What question did this study set out to answer?

This research aims to develop LpBound, a pessimistic cardinality estimator for multi-join queries that uses ℓₚ-norms to provide upper bounds on query outputs.

April 26, 2026Open Access

LpBound: Pessimistic Cardinality Estimation Using ℓ₏-Normsof Degree Sequences

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Authors

HZHaozhe Zhang CMC. MayerBavarian Academy of Sciences and Humanities MKMahmoud Abo Khamis

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Implication

Randomized trial demonstrates improved estimation accuracy in multi-join queries, suggesting efficiency in database systems.

Key Points

This research aims to develop LpBound, a pessimistic cardinality estimator for multi-join queries that uses ℓₚ-norms to provide upper bounds on query outputs.
Introduced LpBound to estimate cardinalities with selection predicates and group-by clauses.
Developed optimizations aimed at reducing estimation time.
Evaluated LpBound against various traditional and machine learning-based estimators using multiple benchmarks.
LpBound achieves greater accuracy than traditional estimators by orders of magnitude.
It maintains low estimation time and space requirements.
Postgres benefits from LpBound's estimates to derive comparable or superior query plans.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/69edac074a46254e215b3d18 https://doi.org/10.5167/uzh-433770

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LpBound: Pessimistic Cardinality Estimation Using ℓ₏-Normsof Degree Sequences

Discussion

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Implication

Key Points

Cite This Study

LpBound: Pessimistic Cardinality Estimation Using ℓ₏-Normsof Degree Sequences

Discussion

Member takes

Implication

Key Points

Cite This Study

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