Developing an Intelligent Framework for Optimizing Apache Spark Tasks to Improve the Efficiency of Memory Configuration

Due to the massive data generated continuously in recent emerging applications, big data analytics must ease and speed up the process. Optimal configuration of memory and computational resources is critical for maximizing Apache Spark applications' performance and resource efficiency. This paper introduces an intelligent framework that dynamically generates tailored configuration parameters-including memory allocation, the number of executors, and cores per executor-based on the specific characteristics of a Spark job, such as data volume and execution plan complexity. The framework offers precise, data-driven recommendations that significantly enhance Spark job performance by leveraging historical job performance data, machine learning models, and heuristic-driven analysis. By automating this traditionally manual and time-consuming process, the framework improves resource utilization and job efficiency. It empowers users to achieve optimal configurations without requiring deep expertise in Spark tuning. This approach is particularly advantageous in large-scale data processing environments, where performance and efficiency are paramount.