High-Throughput Go Task Queue on RabbitMQ for VisionBoarder's AI Image Processing
#Go#RabbitMQ#Pocketbase#PostgreSQL
Client
VisionBoarder - AI Vision Board Platform, Vancouver, Canada
Languages
Go
Fig. 1 — VisionBoarder Task Manager featured view (click to expand)
01
The Problem
Business Impact
AI image generation is computationally expensive and slow. Processing these tasks synchronously would block the UI and provide a terrible user experience.
Constraints
Task data and results had to be persisted and backed up reliably - users couldn't lose their generation requests.
02
Technical Execution
Architectural Strategy
Built a subscriber-publisher task queue in Go using RabbitMQ, designed for low-latency and high-throughput async processing.
Depth of Execution
RabbitMQ handles task distribution across worker nodes. Pocketbase persists all task data and results. Automatic S3 backup of task results. REST API exposes queue functionality to other services. Scales to millions of requests by adding more brokers.
03
Results
✓AI image generation fully async - zero UI blocking.
✓Task data persisted and backed up to S3 automatically.
✓Horizontally scalable by adding RabbitMQ broker nodes.
Future Proofing
Worker node count can be scaled independently of the main application based on queue depth.
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04
Client Feedback
"I have been working with Adarsh for many years now and he always delivers a product better than I expected. He knows what the user wants and is quick to understand and implement new technologies. Adarsh is one of the few who truly 'knows' - not just 'knows about'."
