arXiv:2606.15600v1 Announce Type: cross Abstract: Cardinality-estimation (CE) research ranks estimators by q-error, yet it is well known that q-error is an imperfect proxy for query-plan quality. We give a measurement-driven account of when it is a good proxy and when it is not, and why. Modeling plan selection as an argmin over a piecewise-linear cost landscape, we find that plan regret (the cost of the chosen plan relative to the optimal, under true cardinalities) is governed by plan-cost geometry in a regime-dependent way. (i) For small errors, a true-point condition number kappa predicts r
The paper provides a detailed analysis of a long-standing challenge in database optimization, advancing the theoretical understanding of cardinality estimation errors.
Improved understanding of cardinality estimation directly impacts the efficiency and performance of database systems, which are foundational to virtually all software and AI applications.
This research offers a more nuanced framework for evaluating and developing cardinality estimators, moving beyond simplistic metrics like q-error to consider the specific 'regimes' of error.
- · Database researchers
- · Database developers
- · Cloud infrastructure providers
- · Inefficient database systems
- · Applications reliant on suboptimal query plans
Database query optimizers will become more intelligent in predicting and mitigating plan regret.
Enterprise applications and AI systems will experience performance improvements due to more efficient data access.
The development cycle for new database features and large-scale data processing will accelerate as foundational issues become better understood.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG