Survey of graph database performance on the HPC scalable graph analysis benchmark

  • Authors:
  • D. Dominguez-Sal;P. Urbón-Bayes;A. Giménez-Vañó;S. Gómez-Villamor;N. Martínez-Bazán;J. L. Larriba-Pey

  • Affiliations:
  • DAMA, Universitat Politècnica de Catalunya, Barcelona, Spain;DAMA, Universitat Politècnica de Catalunya, Barcelona, Spain;DAMA, Universitat Politècnica de Catalunya, Barcelona, Spain;DAMA, Universitat Politècnica de Catalunya, Barcelona, Spain;DAMA, Universitat Politècnica de Catalunya, Barcelona, Spain;DAMA, Universitat Politècnica de Catalunya, Barcelona, Spain

  • Venue:
  • WAIM'10 Proceedings of the 2010 international conference on Web-age information management
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

The analysis of the relationship among data entities has lead to model them as graphs. Since the size of the datasets has significantly grown in the recent years, it has become necessary to implement efficient graph databases that can load and manage these huge datasets. In this paper, we evaluate the performance of four of the most scalable native graph database projects (Neo4j, Jena, HypergraphDB and DEX). We implement the full HPC Scalable Graph Analysis Benchmark, and we test the performance of each database for different typical graph operations and graph sizes, showing that in their current development status, DEX and Neo4j are the most efficient graph databases.