Coming from a background in C++ and Data Structures, I recently challenged myself to dive into the web ecosystem. The result? Textalyzer — a high-performance REST API and web client for real-time text analysis.
Throughout this project, I focused heavily on optimization and architecture. Here are the main technical hurdles I overcame:
- Algorithmic Efficiency: Refactored the character frequency logic from a naive $O(N^2)$ approach to $O(N)$ by implementing custom Hash Maps, drastically improving processing time for large texts.
- Data Persistence & Resilience: Integrated a serverless PostgreSQL database (Neon). When I encountered "Cold Start" connection drops on the cloud, I implemented Connection Pooling (pg.Pool) to ensure the server gracefully handles disconnections without crashing.
- Clean Architecture: Applied Separation of Concerns by modularizing the Node.js backend logic, and separating the frontend into clean HTML, CSS, and Vanilla JavaScript files.
The Tech Stack:
- Backend: Node.js, Express, PostgreSQLFrontend: HTML5, CSS3, Asynchronous JS (Fetch API)
- DevOps: Git (SemVer), WSL2, Render (PaaS)
Check out the live project here
Dive into the code on Github, here
Excited for the next challenge! Always open to feedback from the community. 👇
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