Sampling-Based Query Re-Optimization
Summary: Sampling-based, low-cost post-processing detects likely cardinality-estimation errors in an optimizer’s plan and iteratively re-optimizes it with targeted sampling. Minimal integration burden—no changes to optimizer or evaluator—and low overhead demonstrated on PostgreSQL and two commercial systems. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Wentao Wu (University of Wisconsin)
- 2. Jeffrey F. Naughton (University of Wisconsin)
- 3. Harneet Singh (University of Wisconsin)
BibTeX Citation
@inproceedings{wu_sigmod16,
title = {{Sampling-Based Query Re-Optimization}},
author = {Wu, Wentao and Naughton, Jeffrey F. and Singh, Harneet},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2882914},
url = {https://dl.acm.org/doi/10.1145/2882903.2882914},
year = {2016}
}
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