How do you manage background jobs and scheduled tasks?

Background jobs handle work that shouldn't block HTTP responses: sending emails, processing images, generating reports, syncing data.

Job queues (BullMQ, Bee-Queue for Node; Celery for Python) manage work. Your app enqueues jobs, worker processes consume them. Redis typically backs these queues. This decouples producing work from processing it.

Key job design principles: make jobs idempotent (running twice produces same result), include all needed data in the job payload, handle failures gracefully with retries, and set reasonable timeouts.

Retries with exponential backoff handle transient failures: wait 1s, then 2s, then 4s. After max retries, move to a dead-letter queue for investigation. Alert on dead-letter queue growth.

Scheduled tasks (cron jobs) run at fixed intervals. Traditional cron works for simple cases. For distributed systems, use job queue scheduling features or dedicated schedulers that ensure only one instance runs the job.

Monitoring matters: track queue depths, job durations, failure rates. Growing queues indicate you need more workers. High failure rates need investigation.

Serverless options: cloud functions triggered by queues (AWS Lambda + SQS, Cloud Functions + Pub/Sub) scale automatically and you pay per execution. Good for variable workloads.

Start simple. A single worker process handling a Redis queue covers most needs. Scale workers horizontally when queues back up.