Comparison of JSON and XML Data Formats in Document Stored NoSql Database Replication Processes

- Rianto, Muhamad Arsyad Rifansyah, Rohmat Gunawan, Irfan Darmawan, Alam Rahmatulloh

Abstract


The current trend of solutions in storing large amounts of data is using the NoSQL Database. A document stored is one type of NoSQL database that uses the JavaScript Object Notation (JSON) and eXtensible Markup Language (XML) data formats in data storage. High Availability database is significant to support cloud-based applications and services. Replication is one solution to maintain the consistency of source data and target data. This study aims to determine the performance of JSON and XML data formats in the document stored NoSQL Database replication process. In this study, ArangoDB, RethinkDB, and MongoDB were chosen for use in the trial process of replication from master-server to slave-server with two different data formats, JSON and XML. Data transfer, CPU usage, memory usage, and execution time are measured in each trial. Based on research and experiments that have been carried out, the JSON data format consumes bandwidth with an average value smaller than the XML data format; this occurs in MongoDB, CouchDB, and RethinkDB. In CPU usage, JSON data format, on average, consumes less CPU compared to the XML data format. This is the case with MongoDB. While on CouchDB and RethinkDB, the average CPU usage for XML and JSON data formats does not show a significant difference. The average memory usage for the JSON data format is smaller than the XML data format. The average execution time of the XML data format a little faster than the JSON data format.

Keywords


Data; JSON; NoSQL; replication; XML.

Full Text:

PDF

References


A. Corbellini, C. Mateos, A. Zunino, D. Godoy, and S. Schiaf, “Persisting big-data: The NoSQL landscape,†pp. 1–23, 2016, doi: 10.1016/j.is.2016.07.009.

H. Gujral, A. Sharma, and P. Kaur, “Empirical Investigation of Trends in NoSQL-Based Big-Data Solutions in the Last Decade,†2018 11th Int. Conf. Contemp. Comput. IC3 2018, pp. 1–3, 2018, doi: 10.1109/IC3.2018.8530582.

A. Makris, K. Tserpes, V. Andronikou, and D. Anagnostopoulos, “A Classification of NoSQL Data Stores Based on Key Design Characteristics,†Procedia Comput. Sci., vol. 97, pp. 94–103, 2016, doi: 10.1016/j.procs.2016.08.284.

G. Bathla, R. Rani, and H. Aggarwal, “Comparative study of NoSQL databases for big data storage,†Int. J. Eng. Technol., vol. 7, no. 2, pp. 83–87, 2018, doi: 10.14419/ijet. v7i2.6.10072.

S. Bjeladinovic, “A fresh approach for hybrid SQL/NoSQL database design based on data structuredness,†Enterp. Inf. Syst., vol. 12, no. 8–9, pp. 1202–1220, 2018, doi: 10.1080/17517575.2018.1446102.

C. Gomes, E. Borba, E. Tavares, and M. N. D. O. Junior, “Performability model for assessing NoSQL DBMS consistency,†SysCon 2019 - 13th Annu. IEEE Int. Syst. Conf. Proc., pp. 1–6, 2019, doi: 10.1109/SYSCON.2019.8836757.

Y. Li and S. Manoharan, “A performance comparison of SQL and NoSQL databases,†IEEE Pacific RIM Conf. Commun. Comput. Signal Process. - Proc., no. November, pp. 15–19, 2013, doi: 10.1109/PACRIM.2013.6625441.

V. Abramova, J. Bernardino, and P. Furtado, “SQL or NoSQL? Performance and scalability evaluation,†Int. J. Bus. Process Integr. Manag., vol. 7, no. 4, pp. 314–321, 2015, doi: 10.1504/IJBPIM.2015.073655.

B. Acharya, A. K. Jena, J. M. Chatterjee, R. Kumar, and D.-N. Le, “NoSQL Database Classification,†Int. J. Knowledge-Based Organ., vol. 9, no. 1, pp. 50–65, 2018, doi: 10.4018/ijkbo.2019010105.

A. Davoudian, L. Chen, and M. Liu, “A Survey on NoSQL Stores,†ACM Comput. Surv., vol. 51, no. 2, pp. 1–43, 2018, doi: 10.1145/3158661.

