Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation

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Abstract

Cardinality estimation is a fundamental and critical problem in databases. Recently, many estimators based on deep learning have been proposed to solve this problem and they have achieved promising results. However, these estimators struggle to provide accurate results for complex queries, due to not capturing real inter-column and inter-table correlations. Furthermore, none of these estimators contain the uncertainty information about their estimations. In this paper, we present a join cardinality estimator called Fauce. Fauce learns the correlations across all columns and all tables in the database, it also contains the uncertainty information of each estimation. Among all studied learned estimators, our results are promising: (1) Fauce has the smallest model size; (2) It has the fastest inference speed; (3) Compared with the state of the art estimator, Fauce has 10 times faster inference speed, and provides 1.3 to 6.7 times smaller estimation errors for complex queries; (4) To the best of our knowledge, Fauce is the first estimator that incorporates cardinality estimation uncertainty information into a deep learning model.