aperture VS DIStage

Anyone interested in load shedding and graceful degradation with request prioritization should check out the Aperture OSS project.

https://github.com/fluxninja/aperture

I agree that queues can problem especially when misconfigured. But some amount of queuing is necessary, to absorb short spikes in demand vs capacity. Also, queues can be helpful to re-order requests based on criticality which won't be possible with zero queue size - in which case we have to immediately drop a request or admit it without considering it's priority.

I think it is beneficial to re-think how we tune queues. Instead of setting a queue size, we should be tuning the max permissible latency in the queue which is what a request timeout actually is. That way, you stay within the acceptable response time SLA while keeping only the serve-able requests in the queue.

Aperture, an open-source load management platform took this approach. Each request specifies a timeout for which it is willing to stay in the queue. And weighted fair queuing scheduler then allocates the capacity (a request quota or max number of in-flight request) across requests based on the priority and tokens (request heaviness) of each request.

Read more about the WFQ scheduler in Aperture: https://docs.fluxninja.com/concepts/scheduler

Link to Aperture's GitHub: https://github.com/fluxninja/aperture

Would love to hear your thoughts on our approach!

For those keen to dive deeper, I highly recommend exploring both the Twitter Space and Aperture: [Twitter Spaces]: https://twitter.com/kelseyhightower/status/1689355284802629633?s=20 [GitHub repo]: https://github.com/fluxninja/aperture

Very interesting blog post! Our team has been working intensively in this area for the last couple of years - flow control, load shedding, controllability (PID control), and so on.

We have open-sourced our work at - https://github.com/fluxninja/aperture

We would love feedback from folks reading this blog post!

Disclaimer: I am one of the co-authors of the Aperture project. There are several interesting ideas we have built into this project and I will be happy to dive into the technical details as well.

It's a blog on an open-source project that precisely tells you how to implement adaptive rate limiting.

Just click around a bit:

- https://github.com/fluxninja/aperture

- https://docs.fluxninja.com/use-cases/adaptive-service-protec...

Note: I am one of the authors' of this project.

At the time of writing, the first sample image on that page is this:

https://coderabbit.ai/assets/section-1-f9a48066.png

which recommends adding a "maxIterations" counter to the "for len(executedComponents) ..." loop here:

https://github.com/fluxninja/aperture/blob/26e00ea818c7c28da...

HOWEVER

- the review has failed to notice the logic using "numExecutedBefore" (around line 377) that already prevents the specific bug it is suggesting a fix for

- the suggested change decrements "maxIterations" inside the "for ... range circuit.components {" loop which means it isn't counting iterations, it's counting components

This kind of suggestion is particularly nasty because it's unlikely that the test suite populates enough components to hit "maxIterations" - so an inattentive reader could accept it, get a green build, and then deploy a production bug!

The need an adaptive protection system like Aperture[0] to mitigate overloads.

[0]: https://github.com/fluxninja/aperture

It’s customized to our policy spec. But you can learn from this and adapt it to your spec.

https://github.com/fluxninja/aperture/blob/main/scripts/json...

Right.

We delivered many talks on that subject and implemented an ultimate tool for that: https://github.com/7mind/izumi (the links to the talks are in the readme).

The library is for Scala, though all the principles may be reused in virtually any environment.

One of the notable mainstream (but dated) approaches is OSGi.