A. Gupta, S. Tyagi, N. Panwar, S. Sachdeva, and U. Saxena, “NoSQL databases: Critical analysis and comparison,†2017 Int. Conf. Comput. Commun. Technol. Smart Nation, IC3TSN 2017, vol. 2017-Octob, pp. 293–299, 2018, doi: 10.1109/IC3TSN.2017.8284494.

R. Gunawan, A. Rahmatulloh, and I. Darmawan, “Performance Evaluation of Query Response Time in The Document Stored NoSQL Database,†in 2019 16th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering, 2019, pp. 1–6, doi: 10.1109/QIR.2019.8898035.

M. M. Patil, A. Hanni, C. H. Tejeshwar, and P. Patil, “A qualitative analysis of the performance of MongoDB vs MySQL database based on insertion and retrieval operations using a web/android application to explore load balancing-Sharding in MongoDB and its advantages,†Proc. Int. Conf. IoT Soc. Mobile, Anal. Cloud, I-SMAC 2017, pp. 325–330, 2017, doi: 10.1109/I-SMAC.2017.8058365.

G. Haughian, R. Osman, and W. J. Knottenbelt, “Benchmarking replication in Cassandra and MongoDB NoSQL datastores,†Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9828 LNCS, no. 3, pp. 152–166, 2016, doi: 10.1007/978-3-319-44406-2_12.

W. Hendricks, “Review of NoSQL Data Stores: Using a reactive three-tier application for software developers to achieve a high availability application design architecture,†pp. 71–77, 2019.

R. Shrestha, “High Availability and Performance of Database in the Cloud - Traditional Master-slave Replication versus Modern Cluster-based Solutions,†no. Closer, pp. 413–420, 2017, doi: 10.5220/0006294604130420.

E. Tang and Y. Fan, “Performance comparison between five NoSQL databases,†Proc. - 2016 7th Int. Conf. Cloud Comput. Big Data, CCBD 2016, pp. 105–109, 2017, doi: 10.1109/CCBD.2016.030.

K. Tabet, R. Mokadem, and M. R. Laouar, “Towards a new data replication strategy in MongoDB systems,†ACM Int. Conf. Proceeding Ser., 2018, doi: 10.1145/3213187.3287609.

K. Tabet, R. Mokadem, and M. R. Laouar, “A data replication strategy for document-oriented NoSQL systems,†Int. J. Grid Util. Comput., vol. 10, no. 1, pp. 53–62, 2019, doi: 10.1504/IJGUC.2019.097227.

K. Ma and B. Yang, “Stream-based live data replication approach of in-memory cache,†Concurr. Comput., vol. 29, no. 11, 2017, doi: 10.1002/cpe.4052.

H. Hashem and D. Ranc, “Evaluating NoSQL document-oriented data model,†Proc. - 2016 4th Int. Conf. Futur. Internet Things Cloud Work. W-FiCloud 2016, pp. 51–56, 2016, doi: 10.1109/W-FiCloud.2016.26.

A. R. Breje, R. Gyorödi, C. Gyorödi, D. Zmaranda, and G. Pecherle, “Comparative study of data sending methods for XML and JSON models,†Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 12, pp. 198–204, 2018, doi: 10.14569/IJACSA.2018.091229.

A. Å imec and M. MagliÄić, “Comparison of JSON and XML Data Formats,†Cent. Eur. Conf. Inf. Intell. Syst., pp. 272–275, 2014.

Z. U. Haq, G. F. Khan, and T. Hussain, “A Comprehensive analysis of XML and JSON web technologies,†New Dev. Circuits, Syst. Signal Process. Commun. Comput., pp. 102–109, 2014.

C. O. Truica, F. Radulescu, A. Boicea, and I. Bucur, “Performance evaluation for CRUD operations in asynchronously replicated document-oriented database,†Proc. - 2015 20th Int. Conf. Control Syst. Comput. Sci. CSCS 2015, pp. 191–196, 2015, doi: 10.1109/CSCS.2015.32.

Y. Gu et al., “Analysis of Data Replication Mechanism in NoSQL Database MongoDB,†pp. 66–67, 2015.




DOI: http://dx.doi.org/10.18517/ijaseit.11.3.11570

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development