Cardinality Estimation of an SQL Query Using Recursive Neural Networks

Authors

  • Davit S. Karamyan Yerevan State University

DOI:

https://doi.org/10.51408/1963-0058

Keywords:

Recursive neural networks, Representation learning, Cardinality estimation, Compositionality, Complex logical expressions

Abstract

To learn complex interactions between predicates and accurately estimate the cardinality of an SQL query, we develop a novel framework based on recursive tree-structured neural networks, which take into account the natural properties of logical expressions: compositionality and n-ary associativity. The proposed architecture is an extension of MSCN (multi-set convolutional network) for queries containing both conjunction and disjunction operators. The goal is to represent an arbitrary logical expression in a continuous vector space by combining sub-expression vectors according to the operator type. We compared the proposed approach with the histogram-based approach on the real-world dataset and showed that our approach significantly outperforms histograms with a large margin.

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Published

2021-12-10

How to Cite

Karamyan, D. S. . (2021). Cardinality Estimation of an SQL Query Using Recursive Neural Networks. Mathematical Problems of Computer Science, 54, 41–52. https://doi.org/10.51408/1963-0058