Learned cardinalities: Estimating correlated joins with deep learning
We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set convolutional network, tailored to representing relational query plans, that employs set semantics to capture query features and true cardinalities. MSCN builds on sampling-based estimation, addressing its weaknesses when no sampled tuples qualify a predicate, and in capturing join-crossing correlations. Our evaluation of MSCN using a real-world dataset shows that deep learning signiicantly enhances the quality of cardinality estimation, which is the core problem in query optimization.
|9th Biennial Conference on Innovative Data Systems Research, CIDR 2019|
|Organisation||Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands|
Kipf, A, Kipf, T, Radke, B, Leis, V, Boncz, P.A, & Kemper, A. (2019). Learned cardinalities: Estimating correlated joins with deep learning. In CIDR 2019 - 9th Biennial Conference on Innovative Data Systems Research. doi:10.48550/arXiv.1809.00677