Category
Web Development
Client
Artboard Studio
Start Date
August 20, 2024
Designer
Sooraj
Project Description
Developed a robust API using FastAPI and Python, consolidating diverse functionalities such as Base64 encoding, QR code generation, flight tracking, and rail tracking into a single platform. The system efficiently handles over 50,000 requests per month, leveraging a load balancer for scalability and high availability.
THE STORY
It all started during a late-night brainstorming session while I was juggling multiple tasks. I realized I was constantly switching between different tools—generating QR codes, encoding data in Base64, and tracking flights and trains for personal projects. Each task required a separate service, and managing them felt inefficient and time-consuming.
That’s when the idea hit me: why not create a single API to handle it all? An API that could not only simplify these tasks but also scale to support others facing the same challenges. I envisioned a solution where everything was in one place, accessible through a seamless interface.
Fueled by this vision, I chose FastAPI for its speed and simplicity, and Python for its versatility. Over the next few weeks, I meticulously designed and implemented the API, adding features like QR code generation, flight tracking, and rail tracking. To ensure it could handle high traffic, I integrated a load balancer, enabling the system to process over 50k+ requests per month effortlessly.
What started as a personal necessity grew into a scalable solution that simplified workflows for countless others.
OUR APPROACH
When building the API, I focused on efficiency, scalability, and simplicity to ensure it could handle diverse functionalities while maintaining high performance. Here's how I approached it:
Design and Development
- I chose FastAPI as the framework for its asynchronous capabilities and speed, perfect for handling high-concurrency tasks.
- Python’s extensive library ecosystem allowed me to seamlessly integrate features like Base64 encoding, QR code generation, flight tracking, and rail tracking.
- To keep the codebase modular, each functionality was developed as a separate service, making the API easy to maintain and extend.
Handling High Traffic
- Anticipating significant traffic, I implemented asynchronous request handling to optimize resource usage and reduce response times.
- A load balancer was deployed to distribute requests across multiple instances, ensuring the system could handle over 50,000 requests per month without downtime.
- I incorporated caching mechanisms for frequently requested data, reducing redundant processing and improving response times.
Hosting and Deployment
- The API was hosted on a cloud platform with auto-scaling capabilities, ensuring it could adapt to traffic spikes without manual intervention.
- I used containerization with Docker to ensure consistent deployments and simplified scaling across environments.
- A CI/CD pipeline was set up to automate testing, building, and deploying updates, minimizing potential disruptions.
Monitoring and Optimization
- I integrated monitoring tools to track API performance and detect issues in real time. Metrics like request latency, error rates, and server utilization were key to maintaining reliability.
- Regular optimizations, such as refactoring code and tuning database queries, ensured the system remained responsive and efficient.
By combining thoughtful design, modern tools, and robust infrastructure, I built an API that is not only versatile but also resilient and capable of handling large-scale traffic seamlessly.