kt-search
dagger
kt-search | dagger | |
---|---|---|
3 | 93 | |
89 | 10,324 | |
- | 2.9% | |
9.1 | 9.9 | |
5 days ago | about 3 hours ago | |
Kotlin | Go | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
kt-search
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Feature Flags in a CI Pipeline
I use matrix tests with github actions to test my kt-search client with different versions of elastisearch and opensearch. Pretty easy to set up: https://github.com/jillesvangurp/kt-search/blob/master/.gith...
Basically it fires up elasticsearch using docker-compose and then the integration tests run against that. You could use a similar strategy to test different feature flag combinations.
For some of our private projects, we use kts to generate the github action yaml files using this: https://github.com/krzema12/github-workflows-kt
Well worth checking out if you have more complex workflows. Yaml is just horrible in terms of copy paste reuse. Also nice to get some compile time safety and auto complete with our action files.
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Kts Scripting of Yaml & Json Dialects
Whatever you are dealing with, you can create a Kotlin DSL for it. If you have any Json dialect, checkout my JsonDsl library, which is part of kt-search. With that you can create simple Kotlin classes to model your DSL using type safe properties and have a run-time modifiable map to add anything it doesn't model. Creating a Yaml version of this is very straightforward and likely something I might do at some point (pull requests welcome).
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Best way to integrate elastic app search into an android app using Kotlin?
jillesvangurp / kt-search
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 β¦ boot! β¦ 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? π€·ββοΈ
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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Whatβs 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 β 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 β declarative pipeline orchestrator based on HCL and Terraform
Is this similar to Dagger[1] ?
[1] https://dagger.io
What are some alternatives?
ktor-arrow-example - Real World implementation - "The mother of all demo apps". Written in Kotlin, with Ktor, Arrow, SqlDelight, KotlinX Serialization, etc.
earthly - Super simple build framework with fast, repeatable builds and an instantly familiar syntax β like Dockerfile and Makefile had a baby.
KStore - A tiny Kotlin multiplatform library that assists in saving and restoring objects to and from disk using kotlinx.coroutines, kotlinx.serialisation and okio
pipeline - A cloud-native Pipeline resource.
appbase-droid - Elasticsearch and appbase.io library for Android (and Java)
gitlab-ci-local - Tired of pushing to test your .gitlab-ci.yml?
github-workflows-kt - Authoring GitHub Actions workflows in Kotlin. You won't go back to YAML!
act - Run your GitHub Actions locally π
kt-search-kts - Adds kts scripting to kt-search and provides some convenience around using that from kts.
aws-cdk - The AWS Cloud Development Kit is a framework for defining cloud infrastructure in code
maven-simple - Example Maven project demonstrating the use of
dagster - An orchestration platform for the development, production, and observation of data assets.