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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
6.9052796e-05
Overall Rank
3,949 | 73.46%
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
3,563 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.2042148e-05
4,258 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.6994722e-05
4,683 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4716143e-05
5,871 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 5.9639223e-05
6,576 Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All? 2025 CIDR 5.7448779e-05
8,332 Learned Offline Query Planning via Bayesian Optimization 2025 SIGMOD 5.3528188e-05
8,352 Towards Building Autonomous Data Services on Azure 2023 SIGMOD 5.3488341e-05
9,276 BASE: Bridging the Gap between Cost and Latency for Query Optimization 2023 VLDB 5.204289e-05
9,670 Low Rank Learning for Offline Query Optimization 2025 SIGMOD 5.1452097e-05
10,030 QO-Insight: Inspecting Steered Query Optimizers 2023 VLDB 5.0925155e-05
10,309 Intra-Query Runtime Elasticity for Cloud-Native Data Analysis 2025 SIGMOD 5.0386264e-05
10,350 Survivorship Bias in Industrial Database Workloads 2026 CIDR 4.9793485e-05
10,596 SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer 2026 SIGMOD 4.9793485e-05
10,693 Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking 2026 SIGMOD 4.9793485e-05
10,923 TQP++: Bridging ML Compilers and Analytical Query Processing on GPUs 2026 VLDB 4.9793485e-05
10,941 Ultron: History-Based Query Optimization at Databricks 2026 VLDB 4.9793485e-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 0.00061066921
30 SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets 2008 VLDB 0.00050495102
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046284649
78 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036684414
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
224 Self-Driving Database Management Systems 2017 CIDR 0.00024013745
362 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019989474
491 Database Tuning Advisor for Microsoft SQL Server 2005 2004 VLDB 0.00017413042
555 SageDB: A Learned Database System 2019 CIDR 0.00016506678
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011226878
1,433 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010677711
1,815 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5791737e-05
2,834 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9560627e-05
3,545 Computation Reuse in Analytics Job Service at Microsoft 2018 SIGMOD 7.2134803e-05
5,110 Steering Query Optimizers: A Practical Take on Big Data Workloads 2021 SIGMOD 6.269351e-05
5,974 Towards instance-optimized data systems 2021 VLDB 5.9305575e-05
6,245 The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward 2021 VLDB 5.837329e-05
7,363 PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! 2021 VLDB 5.5418564e-05
7,759 AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft 2020 VLDB 5.4575614e-05
7,806 Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward 2021 VLDB 5.4500623e-05
8,346 SparkCruise: Workload Optimization in Managed Spark Clusters at Microsoft 2021 VLDB 5.3510511e-05
9,358 Phoebe: A Learning-based Checkpoint Optimizer 2021 VLDB 5.1869771e-05
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