TAG: An Efficient Storage System Towards Transactional and Analytical Processing on Property Graphs

The property graph, as the most widely adopted graph data model, is utilized extensively in various graph systems. However, these systems encounter challenges with regards to high latency, particularly when it comes to graph analysis workloads. Conversely, graph analysis systems are geared towards simple graphs and have limited transactional workload support. There is a growing demand for a graph storage system that can efficiently handle both workloads on the property graph. In this paper, we propose TAG, a graph storage system that surpasses the performance of transactional graph systems and achieves comparable results to that of graph analysis systems. We introduce a novel hybrid architecture for graph storage, incorporating in-memory indexes to enhance graph topology queries and label-based pages to optimize access to the properties. Through experimental evaluations, we demonstrate the superiority of TAG over state-of-the-art graph databases.