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Large scale graph processing systems: survey and an experimental evaluation

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A Correction to this article was published on 28 February 2018

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Abstract

Graph is a fundamental data structure that captures relationships between different data entities. In practice, graphs are widely used for modeling complicated data in different application domains such as social networks, protein networks, transportation networks, bibliographical networks, knowledge bases and many more. Currently, graphs with millions and billions of nodes and edges have become very common. In principle, graph analytics is an important big data discovery technique. Therefore, with the increasing abundance of large graphs, designing scalable systems for processing and analyzing large scale graphs has become one of the most timely problems facing the big data research community. In general, scalable processing of big graphs is a challenging task due to their size and the inherent irregular structure of graph computations. Thus, in recent years, we have witnessed an unprecedented interest in building big graph processing systems that attempted to tackle these challenges. In this article, we provide a comprehensive survey over the state-of-the-art of large scale graph processing platforms. In addition, we present an extensive experimental study of five popular systems in this domain, namely, GraphChi, Apache Giraph, GPS, GraphLab and GraphX. In particular, we report and analyze the performance characteristics of these systems using five common graph processing algorithms and seven large graph datasets. Finally, we identify a set of the current open research challenges and discuss some promising directions for future research in the domain of large scale graph processing.

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  • 28 February 2018

    The original version of this article unfortunately contained a mistake in the acknowledgement statement.

Notes

  1. http://www.insidefacebook.com/2012/10/04/facebook-reaches-billion-user-milestone/.

  2. http://hadoop.apache.org/.

  3. http://pig.apache.org/.

  4. https://hive.apache.org/.

  5. http://giraph.apache.org/.

  6. http://hama.apache.org/.

  7. http://graphlab.com/.

  8. https://github.com/GraphExperiments.

  9. http://systemg.research.ibm.com/analytics-search-gbase.html.

  10. http://www.gzip.org/.

  11. http://www.cs.cmu.edu/~pegasus/.

  12. http://zookeeper.apache.org/.

  13. http://infolab.stanford.edu/gps/.

  14. http://www.cse.cuhk.edu.hk/pregelplus/.

  15. http://pregelix.ics.uci.edu/.

  16. http://graphlab.org/projects/graphchi.html.

  17. http://research.microsoft.com/en-us/projects/trinity/.

  18. https://code.google.com/p/signal-collect/.

  19. http://akka.io/.

  20. https://spark.apache.org/.

  21. http://wshan.net/turbograph.

  22. http://www.cs.cornell.edu/bigreddata/grace/.

  23. http://snap.stanford.edu/data/web-Amazon.html.

  24. https://snap.stanford.edu/data/com-Friendster.html.

  25. https://snap.stanford.edu/data/cit-Patents.html.

  26. https://snap.stanford.edu/data/wiki-Talk.html.

  27. http://swat.cse.lehigh.edu/projects/lubm/.

  28. Please refer to a table with specification of EC2 instances on http://aws.amazon.com/ec2/instance-types/.

  29. http://aws.amazon.com/cloudwatch/.

  30. http://docs.aws.amazon.com/AmazonCloudWatch/latest/DeveloperGuide/mon-scripts-perl.html.

  31. http://hortonworks.com/hadoop/ambari/.

  32. http://snap.stanford.edu/data/web-Amazon.html.

  33. http://snap.stanford.edu/data/.

  34. http://law.di.unimi.it/datasets.php.

  35. http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html.

  36. http://stratosphere.eu/.

  37. http://neo4j.com/.

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Acknowledgments

This work was supported by King Abdulaziz City for Science and Technology (KACST) Project 11-INF1990-03.

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Authors and Affiliations

  1. King Abdulaziz University, Jeddah, Saudi Arabia

    Omar Batarfi, Ayman G. Fayoumi & Ahmed Barnawi

  2. Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

    Radwa El Shawi

  3. University of New South Wales, Sydney, Australia

    Reza Nouri, Seyed-Mehdi-Reza Beheshti & Sherif Sakr

  4. King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia

    Sherif Sakr

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  1. Omar Batarfi
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  2. Radwa El Shawi
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  6. Ahmed Barnawi
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Correspondence to Sherif Sakr.

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A correction to this article is available online at https://doi.org/10.1007/s10586-018-2132-0.

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Batarfi, O., Shawi, R.E., Fayoumi, A.G. et al. Large scale graph processing systems: survey and an experimental evaluation. Cluster Comput 18, 1189–1213 (2015). https://doi.org/10.1007/s10586-015-0472-6

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