Celery

1. Introduction and Brokers

   Distributed task queue concepts, message brokers like RabbitMQ and Redis, workers, and result backends.

2. First Steps with Celery

   Creating a Celery app, defining a task, running a worker, calling tasks, and keeping results.

3. Application

   The Celery app instance, configuration, task autodiscovery, and app finalization.

4. Tasks

   Task definition, bind and self, retries, task states, idempotency, rate limits, time limits, and error handling.

5. Calling Tasks

   delay and apply_async, countdown and eta, expires, serializers, callbacks, and AsyncResult.

6. Canvas: Designing Workflows

   Signatures, chains, groups, chords, map and starmap, partials, and workflow composition.

7. Workers

   Starting workers, concurrency, autoscaling, prefork, remote control, revoking tasks, and graceful shutdown.

8. Concurrency Pools

   Prefork, threads, gevent and eventlet pools, and choosing a concurrency model.

9. Routing Tasks

   Queues, exchanges, routing keys, task_routes, and priority queues.

10. Periodic Tasks

    Celery beat, crontab and interval schedules, beat_schedule, and dynamic schedulers.

11. Configuration and Defaults

    Settings for broker, result backend, serialization, acks_late, prefetch, time zones, and task behavior.

12. Optimizing

    Prefetch multiplier, task granularity, fair scheduling, and avoiding result overhead.

13. Monitoring and Management

    celery inspect, events, Flower, remote control commands, and health monitoring.

14. Signals

    Task, worker, and beat signals for hooking into lifecycle events.

15. Security

    Message signing, serializer safety, broker authentication, and TLS.

16. Testing with Celery

    Eager mode, test workers, pytest fixtures, and mocking tasks.

17. Using Celery with Django

    Django integration, autodiscovery of tasks, settings namespace, and transaction.on_commit.

Each section links to the official page. Titles and summaries by Extra Credit; the docs belong to their authors.