Optimizing Declarative Graph Queries at Large Scale

  • Qizhen Zhang | University of Pennsylvania

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.

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Speaker Details

Qizhen is a third-year PhD student at the University of Pennsylvania in Computer Science. He is co-advised by Prof. Boon Thau Loo and Prof. Vincent Liu. Qizhen’s research mainly focuses on systems for large-scale data processing. Particularly, he works on improving the performance and the reliability of big data systems in clouds/data centers. He is also interested in other aspects of data management and network infrastructure.

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