A Theoretical Framework for Optimizing Spark Distributed Computing Performance: A Systematic Analysis from Data Skew to Task Scheduling

Abstract

The performance of Apache Spark is affected by the interaction among data allocation, traffic, memory pressure, actuator configuration and scheduling strategy. Treating these factors as independent adjustment factors usually leads to local improvement and will only transfer the bottleneck to other places. In this paper, a theoretical framework is developed, which connects the main mechanisms of Spark execution and explains how performance problems spread between layers. Data skew is used as a starting point, because uneven keys or partitions can enlarge the shuffle block, trigger overflow, create wanderers and delay the end of the internship. The framework divides optimization into four interrelated layers: workload and data feature description, physical plane and data movement control, actuator and memory alignment, and real-time adaptive scheduling. It also provides a diagnosis sequence, starting with measurable symptoms, and selecting the intervention measures closest to these symptoms. The study did not propose a new benchmark. His contribution is a systematic summary, clarifying the application conditions, trade-offs and limitations of common Spark optimization strategies.