Abstract
NoSQL databases offer flexibility in the data model. The document-based databases may have some data models built with embedded documents, and others made with referenced documents. The challenge lies in choosing the structure of the data. This paper proposes a study to analyze if different data models can have an impact on the performance of database queries. To this end, we created three data models: embedded, referenced, and hybrid. We ran experiments on each data model in a MongoDB cluster, comparing the response time of 3 different queries in each model. Results showed a disparity in performance between the data models. We also evaluated the use of indexes in each data model. Results showed that, depending on the type of query and field searched some types of indexes presented higher performance compared to others. Additionally, we carried out an analysis of the space occupied on the storage disk. This analysis shows that the choice of model also affects disk space for storing data and indexes.
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DB-Engines lists the most popular database management systems on a monthly basis. Ranking of November 2017. Link: https://db-engines.com/en/ranking.
References
Kang, Y.S., Park, I.H., Rhee, J., Lee, Y.H.: MongoDB-based repository design for IoT-generated RFID sensor big data. IEEE Sens. J. 16, 485–497 (2016)
Chickerur, S., Goudar, A., Kinnerkar, A.: Comparison of relational database with document-oriented database mongodb for big data applications. In: 8th International Conference on Advanced Software Engineering and Its Applications ASEA, pp. 41–47. IEEE (2015)
Li, Y., Manoharan, S.: A performance comparison of SQL and NoSQL databases. IEEE Pacific Rim Conference on Communications, Computers and Signal Processing PACRIM 2013, 15–19 (2013)
Kanoje, S., Powar, V., Mukhopadhyay, D.: Using MongoDB for Social Networking Website. arXiv preprint: arXiv:1503.06548 (2015)
Alekseev, A.A., Osipova, V.V., Ivanov, M.A., Klimentov, A., Grigorieva, N.V., Nalamwar, H.S.: Efficient data management tools for the heterogeneous big data warehouse. Phys. Particles Nucl. Lett. 13, 689–692 (2016)
Jiang, W., Zhang, L., Liao, X., Jin, H., Peng, Y.: A novel clustered MongoDB-based storage system for unstructured data with high availability. Computing 96, 455–478 (2014)
Kanade, A., Gopal, A.: A novel approach of hybrid data model in MongoDB. IUP J. Comput. Sci. 9 (2015)
Xiang, L., Huang, J., Shao, X., Wang, D.: A MongoDB-based management of planar spatial data with a flattened R-tree. ISPRS - Int. J. Geo-Inf. 5 (2016)
Banker, K.: MongoDB in Action. Manning Publications (2016)
Corbellini, A., Mateos, C., Zunino, A., Godoy, D., Schiaffino, S.: Persisting big-data: the NoSQL landscape. Inf, Syst (2017)
Vera, H., Wagner, B., Maristela, H., Valeria, G., Fernanda, H.: Data modeling for NoSQL document-oriented databases. In: CEUR Workshop Proceedings (2015)
Sadalage, P.J., Fowler, M.: NoSQL Distilled: a Brief Guide to the Emerging World of Polyglot Persistence. Pearson Education (2012)
FIES.: Fund of Student Funding. http://sisfiesportal.mec.gov.br/index.php. FIES dataset, http://www.fnde.gov.br/dadosabertos/dataset/fundo-de-financingestudantil-fies/ or http://dados.gov.br/dataset/fundo-de-financiamento-estudantil-fies/. Accessed April 2017
MongoDB, Inc.: The MongoDB 3.4 Manual. https://docs.mongodb.com/manual/. Accessed April 2017
Repository at GitHub (2017). https://github.com/reisdebora/mongodatamodels
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Reis, D.G., Gasparoni, F.S., Holanda, M., Victorino, M., Ladeira, M., Ribeiro, E.O. (2018). An Evaluation of Data Model for NoSQL Document-Based Databases. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 745. Springer, Cham. https://doi.org/10.1007/978-3-319-77703-0_61
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DOI: https://doi.org/10.1007/978-3-319-77703-0_61
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