Evaluation of SQL and NoSQL Databases on Parallel Processing

Authors

  • Iyas Qaddara Department of Computer Science, Faculty of Information Technology, Al-Ahliyya Amman University, Jordan
  • Yousef Alraba’nah Department of Software Engineering, Faculty of Information Technology, Al-Ahliyya Amman University, Jordan
  • Mohammad O. Hiari Department of Networks and Cybersecurity, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, Jordan
Volume: 15 | Issue: 4 | Pages: 24298-24304 | August 2025 | https://doi.org/10.48084/etasr.10620

Received: 23 February 2025 | Revised: 12 April 2025 | Accepted: 22 April 2025 | Online: 13 July 2025


Corresponding author: Iyas Qaddara

Abstract

This study performs an independent examination of the performance exhibited by NoSQL and SQL databases regarding key-value stores in parallel processing using a simulation model containing 15 nodes. Specifically, the focus is on comparing the efficiency of read, write, delete, and instantiate operations within the context of key-value stores implemented with both NoSQL (MongoDB, RavenDB, and Cassandra) and SQL (MS-SQL) databases within the average time of three runs. The results show that SQL achieved better read operation times, while NoSQL databases, especially RavenDB, had better results in the other operations.

Keywords:

SQL, NoSQL, simulation model, parallel processing

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Author Biographies

Yousef Alraba’nah, Department of Software Engineering, Faculty of Information Technology, Al-Ahliyya Amman University, Jordan

Department of Software Engineering, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, Jordan

Mohammad O. Hiari, Department of Networks and Cybersecurity, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, Jordan

Department of Networks and Cybersecurity, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, Jordan

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How to Cite

[1]
I. Qaddara, Y. Alraba’nah, and M. O. Hiari, “Evaluation of SQL and NoSQL Databases on Parallel Processing”, Eng. Technol. Appl. Sci. Res., vol. 15, no. 4, pp. 24298–24304, Aug. 2025.

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