gMark: Controlling Workload Diversity in Benchmarking Graph Databases

Massive graph data sets are pervasive in contemporary application domains. Hence, graph database systems are becoming increasingly important. In the study of these systems, it is vital that the research community has shared benchmarking solutions for the generation of database instances and query workloads having predictable and controllable properties. Similarly to TPC benchmarks for relational databases, benchmarks for graph databases have been important drivers for the Semantic Web and graph data management communities. In this paper, we present the design and engineering principles of gMark, a domain- and query language-independent graph benchmark exhibiting flexible schema and workload chokepoints. A core contribution of gMark is its ability to target and control the diversity of properties of both the generated graph instances and the generated query workloads coupled to these instances. A further novelty is the support of recursive regular path queries, a fundamental graph query paradigm. We illustrate the flexibility and practical usability of gMark by showcasing the frameworku0027s capabilities in generating high quality graphs and workloads, and its ability to encode user-defined schemas across a variety of application domains.