Advertisement

Pocket Data: The Need for TPC-MOBILE

  • Oliver KennedyEmail author
  • Jerry Ajay
  • Geoffrey Challen
  • Lukasz Ziarek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9508)

Abstract

Embedded database engines such as SQLite provide a convenient data persistence layer and have spread along with the applications using them to many types of systems, including interactive devices such as smartphones. Android, the most widely-distributed smartphone platform, both uses SQLite internally and provides interfaces encouraging apps to use SQLite to store their own private structured data. As similar functionality appears in all major mobile operating systems, embedded database performance affects the response times and resource consumption of billions of smartphones and the millions of apps that run on them—making it more important than ever to characterize smartphone embedded database workloads. To do so, we present results from an experiment which recorded SQLite activity on 11 Android smartphones during one month of typical usage. Our analysis shows that Android SQLite usage produces queries and access patterns quite different from canonical server workloads. We argue that evaluating smartphone embedded databases will require a new benchmarking suite and we use our results to outline some of its characteristics.

Keywords

Sqlite Client-side Android Smartphone Embedded database 

References

  1. 1.
    Ahmed, S.: MobiGen: a mobility generator for environment aware mobility model (2009). http://arrow.monash.edu.au/hdl/1959.1/109933
  2. 2.
    Box, D., Hejlsberg, A.: LinQ: NET language-integrated query. MSDN Developer Centre 89 (2007)Google Scholar
  3. 3.
    Campbell, A.T., Eisenman, S.B., Lane, N.D., Miluzzo, E., Peterson, R.A., Lu, H., Zheng, X., Musolesi, M., Fodor, K., Ahn, G.-S.: The rise of people-centric sensing. IEEE Internet Comput. 12(4), 12–21 (2008)CrossRefGoogle Scholar
  4. 4.
    Cheung, A., Arden, O., Madden, S., Solar-Lezama, A., Myers, A.C.: StatusQuo: making familiar abstractions perform using program analysis. In: CIDR (2013)Google Scholar
  5. 5.
    Cooper, B.F., Silberstein, A., Tam, E., Ramakrishnan, R., Sears, R.: Benchmarking cloud serving systems with YCSB. In: SOCC. ACM, New York, NY, USA (2010)Google Scholar
  6. 6.
    Transaction Processing Performance Council. TPC-C specification. http://www.tpc.org/tpcc/
  7. 7.
    Transaction Processing Performance Council. TPC-DS specification. http://www.tpc.org/tpcds/
  8. 8.
    Transaction Processing Performance Council. TPC-H specification. http://www.tpc.org/tpch/
  9. 9.
    Dittrich, J.: The case for small data management. In: CIDR (2015)Google Scholar
  10. 10.
    Jeong, S., Lee, K., Lee, S., Son, S., Won, Y.: I/O stack optimization for smartphones. In: USENIX ATC, pp. 309–320. USENIX Association, Berkeley, CA, USA (2013)Google Scholar
  11. 11.
    Kang, W.-H., Lee, S.-W., Moon, B., Gi-Hwan, O., Min, C.: X-FTL: Transactional FTL for SQLite databases. In: SIGMOD (2013)Google Scholar
  12. 12.
    Kim, J.-M., Kim, J.-S.: AndroBench: benchmarking the storage performance of android-based mobile devices. In: Sambath, S., Zhu, E. (eds.) Frontiers in Computer Education. AISC, vol. 133, pp. 667–674. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Klasnja, P., Consolvo, S., McDonald, D.W., Landay, J.A., Pratt, W.: Using mobile & personal sensing technologies to support health behavior change in everyday life: lessons learned. In: AMIA (2009)Google Scholar
  14. 14.
    Lam, S.C.K., Wong, K.L., Wong, K.O., Wong, W., Mow, W.H.: A smartphone-centric platform for personal health monitoring using wireless wearable biosensors. In: ICICS, December 2009Google Scholar
  15. 15.
    Madden, S.R., Franklin, M.J., Hellerstein, J.M., Hong, W.: TinyDB: an acquisitional query processing system for sensor networks. ACM TODS 30(1), 122–173 (2005)CrossRefGoogle Scholar
  16. 16.
    Nandugudi, A., Maiti, A., Ki, T., Bulut, F., Demirbas, M., Kosar, T., Qiao, C., Ko, S.Y., Challen, G.: PhoneLab: a large programmable smartphone testbed. In: SenseMine, pp. 4:1–4:6 (2013)Google Scholar
  17. 17.
    O’Neil, P., O’Neil, E., Chen, X., Revilak, S.: The star schema benchmark and augmented fact table indexing. In: Nambiar, R., Poess, M. (eds.) TPCTC 2009. LNCS, vol. 5895, pp. 237–252. Springer, Heidelberg (2009)Google Scholar
  18. 18.
    Owens, M., Allen, G.: SQLite. Springer, Heidelberg (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Oliver Kennedy
    • 1
    Email author
  • Jerry Ajay
    • 1
  • Geoffrey Challen
    • 1
  • Lukasz Ziarek
    • 1
  1. 1.University at BuffaloBuffaloUSA

Personalised recommendations