Relevant bibliographies by topics / Elasticsearch performance benchmarks

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  1. Journal articles
  2. Dissertations / Theses

Academic literature on the topic 'Elasticsearch performance benchmarks'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Elasticsearch performance benchmarks"

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Sipilov, I. "ADVANCED OPTIMIZATION OF LARGE-SCALE SEARCH FUNCTIONALITY USING ELASTICSEARCH." Sciences of Europe, no. 151 (October 27, 2024): 105–9. https://doi.org/10.5281/zenodo.13999042.

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This article presents a detailed case study of the application of Elasticsearch in optimizing large-scale search functionality for the recruitment platform NannyServices.ca. By integrating Elasticsearch’s core algorithms such as inverted indexing, BM25 scoring, and sharding techniques with custom user-driven relevance models, we enhanced both the speed and accuracy of search results. The system efficiently indexed 300,000 profiles within 240 seconds, dramatically improving the search experience for thousands of users. This paper provides a deep dive into the architectural decisions, algorithmic customizations, and performance benchmarks, underlining the mathematical foundation and technological advantages of Elasticsearch in large-scale search applications.
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Arjun Malhotra. "Real-time geospatial risk analytics pipeline: architecture diagram of Kafka-Kubernetes feature engineering system for insurance underwriting." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2673–79. https://doi.org/10.30574/wjaets.2025.15.2.0846.

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This article presents a scalable real-time feature engineering architecture for insurance risk analytics that leverages Kafka, Kubernetes, and Elasticsearch to enable instant decision-making in regulated environments. The article streamed event data through stateful transformations while maintaining regulatory compliance, with particular focus on geographic risk concentration analysis using Census Block data and advanced spatial algorithms. The architecture implements bidirectional feedback loops that continuously refine feature importance weights based on quote outcomes, while comprehensive audit trails and data lineage tracking ensure complete traceability for regulatory oversight. Performance benchmarks demonstrate significant improvements over traditional batch processing approaches, with the architecture enabling sub-second feature extraction even during peak load periods. The article contributes architectural patterns for stateful stream processing, spatial risk aggregation methodologies, and validation frameworks specifically designed for the stringent requirements of insurance underwriting.
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Shireen Fathi Malo. "Intelligent Semantic Search for Academic Journals Using AI and NLP Techniques." Journal of Information Systems Engineering and Management 10, no. 41s (2025): 404–20. https://doi.org/10.52783/jisem.v10i41s.7884.

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The exponential growth of academic literature has rendered traditional keyword-based search engines increasingly inadequate for scholars seeking contextually relevant research. This study presents the design and implementation of an intelligent semantic search engine tailored for academic journals, integrating state-of-the-art Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques. The proposed system leverages sentence transformer models (all-mpnet-base-v2) for semantic embeddings, enabling vector-based similarity searches, alongside spaCy for tokenization and entity recognition to enhance syntactic understanding. An ontology-based matching mechanism further aligns user queries with domain-specific research topics, while fuzzy matching and regular expressions improve error tolerance and numeric filtering (e.g., CiteScore, Impact Factor). The system architecture combines these NLP layers with Elasticsearch's hybrid search capabilities to process and rank peer-reviewed journal metadata sourced from Scopus and DOAJ. A modular FastAPI-based backend ensures scalability and responsiveness, while a lightweight frontend interface facilitates interactive user input. This research contributes a novel hybrid framework that unites neural semantic models with structured query construction, addressing limitations in current scholarly search systems. The study also introduces a benchmark methodology for evaluating semantic search performance in academic contexts, with implications for enhancing research efficiency, interdisciplinary discovery, and access to high-impact literature.
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Hlybovets, А., and D. Zvazhii. "IMPLEMENTATION OF A SUFFIX TREE-BASED INDEX FOR SEARCHING SUBSTRINGS IN A LARGE DBMS." KIBERNETYKA TA SYSTEMNYI ANALIZ, 2025, 115–27. https://doi.org/10.34229/kca2522-9664.25.2.11.

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The article considers the advantages and disadvantages of implementing a suffix tree-based index to optimize substring search operations in a DBMS when working with large data. The theoretical characteristics of the complexity of operations for suffix trees are presented. Experimental estimates of the time complexity of substring search operations for suffix trees and database management systems such as Elasticsearch, PostgreSQL, MySQL, ClickHouse are carried out. Based on the results obtained, the hypothesis about the potential efficiency of implementing an index based on suffix trees to optimize substring search operations in a DBMS is confirmed. Keywords: suffix tree, index, string searching, performance benchmark, Elasticsearch, PostgreSQL, MySQL, ClickHouse.
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5

Ansari, Mohammad Hasan, Vahid Tabatab Vakili, and Behnam Bahrak. "Evaluation of big data frameworks for analysis of smart grids." Journal of Big Data 6, no. 1 (2019). http://dx.doi.org/10.1186/s40537-019-0270-8.

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AbstractWith the rapid development of smart grids and increasing data collected in these networks, analyzing this massive data for applications such as marketing, cyber-security, and performance analysis, has gained popularity. This paper focuses on analysis and performance evaluation of big data frameworks that are proposed for handling smart grid data. Since obtaining large amounts of smart grid data is difficult due to privacy concerns, we propose and implement a large scale smart grid data generator to produce massive data under conditions similar to those in real smart grids. We use four open source big data frameworks namely Hadoop-Hbase, Cassandra, Elasticsearch, and MongoDB, in our implementation. Finally, we evaluate the performance of different frameworks on smart grid big data and present a performance benchmark that includes common data analysis techniques on smart grid data.
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Dissertations / Theses on the topic "Elasticsearch performance benchmarks"

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Selander, Nizar. "Resource utilization comparison of Cassandra and Elasticsearch." Thesis, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18665.

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Elasticsearch and Cassandra are two of the widely used databases today withElasticsearch showing a more recent resurgence due to its unique full text searchfeature, akin to that of a search engine, contrasting with the conventional querylanguage-based methods used to perform data searching and retrieval operations. The demand for more powerful and better performing yet more feature rich andflexible databases has ever been growing. This project attempts to study how the twodatabases perform under a specific workload of 2,000,000 fixed sized logs and underan environment where the two can be compared while maintaining the results of theexperiment meaningful for the production environment which they are intended for. A total of three benchmarks were carried, an Elasticsearch deployment using defaultconfiguration and two Cassandra deployments, a default configuration a long with amodified one which reflects a currently running configuration in production for thetask at hand. The benchmarks showed very interesting performance differences in terms of CPU,memory and disk space usage. Elasticsearch showed the best performance overallusing significantly less memory and disk space as well as CPU to some degree. However, the benchmarks were done in a very specific set of configurations and a veryspecific data set and workload. Those differences should be considered whencomparing the benchmark results.
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