Developed and integrated diagnostic logging for Python worker termination within the xupefei/spark repository, focusing on enhancing observability and debugging for rare shutdown scenarios. The solution introduced targeted logging of exceptions during the Python runner’s shutdown phase, enabling faster root-cause analysis and reducing diagnostic effort for customer-reported failures. Leveraging Scala for backend development and logging, the implementation aligned with SPARK-51608 practices and maintained runtime performance with minimal overhead. The feature was seamlessly incorporated into the existing Python worker lifecycle, ensuring traceability through commit history and supporting ongoing audits. No bugs were reported or fixed during this period, reflecting focused feature delivery.