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An Analysis of Distributed Programming Models and Frameworks for Large-scale Graph Processing
IETE Journal of Research ( IF 1.5 ) Pub Date : 2020-04-30 , DOI: 10.1080/03772063.2020.1754139
Alejandro Corbellini 1 , Daniela Godoy 1 , Cristian Mateos 1 , Silvia Schiaffino 1 , Alejandro Zunino 1
Affiliation  

ABSTRACT

In recent years, processing and analysing large graphs has become a major need in many research areas. Distributed graph processing programming models and frameworks arised as a natural solution to process linked data of large volumes, such as data originating from social media. These solutions are distributed by design and help developers to perform operations on the graph, sometimes reaching almost real-time performance even on huge graphs. Some of the available graph processing frameworks exploit generic data processing models, like MapReduce, while others were specifically built for graph processing, introducing techniques such as vertex or edge partitioning and graph-oriented programming models. In this work, we analyse the properties of recent and widely popular frameworks – from the perspective of the adopted programming model – designed to process large-scale graphs with the goal of assisting software developers/designers in choosing the most adequate tool.



中文翻译:

大规模图处理的分布式编程模型和框架分析

摘要

近年来,处理和分析大图已成为许多研究领域的主要需求。分布式图形处理编程模型和框架作为处理大量链接数据(例如源自社交媒体的数据)的自然解决方案而出现。这些解决方案是按设计分发的,可帮助开发人员在图上执行操作,有时甚至在巨大的图上也能达到几乎实时的性能。一些可用的图处理框架利用了通用数据处理模型,如 MapReduce,而另一些则是专门为图处理构建的,引入了诸如顶点或边划分和面向图的编程模型等技术。在这项工作中,

更新日期:2020-04-30
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