What is Message Queues?
A message queue is a buffer that holds tasks or events so they can be processed asynchronously by worker services. It decouples the parts of your system that produce work from the parts that do it.
In plain English
A message queue works like a deli counter with numbered tickets. Customers take a ticket (submit a task) and can leave — they don't have to stand at the counter waiting. The deli staff work through tickets in order without being overwhelmed by everyone demanding service simultaneously.
How it works
A producer service sends messages — jobs, events, or commands — to a queue. One or more consumer workers pull messages off the queue and process them independently of the producer. If a consumer crashes mid-processing, the message stays in the queue and gets retried.
Why it matters for AI-built apps
Long-running tasks like sending emails, resizing images, or running AI inference shouldn't block an HTTP request. Without a queue, these tasks either time out or force users to wait. Queues let you return an immediate response to the user and handle the work in the background.
Best practices
Design consumers to be idempotent — processing the same message twice should produce the same result. Set dead-letter queues to capture messages that fail repeatedly so you can inspect and replay them. Monitor queue depth as a leading indicator of consumer lag.
Frequently asked questions
What's a good queue service to start with?
For simple use cases, Upstash QStash or Redis with BullMQ are easy to set up. For higher scale, AWS SQS or Google Cloud Tasks are fully managed and reliable.
How is a message queue different from a database job table?
A dedicated queue is optimized for high-throughput, concurrent reads and provides built-in retry, visibility timeouts, and dead-lettering. A job table works for low volume but becomes a bottleneck under load.
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