eISSN: 3087-4289 editor@aimjournals.com
All Journals
Submit your article

International Journal of Modern Computer Science and IT Innovations

Open Access Peer Review International
Open Access

A Comparative Benchmark Analysis of Transactional and Analytical Performance in PostgreSQL and MySQL

DOI
PDF
Martin Schneider 1 , Diego Martínez 1 ,
4 Faculty of Computer Science and Engineering, Technical University of Munich (TUM), Munich, Germany
4 Department of Computer Science, Universidad de Buenos Aires (UBA), Buenos Aires, Argentina

Abstract

Background: PostgreSQL and MySQL are the world's leading open-source relational database management systems (RDBMS), yet the choice between them remains a critical and complex decision for system architects. While historical benchmarks exist, the continuous evolution of both systems necessitates an updated, rigorous performance evaluation that reflects modern hardware and diverse application workloads.

Methods: This study conducts a comprehensive benchmark analysis of the latest stable versions, PostgreSQL 16 and MySQL 8.0, on a dedicated, high-performance physical server. Using a composite benchmarking approach, we evaluated performance across three distinct, industry-standard workload profiles: a simple, high-concurrency Online Transaction Processing (OLTP) workload using SysBench; a complex, multi-table OLTP workload using the TPC-C benchmark; and a decision-support, Online Analytical Processing (OLAP) workload using the 22 queries of the TPC-H benchmark. Key performance metrics, including throughput (TPS), 95th percentile latency, and query execution time, were systematically collected.

Results: Our findings reveal a distinct performance dichotomy. MySQL demonstrated superior throughput and lower latency in simple OLTP scenarios, achieving up to 21% higher peak TPS than PostgreSQL under moderate concurrency. However, its performance degraded under heavy client load. Conversely, PostgreSQL exhibited greater stability and scalability, outperforming MySQL by 14% in the complex TPC-C workload. In the analytical TPC-H benchmark, PostgreSQL showed a profound advantage, completing the full query suite in less than one-third of the time required by MySQL, highlighting its superior query optimizer and execution engine for complex analytical tasks.

Conclusion: The optimal database choice is fundamentally workload-dependent. MySQL is highly proficient for applications dominated by simple, high-volume read/write operations. PostgreSQL is the more robust and versatile choice for applications with complex transactional logic, mixed transactional and analytical requirements, and the need for predictable performance under high contention. These findings provide empirical guidance for architects to align database selection with specific application performance profiles.

