University System – Video Encoding Platform

Objective & Constraints

Handling concurrent video encoding jobs efficiently without impacting backend performance. The university's distance learning portal allowed professors to upload massive lecture videos. The monolithic backend attempted to process these synchronously, leading to timeout errors, exhausted memory, and a completely frozen application during peak upload hours.

Strategic Implementation

Leveraged AWS SQS to decouple video processing from the main backend and used Python microservices for encoding. By introducing an asynchronous queue, the web application could instantly acknowledge uploads while background workers handled the CPU-intensive transcoding tasks at their own pace.

Protocol Execution

• Decoupling: Integrated AWS SQS to manage the video processing queue, providing fault tolerance, retries, and dead-letter queues for failed encoding jobs.
• Processing: Developed scalable Python microservices utilizing FFmpeg for real-time encoding, containerized and deployed on AWS ECS.
• Security: Secured the platform with IAM-based access control, presigned S3 URLs for secure uploads/downloads, and strict network segmentation.
• Efficiency: Optimized resource usage for concurrent encoding jobs by configuring ECS to scale based on the depth of the SQS queue, ensuring cost-efficient processing.

Professional Reflection

"Decoupling heavy workloads via message queues is a textbook architectural pattern, but seeing it instantly resolve cascading system failures in production was a stark reminder of its necessity."