Benchmarking Graph Data Management and Processing Systems: A Survey

TL;DR

This survey reviews 20 benchmarks for graph data systems over 15 years, highlighting gaps in workload diversity, metrics, and emerging areas like graph streaming and machine learning.

Contribution

It categorizes existing benchmarks, identifies key issues, and outlines future challenges in benchmarking graph data management and processing systems.

Findings

Few benchmarks support high workload scenarios

Limited benchmarks for graph stream processing and machine learning

Existing benchmarks rely on traditional metrics

Abstract

The development of scalable, representative, and widely adopted benchmarks for graph data systems have been a question for which answers has been sought for decades. We conduct an in-depth study of the existing literature on benchmarks for graph data management and processing, covering 20 different benchmarks developed during the last 15 years. We categorize the benchmarks into three areas focusing on benchmarks for graph processing systems, graph database benchmarks, and bigdata benchmarks with graph processing workloads. This systematic approach allows us to identify multiple issues existing in this area, including i) few benchmarks exist which can produce high workload scenarios, ii) no significant work done on benchmarking graph stream processing as well as graph based machine learning, iii) benchmarks tend to use conventional metrics despite new meaningful metrics have been around for years, iv) increasing number of big data benchmarks appear with graph processing workloads. Following these observations, we conclude the survey by describing key challenges for future research on graph data systems benchmarking.