GRBench: A Comprehensive Benchmark Evaluation for Graph-relational Data Management
2026-08-31 • Databases
Databases
AI summaryⓘ
The authors created GRBench, a new way to test database systems that handle both structured tables and graph data together. They built it using a real dataset and designed it to check how different parts of these systems work, like storage and query processing. Unlike past tests, GRBench offers detailed and fair comparisons across different system designs. The authors used it to understand the strengths and weaknesses of current methods and to suggest improvements for future database systems.
graph-relational datadatabase systemsquery processingbenchmarkingdata modelingmulti-model databasesquery optimizationstorage designgraph datarelational databases
Authors
Zepeng Liu, Xinxin Huang, Xuanming Liu, Sheng Wang, Zhiyong Peng
Abstract
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.