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)

Paper ID: 4264
Venue: SIGMOD
Year: 2009
Pagerank: 5.6147364e-05
Overall Rank: 7,448 | 48.91%
DOI: 10.1145/1559845.1559976

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
6,938	Guided automated learning for query workload re-optimization	2019	VLDB	5.733869e-05
9,846	QO-Insight: Inspecting Steered Query Optimizers	2023	VLDB	5.2094004e-05

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.

Rank	Cited Paper	Year	Venue	Pagerank

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