celery-worker

Celery worker configuration for production uses late acknowledgments and reject-on-worker-lost settings to ensure background tasks are reprocessed if workers crash, preventing task loss in distributed Python applications.

Celery workers can hang when tasks exceed time limits without proper configuration. Setting worker prefetch limits, concurrency parameters, and hard time limits prevents hung tasks and ensures reliable asynchronous execution.

Queue routing in Celery directs tasks to specific queues for workload separation and prioritization. You define task routes and manage queues so that emails, file processing, and long computations run on dedicated workers.

Task_acks_late and reject_on_worker_lost are needed when workers may crash during processing. Late acknowledgment delays task completion signals until after execution, while reject-on-worker-lost requeues tasks if a worker disappears unexpectedly.

Yes, this Celery worker setup targets distributed Python applications that need reliable asynchronous execution for sending emails, processing uploaded files, or running long computations with robust error handling and queue management.