English(EN) Filtered ANN as a Phase Transition: When Selectivity-Estimation Error Causes Plan Regret

新研究探讨查询优化错误和计划遗憾

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-16 04:00

两篇新研究论文探讨了大规模数据系统中查询优化的细微差别，重点关注估计误差如何影响性能。第一篇论文《Filtered ANN 作为一种相变》分析了近似最近邻搜索，并确定了性能显著下降的关键误差区域。第二篇论文《q-error 何时能预测计划遗憾？》研究了基数估计，并提出了新的指标，如 ACS-infinity，这些指标比传统的 q-error 更能预测查询计划的质量，尤其对于复杂的学习估计器。 AI

排序理由在 arXiv 上发表了两篇学术论文，详细介绍了查询优化和基数估计的理论和实证研究结果。

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报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Madhulatha Mandarapu, Sandeep Kunkunuru · 2026-06-16 04:00

Filtered ANN as a Phase Transition: When Selectivity-Estimation Error Causes Plan Regret

arXiv:2606.16341v1 Announce Type: new Abstract: A filtered approximate-nearest-neighbor (ANN) query returns the k nearest vectors among those satisfying an attribute predicate P of selectivity s. The best execution strategy -- pre-filter, post-filter, or in-filter -- changes with…
arXiv cs.LG TIER_1 English(EN) · Madhulatha Mandarapu, Sandeep Kunkunuru · 2026-06-16 04:00

When Does q-error Predict Plan Regret? Three Regimes of Cardinality-Estimation Error

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, …

报道来源 [2]

Filtered ANN as a Phase Transition: When Selectivity-Estimation Error Causes Plan Regret

When Does q-error Predict Plan Regret? Three Regimes of Cardinality-Estimation Error

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