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On an Information Theoretic Approach to Cardinality Estimation (Invited Talk)

Author Hung Q. Ngo

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Hung Q. Ngo
  • RelationalAI Inc., Berkeley, CA, USA

Acknowledgements

I would like to thank the technical program committee and the program chair Dan Olteanu of ICDT 2022 for inviting me to give a keynote talk at the conference

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Hung Q. Ngo. On an Information Theoretic Approach to Cardinality Estimation (Invited Talk). In 25th International Conference on Database Theory (ICDT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 220, pp. 1:1-1:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.ICDT.2022.1

Abstract

This article is a companion to an invited talk at ICDT'2022 with the same title. Cardinality estimation is among the most important problems in query optimization. It is well-documented that, when query plans go haywire, in most cases one can trace the root cause to the cardinality estimator being far off. In particular, traditional cardinality estimation based on selectivity estimation may sometimes under-estimate cardinalities by orders of magnitudes, because the independence or the uniformity assumptions do not typically hold. This talk outlines an approach to cardinality estimation that is "model-free" from a statistical stand-point. Being model-free means the approach tries to avoid making any distributional assumptions. Our approach is information-theoretic, and generalizes recent results on worst-case output size bounds of queries, allowing the estimator to take into account histogram information from the input relations. The estimator turns out to be the objective of a maximization problem subject to concave constraints, over an exponential number of variables. We then explain how the estimator can be computed in polynomial time for some fragment of these constraints. Overall, the talk introduces a new direction to address the classic problem of cardinality estimation that is designed to circumvent some of the pitfalls of selectivity-based estimation. We will also explain connections to other fundamental problems in information theory and database theory regarding information inequalities. The talk is based on (published and unpublished) joint works with Mahmoud Abo Khamis, Sungjin Im, Hossein Keshavarz, Phokion Kolaitis, Ben Moseley, XuanLong Nguyen, Kirk Pruhs, Dan Suciu, and Alireza Samadian Zakaria

Subject Classification

ACM Subject Classification
  • Information systems → Query optimization
  • Information systems → Query planning
  • Information systems → Join algorithms
Keywords
  • Cardinality Estimation
  • Information Theory
  • Polymatroid Bound
  • Worst-case Optimal Join

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