针对复杂查询执行机制，提出可预测性能的新型成本估算方法
CONCERTO: Complex Query Execution Mechanism-Aware Learned Cost Estimation
- 为每个物理算子建立独立资源成本模型
- 用图注意力网络校准并行执行的成本影响
- 适合需要高精度查询性能预测的数据库系统研发者
随着大规模数据分析需求的增长，许多数据库管理系统（DBMS）采用了复杂的底层查询执行机制，包括向量化操作、并行执行和动态流水线调整。然而，现有方法大多聚焦于传统的树形查询计划和静态串行执行器，缺乏对这些复杂执行机制及其相互作用的针对性查询性能预测（QPP）方法。为此，本文提出 CONCERTO——一种面向复杂查询执行机制的、基于学习的成本估算方法。CONCERTO 首先为每个物理算子建立独立的资源成本模型；随后构建一个由数据流树主干和并发算子间资源竞争关系组成的有向无环图（DAG）；通过引入额外注意力机制的图注意力网络（GATs）校准并行算子执行的成本影响；再利用时间卷积网络（TCNs）提取并聚合成本向量树，最终实现高效的查询性能预测。实验结果表明，CONCERTO 在预测精度上优于现有方法。
原文摘要 · Abstract (English)
With the growing demand for massive data analysis, many DBMSs have adopted complex underlying query execution mechanisms, including vectorized operators, parallel execution, and dynamic pipeline modifications. However, there remains a lack of targeted Query Performance Prediction (QPP) methods for these complex execution mechanisms and their interactions, as most existing approaches focus on traditional tree-shaped query plans and static serial executors. To address this challenge, this paper proposes CONCERTO, a Complex query executiON meChanism-awaE leaRned cosT estimatiOn method. CONCERTO first establishes independent resource cost models for each physical operator. It then constructs a Directed Acyclic Graph (DAG) consisting of a dataflow tree backbone and resource competition relationships among concurrent operators. After calibrating the cost impact of parallel operator execution using Graph Attention Networks (GATs) with additional attention mechanisms, CONCERTO extracts and aggregates cost vector trees through Temporal Convolutional Networks (TCNs), ultimately achieving effective query performance prediction. Experimental results demonstrate that CONCERTO achieves higher prediction accuracy than existing methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。