Imagine you're designing an image processing system. Users upload photos, and your service needs to generate thumbnails, extract metadata, run content moderation, and update search indexes. If you process these synchronously in the upload request, users wait 30 seconds while your endpoint becomes a fragile chain of service calls.

The solution is obvious: decouple with a message queue. But now you face a choice. Do you spin up a RabbitMQ cluster? Deploy Kafka and manage ZooKeeper? Or do you use something simpler?

This is where Amazon SQS shines. Launched in 2006 as one of AWS's first services, SQS has quietly become the backbone of countless production systems. It's not the most feature-rich message broker, but that's precisely its strength. SQS trades flexibility for operational simplicity, and for many workloads, that's exactly the right trade-off.

There are no servers to provision, no clusters to configure, no replication to worry about. You create a queue, start sending messages, and AWS handles everything else, scaling from zero to millions of messages without you lifting a finger.

In system design interviews, the ability to recognize when SQS's simplicity is the right choice, and when you need something more powerful, demonstrates mature engineering judgment.

This article covers the practical knowledge you need: queue types and their trade-offs, message lifecycle, scaling patterns, AWS integrations, and how SQS compares to alternatives. By the end, you'll be able to confidently design systems using SQS and explain your choices.

1. When to Choose SQS