argocd-example-apps VS argo

I have not yet had the opportunity to test flux extensively. Regarding Argo examples, the Argo team themself maintain such a repo: https://github.com/argoproj/argocd-example-apps

Second, when dealing with OCI helm charts, look up the umbrella chart model https://github.com/argoproj/argocd-example-apps/blob/master/helm-dependency/README.md. This basically lets you create a helm chat that doesn’t do anything but call your next helm chart as a dependency. I use this with OCI stores helm charts all over the place. Also, in the next ArgoCD release, you should be able to get multiple sources for a sync, but we’ll see when that comes out

argocd app create helm-guestbook --repo https://github.com/argoproj/argocd-example-apps.git --path helm-guestbook --dest-server https://kubernetes.default.svc --dest-namespace default

Let’s Fork a sample repo, for example, like this one found here: https://github.com/argoproj/argocd-example-apps

apiVersion: argoproj.io/v1alpha1 kind: Application metadata: name: guestbook namespace: argocd spec: project: default source: repoURL: https://github.com/argoproj/argocd-example-apps.git targetRevision: HEAD path: guestbook destination: server: https://kubernetes.default.svc namespace: guestbook

For example if I point to https://github.com/argoproj/argocd-example-apps, from the UI, I can see a new repository but no applications

extraObjects: - apiVersion: argoproj.io/v1alpha1 kind: Application metadata: name: my-app namespace: argocd spec: project: default source: repoURL: 'https://github.com/argoproj/argocd-example-apps' path: guestbook targetRevision: HEAD destination: server: 'https://kubernetes.default.svc' namespace: test syncPolicy: automated: {} syncOptions: - CreateNamespace=true EOF

❯ cd ~/git ❯ gh repo fork https://github.com/argoproj/argocd-example-apps.git --clone ✓ Created fork e-minguez/argocd-example-apps Cloning into 'argocd-example-apps'... ... From github.com:argoproj/argocd-example-apps * [new branch] master -> upstream/master ✓ Cloned fork

Like Argo Workflows?

https://github.com/argoproj/argo-workflows

Dagger looks more comparable to Argo Workflows: https://argoproj.github.io/argo-workflows/ That's the first of the Argo projects, which can run multi-step workflows within containers on Kubernetes.

For what it's worth, my colleagues and I have had great luck with Argo Workflows and wrote up a blog post about some of its advantages a few years ago: https://www.interline.io/blog/scaling-openstreetmap-data-wor...

Despite other alternatives like Circle CI, Travis CI, GitLab CI or even self-hosted options using open-source projects like Tekton or Argo Workflow, the reason for choosing GitHub Actions was straightforward: GitHub Actions, in conjunction with the GitHub ecosystem, offers a user-friendly experience and access to a rich software marketplace.

author seems to be describing the kind of patterns you might make with https://argoproj.github.io/argo-workflows/ . or see for example https://github.com/couler-proj/couler , which is an sdk for describing tasks that may be submitted to different workflow engines on the backend.

it's a little confusing to me that the author seems to object to "pipelines" and then equate them with messaging-queues. for me at least, "pipeline" vs "workflow-engine" vs "scheduler" are all basically synonyms in this context. those things may or may not be implemented with a message-queue for persistence, but the persistence layer itself is usually below the level of abstraction that $current_problem is really concerned with. like the author says, eventually you have to track state/timestamps/logs, but you get that from the beginning if you start with a workflow engine.

i agree with author that message-queues should not be a knee-jerk response to most problems because the LoE for edge-cases/observability/monitoring is huge. (maybe reach for a queue only if you may actually overwhelm whatever the "scheduler" can handle.) but don't build the scheduler from scratch either.. use argowf, kubeflow, or a more opinionated framework like airflow, mlflow, databricks, aws lamda or step-functions. all/any of these should have config or api that's robust enough to express rate-limit/retry stuff. almost any of these choices has better observability out-of-the-box than you can easily get from a queue. but most importantly.. they provide idioms for handling failure that data-science folks and junior devs can work with. the right way to structure code is just much more clear and things like structuring messages/events, subclassing workers, repeating/retrying tasks, is just harder to mess up.

Argo Workflows + Argo Events

Drone, or Tekton, Argo Workflows if you’re on k8s

Check out Argo Workflows.

go-git has a lot of bugs and is not actively maintained. The bug even affects Argo Workflow, which caused our data pipeline to fail unexpectedly (reference: https://github.com/argoproj/argo-workflows/issues/10091)