Leveraging Query Logs and Machine Learning for Parametric Query Optimization
Summary: Decouples PQO cache population from plan selection: log-driven optimization compactly chooses cached plans, while ML rapidly maps parameter instances to the best plan. An execution-aware selector also mitigates optimizer cost-estimate errors, delivering low suboptimality and microsecond-scale latency. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kapil Vaidya (Massachusetts Institute of Technology)
- 2. Anshuman Dutt (Microsoft)
- 3. Vivek Narasayya (Microsoft)
- 4. Surajit Chaudhuri (Microsoft)
BibTeX Citation
@article{vaidya_vldb22,
title = {{Leveraging Query Logs and Machine Learning for Parametric Query Optimization}},
author = {Vaidya, Kapil and Dutt, Anshuman and Narasayya, Vivek and Chaudhuri, Surajit},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {3},
pages = {401--413},
doi = {10.14778/3494124.3494126},
url = {https://doi.org/10.14778/3494124.3494126},
year = {2022}
}
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