A Comparative Benchmark Analysis of Transactional and Analytical Performance in PostgreSQL and MySQL
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
References
Similar Articles
- Dr. Elena M. Petrovic, Dr. Rajan V. Subramaniam, A COMPREHENSIVE REVIEW AND EMPIRICAL ASSESSMENT OF DATA AUGMENTATION TECHNIQUES IN TIME-SERIES CLASSIFICATION , International Journal of Modern Computer Science and IT Innovations: Vol. 2 No. 07 (2025): Volume 02 Issue 07
- Prof. Elena Rostova, Dr. Kenji Tanaka, Enhancing Stability in Distributed Signed Networks via Local Node Compensation , International Journal of Modern Computer Science and IT Innovations: Vol. 2 No. 09 (2025): Volume 02 Issue 09
- Daniela Costa, Rafael Lima, Dynamic Deep Neural Network Partitioning For Low-Latency Edge-Assisted Video Analytics: A Learning-To-Partition Approach , International Journal of Modern Computer Science and IT Innovations: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Hakim Bin Abdullah, Marcus Tanaka, The Fusion of Enterprise Resource Planning and Artificial Intelligence: Leveraging SAP Systems for Predictive Supply Chain Resilience and Performance , International Journal of Modern Computer Science and IT Innovations: Vol. 2 No. 07 (2025): Volume 02 Issue 07
- Rahul van Dijk, Advancing Circular Business Models through Big Data and Technological Integration: Pathways for Sustainable Value Creation , International Journal of Modern Computer Science and IT Innovations: Vol. 2 No. 12 (2025): Volume 02 Issue 12
- Dr. Emiliano R. Vassalli, Event-Driven Architectures in Fintech Systems: A Comprehensive Theoretical, Methodological, and Resilience-Oriented Analysis of Kafka-Centric Microservices , International Journal of Modern Computer Science and IT Innovations: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Prof. Elise Vandermark, INTEGRATING LAKEHOUSE ARCHITECTURES AND CLOUD DATA WAREHOUSING FOR NEXT-GENERATION ENTERPRISE ANALYTICS , International Journal of Modern Computer Science and IT Innovations: Vol. 2 No. 12 (2025): Volume 02 Issue 12
- Victor E. Halden, Integrating AI-Driven Automation into Modern DevOps: Advancements, Challenges, and Strategic Implications in Software Engineering , International Journal of Modern Computer Science and IT Innovations: Vol. 3 No. 02 (2026): Volume 03 Issue 02
- Dr. Eleanor Whitfield, Architecting Secure and Cost-Optimized Iot-Cloud Ecosystems: Integrating AI-Driven Intrusion Detection, Multi-Path Routing, And Intelligent Workload Scheduling in Distributed Systems , International Journal of Modern Computer Science and IT Innovations: Vol. 3 No. 01 (2026): Volume 03 Issue 01
You may also start an advanced similarity search for this article.