# A Performance Benchmark for the PostgreSQL and MySQL Databases
## 1 Introduction
Studying execution times under varying conditions is crucial because databases directly impact application performance, to ensure that databases are continuously tailored to users' authentication needs. There are many research contributions on database benchmarking; however, research gaps exist where they remain generic and not specifically tailored towards continuous user  
Figure 1: Continuous authentication architecture. authentication scenarios. This study focuses on creating frameworks reflecting real-world conditions of continuous user authentication before presenting a proposed solution and research questions and providing recent literature summaries highlighting the importance of addressing these gaps:  
* Continuous Authentication Challenges: Continuous authentication systems face unique challenges such as fast data updates and efficient query processing, which are critical for minimizing latency [2; 10]. These challenges emphasize the need for specialized benchmarking frameworks that can evaluate database performance under such demanding conditions. * Importance of Database Performance: Studies have shown that efficient database management is crucial in scenarios involving large volumes of data, such as continuous user authentication [11]. Our proposal's focus on data latency aligns with findings that highlight the impact of database performance on overall system efficiency. * Innovative Approaches: New developments in continuous authentication technologies, like using behavioral biometrics and machine learning models, make it even more important to have strong backend systems that can handle a lot of data coming in quickly [12; 13]. This further supports the necessity for a tailored benchmarking approach such as that proposed in this study. In this study, we propose a novel benchmarking database considering the following key research questions:  
* What are the factors contributing to latency in continuous user authentication systems, and how can they be minimized? This study will highlight the importance of understanding latency, particularly data latency, in systems where continuous user authentication is critical. This involves identifying specific causes of latency in database operations and exploring methods to mitigate them. * Which database engine is most suitable for continuous user authentication applications, considering the need for low latency? This study proposes developing a universal benchmarking framework to evaluate databases under practical conditions similar to those found in continuous user authentication scenarios. This involves comparing PostgreSQL and MySQL to determine which database engine better handles structured and tabular data efficiently. * How can a benchmarking framework be designed to evaluate database performance in production-like scenarios for continuous authentication? This research intends to create a Python-based framework that benchmarks databases based on various operations typical in continuous user authentication environments. This framework aims to identify the best database fit for such applications and can be adapted to other databases with minimal code changes. The remaining sections of this paper are arranged as follows: A summary of the PostgreSQL and MySQL technologies will be given in Section 2, and a thorough explanation of the suggested benchmarking framework and the experimental setup will be given in Section 3. The framework experiments and their findings are covered in detail in Section 4, and our concluding thoughts are provided in Section 5.