Launching mvps for it startups: python/django/postgresql in location-tracking applications

2024;
: pp. 1-5
1
Ivan Franko National University
2
Ivan Franko National University
3
Ivan Franko National University

In this study, we develop and evaluate performance of a location-tracking application built using a Python/Django/PostgreSQL stack. Through rigorous experimentation and its analysis, we scrutinize the efficiency of WebSocket connections in facilitating real-time commun­ication and data retrieval. By comparing our findings with the previous research results available on an ASP.NET stack, we contribute to understanding of the technologies for stack selection and the strategies used for optimization of the location-tracking applications. Our results offer valuable guidance for the developers striving to create robust and scalable solutions in this burgeoning domain.

  1. V. Franiv, S. Vasyluk, O. Biletskyi, and I. Franiv, “An Investigation into the Efficiency of Specific Data­bases for Tracking Purposes in Scope of IT Startup,” in Proc. IEEE 13th Intl. Conf. on Electronics and Inf. Technol. (ELIT), pp. 186-190, Lviv, Ukraine 2023. https://doi.org/10.1109/ELIT61488.2023.10310719 
  2. F.Ahmed, M. Phillips, S. Phillips, and K.-Y. Kim, “Comparative Study of Seamless Asset Location and Tracking Technologies,” Procedia Manufacturing, vol. 51, pp. 1138–1145, 2020. 
    https://doi.org/10.1016/j.promfg.2020.10.160 
  3. M. Akkaya,  Startup Valuation,  IGI Global, pp. 137–156, 2019. 
    https://doi.org/10.4018/978-1-7998-1086-5.ch008 
  4. A.Mathur and H. Agarwal, “Study of Challenges Faced by Startup Industries,” A referred & peer-reviewed quarterly research journal, vol. 48,  pp 58–67, 2023.
  5. D. Esposito, “Building Web Solutions with ASP.NET and ADO.NET,” Redmond: Microsoft Press, 2002.
  6. K. Padaliya,  “C# Programming with .Net Framework,” 2019.
  7. W.S. Vincent, Django for APIs: Build web APIs with Python and Djang. New York, USA: WelcomeToCode, 2020.
  8. S. Matam and J. Jain, Pro Apache JMeter: web applic­a­­tion performance testing, Apress, USA, 2017. https://doi.org/10.1007/978-1-4842-2961-3 
  9. C. H. Lee and Y. L. Zheng, “SQL-to-NoSQL Schema Denormalization and Migration: A Study on Content Management Systems,” in Proc. IEEE Intl. Conf. on Systems, Man, and Cybernetics (SMC), pp. 2022–2026, 2015. https://doi.org/10.1109/SMC.2015.353 
  10. M. Abu Kausar, M. Nasar, and A. Soosaimanickam, “A Study of Performance and Comparison of NoSQL Data­bases: MongoDB, Cassandra, and Redis Using YCSB,” Indian J. Sci. Technol., vol. 15, pp. 1532–1540, 2022. https://doi.org/10.17485/IJST/v15i31.1352
  11. J. R. Lourenco, B. Cabral, P. Carreiro, M. Vieira, and J. Bernardino, “Choosing the right NoSQL database for the job: a quality attribute evaluation,” J. Big Data, vol. 2, pp. 1–26, 2015.
    https://doi.org/10.1186/s40537-015-0025-0 
  12. L. Vokorokos, M. Uchnar, and L. Lescisin, “Perform­ance optimization of applications based on non-relat­io­nal databases,” in Proc. Conf. on Emerging eLearn­ing Technol. and Appl. (ICETA), pp. 371–376, 2016. https://doi.org/10.1109/ICETA.2016.7802079
  13. N. Jatana, S. Puri, M. Ahuja, I. Kathuria, and D. Gosain, “A survey and comparison of relational and non-relational database,” Intl. J. Engin. Res. & Technol., vol. 1, pp. 104–118, 2012.
  14. J. Han, E. Haihong, G. Le, and J. Du, “Survey on NoSQL database,” in Proc.  6th Intl. Conf. on Pervasive Comput. and Appl. (ICPCA), pp. 363–366, 2011. https://doi.org/10.1109/ICPCA.2011.6106531 
  15. K. Fraczek and M. Plechawska-Wojcik, “Comparative analysis of relational and non-relational databases in the context of performance in web applications,” in Proc. Conf.: Beyond Databases, Architectures and Structures, pp. 205–213, 2017. https://doi.org/10.1007/978-3-319-58274-0_13
  16. S. Gupta and G. Narsimha, “Efficient Query Analysis and Performance Evaluation of the Nosql Data Store for Big Data,” in Proc.  1st Intl. Conf. on Comput. Intel­lig­ence and Informatics. Springer (Singapore), pp. 549–558, 2017. https://doi.org/10.1007/978-981-10-2471-9_53
  17.  K.Chodorow and M. Dirolf, MongoDB: The Definitive Guide, O’Reilly Media, 2010.
  18. D. Sullivan, NoSQL for Mere Mortals, Addison-Wesley, 2015.
  19. E. Brewer, “CAP twelve years later: how the rules have changed”, Computer, vol. 45, pp. 23–29, 2012. https://doi.org/10.1109/MC.2012.37
  20.  X. Li, Z. Ma, and H. Chen, “QODM: A query-oriented data modeling approach for NoSQL databases,” Advanced Research and Technology in Industry Applications (WARTIA), pp. 338–345, 2014.
    https://doi.org/10.1109/WARTIA.2014.6976265
  21. Y. Li and S. Manoharan, “A performance comparison of SQL and NoSQL databases,” in Proc. IEEE Pacific Rim Conf. on Communications, Computers and Signal Processing (PACRIM), pp. 15–19, 2013. https://doi.org/10.1109/PACRIM.2013.6625441