Optimizing Declarative Graph Queries at Large Scale

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Microsoft Research
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Published on ● Video Link: https://www.youtube.com/watch?v=e2eQ351gK1c


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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.

Talk slides: https://www.microsoft.com/en-us/research/uploads/prod/2019/09/Optimizing-Declarative-Graph-Queries-at-Large-Scale-SLIDES.pdf

Learn more about this and other talks at Microsoft Research: https://www.microsoft.com/en-us/research/video/optimizing-declarative-graph-queries-at-large-scale/



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Tags:
GraphRex
graph processing
graph analysis
Graph Queries
data analysis
data analytics
information retrieval
Qizhen Zhang
social networks
large-scale graphs
Microsoft Research
MSR