python-background-jobs

# Python Background Jobs & Task Queues Decouple long-running or unreliable work from request/response cycles. Return immediately to the user while background workers handle the heavy lifting asynchronously. ## When to Use This Skill - Processing tasks that take longer than a few seconds - Sending emails, notifications, or webhooks - Generating reports or exporting data - Processing uploads or media transformations - Integrating with unreliable external services - Building event-driven architectures ## Core Concepts ### 1. Task Queue Pattern API accepts request, enqueues a job, returns immediately with a job ID. Workers process jobs asynchronously. ### 2. Idempotency Tasks may be retried on failure. Design for safe re-execution. ### 3. Job State Machine Jobs transition through states: pending → running → succeeded/failed. ### 4. At-Least-Once Delivery Most queues guarantee at-least-once delivery. Your code must handle duplicates. ## Quick Start This skill uses Celery for examples, a widely adopted task queue. Alternatives like RQ, Dramatiq, and cloud-native solutions (AWS SQS, GCP Tasks) are equally valid choices. ```python from celery import Celery app = Celery("tasks", broker="redis://localhost:6379") @app.task def send_email(to: str, subject: str, body: str) -> None: # This runs in a background worker email_client.send(to, subject, body) # In your API handler send_email.delay("user@example.com", "Welcome!", "Thanks for signing up") ``` ## Fundamen...