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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Performance Optimization of Search Systems using Lucene Segment-Level Caches |
| Authors Name: | Rohit Reddy Kommareddy |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_204382
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| Published Paper Id: | IJRTI2506005 |
| Published In: | Volume 10 Issue 6, June-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i6.204382 |
| Abstract: | 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. |
| Keywords: | Lucene, Segment-Level Cache, Information Retrieval, Query Optimization, Machine Learning, Reinforcement Learning, AutoML, Search Architecture, Cache Management, Performance Tuning |
| Cite Article: | "Performance Optimization of Search Systems using Lucene Segment-Level Caches", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.a28-a37, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506005.pdf |
| Downloads: | 000573 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2506005
Registration ID:204382
Published In: Volume 10 Issue 6, June-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i6.204382
Page No: a28-a37 Country: Chennai, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2506005 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2506005 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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