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Q-error Bounds of Random Uniform Sampling for Cardinality Estimation

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arxiv 2108.02715 v2 pith:J7OON54S submitted 2021-08-05 math.ST cs.DBstat.TH

Beibin Li , Yao Lu , Chi Wang , Srikanth Kandula This is my paper · ORCID
classification math.STcs.DBstat.TH
keywords cardinalityq-errorrandomsamplinguniformboundestimationsample
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Random uniform sampling has been studied in various statistical tasks but few of them have covered the Q-error metric for cardinality estimation (CE). In this paper, we analyze the confidence intervals of random uniform sampling with and without replacement for single-table CE. Results indicate that the upper Q-error bound depends on the sample size and true cardinality. Our bound gives a rule-of-thumb for how large a sample should be kept for single-table CE.

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  1. PLANSIEVE: Real-time Suboptimal Query Plan Detection Through Incremental Refinements

    cs.DB 2025-01 conditional novelty 5.0 of 10

    PLANSIEVE uses a transformer and L1-error to classify query plans as suboptimal during optimization, using surrogate cardinalities refined with observed execution results.

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