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The G* graph database: efficiently managing large distributed dynamic graphs

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Abstract

From sensor networks to transportation infrastructure to social networks, we are awash in data. Many of these real-world networks tend to be large (“big data”) and dynamic, evolving over time. Their evolution can be modeled as a series of graphs. Traditional systems that store and analyze one graph at a time cannot effectively handle the complexity and subtlety inherent in dynamic graphs. Modern analytics require systems capable of storing and processing series of graphs. We present such a system. G* compresses dynamic graph data based on commonalities among the graphs in the series for deduplicated storage on multiple servers. In addition to the obvious space-saving advantage, large-scale graph processing tends to be I/O bound, so faster reads from and writes to stable storage enable faster results. Unlike traditional database and graph processing systems, G* executes complex queries on large graphs using distributed operators to process graph data in parallel. It speeds up queries on multiple graphs by processing graph commonalities only once and sharing the results across relevant graphs. This architecture not only provides scalability, but since G* is not limited to processing only what is available in RAM, its analysis capabilities are far greater than other systems which are limited to what they can hold in memory. This paper presents G*’s design and implementation principles along with evaluation results that document its unique benefits over traditional graph processing systems.

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Notes

  1. These systems cannot readily take advantage of commonalities among graphs and thereby suffer high space overhead. For example, one may consider using a relation to store edges of a series of graphs. In this case, for an edge contained in 100 snapshots, there will be 100 tuples for that edge, each differentiated by snapshot ID. This incurs high space overhead compared to our system, which supports deduplicated storage as described throughout this paper.

  2. In this paper, we focus on managing graphs that correspond to periodic snapshots of an evolving network. Logging the input data allows G* to reconstruct graphs as of any points in the past by using periodic snapshots and log data. This feature is not further discussed in this paper.

  3. The current G* implementation assigns each vertex to a server based on the hash value of the vertex ID. We are developing data distribution techniques that can reduce the edges whose end points are assigned to different servers.

  4. As mentioned in the cost analysis of the put(v, d, g) method, updating the CGI for a version of vertex v also requires a lookup via maps(v).

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Acknowledgments

This research was supported by NSF CAREER award IIS-1149372 and also supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the “IT Consilience Creative Program” (NIPA-2013-H0203-13-1001) supervised by the NIPA (National IT Industry Promotion Agency).

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Authors and Affiliations

  1. Department of Computer Science, State University of New York, Albany, NY, USA

    Alan G. Labouseur, Jeremy Birnbaum, Paul W. Olsen Jr., Sean R. Spillane, Jayadevan Vijayan & Jeong-Hyon Hwang

  2. Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang, Korea

    Wook-Shin Han

  3. Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea

    Wook-Shin Han

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  1. Alan G. Labouseur
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Correspondence to Alan G. Labouseur.

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Communicated by Haixun Wang and Jeffrey Xu Yu.

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Labouseur, A.G., Birnbaum, J., Olsen, P.W. et al. The G* graph database: efficiently managing large distributed dynamic graphs. Distrib Parallel Databases 33, 479–514 (2015). https://doi.org/10.1007/s10619-014-7140-3

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