ROME: Robust Query Optimization via Parallel Multi-Plan Execution

Summary: ROME: non-intrusive robust query processing via parallel multi-plan execution on top of any SQL engine. Select complementary plans to hedge cardinality-estimation errors; first finisher wins. Cost-based greedy/exhaustive plan selection via diversity/probabilistic overhead models. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID: h87fa625e7d83f360
Venue: SIGMOD
Year: 2024
Pagerank: 5.3030089e-05
Overall Rank: 8,550 | 42.69%
DOI: 10.1145/3654973

Incoming Non-self Citations Over Time

Authors

1. Ziyun Wei (Cornell University)
2. Immanuel Trummer (Cornell University)

BibTeX Citation

@inproceedings{wei_sigmod24,
        title = {{ROME: Robust Query Optimization via Parallel Multi-Plan Execution}},
        author = {Wei, Ziyun and Trummer, Immanuel},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3654973},
        url = {https://dl.acm.org/doi/10.1145/3654973},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,767	PAR2QO: Parametric Penalty-Aware Robust Query Optimization	2025	VLDB	5.115551e-05
10,812	OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning	2026	VLDB	4.9605343e-05
10,998	Real-time SQL Plan Management in Oracle	2026	VLDB	4.9605343e-05
11,011	Ultron: History-Based Query Optimization at Databricks	2026	VLDB	4.9605343e-05

Outgoing Citations (Sorted by Pagerank)

Showing 27 of 27 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
15	How Good Are Query Optimizers, Really?	2016	VLDB	0.00061463175
53	Eddies: Continuously Adaptive Query Processing	2000	SIGMOD	0.00040739114
84	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035920657
98	LEO - DB2's LEarning Optimizer	2001	VLDB	0.00034050614
142	Neo: A Learned Query Optimizer	2019	VLDB	0.00029114937
320	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00021128745
355	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00020146245
453	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.0001801482
568	Towards a Robust Query Optimizer: A Principled and Practical Approach	2005	SIGMOD	0.00016221103
838	Proactive Re-Optimization	2005	SIGMOD	0.00013528646
1,080	Multi-dimensional Selectivity Estimation Using Compressed Histogram Information	1999	SIGMOD	0.00012114187
1,124	Least Expected Cost Query Optimization: An Exercise in Utility	1999	PODS	0.00011898749
1,156	QuickSel: Quick Selectivity Learning with Mixture Models	2020	SIGMOD	0.00011759683
1,261	Sampling-Based Query Re-Optimization	2016	SIGMOD	0.00011289724
1,483	Skew in Parallel Query Processing	2014	PODS	0.00010500209
1,609	SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning	2019	SIGMOD	0.0001007196
1,622	AniPQO: Almost Non-intrusive Parametric Query Optimization for Nonlinear Cost Functions	2003	VLDB	0.00010038595
1,831	Least Expected Cost Query Optimization: What Can We Expect?	2002	PODS	9.5366955e-05
1,987	Plan Bouquets: Query Processing without Selectivity Estimation	2014	SIGMOD	9.2323722e-05
3,531	Identifying Robust Plans through Plan Diagram Reduction	2008	VLDB	7.2065332e-05
3,785	Parallelizing Query Optimization	2008	VLDB	7.0012696e-05
4,202	Kepler: Robust Learning for Faster Parametric Query Optimization	2023	SIGMOD	6.7258607e-05
4,817	Permutable Compiled Queries: Dynamically Adapting Compiled Queries without Recompiling	2021	VLDB	6.388743e-05
6,154	Parallelizing Extensible Query Optimizers	2009	SIGMOD	5.8543039e-05
6,405	QUEST: An Exploratory Approach to Robust Query Processing	2014	VLDB	5.7791099e-05
6,812	dbET: Execution Time Distribution-based Plan Selection	2023	SIGMOD	5.6597129e-05
9,656	Parallelizing Query Optimization on Shared-Nothing Architectures	2016	VLDB	5.1303051e-05

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