Question 1

How do I scale RQ workers for high traffic?

Accepted Answer

Use rq worker-pool to run multiple processes on a single machine, or deploy distributed workers across servers connected to the same Redis instance. For production, integrate with process managers like systemd or Kubernetes for resilience and auto-scaling.

Question 2

RQ or Celery: which should I pick for background jobs?

Accepted Answer

Choose RQ for simplicity and Redis-only setups; it's lighter and faster to start. Celery is better for complex needs like multiple brokers (RabbitMQ), advanced routing, workflow orchestration, or if you need features like task result backends beyond Redis.

Question 3

How to schedule a job to run every hour in RQ?

Accepted Answer

Use the built-in cron support: define the job with cron.register() in a config file using cron syntax (e.g., '0 * * * *' for hourly), then run rq cron to enqueue it automatically at intervals.

Question 4

What happens to failed jobs in RQ?

Accepted Answer

Jobs without retries move to a failed queue where they can be inspected or requeued manually. With retry configured, RQ automatically re-queues them up to the max attempts, with optional delays between retries as per the Retry settings.

Question 5

How to integrate RQ with a Django project?

Accepted Answer

Use the django-rq package, which provides Django management commands, settings integration, and seamless queue setup. It simplifies worker management and allows enqueuing jobs directly from Django views or models.

Question 6

Can RQ handle async/await Python functions?

Accepted Answer

Not natively; RQ workers are designed for synchronous functions. For async tasks, you need to wrap them in sync code or use alternatives like ARQ that are built for asyncio, as RQ's process-based model doesn't support event loops directly.

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