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}
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