Task Queues &
Async Job Processing

What you'll find here

Distributed job systems are notoriously subtle: a misconfigured visibility timeout silently causes duplicate processing; the wrong broker choice creates a throughput ceiling under load; unbounded queues trigger cascading OOM crashes. This site collects battle-tested patterns, configuration recipes, and architectural decision frameworks from production deployments.

Whether you're wiring up your first Celery deployment with a Redis broker, tuning BullMQ concurrency limits for a Node.js service, or designing a multi-region queue partitioning strategy for AWS SQS — you'll find actionable guidance grounded in real operational trade-offs.

Content is organized into three main tracks: foundational concepts that apply regardless of framework, framework-specific deep dives for the most widely-used async job ecosystems, and the observability and monitoring practices that keep a worker fleet healthy in production.

Eighty-two guides now cover the full operational surface — retry policy and backoff, priority and per-tenant fairness, dead-letter handling and replay, graceful shutdown and zero-downtime deploys, and the service level objectives and burn-rate alerting that tell you when any of it has stopped working.

Start here

New to the site? These five pages cover the decisions that shape every other one.

• Queue Fundamentals & Architecture — delivery semantics, backpressure, and a decision framework for designing a queue.
• Retry Strategies & Backoff — exponential backoff, jitter, retry budgets, and where the attempt ceiling belongs.
• Dead-Letter Queues & Poison Messages — quarantining what cannot succeed, and replaying it safely afterwards.
• Graceful Shutdown & Worker Deployments — why every deploy is a test of your redelivery guarantees.
• SLOs & Alerting for Job Queues — queue-time objectives, error budgets, and alerts that fire on impact.