Learned Cardinalities: Estimating Correlated Joins with Deep Learning [article]

Andreas Kipf, Thomas Kipf, Bernhard Radke, Viktor Leis, Peter Boncz, Alfons Kemper
2018 arXiv   pre-print
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 significantly enhances the
more » ... ity of cardinality estimation, which is the core problem in query optimization.
arXiv:1809.00677v2 fatcat:2oqbfpvop5h2pbsovvha5p3xzq