Iranian Journal of Information Processing and Management

Iranian Journal of Information Processing and Management

Performance evaluation of database models in social network systems

Document Type : Original Article

Authors
Mojan Safaeinejad
soheila karbasi
Department of Computer Engineering; Golestan University; Gorgan, Iran
10.22034/jipm.2023.707244
Abstract
In this research, the data models of widely used social networks, their advantages and disadvantages, as well as the things that need to be taken into account for storing and retrieving social network data, have been reviewed and presented. The usage of social networks has grown significantly in the past few years, and the result of this growth is the production of a large amount of data. On the other hand, it is necessary to effectively store and analyze social network data for all kinds of businesses today. The types of common storage methods based on the relational data model are not scalable for large amounts of information and therefore are not optimal and efficient. As a result, the use of storage methods based on non-relational models along with relational models have become very common. In this research, a large dataset of Twitter social network data, which includes 1,581,468 tweets from posts sent by 300,000 Persian users of this social network, is stored in three databases: MySQL, MongoDB, and Redis, and the performance of each of them with several different queries has been examined and compared. In addition to measuring the time spent to perform operations on the data, the amount of space occupied by the databases was also examined. Considering the advantages and limitations associated with the structure of all three databases, from the obtained values, it can be concluded that if the data has a specific structure, a relational database like MySQL is a good choice. If the data is unstructured or structured with the potential for rapid growth, NoSQL models will perform better. In addition, storing data temporarily and at high speed in key-value databases such as Redis, which store data in memory, are more suitable. Also, in terms of execution time, MongoDB executes queries very fast compared to MySQL and Redis, which is a proof that NoSQL databases show better performance and scalability for most operations in large datasets.
Keywords
Social Networks
Database
Big Data model
Scalability
Subjects
References:
Auradkar, A., C. Botev, S. Das, D. De Maagd, A. Feinberg, P. Ganti, L. Gao, B. Ghosh, K. Gopalakrishna, B. Harris, et al. Data infrastructure at LinkedIn. 2012. IEEE 28th International Conference on Data Engineering. pp. 1370-1381: IEEE. Arlington, VA, USA USA. ISBN: 978-1-4673-1640-8.
Bhogal, J. & I. Choksi. 2015. Handling big data using NoSQL. In: IEEE 29th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2015. South Korea.
Borthakur, D., J. Gray, J. S. Sarma, K. Muthukkaruppan, N. Spiegelberg, H. Kuang, K. Ranganathan, D. Molkov, A. Menon, S. Rash et al. 2011. Apache hadoop goes realtime at facebook. Proceedings of the 2011 ACM SIGMOD International Conference on Management of data. ACM. pp. 1071–1080. Athens, Greece.
Bronson, N., Z. Amsden, G. Cabrera, P. Chakka, P. Dimov, H. Ding, J. Ferris, A. Giardullo, S. Kulkarni, H. Li, et al. 2013. {TAO}: Facebook’s distributed data store for the social graph. In 2013 {USENIX} Annual Technical Conference ({USENIX} {ATC} 13), pages 49–60. San Jose CA.
Caers, R., T. D. Feyter, M. D. Couck, T. Stough, C. Vigna, C. D. Bois. 2013. Facebook: A literature review. New Media & Society. 15 (6): 982 - 1002.
Chen, S., X. Tang, H. Wang, H. Zhao & M. Guo. 2016. Towards scalable and reliable in-memory storage system: A case study with Redis. IEEE Trustcom/BigDataSE/ ISPA, 2016, pp. 1660-1667. doi: 10.1109/TrustCom.2016.0255.
