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New research probes query optimization errors and plan regret

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore the nuances of query optimization in large-scale data systems, focusing on how estimation errors impact performance. The first paper, "Filtered ANN as a Phase Transition," analyzes approximate nearest neighbor searches and identifies critical error regions where performance degrades significantly. The second paper, "When Does q-error Predict Plan Regret?", investigates cardinality estimation and proposes new metrics, like ACS-infinity, that better predict query plan quality than traditional q-error, especially for complex learned estimators. AI

RANK_REASON Two academic papers published on arXiv detailing theoretical and empirical findings on query optimization and cardinality estimation.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Madhulatha Mandarapu, Sandeep Kunkunuru ·

    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…

  2. arXiv cs.LG TIER_1 English(EN) · Madhulatha Mandarapu, Sandeep Kunkunuru ·

    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, …

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