Concourse VS act

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> Why bother, when Dagger caches everything automatically?

The fear with needing to run `npm ci` (or better, `pnpm install`) before running dagger is on the amount of time required to get this step to run. Sure, in the early days, trying out toy examples, when the only dependencies are from dagger upstream, very little time at all. But what happens when I start pulling more and more dependencies from the Node ecosystem to build the Dagger pipeline? Your documentation includes examples like pulling in `@google-cloud/run` as a dependency: https://docs.dagger.io/620941/github-google-cloud#step-3-cre... and similar for Azure: https://docs.dagger.io/620301/azure-pipelines-container-inst... . The more dependencies brought in - the longer `npm ci` is going to take on GitHub Actions. And it's pretty predictable that, in a complicated pipeline, the list of dependencies is going to get pretty big - at least a dependency per infrastructure provider we use, plus inevitably all the random Node dependencies that work their way into any Node project, like eslint, dotenv, prettier, testing dependencies... I think I have a reasonable fear that `npm ci` just for the Dagger pipeline will hit multiple minutes, and then developers who expect linting and similar short-run jobs to finish within 30 seconds are going to wonder why they're dealing with this overhead.

It's worth noting that one of Concourse's problems was, even with webhooks setup for GitHub to notify Concourse to begin a build, Concourse's design required it to dump the contents of the webhook and query the GitHub API for the same information (whether there were new commits) before starting a pipeline and cloning the repository (see: https://github.com/concourse/concourse/issues/2240 ). And that was for a CI/CD system where, for all YAML's faults, for sure one of its strengths is that it doesn't require running `npm ci`, with all its associated slowness. So please take it on faith that, if even a relatively small source of latency like that was felt in Concourse, for sure the latency from running `npm ci` will be felt, and Dagger's users (DevOps) will be put in an uncomfortable place where they need to defend the choice of Dagger from their users (developers) who go home and build a toy example on AlternateCI which runs what they need much faster.

> I will concede that Dagger’s clustering capabilities are not great yet

Herein my argument. It's not that I'm not convinced that building pipelines in a general-purpose programming language is a better approach compared to YAML, it's that building pipelines is tightly coupled with the infrastructure that runs the pipelines. One aspect of that is scaling up compute to meet the requirements dictated by the pipeline. But another aspect is that `npm ci` should not be run before submitting the pipeline code to Dagger, but after submitting the pipeline code to Dagger. Dagger should be responsible for running `npm ci`, just like Concourse was responsible for doing all the interpolation of the `((var))` syntax (i.e. you didn't need to run some kind of templating before submitting the YAML to Concourse). If Dagger is responsible for running `npm ci` (really, `pnpm install`), then it can maintain its own local pnpm store / pipeline dependency caching, which would be much faster, and overcome any shortcomings in the caching system of GitHub Actions or whatever else is triggering it.

> Imagine you live in a world where no part of the build has to repeat unless the changes actually impacted it. A world in which all builds happened with automatic parallelism. A world in which you could reproduce very reliably any part of the build on your laptop.

That sounds similar to https://concourse-ci.org/

I quite like it, but it never seemed to gain traction outside of Cloud Foundry.

I used Concourse[0] for a while. No real complaints, the visibility is nice but the functionality isn't anything new.

[0] https://concourse-ci.org/

We run https://concourse-ci.org/ on our own hardware at our office. (as a side note, running your own hardware, you realise just how abysmally slow most cloud servers are.)

Concourse: https://concourse-ci.org

We use https://concourse-ci.org/ at the moment and have been reasonably happy with it, however it only has support for linux containers at the moment, no windows containers. (MacOS doesn't have a containers primitive yet unfortunately)

To speed up your development cycle, install and use the act tool to test-run your action directly in your development environment. This tool lets you invoke a GitHub workflow right on your local machine and will save you the round-trips of pushing each change to GitHub to see if it works.

When it comes to the alternatives to tmate, there is another great debugging tool that you could check out. It is called act and it allows you to run GitHub Actions code on your local machine making debugging even easier. It has its own limitations and some learning curve but overall it is another tool you should use if you can’t fix the CI bugs by connecting directly into the running action with the tmate.

Link: https://github.com/nektos/act

Could you upload your build of GitHub's runner image to Docker Hub?

This would be quite useful for users of other GitHub Actions clones like act [0].

[0]: https://github.com/nektos/act

> These kinds of commit messages are typically an indicator of a broken process where somebody needs to commit to see something happen, like a deployment or build process, and aren't able to assert that stuff works locally.

This is one of my biggest pet peeves with services like github actions. Something running locally like "act" [1] isn't sufficient because it doesn't have everything github has and is extra friction anyway to get everyone to use it for testing.

[1] https://github.com/nektos/act

View on GitHub

If you use Github actions, act is incredibly useful. It can be used to test your GH actions, but also serves as an interface for running tasks locally.

That's something that often is difficult or basically impossible. Except for maybe GitHub actions through Act (https://github.com/nektos/act). I'd still lean to something in the yaml sphere if it eventually would be used in deployment pipelines and such. For example a solution incorporating ansible.

It also seems to me that the argument you make is mostly focused on the building step? Earthly certainly seems focused on that aspect.

I feel I'm being trolled, but I'll bite and accept the resulting downvotes

I don't think treating every mention of act as an opportunity for airing of personal grievances is helpful in a discussion when there's already ample reports of people's concrete issues with it, had one looked at the 800 issues in its repo https://github.com/nektos/act/issues?q=is%3Aissue or the 239 from gitea's for https://gitea.com/gitea/act_runner/issues or whatever is going on with Forgejo's fork https://code.forgejo.org/forgejo/act .

But, as for me specifically, there are two and a half answers: I wanted to run VSCodium's build locally, which act for sure puked about. Then, while trying to troubleshoot that, I thought I'd try something simpler and have it run the lint job from act's own repo <https://github.com/nektos/act/blob/1252e551b8672b1e16dc8835d...> to rule out "you're holding it wrong" type junk. It died with

  [checks/lint]     Failure - Main actions/setup-go@v3

https://twitter.com/mitsuhiko/status/1720410479141487099 :

> GitHub Actions currently charges $0.16 per minute* for the macOS M1 Runners. That comes out to $84,096 for 1 machine year*

GitHub Runner is written in Go; it fetches tasks from GitHub Actions and posts the results back to the Pull Request that spawned the build.

nektos/act is how Gitea Actions builds GitHub Actions workflow YAML build definition documents. https://github.com/nektos/act

https://twitter.com/MatthewCroughan/status/17200423527675700... :

> This is the macOS Ventura installer running in 30 VMs, in 30 #nix derivations at once. It gets the installer from Apple, automates the installation using Tesseract OCR and TCL Expect scripts. This is to test the repeatability. A single function call `makeDarwinImage`.

With a Multi-Stage Dockerfile/Containerfild, you can have a dev environment like xcode or gcc+make in the first stage that builds the package, and then the second stage the package is installed and tested, and then the package is signed and published to a package repo / app store / OCI container image repository.

SLSA now specifies builders for signing things correctly in CI builds with keys in RAM on the build workers.

"Build your own SLSA 3+ provenance builder on GitHub Actions" https://slsa.dev/blog/2023/08/bring-your-own-builder-github