Das, A., A. Mitra, S. N. Bhagat & S. Paul. 2020. Issues and Concepts of Graph Database and a Comparative Analysis on list of Graph Database tools. International Conference on Computer Communication and Informatics (ICCCI), pp. 1-6: IEEE. Coimbatore, India.
Davoudian, A., L. Chen & M. Liu. 2018. A survey on NoSQL stores. ACM Computing Surveys (CSUR) 51 (2): 1-43.
Dawodi, M., M. H. Hedayati, J. A. Baktash & A. L. Erfan. 2019. Facebook MySQL Performance vs MySQL Performance. IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2019, pp. 0103-0109: IEEE. Vancouver, British Columbia, Canada.
Dipina Damodaran, B., S. Salim & S. M. Vargese. 2016. Performance evaluation of MySQL and MongoDB databases. International Journal on Cybernetics & Informatics (IJCI). vol. 5: ?
Fernandes, D. & J. Bernardino. 2018. Graph databases comparison: Allegrograph, arangodb, infinitegraph, neo4j, and orientdb. Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, INSTICC. SciTePress. doi: 10.5220/0006910203730380. ISBN 978-989-758-318-6 pp. 373–380.
Filip, P. & L. Čegan. 2020. Comparison of mysql and mongodb with focus on performance. International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS). pp. 184-187: IEEE. Jakarta, Indonesia.
Gašpar, D. & M. Mabić. 2017. NoSQL databases as social networks storage systems. Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Dubrovnik, Croatia. pages 251-257.
Han, J., E. Haihong, G. Le & J. Du. 2011. Survey on NoSQL database. 6th international conference on pervasive computing and applications. pp. 363-366: IEEE. Port Elizabeth, South Africa.
Jose, B. & S. Abraham. 2017. Exploring the merits of nosql: A study based on mongodb. International Conference on Networks & Advances in Computational Technologies (NetACT). pp. 266-271: IEEE. DOI: 10.1109/NetACT41440.2017
Jose, B. & S. Abraham. 2020. Performance analysis of NoSQL and relational databases with MongoDB and MySQL. Materials today: PROCEEDINGS. 24: 2036-2043.
Liu, W., A. Sidhu, A. M. Beacom & T. W. Valente. 2017. Social network theory. The international encyclopedia of media effects. Hoboken: Wiley. pp. 1-12.
Mathew, A. B. & S. M. Kumar. 2015. Analysis of data management and query handling in social networks using NoSQL databases, International Conference on Advances in Computing, Communications and Informatics (ICACCI). pp. 800-806: IEEE. Kochi, India.
Medhi, S. & H. K. Baruah. 2017. Relational database and graph database: A comparative analysis. Journal of Process Management- New Technologies 5 (2): 1-9.
Ongo, G. and G. P. Kusuma. 2018. Hybrid database system of MySQL and MongoDB in web application development. International Conference on Information Management and Technology (ICIMTech). pp. 256-260: IEEE. Jakarta, Indonesia.
Palanisamy, S. & P. SuvithaVani. 2020. A survey on RDBMS and NoSQL Databases MySQL vs MongoDB. International Conference on Computer Communication and Informatics (ICCCI). pp. 1-7: IEEE. Coimbatore, India.
Ramesh, D. & A. Kumar. 2018. Query Driven implementation of Twitter base using Cassandra. International Conference on Current Trends towards Converging Technologies (ICCTCT). pp. 1-4: IEEE. Coimbatore, India.
Roein Fard, F., F. Bahrampour & H. Jahanshahi Nokandeh. 2018. A Study of NoSQL and MongoDB Application, 6th National Conference on Applied Research in Computer Engineering and Information Technology. Tehran https://civilica.com/doc/1011692.  
Sharma, M., V. D. Sharma & M. M. Bundele. 2018. Performance Analysis of RDBMS and No SQL Databases: PostgreSQL, MongoDB and Neo4j. 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE). pp. 1-5: IEEE. Jaipur, India.
Tang, E. & Y. Fan. 2016. Performance comparison between five NoSQL databases. 7th International Conference on Cloud Computing and Big Data (CCBD). pp. 105-109: IEEE. Macau, China.

  • Receive Date 01 February 2023
  • Revise Date 16 August 2023
  • Accept Date 16 August 2023