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Recently developed machine learning (ML) models for cardinality estimation improve the accuracy significantly, which inspires database developers to tap learned cardinality estimators into RDBMS. However, query optimization is a complicated task involving multiple factors, and accurate estimations may not necessarily procure optimal query plans. In this demonstration, we present an interactive platform that facilitates the investigation of various learned cardinality estimators and their utilities in query optimization in real RDBMS. We bridge the model zoo and the optimizer of PostgreSQL and provide a graphical interface for users to configure parameters and visualize the results interactively. In addition, our platform exposes extensible interfaces for users to deploy plug-and-play ML estimators and datasets. The demonstration video can be found on YouTube https://www.youtube.com/watch?v=ay3w7V1JhUU .

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Investigating Machine Learning Models for Cardinality Estimation: An interactive Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Rui Li,
  • Kangfei Zhao,
  • Mengyu Li,
  • Jeffrey Xu Yu

摘要

Recently developed machine learning (ML) models for cardinality estimation improve the accuracy significantly, which inspires database developers to tap learned cardinality estimators into RDBMS. However, query optimization is a complicated task involving multiple factors, and accurate estimations may not necessarily procure optimal query plans. In this demonstration, we present an interactive platform that facilitates the investigation of various learned cardinality estimators and their utilities in query optimization in real RDBMS. We bridge the model zoo and the optimizer of PostgreSQL and provide a graphical interface for users to configure parameters and visualize the results interactively. In addition, our platform exposes extensible interfaces for users to deploy plug-and-play ML estimators and datasets. The demonstration video can be found on YouTube https://www.youtube.com/watch?v=ay3w7V1JhUU .