DistJoin：一种基于自适应神经谓词调制的解耦连接基数估计器
DistJoin: A Decoupled Join Cardinality Estimator based on Adaptive Neural Predicate Modulation
AI总结：
DistJoin提出解耦多自回归分布预测与自适应神经谓词调制，解决基数估计三难困境，首个同时支持等值与非等值连接，兼具高准确性和快速更新。
摘要：
近年来，关于学习型基数估计的研究取得了显著进展。然而，现有方法仍面临不同的挑战，阻碍了它们在生产环境中的实际部署。我们将这些挑战定义为“基数估计的三难困境”，即学习型基数估计方法难以在通用性、准确性和可更新性之间取得平衡。为了解决这些挑战，我们引入了DistJoin，一种基于多自回归模型的高效分布预测的连接基数估计器。我们的贡献有三方面：（1）我们提出了一种通过解耦方式利用各个表的概率分布来估计连接基数的方法。（2）为了满足DistJoin的效率要求，我们开发了自适应神经谓词调制（ANPM），一种高吞吐量的分布估计模型。（3）我们通过形式化的方差分析证明，现有的类似方法存在方差累积问题。为了缓解这一问题，DistJoin采用基于选择性的方法来推断连接基数，有效减少了方差。总之，DistJoin不仅代表了首个同时支持等值连接和非等值连接的数据驱动方法，而且展示了卓越的准确性，同时支持快速灵活的更新。实验结果表明，与现有方法相比，DistJoin在准确性、对数据更新的鲁棒性、通用性以及可比的更新和推理速度方面均达到了最高水平。
英文摘要：
Research on learned cardinality estimation has made significant progress in recent years. However, existing methods still face distinct challenges that hinder their practical deployment in production environments. We define these challenges as the ``Trilemma of Cardinality Estimation'', where learned cardinality estimation methods struggle to balance generality, accuracy, and updatability. To address these challenges, we introduce DistJoin, a join cardinality estimator based on efficient distribution prediction using multi-autoregressive models. Our contributions are threefold: (1) We propose a method to estimate join cardinality by leveraging the probability distributions of individual tables in a decoupled manner. (2) To meet the requirements of efficiency for DistJoin, we develop Adaptive Neural Predicate Modulation (ANPM), a high-throughput distribution estimation model. (3) We demonstrate that an existing similar approach suffers from variance accumulation issues by formal variance analysis. To mitigate this problem, DistJoin employs a selectivity-based approach to infer join cardinality, effectively reducing variance. In summary, DistJoin not only represents the first data-driven method to support both equi and non-equi joins simultaneously but also demonstrates superior accuracy while enabling fast and flexible updates. The experimental results demonstrate that DistJoin achieves the highest accuracy, robustness to data updates, generality, and comparable update and inference speed relative to existing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
