Name
Abstract | ||
|---|---|---|
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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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3299869.3300064 | Proceedings of the 2019 International Conference on Management of Data |
Keywords | Field | DocType |
datacenter networks, datalog optimizations, distributed systems, graph analytics | Data mining,Graph,Computer science,Implementation,Power graph analysis,Theoretical computer science,Ranging,Operator (computer programming),Order of magnitude,Network conditions,Scalability | Conference |
ISSN | ISBN | Citations |
0730-8078 | 978-1-4503-5643-5 | 1 |
PageRank | References | Authors |
0.35 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qizhen Zhang | 1 | 23 | 4.74 |
| Akash Acharya | 2 | 1 | 0.35 |
| Hongzhi Chen | 3 | 47 | 13.00 |
| Simran Arora | 4 | 1 | 0.68 |
| Ang Chen | 5 | 15 | 5.02 |
| Vincent Liu | 6 | 1 | 1.36 |
| Boon Thau Loo | 7 | 2118 | 131.09 |