Software Computing
This thesis examines the performance and use of three popular Python frameworks for API development: Flask, Falcon, and Bottle, aiming to determine which of them offers the greatest utility. This research is significant for both developers and businesses as it facilitates the selection of an appropriate tool for developing efficient and responsive APIs.
To reach these conclusions, we developed comparable APIs with each of these frameworks. We started by setting up an integrated development environment and planning the API concepts to be developed. Each API was designed to support basic CRUD operations (Create, Read, Update, Delete).
After developing the APIs, we performed performance measurements, where we measured response time, the number of requests sent, and throughput. Additionally, we evaluated the ease of implementation and the scalability of each framework.
We continued with an analysis of community support, where we examined developer activities on two platforms Stack Overflow and Reddit, and the availability of learning resources. Flask proved to be the tool with the most support and accessible resources, while Falcon offers highly specialized support.
As part of the research, we also tested the use of artificial intelligence, specifically ChatGPT 4.0, as a tool for learning the Bottle framework. We found that ChatGPT 4.0 is effective for learning both basic and advanced API development techniques, although additional resources are beneficial for a deeper understanding.
During the learning process, we used ChatGPT to obtain additional information and advice on best practices for API development, which facilitated the learning process and increased work efficiency.
Based on these measurements and findings, we concluded that Flask has greater community support than Falcon, while Bottle excels in response speed. ChatGPT 4.0 proved to be an effective tool for learning how to use the Bottle framework.
It was also found that it is possible to develop an API with the Flask framework in fewer steps using artificial intelligence. Further research could include an in-depth analysis of performance under various loads, a qualitative analysis of community support, and a comparison of the development of more complex APIs.
This would provide a comprehensive understanding of the advantages and limitations of each framework in different use cases.
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Academia
Design and development:
The investment voucher for digital marketing-web design is co-financed by the Republic of Slovenia and the European Union from the European Regional Development Fund.
