Enhancing Python Object-Relational Mapping Performance using Just-in-Time Compiler

In recent years, Python-based Object-Relational Mapping (ORM) frameworks have gained popularity for their ability to simplify database interactions by abstracting SQL queries and schema management. However, these frameworks often suffer from performance bottlenecks, especially in largescale applications with complex data relationships. This research explores the integration of Just-In-Time (JIT) compilation to optimize the performance of Python ORM frameworks. A custom ORM framework, JITORM, is developed and compared to traditional ORM frameworks such as SQLAlchemy, Pony ORM, and Tortoise ORM. The evaluation focuses on key performance metrics: execution time, memory usage, and CPU utilization across varying dataset sizes (50,000 to 200,000 records). The results demonstrate that JITORM significantly outperforms traditional ORMs, especially in dataintensive operations, by reducing response times, optimizing memory usage, and enhancing scalability. This study suggests that JIT-enhanced ORM frameworks can offer substantial performance improvements, particularly for applications with high data demands and resource constraints. Further research is recommended to explore additional features and real-world applications of JITORM.