My app queues background jobs (emails, image processing, report generation, third-party API calls). Jobs usually process fine, but I've never checked what happens when jobs arrive faster than workers can handle them. Make my queues safe under overload: 1. Measure the baseline: jobs enqueued per minute at normal load, average job duration, and worker throughput. From that, calculate how much headroom I have and how fast a backlog grows when the producer outpaces the workers (a job that takes 2 seconds with one worker clears 30/minute; anything above that is a growing queue). 2. Add visibility: queue depth and age-of-oldest-job metrics, with an alert when depth or age crosses a threshold. A queue that silently grows for days turns a small hiccup into users getting welcome emails three days late. 3. Define overload behavior per job type: - Shed or defer non-urgent work (analytics, cleanup) when the queue is deep. - Coalesce duplicate jobs (five 'reindex user X' jobs can become one). - Keep user-facing-critical jobs (purchase receipts, password resets) prioritized, on a separate queue if needed, so a report-generation spike doesn't delay receipts. 4. Bound retries: cap retry counts, use exponential backoff, and send permanently failing jobs to a dead-letter queue I actually check, instead of retrying forever and clogging the worker. 5. Fix the producers: jobs enqueued in loops per-user or per-row (send this email to 50,000 users) should be batched or rate-limited so one action can't flood the queue. Don't just add more workers as the plan; if the job hits a shared resource (database, third-party API), more workers move the bottleneck and can take the database down with it. The deliverable is the throughput math, the monitoring and alerts, the per-type overload behavior, and the retry/dead-letter setup.