An Evaluation of Alternative Physical Graph Data Designs for Processing Interactive Social Networking Actions

  • Shahram Ghandeharizadeh
  • Reihane Boghrati
  • Sumita Barahmand
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8904)

Abstract

This study quantifies the tradeoff associated with alternative physical representations of a social graph for processing interactive social networking actions. We conduct this evaluation using a graph data store named Neo4j deployed in a client-server (REST) architecture using the BG benchmark. In addition to the average response time of a design, we quantify its SoAR defined as the highest observed throughput given the following service level agreement: 95 % of actions to observe a response time of 100 ms or faster. For an action such as computing the shortest distance between two members, we observe a tradeoff between speed and accuracy of the computed result. With this action, a relational data design provides a significantly faster response time than a graph design. The graph designs provide a higher SoAR than a relational one when the social graph includes large member profile images stored in the data store.

References

  1. 1.
    Amsden, Z., Bronson, N., Cabrera III, G., Chakka, P., Dimov, P., Ding, H., Ferris, J., Giardullo, A., Hoon, J., Kulkarni, S., Lawrence, N., Marchukov, M., Petrov, D., Puzar, L., Venkataramani, V.: TAO: how facebook serves the social graph. In: SIGMOD Conference (2012)Google Scholar
  2. 2.
    Angles, R., Boncz, P., Larriba-Pey, J., Fundulaki, I., Neumann, T., Erling, O., Neubauer, P., Martinez-Bazan, N., Kostev, V., Toma, I.: The Linked data benchmark council: a graph and RDF industrybenchmarking effort. SIGMOD Rec. 43, 27–31 (2014)CrossRefGoogle Scholar
  3. 3.
    Angles, R., Prat-Pérez, A., Dominguez-Sal, D., Larriba-Pey, J.: Benchmarking database systems for social network applications. In: First International Workshop on Graph Data Management Experiences and Systems, GRADES 2013 (2013)Google Scholar
  4. 4.
    Armstrong, T., Ponnekanti, V., Borthakur, D., Callaghan, M.: LinkBench: a database benchmark based on the facebook social graph. In: ACM SIGMOD, June 2013Google Scholar
  5. 5.
    Bai, X., Junqueira, F.P, Silberstein, A.: Cache refreshing for online social news feeds. In: CIKM (2013)Google Scholar
  6. 6.
    Barahmand, S.: Benchmarking interactive social networking actions. Ph.D. thesis, Computer Science Department, USC (2014)Google Scholar
  7. 7.
    Barahmand, S., Ghandeharizadeh, S.: BG: a benchmark to evaluate interactive social networking actions. In: Proceedings of 2013 CIDR, January 2013Google Scholar
  8. 8.
    Barahmand, S., Ghandeharizadeh, S.: Benchmarking correctness of operations in big data applications. In: Proceedings of IEEE MASCOTS (2014)Google Scholar
  9. 9.
    Barahmand, S., Ghandeharizadeh, S.: Extensions of BG for testing and benchmarking alternative implementations of feed following. In: ACM SIGMOD Workshop on Reliable Data Services and Systems (RDSS), June 2014Google Scholar
  10. 10.
    Barahmand, S., Ghandeharizadeh, S., Yap, J.: A comparison of two physical data designs for interactive social networking actions. In: CIKM (2013)Google Scholar
  11. 11.
    Boncz, P.: LDBC: benchmark for graph and RDF data management. In: IDEAS, October 2013Google Scholar
  12. 12.
    Transaction Processing Performance Council. TPC Benchmarks. http://www.tpc.org/information/benchmarks.asp
  13. 13.
    Nishtala, R., et al.: Scaling memcache at Facebook. In: NSDI (2013)Google Scholar
  14. 14.
    Ghandeharizadeh, S., Barahmand, S.: A mid-flight synopsis of the BG social networking benchmark. In: Rabl, T., Raghunath, N., Poess, M., Bhandarkar, M., Jacobsen, H.-A., Baru, C. (eds.) WBDB 2013. LNCS, vol. 8585, pp. 19–31. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Gray, J.: The Benchmark Handbook for Database and Transaction Systems, 2nd edn. Morgan Kaufmann, San Mateo (1993). ISBN 1055860-292-5MATHGoogle Scholar
  16. 16.
    Holzschuher, F., Peinl, R.: Performance of graph query languages: comparison of cypher, gremlin and native access in Neo4J. In: Proceedings of the Joint EDBT/ICDT 2013 Workshops, EDBT 2013 (2013)Google Scholar
  17. 17.
    Junqueira, F.P., Leroy, V., Serafini, M., Silberstein, A.: Shepherding social feed generation with sheep. In: SNS (2012)Google Scholar
  18. 18.
    Labouseur, A., Olsen, P., Hwang, J: Scalable and robust management of dynamic graph data. In: VLDB Workshop on Big Dynamic Distributed Data (2013)Google Scholar
  19. 19.
    Sears, R., Ingen, C.V., Gray, J.: To BLOB or not to BLOB: large object storage in a database or a filesystem. Technical report MSR-TR-2006-45, Microsoft Research (2006)Google Scholar
  20. 20.
    Silberstein, A., Machanavajjhala, A., Ramakrishnan, R.: Feed following: the big data challenge in social applications. In: DBSocial (2011)Google Scholar
  21. 21.
    Silberstein, A., Terrace, J., Cooper, B.F., Ramakrishnan, R.: Feeding frenzy: selectively materializing users’ event feeds. In: SIGMOD Conference (2010)Google Scholar
  22. 22.
    The Neo4j Team. The Neo4j Manual V2.1.1, 29 May 2014. http://www.neo4j.org
  23. 23.
    Tesoriero, C.: Getting Started with OrientDB. Packt Publishing Ltd, Birmingham (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shahram Ghandeharizadeh
    • 1
  • Reihane Boghrati
    • 1
  • Sumita Barahmand
    • 1
  1. 1.Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA

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