bcl
dagger
bcl | dagger | |
---|---|---|
7 | 96 | |
14 | 10,358 | |
- | 3.6% | |
9.5 | 9.9 | |
7 days ago | 5 days ago | |
Go | Go | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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bcl
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HCL: Toolkit for Structured Configuration Languages
Another take on replacing HCL with something more sensible:
BCL https://github.com/wkhere/bcl
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Show HN: Togomak – declarative pipeline orchestrator based on HCL and Terraform
I agree with you that HCL sucks when it comes to variables.
Other thing that is funny: no user-defined functions, being unable to use function calls in string interpolations, but allowing variables... so it is like saying: we have this parser and at some points it allows expressions, at some other point not. This seems wrong.
At the same time I agree or at least understand original author's intent to squeeze HCL to maxinum. There is something appealing in HCL visual form, at least when defining resources. Maybe it's just (almost) simplest form of defining such structures that can exists.
This is why I started to work on my own format for configuration, visually similar but with different model of evaluation.
Here is the first attempt: https://github.com/wkhere/bcl
Disclaimer: I named it BCL, 'B' stands for Basic, to somehow relate to HCL and make it easily pronounced. But later I discovered that another BCL is used as Google to configure the Borg platform and seems to be massively hated ;) So I look for the better name..
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Ask HN: Show me your half baked project
I am crafting BCL, own configuration language.
https://github.com/wkhere/bcl
I started this when I was unable to squeeze certain usage patterns from HCL, like: variables living in the same scope as the file, evaluating variables in one pass with parsing, easily using external (environment) variables; plus, a simplified syntax.
The implementation is mostly done: you can defined blocks holding key-value pairs and use numerical, string and bool expressions in them. I will add lists and nested blocks.
At this very moment I am rewriting a parser from yacc-based to a Pratt top-down parser with vm, heavily inspired by the excellent book "Crafting Interpreters".
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That's a Lot of YAML
https://github.com/wkhere/bcl
Will not immediately help for all of YAML usages, but at least for defining resources in a Terraform-like style. In fact, it's already it's already helpful as a replacement for HCL in one internal project, that was a final motivation to hack it.
In a bigger picture, I have no idea how to help with YAML omnipresence in Kubernetes. More than a half of my problems in a $daily_job is how crude is consolidating a final Helm chart from different sources. I am not saying that Helm would be inherently a bad tool or my company has chosen pretty bad way of using it - I guess everyone is doing their best considering the ciscumstances. But manipulating textual templates is just too error prone, and the detection of errors happens too late. I dare to say - Kubernetes would do much better with custom format based on a C-like syntax, instead if trying to prove how cool YAML is, especially when it isn't.
- BCL - a simplified HCL-like configuration format (WIP)
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What Is Wrong with TOML?
Related:
just few days ago I crafted together some ideas i had couple of years already for a configuration language, syntactically like HCL but without HashiCorps idiosyncrasies.
Here it goes, BCL (_Basic_ Configuration Language, for a lack of better name yet), Go prototype, I can code Python port and possibly several other as well..
https://github.com/wkhere/bcl
dagger
- Dagger: Programmable open source CI/CD engine that runs pipelines in containers
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Nix is a better Docker image builder than Docker's image builder
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
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Testcontainers
> 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 :)
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BuildKit in depth: Docker's build engine explained
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.
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Cloud, why so difficult? 🤷♀️
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.
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Share your DevOps setups
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.
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What’s with DevOps engineers using `make` of all things?
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.
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Shellcheck finds bugs in your shell scripts
> 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
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Cicada – A FOSS, Cross-Platform Version of GitHub Actions and Gitlab CI
Check out https://dagger.io/. Write declarative pipelines in code, reproducibly run anywhere.
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Show HN: Togomak – declarative pipeline orchestrator based on HCL and Terraform
Is this similar to Dagger[1] ?
[1] https://dagger.io