Architectural implications on the performance and cost of graph analytics systems

Abstract

Graph analytics systems have gained significant popularity due to the prevalence of graph data. Many of these systems are designed to run in a shared-nothing architecture whereby a cluster of machines can process a large graph in parallel. In more recent proposals, others have argued that a single-machine system can achieve better performance and/or is more cost-effective. There is however no clear consensus which approach is better. In this paper, we classify existing graph analytics systems into four categories based on the architectural differences, i.e., processing infrastructure (centralized vs distributed), and memory consumption (in-memory vs out-of-core). We select eight open-source systems to cover all categories, and perform a comparative measurement study to compare their performance and cost characteristics across a spectrum of input data, applications, and hardware settings. Our results show that the best performing configuration can depend on the type of applications and input graphs, and there is no dominant winner across all categories. Based on our findings, we summarize the trends in performance and cost, and provide several insights that help to illuminate the performance and resource cost tradeoffs across different graph analytics systems and categories.