When your Django application grows beyond simple request-response cycles, you need a robust task queue. Celery with Redis provides exactly that — a battle-tested combination for handling background processing at scale.
In this article, we'll walk through setting up Celery with Redis in a Django project. We'll cover task definition, periodic scheduling with Celery Beat, error handling with automatic retries, and monitoring with Flower.
The key to scalable task processing is understanding the difference between I/O-bound and CPU-bound tasks. For I/O-bound tasks like sending emails or calling external APIs, you can use more concurrent workers. For CPU-bound tasks like image processing or data analysis, you'll want to limit concurrency to match your available cores.
We'll also explore advanced patterns like task chaining, grouping, and chord callbacks. These primitives let you build complex workflows where the output of one task feeds into the next, or where multiple tasks run in parallel and their results are aggregated.
Finally, we'll discuss production deployment strategies including autoscaling workers based on queue depth, graceful shutdown handling, and monitoring task execution times to identify bottlenecks.