Comparative Evaluation of Persistent vs. In-Memory Stream Exchange in Big Data Systems: A Technical Review

Abstract

Modern distributed data processing frameworks face critical architectural decisions regarding intermediate data handling mechanisms between processing stages. This technical review examines the fundamental trade-offs between persistent stream storage and in-memory stream exchange strategies across diverse operational scenarios. The evaluation encompasses performance characteristics, fault tolerance capabilities, resource utilization patterns, and scalability considerations within contemporary big data processing environments. Results demonstrate that in-memory stream exchange delivers superior performance for smaller computational workloads through eliminated storage operations and reduced serialization overhead, while persistent storage approaches provide enhanced durability guarantees and sustained throughput for large-scale processing tasks exceeding available memory capacity. The comparative assessment reveals distinct advantages for each strategy based on workload characteristics, with persistent storage demonstrating superior recovery capabilities and data durability for mission-critical applications, whereas in-memory approaches excel in latency-sensitive scenarios requiring rapid response times. Hybrid implementation strategies emerge as promising solutions that dynamically adapt between exchange mechanisms based on runtime conditions, offering potential for optimized performance across diverse operational environments. The findings provide essential guidance for system architects designing next-generation distributed processing platforms, highlighting the importance of adaptive strategy selection mechanisms that can intelligently balance performance requirements with reliability constraints.