ADVANCED OPTIMIZATION OF LARGE-SCALE SEARCH FUNCTIONALITY USING ELASTICSEARCH
ADVANCED OPTIMIZATION OF LARGE-SCALE SEARCH FUNCTIONALITY USING ELASTICSEARCH
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.
Elasticsearch optimization, large-scale search, inverted indexing, BM25 scoring, relevance ranking, distributed search architecture, geospatial filtering, search latency reduction, custom search algorithms, Elasticsearch performance benchmarks, search scalability, Elasticsearch sharding, function score queries, search system architecture, user-driven relevance models, recruitment platform optimization, real-time search queries, indexing strategy.
Elasticsearch optimization, large-scale search, inverted indexing, BM25 scoring, relevance ranking, distributed search architecture, geospatial filtering, search latency reduction, custom search algorithms, Elasticsearch performance benchmarks, search scalability, Elasticsearch sharding, function score queries, search system architecture, user-driven relevance models, recruitment platform optimization, real-time search queries, indexing strategy.
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