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Degree Sequence Bound for Join Cardinality Estimation

Authors Kyle Deeds , Dan Suciu, Magda Balazinska, Walter Cai

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Author Details

Kyle Deeds
  • University of Washington, Seattle, WA, USA
Dan Suciu
  • University of Washington, Seattle, WA, USA
Magda Balazinska
  • University of Washington, Seattle, WA, USA
Walter Cai
  • University of Washington, Seattle, WA, USA

Funding

This work is supported by National Science Foundation grants NSF IIS 1907997 and NSF-BSF 2109922.

Cite AsGet BibTex

Kyle Deeds, Dan Suciu, Magda Balazinska, and Walter Cai. Degree Sequence Bound for Join Cardinality Estimation. In 26th International Conference on Database Theory (ICDT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 255, pp. 8:1-8:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ICDT.2023.8

Abstract

Recent work has demonstrated the catastrophic effects of poor cardinality estimates on query processing time. In particular, underestimating query cardinality can result in overly optimistic query plans which take orders of magnitude longer to complete than one generated with the true cardinality. Cardinality bounding avoids this pitfall by computing an upper bound on the query’s output size using statistics about the database such as table sizes and degrees, i.e. value frequencies. In this paper, we extend this line of work by proving a novel bound called the Degree Sequence Bound which takes into account the full degree sequences and the max tuple multiplicity. This work focuses on the important class of Berge-Acyclic queries for which the Degree Sequence Bound is tight. Further, we describe how to practically compute this bound using a functional approximation of the true degree sequences and prove that even this functional form improves upon previous bounds.

Subject Classification

ACM Subject Classification
  • Information systems → Query optimization
  • Information systems → Query planning
  • Theory of computation → Database query processing and optimization (theory)
  • Theory of computation → Data modeling
Keywords
  • Cardinality Estimation
  • Cardinality Bounding
  • Degree Bounds
  • Functional Approximation
  • Query Planning
  • Berge-Acyclic Queries

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References

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