Details

Autor(en) / Beteiligte

• Bu, Yingyi
• Borkar, Vinayak
• Jia, Jianfeng
• Carey, Michael J.
• Condie, Tyson

Titel

Pregelix: Big(ger) graph analytics on a dataflow engine

Ist Teil von

• Proceedings of the VLDB Endowment, 2014-10, Vol.8 (2), p.161-172

Erscheinungsjahr

2014

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• There is a growing need for distributed graph processing systems that are capable of gracefully scaling to very large graph datasets. Unfortunately, this challenge has not been easily met due to the intense memory pressure imposed by process-centric, message passing designs that many graph processing systems follow. Pregelix is a new open source distributed graph processing system that is based on an iterative dataflow design that is better tuned to handle both in-memory and out-of-core workloads. As such, Pregelix offers improved performance characteristics and scaling properties over current open source systems (e.g., we have seen up to 15X speedup compared to Apache Giraph and up to 35X speedup compared to distributed GraphLab), and more effective use of available machine resources to support Big(ger) Graph Analytics.