Queueing Requests Queues Your Capacity Problems, Too

That reminds me of this talk[0] by Gil Tene called "How NOT to Measure Latency" at the Strangeloop conference in 2015 (or read this blog post[1] that contains the most important points).

[0] https://www.youtube.com/watch?v=lJ8ydIuPFeU

[1] https://bravenewgeek.com/everything-you-know-about-latency-i...

When I give system design interviews, candidates that start adding queues reflexively to the design always do poorly.

Queueing is only useful for a few cases, IMO:

* The request is expensive to reject. For example, the inputs to the rejected request also came from expensive requests or operations (like a file upload). So rejecting the request because of load will multiply the load on other parts of the system. You still need backpressure or forwardpressure (autoscaling).

* Losing a request is expensive, delaying the result is not. Usually you want a suitably configured durable queueing system (e.g. Kafka) if you have this scenario.

* A very short queue is acceptable if it's necessary that downstream resources are kept 100% busy. A good example of this is in a router, the output to a slower link might queue 1-2 packets so that there is always something to send, which maximizes throughput.

* If you have very bursty traffic, you can smooth the bursts to fit in your capacity. But this runs the danger of having the queue always full, which you have to manage with load shedding (either automated or manual).

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An underappreciated queue type is LIFO (last-in, first-out). It sounds unfair, but it keeps you from moving the median response time at the cost of the maximum response time, and it behaves well when full. It fails over into either responding quickly or just rejecting requests when full, so it works well for dealing with bursty traffic.

Use a stack? LIFO.

As long as you have capacity to keep it mostly empty, it's fine. When requests backup, at least some people will still get quick responses, instead of making everyone suffer.

The author speculates about ways to deal with an overloaded queue.

Kingmans Formula says that as you approach 100% utilization, waiting times explode.

The correct way to deal with this is bounded queue lengths and back pressure. I.e don’t deal with an overloaded queue, don’t allow an overloaded queue.

Hit with machine generated art, so awful. Is the rest of it also generated?