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Distributed graph cube generation using Spark framework

  • Seok Kang
  • Suan Lee
  • Jinho Kim
Article
  • 8 Downloads

Abstract

Graph OLAP is a technology that generates aggregates or summaries of a large-scale graph based on the properties (or dimensions) associated with its nodes and edges, and in turn enables interactive analyses of the statistical information contained in the graph. To efficiently support these OLAP functions, a graph cube is widely used, which maintains aggregate graphs for all dimensions of the source graph. However, computing the graph cube for a large graph requires an enormous amount of time. While previous approaches have used the MapReduce framework to cut down on this computation time, the recently developed Spark environment offers superior computational performance. To leverage the advantages of Spark, we propose the GraphNaïve and GraphTDC algorithms. GraphNaïve sequentially computes graph cuboids for all dimensions in a graph, while GraphTDC computes them after first creating an execution plan. We also propose the Generate Multi-Dimension Table method to efficiently create a multidimensional graph table to express the graph. Evaluation experiments demonstrated that the GraphTDC algorithm significantly outperformed Spark SQL’s built-in library DataFrame, as the size of graphs increased.

Keywords

Distributed parallel processing Spark framework Resilient distributed dataset Graph cube Data cube Online analytical processing 

Notes

Acknowledgements

This research was supported by Korea Electric Power Corporation. (Grant Number: R18XA05) and by the Industrial Technology Innovation Program (Project#: 10052797), through the Korea Evaluation Institute of Industrial Technology (Keit), funded by the Ministry of Trade, Industry and Energy.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceKangwon National UniversityChuncheonKorea

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