Celery From Zero

Learn the distributed task queue that runs background work for Python web apps: the broker/worker/result model, defining and calling tasks, results and state, retries and error handling, scheduled tasks with Celery Beat, and production scaling, monitoring, and the pitfalls. Move slow work off the request and run it reliably.

1. What Celery Is & Why Some work is too slow to do inside a web request. Celery moves it to the background: you define tasks, your app enqueues them, and separate worker processes run them through a broker.
2. The Broker & Worker Meet the two processes that make Celery work: the broker (a Redis or RabbitMQ queue holding pending task messages) and the worker (a separate process that drains the queue and runs tasks).
3. Defining & Calling Tasks How to register a function as a Celery task, send it to the worker with .delay() and .apply_async(), and why arguments must be serializable simple data.
4. Results & State How Celery stores task return values and status in a result backend, how to query them with AsyncResult, what the task states mean, and when you actually need results at all.
5. Retries & Error Handling Why background tasks fail, how to retry transient errors with backoff, why retried tasks must be idempotent, the acks_late trade-off, and how to surface permanent failures.
6. Scheduled Tasks with Celery Beat Run Celery tasks on a timetable with Celery Beat — a scheduler that enqueues jobs on intervals or crontab schedules, while your normal workers do the actual work.
7. Production: Scaling, Monitoring & Pitfalls Scaling Celery with more workers and dedicated queues, seeing your tasks with Flower and metrics, a pitfall cheat-card, worker reliability knobs, and the deployment shape that makes Celery rock-solid.
8. Where to Go Next You can define, call, retry, schedule, scale, and monitor background tasks. Now plug Celery into your web framework, compose tasks with canvas, weigh the alternatives honestly, and build something real.