Keywords

Database Performance, Benchmarking, PostgreSQL, MySQL, OLTP

References

📄 Yao, S. B., Hevner, A. R., & Young-Myers, H. (1987). Analysis of database system architectures using benchmarks. IEEE Transactions on Software Engineering, SE-13, 709–725.
📄 Bonthu, C., Kumar, A., & Goel, G. (2025). Impact of AI and machine learning on master data management. Journal of Information Systems Engineering and Management, 10(32s), 46–62. https://doi.org/10.55278/jisem.2025.10.32s.46
📄 Heck, W. (2009). Using MySQL in your organisation. In Proceedings of the INTED2009, IATED, Valencia, Spain, 9–11 March 2009; pp. 3686–3694.
📄 Shaik, B., & Vallarapu, A. (2018). Beginning PostgreSQL on the Cloud: Simplifying Database as a Service on Cloud Platforms. Apress: New York, NY, USA.
📄 Reddy Gundla, S. (2025). PostgreSQL tuning for cloud-native Java: Connection pooling vs. reactive drivers. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3479
📄 Comer, D. (1979). Ubiquitous B-tree. ACM Computing Surveys (CSUR), 11, 121–137.
📄 Han, R., John, L. K., & Zhan, J. (2018). Benchmarking Big Data Systems: A Review. IEEE Transactions on Services Computing, 11, 580–597.
📄 Stonebraker, M., Rowe, L. A., & Hirohama, M. (1990). The implementation of POSTGRES. IEEE Transactions on Knowledge and Data Engineering, 2, 125–142.
📄 Nagaraj, V. (2025). Ensuring low-power design verification in semiconductor architectures. Journal of Information Systems Engineering and Management, 10(45s), 703–722. https://doi.org/10.52783/jisem.v10i45s.8903
📄 Almeida, D. (2023). Performance Comparison of Redis, Memcached, MySQL, and PostgreSQL: A Study on Key-Value and Relational Databases. In Proceedings of the 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon), Singapore, 18–19 Aug 2023; pp. 902–907.
📄 Lulla, K. L., Chandra, R. C., & Sirigiri, K. S. (2025). Proxy-based thermal and acoustic evaluation of cloud GPUs for AI training workloads. The American Journal of Applied Sciences, 7(7), 111–127. https://doi.org/10.37547/tajas/Volume07Issue07-12
📄 Salunke, S. V., & Ouda, A. (2023). Ensemble Learning to Enhance Continuous User Authentication For Real World Environments. In Proceedings of the 2023 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Istanbul, Türkiye, 4–7 Jul 2023; pp. 102–108.
📄 Rangu, S. (2025). Analyzing the impact of AI-powered call center automation on operational efficiency in healthcare. Journal of Information Systems Engineering and Management, 10(45s), 666–689. https://doi.org/10.55278/jisem.2025.10.45s.666
📄 Weng, S., Wang, Q., Qu, L., Zhang, R., Cai, P., Qian, W., & Zhou, A. (2024). Lauca: A Workload Duplicator for Benchmarking Transactional Database Performance. IEEE Transactions on Knowledge and Data Engineering, 36, 3180–3194.
📄 Ciolli, G., Mejías, B., Angelakos, J., Kumar, V., & Riggs, S. (2023). PostgreSQL 16 Administration Cookbook. Packt Publishing Ltd.: Birmingham, UK.
📄 Bartunov, O., & Sigaev, T. (2024). Full-Text Search in PostgreSQL. PostgreSQL Documentation. Retrieved October 17, 2024, from https://www.postgresql.org/docs/current/textsearch.html
📄 Reddy Dhanagari, M. (2025). Aerospike: The key to high-performance real-time data processing. Journal of Information Systems Engineering and Management, 10(45s), 513–531. https://doi.org/10.55278/jisem.2025.10.45s.513
📄 Tongkaw, S., & Tongkaw, A. (2016). A comparison of database performance of MariaDB and MySQL with OLTP workload. In Proceedings of the 2016 IEEE Conference on Open Systems (ICOS), Langkawi, Malaysia, 10–12 Oct 2016; pp. 117–119.
📄 Edrah, M., & Ouda, A. (2024). Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System. Computers, 13, 159.
📄 Bonthu, C., & Goel, G. (2025). Autonomous supplier evaluation and data stewardship with AI: Building transparent and resilient supply chains. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3854
📄 Bansal, P., & Ouda, A. (2022). Study on integration of FastAPI and machine learning for continuous authentication of behavioral biometrics. In Proceedings of the 2022 ISNCC, Shenzhen, China, 19–22 Jul 2022; pp. 1–6.
📄 Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt, I., & Warfield, A. (2003). Xen and the art of virtualization. ACM SIGOPS Operating Systems Review, 37, 164–177.
