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A Simple Algorithm for Worst Case Optimal Join and Sampling

Authors Florent Capelli , Oliver Irwin , Sylvain Salvati

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Author Details

Florent Capelli
  • Université d'Artois, CNRS, UMR 8188 - CRIL, F-62300 Lens, France
Oliver Irwin
  • Université de Lille, CNRS, Inria, UMR 9189 - CRIStAL, F-59000 Lille, France
Sylvain Salvati
  • Université de Lille, CNRS, Inria, UMR 9189 - CRIStAL, F-59000 Lille, France

Funding

This work was supported by project ANR KCODA, ANR-20-CE48-0004.

Cite As Get BibTex

Florent Capelli, Oliver Irwin, and Sylvain Salvati. A Simple Algorithm for Worst Case Optimal Join and Sampling. In 28th International Conference on Database Theory (ICDT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 328, pp. 23:1-23:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ICDT.2025.23

Abstract

We present an elementary branch and bound algorithm with a simple analysis of why it achieves worstcase optimality for join queries on classes of databases defined respectively by cardinality or acyclic degree constraints. We then show that if one is given a reasonable way for recursively estimating upper bounds on the number of answers of the join queries, our algorithm can be turned into algorithm for uniformly sampling answers with expected running time Õ(UP/OUT) where UP is the upper bound, OUT is the actual number of answers and Õ(⋅) ignores polylogarithmic factors. Our approach recovers recent results on worstcase optimal join algorithm and sampling in a modular, clean and elementary way.

Subject Classification

ACM Subject Classification
  • Information systems → Relational database model
  • Theory of computation → Branch-and-bound
Keywords
  • join queries
  • worst-case optimality
  • uniform sampling

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References

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