Apache Giraph is an iterative graph processing system built for high scalability.
Apache Giraph is an iterative graph processing system built for high scalability.
For example, it is currently used at Facebook to analyze the social graph formed by users and their connections.
Giraph originated as the open-source counterpart to Pregel, the graph processing architecture developed at Google and described in this paper.
Both systems are inspired by the Bulk Synchronous Parallel model of distributed computation introduced by Leslie Valiant.
Bulk Synchronous Parallel (BSP) abstract computer is a bridging model for designing parallel algorithms. It differs from Parallel random access machine (PRAM) by not talking communication and synchronization for granted. An important part of analyzing a BSP algorithm rests in qualifying the synchronization and the communication needed.
Giraph adds several features beyond the basic Pregel model, including master computation, sharded aggregators, edge-oriented input, out-of-core computation, and more.
With a steady development cycle and a growing community of users worldwide, Giraph is a natural choice for unleashing the potential of structured datasets at a massive scale.
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