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.
The members of DAMA-UPC thank the Ministry of Science and Innovation of Spain and Generalitat de Catalunya, for grant numbers TIN2009-14560-C03-03 and GRC-1087 respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
AllegroGraph. AllegroGraph website, http://www.franz.com/agraph/ (last retrieved in May 2010)
Apache Lucene (September 2008), Lucene website, http://lucene.apache.org/
Bader, D., Feo, J., Gilbert, J., Kepner, J., Koetser, D., Loh, E., Madduri, K., Mann, B., Meuse, T., Robinson, E.: HPC Scalable Graph Analysis Benchmark v1.0. HPC Graph Analysis (February 2009)
Bader, D., Madduri, K.: Parallel algorithms for evaluating centrality indices in real-world networks. In: ICPP, pp. 539–550 (2006)
BerkeleyDB. BerkeleyDB website, http://www.oracle.com/database/berkeley-db/index.html (last retrieved in March 2010)
Brandes, U.: A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25(2), 163–177 (2001)
Chakrabarti, D., Zhan, Y., Faloutsos, C.: R-mat: A recursive model for graph mining. In: Jonker, W., Petković, M. (eds.) SDM 2004. LNCS, vol. 3178. Springer, Heidelberg
Chang, F., Dean, J., Ghemawat, S., et al.: Bigtable: A distributed storage system for structured data. ACM Trans. Comput. Syst. 26(2) (2008)
HypergraphDB. HypergraphDB website, http://www.kobrix.com/hgdb.jsp (last retrieved in March 2010)
Infogrid. Blog, http://infogrid.org/blog/2010/03/operations-on-a-graph-databae-part-4 (last retrieved in March 2010)
Jena-RDF. Jena documentation, http://jena.sourceforge.net/documentation.html (last retrieved in March 2010)
Leskovec, J., Lang, L., Dasgupta, A., Mahoney, M.: Statistical properties of community structure in large social and information networks. In: WWW, pp. 695–704 (2008)
Martínez-Bazan, N., Muntés-Mulero, V., Gómez-Villamor, S., et al.: Dex: high-performance exploration on large graphs for information retrieval. In: CIKM, pp. 573–582 (2007)
Neo4j. The neo database (2006), http://dist.neo4j.org/neo-technology-introduction.pdf
Neo4j. Batch Insert, http://wiki.neo4j.org/content/Batch_Insert (last retrieved in March 2010)
Neo4j. Neo4j wiki documentation, http://wiki.neo4j.org/content/Main_Page (last retrieven in March 2010)
Olson, M., Bostic, K., Seltzer, M.: Berkeley db. In: USENIX Annual Technical Conference, FREENIX Track, pp. 183–191. USENIX (1999)
Sesame. Open RDF website, http://www.openrdf.org/ (last retrieved in May 2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dominguez-Sal, D., Urbón-Bayes, P., Giménez-Vañó, A., Gómez-Villamor, S., Martínez-Bazán, N., Larriba-Pey, J.L. (2010). Survey of Graph Database Performance on the HPC Scalable Graph Analysis Benchmark. In: Shen, H.T., et al. Web-Age Information Management. WAIM 2010. Lecture Notes in Computer Science, vol 6185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16720-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-16720-1_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16719-5
Online ISBN: 978-3-642-16720-1
eBook Packages: Computer ScienceComputer Science (R0)