OpenSearch at Scale: Architecting High-Performance Distributed Search Solutions for Enterprise Data Retrieval

This technical guide explores the implementation of OpenSearch as a high-performance, distributed search solution for organizations requiring millisecond response times with large-scale datasets. The article examines architectural considerations for optimal performance, including strategic approaches to shard configuration, memory allocation, and replication design based on write frequency patterns. It details effective data modeling practices, emphasizing the importance of appropriate data typing, text analyzers, and keyword normalization to enhance search capabilities. The guide further addresses methodologies for continuous optimization through query pattern analysis and provides a framework for production monitoring to maintain performance at scale. By following these evidence-based recommendations, engineering teams can develop robust search infrastructures that deliver consistent, high-speed access to critical data while effectively managing resources.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-31
DOI
https://doi.org/10.5281/zenodo.17317970
Primary Topic
Healthcare Systems and Technology
Type
article
Field-Weighted Citation Impact
0.00
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article

OpenSearch at Scale: Architecting High-Performance Distributed Search Solutions for Enterprise Data Retrieval

Zenodo (CERN European Organization for Nuclear Research)
Healthcare Systems and Technology
article

OpenSearch at Scale: Architecting High-Performance Distributed Search Solutions for Enterprise Data Retrieval

article en

Abstract

This technical guide explores the implementation of OpenSearch as a high-performance, distributed search solution for organizations requiring millisecond response times with large-scale datasets. The article examines architectural considerations for optimal performance, including strategic approaches to shard configuration, memory allocation, and replication design based on write frequency patterns. It details effective data modeling practices, emphasizing the importance of appropriate data typing, text analyzers, and keyword normalization to enhance search capabilities. The guide further addresses methodologies for continuous optimization through query pattern analysis and provides a framework for production monitoring to maintain performance at scale. By following these evidence-based recommendations, engineering teams can develop robust search infrastructures that deliver consistent, high-speed access to critical data while effectively managing resources.

Zenodo (CERN European Organization for Nuclear Research)
Amazon (United States) (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 38%
Healthcare Systems and Technology
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