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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
6490
Venue
SIGMOD
Year
2022
Pagerank
6.9676473e-05
Overall Rank
3,998 | 72.58%
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 14 of 14 citing papers.

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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
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
30 SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets 2008 VLDB 0.00051174276
43 The Case for Learned Index Structures 2018 SIGMOD 0.00046060254
86 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00035316107
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
234 Self-Driving Database Management Systems 2017 CIDR 0.00023810722
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
501 Database Tuning Advisor for Microsoft SQL Server 2005 2004 VLDB 0.0001738508
568 SageDB: A Learned Database System 2019 CIDR 0.0001641553
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011361878
1,468 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010686496
1,904 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5040429e-05
2,822 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 8.0898536e-05
3,605 Computation Reuse in Analytics Job Service at Microsoft 2018 SIGMOD 7.2640711e-05
5,059 Steering Query Optimizers: A Practical Take on Big Data Workloads 2021 SIGMOD 6.3807509e-05
5,974 Towards instance-optimized data systems 2021 VLDB 6.0230488e-05
6,121 The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward 2021 VLDB 5.9688569e-05
7,619 AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft 2020 VLDB 5.5810604e-05
7,661 Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward 2021 VLDB 5.5736026e-05
8,040 PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! 2021 VLDB 5.5018396e-05
8,175 SparkCruise: Workload Optimization in Managed Spark Clusters at Microsoft 2021 VLDB 5.4737932e-05
9,224 Phoebe: A Learning-based Checkpoint Optimizer 2021 VLDB 5.3035811e-05
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