2019-06-30
Estimating cardinalities with deep sketches
Publication
Publication
Presented at the
ACM SIGMOD International Conference on Management of Data (June 2019), Amsterdam, The Netherlands
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.
| Additional Metadata | |
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| doi.org/10.1145/3299869.3320218 | |
| ACM SIGMOD International Conference on Management of Data | |
| Organisation | Database Architectures |
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Kipf, A., Vorona, D., Müller, J., Kipf, T., Radke, B., Leis, V., … Kemper, A. (2019). Estimating cardinalities with deep sketches. In Proceedings of the ACM International Conference on Management of Data (SIGMOD) (pp. 1937–1940). doi:10.1145/3299869.3320218 |
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