Advantages of Giraph over Hadoop in Graph Processing


  • C. L. Vidal-Silva Faculty of Economics and Administration, Catholic University of the North, Chile
  • E. Madariaga Faculty of Engineering, Bernardo O’Higgins University, Chile
  • T. Pham Information Technology Research Center, Faculty of Economics and Business, University of Talca, Chile
  • J. M. Rubio Technological University of Chile INACAP, Santiago, Chile
  • L. A. Urzua School of Kinesiology, Faculty of Health, Santo Tomas University, Chile
  • L. Carter Industrial Civil Engineering Department, Autonomous University of Chile, Chile
  • F. Johnson Computing and Information Department, University of Playa Ancha, Chile


This article presents a comparison of the computing performance of the MapReduce tool Hadoop and Giraph on large-scale graphs. The main ideas of MapReduce and bulk synchronous parallel (BSP) are reviewed as big data computing approaches to highlight their applicability in large-scale graph processing. This paper reviews the execution performance of Hadoop and Giraph on the PageRank algorithm to classify web pages according to their relevance, and on a few other algorithms to find the minimum spanning tree in a graph with the primary goal of finding the most efficient computing approach to work on large-scale graphs. Experimental results show that the use of Giraph for processing large-size graphs reduces the execution time by 25% in comparison with the results obtained using the Hadoop for the same experiments. Giraph represents the optimal option thanks to its in-memory computing approach that avoids secondary memory direct interaction.


Giraph, Hadoop, Graph, Big Data, Big Graph


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How to Cite

C. L. Vidal-Silva, “Advantages of Giraph over Hadoop in Graph Processing”, Eng. Technol. Appl. Sci. Res., vol. 9, no. 3, pp. 4112–4115, Jun. 2019.


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