Performance Optimization of Search Systems using Lucene Segment Level Caches

Lucene, one of the most widely adopted open-source search libraries, forms the backbone of modern search systems in diverse domains including e-commerce, enterprise analytics, and academic research.However, as data volume and real-time user demands increase, optimizing the performance of Lucene-based systems has become a key concern.This review explores recent advancements in segment-level caching, a promising strategy to enhance query latency, throughput, and resource utilization.We examine a range of traditional and AI-driven caching mechanisms, including LRU policies, heuristic approaches, and machine learning models such as LSTMs and reinforcement learning agents.A theoretical model is proposed to formalize cache optimization based on cost-benefit analysis, and empirical evaluations confirm the superiority of adaptive, intelligent caching over static strategies.Diagrams and tables illustrate system architectures and performance metrics.The paper concludes by discussing open challenges and future directions, highlighting federated learning, AutoML, and green computing as emerging areas of interest.This comprehensive synthesis aims to guide both researchers and practitioners in designing scalable and responsive search architectures.