gMark: Schema-Driven Generation of Graphs and Queries

TL;DR

gMark is a flexible, schema-driven framework for generating controllable graph instances and query workloads, supporting diverse properties, regular path queries, and selectivity estimation, aiding research in graph database systems.

Contribution

It introduces gMark, a novel, domain-independent generator for graph data and queries with controllable properties and schema-driven selectivity estimation.

Findings

Supports regular path queries and schema-driven selectivity estimation.

Enables generation of high-quality, diverse graph instances and workloads.

Demonstrates practical usability across various application domains.

Abstract

Massive graph data sets are pervasive in contemporary application domains. Hence, graph database systems are becoming increasingly important. In the experimental study of these systems, it is vital that the research community has shared solutions for the generation of database instances and query workloads having predictable and controllable properties. In this paper, we present the design and engineering principles of gMark, a domain- and query language-independent graph instance and query workload generator. A core contribution of gMark is its ability to target and control the diversity of properties of both the generated instances and the generated workloads coupled to these instances. Further novelties include support for regular path queries, a fundamental graph query paradigm, and schema-driven selectivity estimation of queries, a key feature in controlling workload chokepoints. We illustrate the flexibility and practical usability of gMark by showcasing the framework's capabilities in generating high quality graphs and workloads, and its ability to encode user-defined schemas across a variety of application domains.