Abstract
Large and dynamic graphs with streaming updates have been gaining traction recently, along with the need for enabling graph analytics in a commodity cluster instead of a high-performance computing facility. Surprisingly, there is a lack of study on scaling out graph data structures to represent sparse dynamic graphs in a commodity cluster, and even the latest work [1] based upon the most common in-memory graph representation CSR [2] is a single-machine case. In this paper we present DISTINGER, a distributed graph representation that handles massive graph analytics with streaming updates. DISTINGER successfully extends a scale-up design to a scale-out graph data structure while maintains its efficiency and scalability. We implement our design and algorithms as a prototype, and compare it to single-site STINGER and state-of-art graph systems. Our experimental evaluation in a real cluster shows that DISTINGER can handle larger graphs than STINGER, and perform graph tasks (PageRank and edge updates) more efficiently than GraphLab and Giraph.
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