The Future of Fashion, Powered by AI: Golang Migration with a GenAI Stitch
Problem Statement
Our client, an online retail company, initially built their business on a Python-based codebase. As the company grew, this system faced two major challenges. Performance bottlenecks: During peak shopping periods like sales and flash deals, the system experienced traffic far beyond its original capacity, leading to performance bottlenecks. This resulted in slow page loading, order processing delays, and system crashes. Increasing maintenance costs: The system became increasingly complex and difficult to maintain, especially as new features and products were added. This escalated the development and maintenance costs, posing significant challenges for the rapidly expanding company.
Client Info
A rapidly growing online retailer conquering the fashion world since 2015
Outcomes
40% faster page loading
2x the usual development speed with 45% lower costs
Customer satisfaction scores (NPS) rose by 17% due to improved performance.
How did BeautifulCode do it?
Recognizing the need for improved scalability and performance, we embarked on a strategic migration to Golang. This decision was driven by several key factors
- Golang's concurrency features: Its built-in goroutines and lightweight channels offered efficient handling of concurrent requests, promising smoother peak traffic periods and faster page loading times. Benchmarks indicated significant performance gains compared to Python
- Scalability for future growth: With the anticipated continued growth of the e-commerce platform, Golang's ability to easily scale horizontally on cloud platforms was crucial
- Maintainability and cost reduction: Golang's simple syntax and static typing offered better code readability and easier debugging, potentially reducing maintenance costs and development time.
However, transitioning a large codebase was not an easy task to undertake. At which point, the team developed a workflow aided by GenAI for code conversion which significantly reduced the human efforts and shortened the duration of the migration.
Implementation
The migration process was executed in a phased approach.
Phase 1 – Proof of Concept
We evaluated Golang's suitability by migrating a critical component of the system. This provided valuable insights into performance gains and potential challenges.
Phase 2 – GenAI-powered Conversion
We created a solution powered by Generative AI that facilitates the migration of business logic from Python to GoLang. This is achieved through the use of OpenAI 3.5-Turbo and a range of customised prompts. Additionally, we have crafted unique workflows for developers to include split-screen interfaces that display both the original Python code and the migrated GoLang code side by side, along other relevant information to aid in the migration process.
Our solution worked well, but it wasn't perfect for the following cases
- Variations in data structures between Python and GoLang led to conversion issues. A prime example is the translation of Python dictionaries into Go maps, which necessitated extra measures to manage nil values and specific key types
- The complexity and intricate control flows present in Python code posed a challenge for the GenAI in translating these structures accurately and idiomatically into GoLang
- Dependency on third-party Python libraries presented a hurdle, as many of these libraries lack direct equivalents in GoLang, requiring manual intervention during the migration process.
These limitations underscore the need for continuous refinement of our AI-assisted approach and the importance of human oversight in ensuring successful code migration.
Phase 3 – Optimization and Testing
Our team optimized the AI-generated GoLang code for better performance and conducted comprehensive testing for functionality and stability, including GenAI-based unit tests, integration testing for system compatibility, and load testing for performance verification.
Technologies Used
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