Estimating Cardinalities with Deep Sketches
Databases
2019-04-18 v1
Abstract
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.
Keywords
Cite
@article{arxiv.1904.08223,
title = {Estimating Cardinalities with Deep Sketches},
author = {Andreas Kipf and Dimitri Vorona and Jonas Müller and Thomas Kipf and Bernhard Radke and Viktor Leis and Peter Boncz and Thomas Neumann and Alfons Kemper},
journal= {arXiv preprint arXiv:1904.08223},
year = {2019}
}
Comments
To appear in SIGMOD'19