International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Query Optimization in Elasticsearch: A Comparative Analysis of Ranking Strategies

Author(s) Ritesh Kumar
Country United States
Abstract Elasticsearch is a widely used distributed search engine, powering applications in enterprise search, e-commerce, security analytics, and knowledge retrieval. As datasets grow, ensuring efficient query execution, accurate ranking, and system scalability becomes a critical challenge. This paper presents a comparative analysis of ranking strategies within Elasticsearch, including BM25 (default model), TF-IDF, Function Score Queries, Learning-to-Rank (LTR), and Vector Search. We evaluate their impact on query performance, retrieval accuracy, and computational efficiency. Additionally, we explore index-level and query-level optimizations, providing practical recommendations for search efficiency. Through real-world case studies, we demonstrate how different ranking models enhance enterprise search, security analytics, and personalized search applications. Finally, we discuss emerging trends such as AI-driven search, hybrid models, and neural ranking techniques, outlining future directions in Elasticsearch ranking optimization. Our findings suggest that while BM25 remains a strong baseline, hybrid models that integrate Function Score Queries, LTR, and Vector Search achieve an optimal balance among precision, recall, and scalability in large-scale applications.
Keywords Elasticsearch, Query Optimization, Ranking Strategies, BM25, Learning-to-Rank (LTR), Function Score Queries, Vector Search, Neural Ranking, Search Performance, Semantic Search, Information Retrieval, Search Relevance
Field Engineering
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-02-06
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.38173
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E-ISSN 2582-2160
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