SafeBound: A Practical System for Generating Cardinality Bounds
Kyle Deeds, Dan Suciu, Magda Balazinska

TL;DR
SafeBound is a practical system that provides guaranteed upper bounds on query cardinalities, improving query optimization efficiency by avoiding underestimates and significantly reducing runtime and planning costs.
Contribution
This paper introduces SafeBound, the first practical system for generating cardinality bounds that handles predicates and extends theoretical frameworks with compression and efficient inference.
Findings
Achieves up to 80% lower runtimes than PostgreSQL
Saves up to 500x in query planning time
Uses up to 6.8x less space than existing methods
Abstract
Recent work has reemphasized the importance of cardinality estimates for query optimization. While new techniques have continuously improved in accuracy over time, they still generally allow for under-estimates which often lead optimizers to make overly optimistic decisions. This can be very costly for expensive queries. An alternative approach to estimation is cardinality bounding, also called pessimistic cardinality estimation, where the cardinality estimator provides guaranteed upper bounds of the true cardinality. By never underestimating, this approach allows the optimizer to avoid potentially inefficient plans. However, existing pessimistic cardinality estimators are not yet practical: they use very limited statistics on the data, and cannot handle predicates. In this paper, we introduce SafeBound, the first practical system for generating cardinality bounds. SafeBound builds on a…
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Taxonomy
TopicsMachine Learning and Algorithms · Data Management and Algorithms · Machine Learning and Data Classification
