URL Shortener
Developed a scalable URL shortener using Python, MongoDB, and Kubernetes.
This project involved designing and implementing a URL shortener as part of a database systems lab. The system was built using Python for backend logic, MongoDB as the database, and tested for scalability with Docker and Kubernetes under various stress conditions. A dashboard was also created to monitor API calls and system performance, providing insights into usage patterns.
Technical Details
The URL shortening logic relied on a custom hashing function to generate unique identifiers for input URLs. The hash function mapped each URL to a fixed-length string using a combination of:
- Base62 Encoding: Encoded hash outputs into a mix of alphanumeric characters to keep shortened URLs compact.
- Hash Collisions: Managed potential hash collisions by appending incremental counters to conflicting hashes.
- Database Integration: Stored mappings between original URLs and their shortened counterparts in MongoDB, ensuring fast retrieval and scalability for high-volume traffic.
Stress Testing
To evaluate system performance, I used Docker containers and Kubernetes clusters to simulate high traffic scenarios. Tests were designed to measure response times, database query performance, and overall API throughput. The system proved robust under load, maintaining low latency and high reliability.