• Media type: E-Book; Video
  • Title: Optimizing Declarative Graph Queries at Large Scale
  • Contributor: Zhang, Qizhen [Author]; Acharya, Akash [Other]; Chen, Hongzhi [Other]; Arora, Simran [Other]; Chen, Ang [Other]; Liu, Vincent [Other]; Loo, Boon [Other]
  • Published: [Erscheinungsort nicht ermittelbar]: ACM SIGMOD, 2019
  • Published in: SIGMOD 2019 ; (Jan. 2019)
  • Extent: 1 Online-Ressource (256 MB, 00:13:54:16)
  • Language: English
  • DOI: 10.5446/42979
  • Identifier:
  • Origination:
  • Footnote: Audiovisuelles Material
  • Description: This paper presents GraphRex, an efficient, robust, scalable, and easy-to-program framework for graph processing on datacenter infrastructure. To users, GraphRex presents a declarative, Datalog-like interface that is natural and expressive. Underneath, it compiles those queries into efficient implementations. A key technical contribution of GraphRex is the identification and optimization of a set of global operators whose efficiency is crucial to the good performance of datacenter-based, large graph analysis. Our experimental results show that GraphRex significantly outperforms existing frameworks-both high- and low-level-in scenarios ranging across a wide variety of graph workloads and network conditions, sometimes by two orders of magnitude
  • Access State: Open Access
  • Rights information: Attribution - Non Commercial (CC BY-NC)