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Large scale graph processing is a major research area for Big Dataexploration. Vertex centric programming models like Pregel are gaining tractiondue to their simple abstraction that allows for scalable execution ondistributed systems naturally. However, there are limitations to this approachwhich cause vertex centric algorithms to under-perform due to poor compute tocommunication overhead ratio and slow convergence of iterative superstep. Inthis paper we introduce GoFFish a scalable sub-graph centric frameworkco-designed with a distributed persistent graph storage for large scale graphanalytics on commodity clusters. We introduce a sub-graph centric programmingabstraction that combines the scalability of a vertex centric approach with theflexibility of shared memory sub-graph computation. We map ConnectedComponents, SSSP and PageRank algorithms to this model to illustrate itsflexibility. Further, we empirically analyze GoFFish using several real worldgraphs and demonstrate its significant performance improvement, orders ofmagnitude in some cases, compared to Apache Giraph, the leading open sourcevertex centric implementation.

GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics