Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Structured Streaming has become a leading framework for large-scale stream processing, yet incorporating external data enrichment poses considerable performance issues that can significantly lower throughput and heighten latency. This article offers an in-depth examination of the effects on performance when enhancing streaming data with external sources like databases, REST APIs, and distributed caches. The micro-batch processing model of Spark, while providing fault tolerance and exactly-once semantics, creates burst traffic patterns that can overwhelm external systems, leading to connection pool exhaustion, API rate limiting, and cascading failures. Through extensive production deployments and benchmarking, It demonstrates that external enrichment operations can reduce throughput by an order of magnitude compared to pure stream processing, transforming systems capable of processing millions of events per second to handling only tens of thousands. The article presents a systematic framework for optimizing batch sizes, implementing multi-tier caching architectures, and employing circuit breaker patterns to achieve sustainable performance. It compares the micro-batch approach with true streaming allocation, though fundamental architectural differences make true streaming systems more suitable for ultra-low latency requirements. The article concludes with best practices for production deployments and identifies emerging research directions, including edge-cloud hybrid architectures, federated learning integration, and hardware acceleration techniques that promise to revolutionize stream processing performance in the coming years.