gMark: Schema-Driven Generation of Graphs and Queries

Abstract

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.

Selectivity Estimation for SPARQL Triple Patterns with Shape Expressions

“…The results of such query is small and constant, since the number of countries and languages is constant; they do not vary even when the dataset size is exponentially increased, and thus such results are expected for some of the generated queries. Actually these kinds of queries are intentionally generated by gMark for benchmarking purposes (check [3]). …”

“…We generate data according to the Social Network Benchmark (SNB) schema of the Linked Data Benchmark Council (LDBC) [5]. The data and the workload tests are generated by gMark [3]. gMark is a graph and query workload generator based on an input schema.…”

Certified Graph View Maintenance with Regular Datalog

“…The optimization issues around RQ evaluation have never been formally addressed in the database literature and, even for the simple UC2RPQ class, current graph database engines perform poorly (see (Bagan et al 2017)). Even though our goal is not to provide a RQ optimizer, we touch base with some simple optimizations.…”

“…These graphshenceforth denoted by G -are diverse in terms of their density (increasing from SNB to WD) and of their in-degree and out-degree distributions (Bagan et al 2017). They represent two extreme cases to be considered in benchmarking graph database engines.…”

“…For our experimental analysis, we generated synthetic datasets and query workloads using gMark (Bagan et al 2017), which allowed us to encode, two state-of-the-art benchmarks: WD, the Waterloo SPARQL Diversity Test Suite (Wat-Div) (Aluç et al 2014), and SNB, the LDBC Social Network Benchmark (Erling et al 2015). These graphshenceforth denoted by G -are diverse in terms of their density (increasing from SNB to WD) and of their in-degree and out-degree distributions (Bagan et al 2017).…”