Steering Query Optimizers: A Practical Take on Big Data Workloads
Summary: Steering query optimizers for big data; Bao adapted to SCOPE. Introduces rule signatures, a pipeline for recurring configs, and a learning method for unseen workloads; evaluated on 150K daily jobs with 7–30% latency savings, up to 90% on subset. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Parimarjan Negi (Massachusetts Institute of Technology)
- 2. Matteo Interlandi (Microsoft)
- 3. Ryan Marcus (Intel; Massachusetts Institute of Technology)
- 4. Mohammad Alizadeh (Massachusetts Institute of Technology)
- 5. Tim Kraska (Massachusetts Institute of Technology)
- 6. Marc Friedman (Microsoft)
- 7. Alekh Jindal (Microsoft)
BibTeX Citation
@inproceedings{negi_sigmod21,
title = {{Steering Query Optimizers: A Practical Take on Big Data Workloads}},
author = {Negi, Parimarjan and Interlandi, Matteo and Marcus, Ryan and Alizadeh, Mohammad and Kraska, Tim and Friedman, Marc and Jindal, Alekh},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457568},
url = {https://dl.acm.org/doi/10.1145/3448016.3457568},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 22 of 22 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 9,342 | Deep Query Optimization | 2019 | SIGMOD |
| 2 | 4,069 | Dynamically Optimizing Queries over Large Scale Data Platforms | 2014 | SIGMOD |
| 3 | 5,850 | Modeling Shifting Workloads for Learned Database Systems | 2024 | SIGMOD |
| 4 | 3,057 | Towards a Hands-Free Query Optimizer through Deep Learning | 2019 | CIDR |
| 5 | 5,042 | Leveraging Query Logs and Machine Learning for Parametric Query Optimization | 2022 | VLDB |
| 6 | 4,677 | AutoSteer: Learned Query Optimization for Any SQL Database | 2023 | VLDB |
| 7 | 4,820 | Continuous Cloud-Scale Query Optimization and Processing | 2013 | VLDB |
| 8 | 2,833 | Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings | 2020 | SIGMOD |
| 9 | 6,293 | Incorporating Super-Operators in Big-Data Query Optimizers | 2020 | VLDB |
| 10 | 3,948 | Deploying a Steered Query Optimizer in Production at Microsoft | 2022 | SIGMOD |