Title: Data-Agnostic Cardinality Learning from Imperfect Workloads Show Chinese title

Title: 从不完美工作负载中学到的数据无关基数学习

Abstract: Cardinality estimation (CardEst) is a critical aspect of query optimization. Traditionally, it leverages statistics built directly over the data. However, organizational policies (e.g., regulatory compliance) may restrict global data access. Fortunately, query-driven cardinality estimation can learn CardEst models using query workloads. However, existing query-driven models often require access to data or summaries for best performance, and they assume perfect training workloads with complete and balanced join templates (or join graphs). Such assumptions rarely hold in real-world scenarios, in which join templates are incomplete and imbalanced. We present GRASP, a data-agnostic cardinality learning system designed to work under these real-world constraints. GRASP's compositional design generalizes to unseen join templates and is robust to join template imbalance. It also introduces a new per-table CardEst model that handles value distribution shifts for range predicates, and a novel learned count sketch model that captures join correlations across base relations. Across three database instances, we demonstrate that GRASP consistently outperforms existing query-driven models on imperfect workloads, both in terms of estimation accuracy and query latency. Remarkably, GRASP achieves performance comparable to, or even surpassing, traditional approaches built over the underlying data on the complex CEB-IMDb-full benchmark -- despite operating without any data access and using only 10% of all possible join templates. Show Chinese abstract

Abstract: 基数估计（CardEst）是查询优化的关键方面。传统上，它利用直接基于数据构建的统计信息。然而，组织政策（例如，法规合规性）可能会限制全局数据访问。幸运的是，基于查询的基数估计可以通过查询工作负载学习CardEst模型。然而，现有的基于查询的模型通常需要访问数据或摘要以获得最佳性能，并且假设训练工作负载完整且平衡的连接模板（或连接图）。在现实场景中，这些假设很少成立，因为连接模板通常是不完整和不平衡的。 我们提出了GRASP，这是一种与数据无关的基数学习系统，旨在在这些现实世界约束下工作。GRASP的组合设计可以推广到未见过的连接模板，并且对连接模板的不平衡具有鲁棒性。它还引入了一种新的每表CardEst模型，用于处理范围谓词的值分布偏移，并且提出了一种新的学习计数草图模型，该模型捕获基础关系之间的连接相关性。 在三个数据库实例中，我们证明了GRASP在不完美工作负载下始终优于现有的基于查询的模型，无论是在估计准确性还是查询延迟方面。值得注意的是，在复杂的CEB-IMDb-full基准测试中，尽管GRASP没有访问任何数据并且仅使用所有可能连接模板的10%，但其性能可与甚至超过基于底层数据的传统方法相媲美或超越。