📄 Salunke, S., Ouda, A., & Gagne, J. (2022). Transfer Learning for Behavioral Biometrics-based Continuous User Authentication. In Proceedings of the 2022 ISNCC, Shenzhen, China, 19–22 Jul 2022; pp. 1–6.
📄 Han, J., Haihong, E., Le, G., & Du, J. (2011). Survey on NoSQL database. In Proceedings of the 6th International Conference on Pervasive Computing and Applications, Port Elizabeth, South Africa, 26–28 Oct 2011; pp. 363–366.
📄 Durgam, S. (2025). CICD automation for financial data validation and deployment pipelines. Journal of Information Systems Engineering and Management, 10(45s), 645–664. https://doi.org/10.52783/jisem.v10i45s.8900
📄 Kaur, K., & Sachdeva, M. (2017). Performance evaluation of NewSQL databases. In Proceedings of the 2017 International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 19–20 Jan 2017; pp. 1–5.
📄 Murach, J. (2019). Murach’s MySQL (3rd ed.). Mike Murach Associates: Fresno, CA, USA.
📄 Seghier, N. B., & Kazar, O. (2021). Performance benchmarking and comparison of NoSQL databases: Redis vs MongoDB vs Cassandra using YCSB tool. In Proceedings of the 2021 ICRAMI, Tebessa, Algeria, 21–22 Sep 2021; pp. 1–6.
📄 Armenatzoglou, N., Basu, S., Bhanoori, N., Cai, M., Chainani, N., Chinta, K., Govindaraju, V., Green, T. J., Gupta, M., Hillig, S., et al. (2022). Amazon Redshift re-invented. In Proceedings of the 2022 International Conference on Management of Data, Paphos, Cyprus, 12–17 Jun 2022; pp. 2205–2217.
📄 Reddy Dhanagari, M. (2025). Aerospike vs. traditional databases: Solving the speed vs. consistency dilemma. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3780
📄 Aref, Y., & Ouda, A. (2024). HSM4SSL: Leveraging HSMs for Enhanced Intra-Domain Security. Future Internet, 16, 148.
📄 Bog, A., Kruger, J., & Schaffner, J. (2008). A composite benchmark for online transaction processing and operational reporting. In Proceedings of the 2008 IEEE Symposium on Advanced Management of Information for Globalized Enterprises (AMIGE), Tianjin, China, 28–29 Sep 2008; pp. 1–5.
📄 Ramsey, P. (2022). Postgres Indexing: When Does BRIN Win? Crunchy Data. Retrieved Oct 13 2024 from https://www.crunchydata.com/blog/postgres-indexing-when-does-brin-win
📄 Bernstein, P. A., & Goodman, N. (1983). Multiversion concurrency control—Theory and algorithms. ACM Transactions on Database Systems (TODS), 8, 465–483.
📄 Zhou, G., Huang, L., Li, Z., Tian, H., Zhang, B., Fu, M., Feng, Y., & Huang, C. (2021). Intever Public Database for Arcing Event Detection: Feature Analysis, Benchmark Test, and Multi-Scale CNN Application. IEEE Transactions on Instrumentation and Measurement, 70, 3518515.
📄 Kubernetes, T. (2019). Kubernetes. Retrieved May 24, 2019, from Kubernetes.io
📄 Abreha, T., & Reuter, A. (1983). Principles of transaction-oriented database recovery. ACM Computing Surveys (CSUR), 15, 287–317.
📄 Callaghan, M. (2024). MySQL and Postgres vs the Insert Benchmark on a Large Server. Retrieved Oct 13 2024 from https://smalldatum.blogspot.com/2024/09/mysql-and-postgres-vs-insert-benchmark.html
📄 Reddy Gundla, S. (2025). PostgreSQL tuning for cloud-native Java. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3479
📄 Kenler, E., & Razzoli, F. (2015). MariaDB Essentials. Packt Publishing Ltd.: Birmingham, UK.
📄 Filip, P., & Cegan, L. (2020). Comparison of MySQL and MongoDB with focus on performance. In Proceedings of the 2020 ICIMCIS, Jakarta, Indonesia, 19–20 Nov 2020; pp. 184–187.
📄 Sabharwal, N., & Edward, S. G. (2019). Hands on Google Cloud SQL and Cloud Spanner. Apress: New York, NY, USA.
📄 Hellerstein, J. M., Naughton, J. F., & Pfeffer, A. (1995). Generalized Search Trees for Database Systems. University of Wisconsin–Madison, Department of Computer Sciences.
📄 A Brief History of PostgreSQL. (2018). In PostgreSQL Documentation. The PostgreSQL Global Development Group: Athens, Greece.
📄 Srilatha, S. (2025). Integrating AI into enterprise content management systems: A roadmap for intelligent automation. Journal of Information Systems Engineering and Management, 10(45s), 672–688. https://doi.org/10.52783/jisem.v10i45s.8904

Similar Articles

11-20 of 32

You may also start an advanced similarity search for this article.