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Fast, Scalable Graph Processing: Apache Giraph on YARN

by on Jul 11, 2013

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Apache Giraph performs offline, batch processing of very large graph datasets on top of a Hadoop cluster. Giraph replaces iterative MapReduce-style solutions with Bulk Synchronous Parallel graph ...

Apache Giraph performs offline, batch processing of very large graph datasets on top of a Hadoop cluster. Giraph replaces iterative MapReduce-style solutions with Bulk Synchronous Parallel graph processing using in-memory or disk-based data sets, loosely following the model of Google`s Pregel. Many recent advances have left Giraph more robust, efficient, fast, and able to accept a variety of I/O formats typical for graph data in and out of the Hadoop ecosystem. Giraph's recent port to a pure YARN platform offers increased performance, fine-grained resource control, and scalability that Giraph atop Hadoop MRv1 cannot, while paving the way for ports to other platforms like Apache Mesos. Come see whats on the roadmap for Giraph, what Giraph on YARN means, and how Giraph is leveraging the power of YARN to become a more robust, usable, and useful platform for processing Big Graph datasets.

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Fast, Scalable Graph Processing: Apache Giraph on YARN Fast, Scalable Graph Processing: Apache Giraph on YARN Presentation Transcript