GRBench：面向图关系数据管理的综合基准评估
GRBench: A Comprehensive Benchmark Evaluation for Graph-relational Data Management
- School of Computer Science, Wuhan University（武汉大学计算机学院）
- Big Data Institute, Wuhan University（武汉大学大数据研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
GRBench是面向图关系数据管理的综合基准，基于真实数据集构建，可评估系统架构并分析设计权衡，为相关系统优化提供指导。
AI中文摘要：
现代数据密集型应用日益要求数据库系统同时管理结构化记录与图数据，由此催生了图关系数据管理，涵盖关系数据与图数据的存储、查询处理及优化。针对这一需求，关系数据库扩展、多模型数据库及专用图关系系统已出现，架构各异，但评估方法未能同步发展。现有关系与图基准大多孤立评估两种模型，而多模型基准对图关系工作负载的覆盖有限；可用的图关系工作负载主要支持功能验证与端到端延迟测量，几乎无法体现存储、算子及优化设计对性能的影响。为评估系统在图关系数据管理中的能力，本文提出GRBench：首先，GRBench从真实世界SciSciNet-v2数据集构建关联图关系模式，并通过保持一致性的子集提取生成可扩展实例；其次，它组织专用查询序列，用于对查询处理及系统组件的受控评估；最后，GRBench提供语义等价的原生查询表述，并通过统一的多维方法评估代表性系统架构。基于该评估，本文分析了设计权衡并识别出开放挑战，以指导未来的系统设计与优化。
英文摘要：
Modern data-intensive applications increasingly require database systems to manage structured records and graph data. This demand gives rise to graph-relational data management, spanning storage, query processing, and optimization across relational and graph data. In response, relational database extensions, multi-model databases, and dedicated graph-relational systems have emerged with diverse architectures. However, evaluation methodologies have not kept pace. Existing relational and graph benchmarks assess the two models largely in isolation, while multi-model benchmarks provide limited coverage of graph-relational workloads. Available graph-relational workloads mainly support functional validation and end-to-end latency measurement, revealing little about how storage, operator, and optimization designs affect performance. To evaluate system capabilities in graph-relational data management, we present GRBench. First, GRBench constructs a linked graph-relational schema from the real-world SciSciNet-v2 dataset and derives scalable instances through consistency-preserving subset extraction. Second, it organizes purpose-built query series for controlled evaluation of query processing and system components. Third, GRBench provides semantically equivalent native query formulations and evaluates representative system architectures through a unified, multidimensional methodology. Based on this evaluation, we analyze design trade-offs and identify open challenges to guide future system design and optimization.
