Advanced Optimization of Large-scale Search Functionality Using Elasticsearch
Year: 2024
Type: article
Abstract: 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.
Source: CyberLeninK (CyberLeninka)
Author: Sipilov I.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6968428685
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