During August 2025, contributed to the apache/spark repository by developing a configurable limit for idle Python workers within the PythonWorkerFactory. This feature, implemented using Scala and Apache Spark, addressed resource management challenges by allowing administrators to cap the number of idle Python workers, thereby improving control over worker pool size. The technical approach focused on enhancing cluster stability and enabling more predictable performance for PySpark workloads. By introducing this configuration, the work laid the foundation for better capacity planning and more efficient resource utilization in Spark clusters, reflecting a targeted backend development effort with attention to operational scalability and maintainability.