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Queueing Requests Queues Your Capacity Problems, Too

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Why This Matters

This article highlights the hidden pitfalls of queueing requests in tech systems, emphasizing that while queues can prevent overloads, they can also cause significant latency issues and hidden capacity problems. Understanding these dynamics is crucial for developers and businesses to design scalable, efficient APIs and services that meet user expectations without incurring excessive costs or delays.

Key Takeaways

Here’s an exchange I had on twitter a few months ago:

twitter, the world’s largest town hall

My intent here is not to single this gentleman out, as from what I can tell, he runs a very successful business and is likely smarter than me in many ways. That said, he and I have a different understanding of queueing, a topic dear to my heart that I’ve written about before:

Allow me to paint you a picture: your p90 latency graph looks perfectly healthy at or around 1 second, while every customer request submitted in the last hour is experiencing latency of 1 hour because they are waiting behind 3.6 million other requests in the queue.

How could this happen?

A Few Numbers

Let’s say you’re providing an API, and real-world constraints lead you to believe that offering a queue-like interface would be a good alternative to your existing real-time request/response model.

Some more specifics:

Your request rate is a flat 1000 requests/second for 24 hours per day, 7 days per week.

Every request takes 1 second to process.

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