Built a distributed task queue system in Python here's what I learned. Most developers think of a web API as the place where work happens. It's not. It's where work gets *accepted*. The real insight behind distributed task queues: → Accept the request instantly → Return a task ID → Do the heavy lifting in the background → Let the client poll for results Here's the stack I used: FastAPI — lightweight HTTP layer that just enqueues and returns RabbitMQ — message broker with priority queue support (0–10) Celery — worker orchestration across 2 replicas × 2 concurrent slots Redis — result backend with no TTL (results persist indefinitely) Flower — real-time monitoring dashboard The architectural decisions that mattered most: task_acks_late: True Workers only acknowledge a task AFTER completing it — not when they pick it up. If a worker crashes mid-job, the message goes back to the queue. Zero data loss. worker_prefetch_multiplier: 1 Each worker holds exactly one task at a time. No hoarding. This makes autoscaling predictable — spin up a new worker, it immediately takes load. Priority queues RabbitMQ supports x-max-priority. High-priority jobs jump the queue without any special routing logic. The result? An API that stays responsive at 1ms response times while workers handle jobs that take minutes — and survives worker crashes without losing a single task. If you're building ML inference, video processing, or any long-running computation — you don't need a bigger server. You need this pattern.