COMPARATIVE ANALYSIS OF THE PERFORMANCE OF NODE AND BUN PLATFORMS IN EDUCATIONAL WEB APPLICATIONS

Abstract

This article presents a comparative analysis of the performance of Node.js and Bun platforms in the context of developing educational web applications. Modern educational information systems are increasingly implemented as web applications, requiring high performance, scalability, and efficient resource utilization. With the emergence of Bun, a new platform positioned as a faster alternative to Node.js, there is a need for scientific investigation of its capabilities, advantages, and limitations in practical application. This study describes an experimental evaluation of a web application’s performance on both platforms and discusses the feasibility of migrating from Node.js to Bun. A roadmap for migrating a web application from Node.js to Bun is also developed. The research involved a series of load tests conducted using the Bombardier utility. Experiments were performed in the Google Cloud Platform (GCP) cloud environment using a standardized test configuration, enabling an objective performance assessment of each platform. The test results demonstrated a significant advantage of Bun over Node.js in terms of the average number of HTTP requests processed per second (RPS). Bun showed a 2.14-fold performance increase, processing an average of 8038 requests per second compared to 3753 requests for Node.js. However, certain compatibility issues between Bun and some modules were identified, necessitating modifications to the web application’s code. This suggests that while Bun holds considerable potential for web development, it is still under active development, and the migration process might require additional effort to adapt the application code. The obtained results are valuable for developers of educational web applications, as the choice of runtime environment directly impacts application performance, access speed to materials, and system scalability. Promising areas for future research include analyzing Bun’s stability in long-term use, its compatibility with existing modules, and optimization under real-world high-load conditions.