hull VS dagger

If you are just starting out and decide to go with writing your own Helm Charts I'd like to suggest our HULL Helm Library Chart for that purpose.

With HULL we have proposed an alternative yet Helm based solution a year ago which that tries to do it upside down by first giving you a documented Kubernetes API style full access to each objects configuration. Only on top of that it provides you further advanced options to (re)introduce abstraction into the mix - only if you need them and they actually improve your configuration. Everything takes place in the values.yaml so there is no digging around in the templates folder and everything is in view.

The HULL library Helm chart provides a single common interface to specifying Kubernetes objects within Helm Charts. The interface itself is based on the Kubernetes API schema itself which is integrated as a JSON schema in the HULL chart. Since all objects are defined directly in the values.yaml under the hull key there is no need to create and maintain custom template files when creating objects with HULL, everything happens in the values.yaml.

Good, now proceed by creating a new empty HULL based Helm chart. The steps are documented here but you will create it from scratch here to understand what is needed.

As a reminder, the goal of this tutorial series is to demonstrate how to build Helm charts based on the HULL library chart by recreating the functionality of the original kubernetes-dashboard Helm chart with a HULL based chart from scratch. When you have followed the previous part of this tutorial on setting up a HULL base chart you have created a for now unconfigured Helm chart named kubernetes-dashboard-hull in the 02_setup subfolder of your working directory (we assume that's ~/kubernetes-dashboard-hull here). You can alternatively download the current chart state here and continue from there. Also you should have checked out and extracted the kubernetes-dashboard Helm chart to kubernetes-dashboard in your working directory because examining it will be frequently required.

the ability to specify any CustomResource as a customresource object instance. For CustomResources you additionally need to specify the kind and apiVersion besides the free form spec of your object.

We have built a Helm Library chart named HULL, it provides amongst other features full access to all defined objects and their properties at creation and deployment time. Think of it as an API to specify Kubernetes objects directly in a Helm charts values.yaml. If there is some functionality you want to add or use in a particular scenario you can just configure it and the Kubernetes objects are as you actually want them to be - every aspect can always be tuned at deploy time if needed without you having to get back to the chart creator via PRs, hack the chart or similar methods. All doable with Helm and the HULL library chart, no other tooling required!

We actually proposed an alternative way to solve the problem if you are Helm with our Helm library chart HULL.

It may be worthwhile to look at the recently added examles, these are more advanced chart values.yamls from products we are deploying this way. You can see it can be pretty concise to define your applications structure with HULL in comparison.

Downsides to this is that you would have to write out the full content of your config in the values.yaml and cannot use the templating capabilities any further. Within the values.yaml no templating is allowed (unless you base your chart on this library chart we have created ;) which may be a more advanced topic if you just got started)

The fact that I couldn't point to one page on the docs that shows the tl;dr or the what problem is this solving

https://docs.dagger.io/quickstart/562821/hello just emits "Hello, world!" which is fantastic if you're writing a programming language but less helpful if you're trying to replace a CI/CD pipeline. Then, https://docs.dagger.io/quickstart/292472/arguments doubles down on that fallacy by going whole hog into "if you need printf in your pipline, dagger's got your back". The subsequent pages have a lot of english with little concrete examples of what's being shown.

I summarized my complaint in the linked thread as "less cowsay in the examples" but to be honest there are upteen bazillion GitHub Actions out in the world, not the very least of which your GHA pipelines use some https://github.com/dagger/dagger/blob/v0.10.2/.github/workfl... https://github.com/dagger/dagger/blob/v0.10.2/.github/workfl... so demonstrate to a potential user how they'd run any such pipeline in dagger, locally, or in Jenkins, or whatever by leveraging reusable CI functions that setup go or run trivy

Related to that, I was going to say "try incorporating some of the dagger that builds dagger" but while digging up an example, it seems that dagger doesn't make use of the functions yet <https://github.com/dagger/dagger/tree/v0.10.2/ci#readme> which is made worse by the perpetual reference to them as their internal codename of Zenith. So, even if it's not invoked by CI yet, pointing to a WIP PR or branch or something to give folks who have CI/CD problems in their head something concrete to map into how GHA or GitLabCI or Jenkins or something would go a long way

> GHA has "service containers", but unfortunately the feature is too basic to address real-world use cases: it assumes a container image can just … boot! … and only talk to the code via the network. Real world use cases often require serialized steps between the test & the dependencies, e.g., to create or init database dirs, set up certs, etc.)

My biased recommendation is to write a custom Dagger function, and run it in your GHA workflow. https://dagger.io

If you find me on the Dagger discord, I will gladly write a code snippet summarizing what I have in mind, based on what you explained of your CI stack. We use GHA ourselves and use this pattern to great effect.

Disclaimer: I work there :)

Dagger (https://dagger.io) is a great way to use BuildKit through language SDKs. It's such a better paradigm, I cannot imagine going back.

Dagger is by the same folks that brought us Docker. This is their fresh take on solving the problem of container building and much more. BuildKit can more than build images and Dagger unlocks it for you.

And suddenly, it's almost painfully obvious where all the pain came from. Cloud applications today are simply a patchwork of disconnected pieces. I have a compiler for my infrastructure, another for my functions, another for my containers, another for my CI/CD pipelines. Each one takes its job super seriously, and keeps me safe and happy inside each of these machines, but my application is not running on a single machine anymore, my application is running on the cloud.

That said I've been moving my CI/CD to https://dagger.io/ which has been FANTASTIC. It's code based so you can define all your pipelines in Go, Python, or Javascript and they all run on containers so I can run actions locally without any special setup. Highly recommended.

You are right make is arcane. But it gets the job done. There are new exciting things happening in this area. Check out https://dagger.io.

> but I'm not convinced it's ready to replace Gitlab CI.

The purpose of Dagger it's not to replace your entire CI (Gitlab in your case). As you can see from our website (https://dagger.io/engine), it works and integrates with all the current CI providers. Where Dagger really shines is to help you and your teams move all the artisanal scripts encoded in YAML into actual code and run them in containers through a fluent SDK which can be written in your language of choice. This unlocks a lot of benefits which are detailed in our docs (https://docs.dagger.io/).

> Dagger has one very big downside IMO: It does not have native integration with Gitlab, so you end up having to use Docker-in-Docker and just running dagger as a job in your pipeline.

This is not correct. Dagger doesn't depend on Docker. We're just conveniently using Docker (and other container runtimes) as it's generally available pretty much everywhere by default as a way to bootstrap the Dagger Engine. You can read more about the Dagger architecture here: https://github.com/dagger/dagger/blob/main/core/docs/d7yxc-o...

As you can see from our docs (https://docs.dagger.io/759201/gitlab-google-cloud/#step-5-cr...), we're leveraging the *default* Gitlab CI `docker` service to bootstrap the engine. There's no `docker-in-docker` happening there.

> It clumps all your previously separated steps into a single step in the Gitlab pipeline.

This is also not the case, we should definitely improve our docs to reflect that. You can organize your dagger pipelines in multiple functions and call them in separate Gitlab jobs as you're currently doing. For example, you can do the following:

```.gitlab-ci.yml

Check out https://dagger.io/. Write declarative pipelines in code, reproducibly run anywhere.

Is this similar to Dagger[1] ?

[1] https://dagger.io