Distributed caching strategies to enhance E-commerce transaction speed

Distributed caching represents a critical architectural strategy for enhancing transaction speed in e-commerce environments. This article examines how strategically positioning frequently accessed data across multiple networked nodes significantly reduces latency while decreasing database load. The assessment framework developed for evaluating caching technologies incorporates both quantitative performance metrics and practical implementation considerations specific to e-commerce workloads. Results demonstrate that in-memory solutions consistently outperform disk-based alternatives, with hybrid caching architectures showing superior performance when aligned with specific data requirements. Economic analysis reveals compelling justifications for distributed caching across various business scales, though implementation challenges including consistency management, cold-start phenomena, and security implications must be addressed. Future directions point toward machine learning for predictive caching, edge computing integration, serverless compatibility, and advanced invalidation strategies that promise to further optimize distributed caching capabilities for next-generation e-commerce platforms.

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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.17312887
Primary Topic
Quantum Mechanics and Applications
Type
article
Field-Weighted Citation Impact
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article

Distributed caching strategies to enhance E-commerce transaction speed

Zenodo (CERN European Organization for Nuclear Research)
Quantum Mechanics and Applications
article

Distributed caching strategies to enhance E-commerce transaction speed

article en

Abstract

Distributed caching represents a critical architectural strategy for enhancing transaction speed in e-commerce environments. This article examines how strategically positioning frequently accessed data across multiple networked nodes significantly reduces latency while decreasing database load. The assessment framework developed for evaluating caching technologies incorporates both quantitative performance metrics and practical implementation considerations specific to e-commerce workloads. Results demonstrate that in-memory solutions consistently outperform disk-based alternatives, with hybrid caching architectures showing superior performance when aligned with specific data requirements. Economic analysis reveals compelling justifications for distributed caching across various business scales, though implementation challenges including consistency management, cold-start phenomena, and security implications must be addressed. Future directions point toward machine learning for predictive caching, edge computing integration, serverless compatibility, and advanced invalidation strategies that promise to further optimize distributed caching capabilities for next-generation e-commerce platforms.

Zenodo (CERN European Organization for Nuclear Research)
Universidad del Zulia (VE)
Industry, innovation and infrastructure
Openalex Percentile: Top 46%
Quantum Mechanics and Applications
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