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From social networks to language modeling, the growing scale and importanceof graph data has driven the development of numerous new graph-parallel systems(e.g., Pregel, GraphLab). By restricting the computation that can be expressedand introducing new techniques to partition and distribute the graph, thesesystems can efficiently execute iterative graph algorithms orders of magnitudefaster than more general data-parallel systems. However, the same restrictionsthat enable the performance gains also make it difficult to express many of theimportant stages in a typical graph-analytics pipeline: constructing the graph,modifying its structure, or expressing computation that spans multiple graphs.As a consequence, existing graph analytics pipelines compose graph-parallel anddata-parallel systems using external storage systems, leading to extensive datamovement and complicated programming model.  To address these challenges we introduce GraphX, a distributed graphcomputation framework that unifies graph-parallel and data-parallelcomputation. GraphX provides a small, core set of graph-parallel operatorsexpressive enough to implement the Pregel and PowerGraph abstractions, yetsimple enough to be cast in relational algebra. GraphX uses a collection ofquery optimization techniques such as automatic join rewrites to efficientlyimplement these graph-parallel operators. We evaluate GraphX on real-worldgraphs and workloads and demonstrate that GraphX achieves comparableperformance as specialized graph computation systems, while outperforming themin end-to-end graph pipelines. Moreover, GraphX achieves a balance betweenexpressiveness, performance, and ease of use.

GraphX: Unifying Data-Parallel and Graph-Parallel Analytics