hof VS aperture

Many business analysts use SQL, have for a long time. They are probably not your target audience. With the problem being JAVA specific, you'd likely want to start there

This sounds similar to the goals of my hof tool (https://github.com/hofstadter-io/hof), lift type definitions out of code so they can be defined in one place, then generate the code for all the places. Is that sounding like what you are after?

Having built something similar, the biggest challenge for users is that they have to use a bespoke language, like WASP here. I suspect that it is also your biggest challenge as well.

Mine is built on CUE, which at least has the potential to become a more widely used language. CUE hasn't reached sufficient maturity for broader adoption yet, so I continue to face this same problem.

https://github.com/hofstadter-io/hof

Here's a big one I needed to get ChatGPT to do something more sophisticated with a JSON object response (predates functions and all that)

https://github.com/hofstadter-io/hof/blob/_dev/flow/chat/pro...

It no longer worked after a model update some time ago, haven't tried recently.

I found codellama to be much better for this and require fewer instructions, an anecdotal validation for smaller, focussed models

I was surprised out how you can define arbitrary grammars using arbitrary formulation and it would follow it. Of course you have to redo the prompt every time there is an update... such a pain

https://github.com/hofstadter-io/hof/blob/_dev/flow/chat/pro...

CUE is indeed a beautiful language, will get those mind juices flowing for sure!

There is more work to be done on the codec implementation, but if you just want to split yaml/json across files, CUE is a great option

You might also like my project, built on CUE: https://github.com/hofstadter-io/hof We have a TUI where you can explore and work with CUE, JSON, Yaml

You can pretty much make up any pseudo grammar like this one, which is a reduced JSON object that is close to CUE: https://github.com/hofstadter-io/hof/blob/_dev/flow/chat/pro...

No need to be formal or use a standard format

Yea, in particular for this project, they have created a bespoke templating system.

You can get the same thing with Go text/templates by adding chat function(s) as custom a helper: https://github.com/hofstadter-io/hof/blob/_dev/lib/templates...

100% one of the best things about building a TUI is not having the pain of modern web development. I do think there is a way to have a CLI & TUI come from the same code, so you can get the best of both, or pick the best for the task at hand.

experiments in progress here: https://github.com/hofstadter-io/hof/tree/_dev/lib/tui

CUE is another interesting language to use from within Go, and is rather natural, given CUE is implemented in Go, but you can also do way more cool things with CUE via the Go API.

We're using CUE to validate and transform data, as input to code gen, the basis for a DAG task engine, and more

https://cuelang.org | https://pkg.go.dev/cuelang.org/[email protected]/cue | https://cuetorials.com/go-api (learn about CUE)

https://github.com/hofstadter-io/hof (where we are doing these things)

here is one of our early examples: https://github.com/hofstadter-io/hof/blob/_dev/flow/chat/pro...

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...