sprig VS aperture

Standard Go templating seems really lacking if you come from something like Jinja. Even with libraries like https://masterminds.github.io/sprig/ (used e.g. for Helm templating) it feels hard to use.

Eleven, you'd be surprised what go template libs are out there like sprig. https://masterminds.github.io/sprig/

If you use Go templates be sure to use Sprig as well to get more usable functions.

I'd highly recommend tossing in the sprig library and depending on how you break up your templates, maybe creating a custom "include" helper instead of using the built in define/template helpers. The advantage of this is that if each template is capable of rendering itself independently, you can potentially render all of your templates in parallel.

The discoverability of golang templates is terrible, IMHO, since it's missing a "dir(locals())" equivalent and every execution environment gets to make its own rules about what pipelines/functions are exposed

Look at helm as an example: https://helm.sh/docs/chart_template_guide/function_list/ is some of them, https://helm.sh/docs/chart_template_guide/accessing_files/#p... are some others, but they also glued in some version of https://masterminds.github.io/sprig/ So, short of (a) knowing that's the case (b) having 3+ bookmarks in your favorite browser to refer to those reference pages, how would anyone know what pipelines are available?

Separately, I dooooo nooooooot understand why every joker has to invent their own new thing when we have like 50 or so templating languages already. Golang may be an outlier in that competition due to the Google Promotion Packet Effect(tm) but how they came up with `{{ range }}{{ end }}` as sane syntax is some true facepalm, to say nothing of the same landmine that ansible stepped on by not switching jinja2's default characters: `{{` is not _yaml safe_

You mentioned something about PHP. We also already have a templating language in the standard library that can be extended (commonly done with sprig).

Plopjs looks interesting and is probably not too hard to write for yourself in Go. You could add something like sprig for some useful template functions.

Not at all. It was "write a helm template using the following imaginary values in a values file". In 30 minutes, I was able to google the Kubernetes api and the sprig functions for templating, complete the assignment, fix a stupid typo the unit test caught, and spend 5 minutes trying to think of any corner cases that hidden unit tests might catch (I don't know if there were any hidden unit tests). The goal of this take-home assessment was to prove that you are not wasting an engineer's time when they call you, not to prove that you should be hired.

Support sprig in Template

They may refer to the definitive shortage of built-in functions. The template engine itself only provides the bare minimum. That's usually not a problem because of template function libraries like https://github.com/Masterminds/sprig

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...