jsii VS dagger

What about jsii? The technology behind AWS sdks: https://aws.github.io/jsii/

Is Stainless similar, different?

The libraries for other supported languages of CDK are built as wrappers for underlying JS or maybe TS code via https://github.com/aws/jsii

So all the core CDK code is written first in JS/TS and then stubs for the other languages are added

Unfortunately this is often done without consideration for how the other supported langs actually work, and artefacts of e.g. JS lack of support for kwargs leak through

This is why e.g. the typing in CDK Python is completely broken - pretty much uniformly the concrete types like "Resource" don't implement their corresponding interface like "IResource" (to a type checker)

(There are many other typing niggles like this but that's the most egregious and pervasive one)

At the end of the day, having to explicitly cast concrete types as their interface to satisfy type checker is a minor annoyance, albeit a stupid one that could have been avoided with more care in the core library.

I could live with that, but I encountered so many bugs and issues trying to use CDK on current project that it's now much clearer to me why every company I worked at previously was using Terraform.

Pretty sure some of those issues are ultimately CloudFormation ones. The cumbersome CF > CDK JS > CDK Python stack is great for obfuscating errors and making debugging hard or impossible though.

Pulumi do something similar, albeit with Go as the core language and Terraform underneath. From what I've seen with a little use they have a much more successful result though, Pulumi Python was not a complete mess, and deploys faster and more reliable with better error feedback. I guess they just took more care to get it right.

Every version of the CDK uses an interop layer and runs on top of the Typescript version

https://github.com/aws/jsii

And as far as TF supports services before CFT. Guess which is easier for an AWS employee to do - getting the CF service team to support a new service or just contribute to Terraform’s open source project?

I know of at least one service where the service team introduced the needed APIs and then an employee of AWS wrote the TF provider and contributed to the project before AWS’s own internal team added it to CFT.

Source: former AWS ProServe employee

To "meet developers where they are" is a beautiful tenet of AWS, and of the CDK, and inspired us to create awesome technology such as JSII and constructs.

Yeah both CDKTF and Pulumi piggyback on JSII, a class translator that supports the languages you've mentioned, which was used as a building base for CDK.

The reason we chose to start with compilation to JavaScript and not some other popular cloud language is because JavaScript is currently one of the most used ones, and in addition, we are using JSII to write our SDK in Typescript and then export it for consumption in other languages, such as Go, Python, Java and others.

The CDKTF CLI is implemented in TypeScript. Providers and resources are implemented in TypeScript as well. jsii is used to compile the providers and resources to the supported languages.

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

Hey, https://dagger.io employee here. We can help with this. I'd recommend taking a look at out Python SDK (https://docs.dagger.io/sdk/python) to build your pipelines and then leverage Dagger Cloud (https://dagger.io/blog/dagger-cloud) for fast production deployments.