Interactive Plan Hints for Query Optimization
Summary: Interactive visual hints to steer the optimizer toward better plans. Demonstration shows a framework for visually specifying hints, far surpassing traditional hinting flexibility and usability, enabling DBAs to handle non-trivial scenarios. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Nicolas Bruno (Microsoft)
- 2. Surajit Chaudhuri (Microsoft)
- 3. Ravi Ramamurthy (Microsoft)
BibTeX Citation
@inproceedings{bruno_sigmod09,
title = {{Interactive Plan Hints for Query Optimization}},
author = {Bruno, Nicolas and Chaudhuri, Surajit and Ramamurthy, Ravi},
series = {{SIGMOD} '09},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1559845.1559976},
url = {https://dl.acm.org/doi/10.1145/1559845.1559976},
year = {2009}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,764 | Guided automated learning for query workload re-optimization | 2019 | VLDB | 5.6873334e-05 |
| 10,030 | QO-Insight: Inspecting Steered Query Optimizers | 2023 | VLDB | 5.0925155e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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