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arxiv 1809.00677 v2 pith:TNWYLYEZ submitted 2018-09-03 cs.DB

Learned Cardinalities: Estimating Correlated Joins with Deep Learning

Andreas Kipf , Thomas Kipf , Bernhard Radke , Viktor Leis , Peter Boncz , Alfons Kemper This is my paper
classification cs.DB
keywords deepestimationlearningmscnquerycardinalitiescardinalityaddressing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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 quality of cardinality estimation, which is the core problem in query optimization.

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