Estimating cardinalities with deep sketches

Andreas Kipf, Dimitri Vorona, Jonas Müller, Thomas Kipf, Bernhard Radke, Viktor Leis, Peter Boncz, Thomas Neumann, Alfons Kemper

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

23 Zitate (Scopus)

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.

OriginalspracheEnglisch
TitelSIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data
Herausgeber (Verlag)Association for Computing Machinery
Seiten1937-1940
Seitenumfang4
ISBN (elektronisch)9781450356435
DOIs
PublikationsstatusVeröffentlicht - 25 Juni 2019
Veranstaltung2019 International Conference on Management of Data, SIGMOD 2019 - Amsterdam, Niederlande
Dauer: 30 Juni 20195 Juli 2019

Publikationsreihe

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Konferenz

Konferenz2019 International Conference on Management of Data, SIGMOD 2019
Land/GebietNiederlande
OrtAmsterdam
Zeitraum30/06/195/07/19

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