Estimating Cardinalities with Deep Sketches

04/17/2019

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We introduce Deep Sketches, which are compact models of databases that allow us to estimate the result sizes of SQL queries. Deep Sketches are powered by a new deep learning approach to cardinality estimation that can capture correlations between columns, even across tables. Our demonstration allows users to define such sketches on the TPC-H and IMDb datasets, monitor the training process, and run ad-hoc queries against trained sketches. We also estimate query cardinalities with HyPer and PostgreSQL to visualize the gains over traditional cardinality estimators.

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Estimating Cardinalities with Deep Sketches

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NN-based Transformation of Any SQL Cardinality Estimator for Handling DISTINCT, AND, OR and NOT

Scardina: Scalable Join Cardinality Estimation by Multiple Density Estimators

Learned Cardinalities: Estimating Correlated Joins with Deep Learning

NeuroCard: One Cardinality Estimator for All Tables

PATSQL: Efficient Synthesis of SQL Queries from Example Tables with Quick Inference of Projected Columns

Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs

Estimating Cardinalities with Deep Sketches

Related Research

Improved Cardinality Estimation by Learning Queries Containment Rates

NN-based Transformation of Any SQL Cardinality Estimator for Handling DISTINCT, AND, OR and NOT

Scardina: Scalable Join Cardinality Estimation by Multiple Density Estimators

Learned Cardinalities: Estimating Correlated Joins with Deep Learning

NeuroCard: One Cardinality Estimator for All Tables

PATSQL: Efficient Synthesis of SQL Queries from Example Tables with Quick Inference of Projected Columns

Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs