Optimizing Node.Js Performance for Production: Memory Management, Clustering, and Monitoring

Bahaa, Taher and Radwa, Ashour (2023) Optimizing Node.Js Performance for Production: Memory Management, Clustering, and Monitoring. International Multi-disciplinary Journal of Education, 1 (6). pp. 603-614. ISSN 2994-9521

Text 603-614+Optimizing+Node.Js+Performance+for+Production.pdf Download (505kB)

Abstract

As Node.js continues to power large-scale, real-time, and high-throughput applications, optimizing its performance in production environments has become increasingly critical. This article delves into the advanced strategies required to maximize Node.js efficiency and reliability in production workloads. It explores the intricacies of memory management, including identifying memory leaks, fine-tuning garbage collection, and leveraging heap snapshots for diagnostics. The article also examines the power of Node.js clustering and multi-process architecture to overcome the limitations of its single-threaded nature, enabling better CPU utilization and horizontal scalability. Furthermore, it highlights essential monitoring practices using tools like Prometheus, Grafana, and built-in Node.js diagnostics to track performance metrics, detect anomalies, and ensure system health in real time. Through a blend of best practices, real-world scenarios, and actionable insights, this guide empowers developers and DevOps teams to build and maintain high-performance Node.js applications that are resilient, scalable, and production-ready.

Item Type:	Article
Subjects:	Q Science > Q Science (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	31 May 2025 05:09
Last Modified:	31 May 2025 05:09
URI:	http://eprints.umsida.ac.id/id/eprint/16133

Actions (login required)

View Item