| Autor: |
Wenyuan Yu, Jingren Zhou, Lei Wang, Zhengping Qian, Longbin Lai, Jingbo Xu, Wenfei Fan, Zhao Li, Zhanning Bai, Yanyan Wang, Xue Li |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Proceedings of the VLDB Endowment. 14:2703-2706 |
| ISSN: |
2150-8097 |
| DOI: |
10.14778/3476311.3476324 |
| Popis: |
Due to diverse graph data and algorithms, programming and orchestration of complex computation pipelines have become the major challenges to making use of graph applications for Web-scale data analysis. GraphScope aims to provide a one-stop and efficient solution for a wide range of graph computations at scale. It extends previous systems by offering a unified and high-level programming interface and allowing the seamless integration of specialized graph engines in a general data-parallel computing environment. As we will show in this demo, GraphScope enables developers to write sequential graph programs in Python and provides automatic parallel execution on a cluster. This further allows GraphScope to seamlessly integrate with existing data processing systems in PyData ecosystem. To validate GraphScope's efficiency, we will compare a complex, multi-staged processing pipeline for a real-life fraud detection task with a manually assembled implementation comprising multiple systems. GraphScope achieves a 2.86× speedup on a trillion-scale graph in real production at Alibaba. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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