGMOD 5.1453041e-05
10,035 QO-Insight: Inspecting Steered Query Optimizers 2023 VLDB 5.0901047e-05
10,319 Intra-Query Runtime Elasticity for Cloud-Native Data Analysis 2025 SIGMOD 5.0362412e-05
10,362 Survivorship Bias in Industrial Database Workloads 2026 CIDR 4.9769913e-05
10,607 SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer 2026 SIGMOD 4.9769913e-05
10,703 Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking 2026 SIGMOD 4.9769913e-05
10,932 TQP++: Bridging ML Compilers and Analytical Query Processing on GPUs 2026 VLDB 4.9769913e-05
10,950 Ultron: History-Based Query Optimization at Databricks 2026 VLDB 4.9769913e-05
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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
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061067652
30 SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets 2008 VLDB 0.00050475202
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046363107
78 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036675568
144 Neo: A Learned Query Optimizer 2019 VLDB 0.00029090793
224 Self-Driving Database Management Systems 2017 CIDR 0.00024011047
361 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00020000855
492 Database Tuning Advisor for Microsoft SQL Server 2005 2004 VLDB 0.00017406029
555 SageDB: A Learned Database System 2019 CIDR 0.0001650754
1,280 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011224914
1,432 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010676754
1,815 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5756946e-05
2,833 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9539771e-05
3,544 Computation Reuse in Analytics Job Service at Microsoft 2018 SIGMOD 7.2108612e-05
5,110 Steering Query Optimizers: A Practical Take on Big Data Workloads 2021 SIGMOD 6.2678118e-05
5,973 Towards instance-optimized data systems 2021 VLDB 5.9281867e-05
6,248 The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward 2021 VLDB 5.8345657e-05
7,356 PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! 2021 VLDB 5.5421826e-05
7,767 AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft 2020 VLDB 5.4550466e-05
7,813 Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward 2021 VLDB 5.4474823e-05
8,349 SparkCruise: Workload Optimization in Managed Spark Clusters at Microsoft 2021 VLDB 5.3485189e-05
9,367 Phoebe: A Learning-based Checkpoint Optimizer 2021 VLDB 5.1845217e-05
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