argo VS argo-cd

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)

The code above will create the argocd Kubernetes namespace and deploy the latest stable manifest. If you would like to install a specific manifest, have a look here.

In this blog post, we will demonstrate how easy and fast it is to deploy Sveltos on an RKE2 cluster with the help of ArgoCD, register two RKE2 Cluster API (CAPI) clusters and create a ClusterProfile to deploy Prometheus and Grafana Helm charts down the managed CAPI clusters.

Argo CD

$version = (Invoke-RestMethod https://api.github.com/repos/argoproj/argo-cd/releases/latest).tag_name Invoke-WebRequest -Uri "https://github.com/argoproj/argo-cd/releases/download/$version/argocd-windows-amd64.exe" -OutFile "argocd.exe"

This is cool - I can think of some projects that are amazing as first contributors, and others I can think of that are terrible.

One thing I think the tool doesn't address is why someone should contribute to a particular project. Having stars is interesting, and a proxy for at least historical activity, but also kind of useless here - take argoproj/argo-cd [1] as an example - 14.5k stars, with a backlog of 2.7k issues and an issue tracker that's a real mess.

Either way, I think this tool is neat for trying to gain some experience in a project purely based on language.

[1] https://github.com/argoproj/argo-cd/issues?q=is%3Aissue+is%3...

In our case, our team went ahead with Solution B, as that was the only solution present when the issue occurred. However, with the release of Argo CD 2.8.0 (released on August 7, 2023), things have changed - for the better :). Now, there are two ways to handle the sharding issue with the Argo CD Application Controller:

$ kubectl create namespace argocd //Next, let's apply the yaml configuration files for ArgoCd $ kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argo-cd/stable/manifests/install.yaml //Now we can view the pods created in the ArgoCD namespace. $ kubectl get pods -n argocd //To interact with the API Server we need to deploy the CLI: $ curl --silent --location -o /usr/local/bin/argocd https://github.com/argoproj/argo-cd/releases/download/v2.4.7/argocd-linux-amd64 $ chmod +x /usr/local/bin/argocd //Expose argocd-server $ kubectl patch svc argocd-server -n argocd -p '{"spec": {"type": "LoadBalancer"}}' //Wait about 2 minutes for the LoadBalancer creation $ kubectl get svc -n argocd //Get pasword and decode it. $ kubectl get secret argocd-initial-admin-secret -n argocd -o yaml $ echo WXVpLUg2LWxoWjRkSHFmSA== | base64 --decode

From here, we can explore other developments and tutorials on Kubernetes, such as o11y or observability (PLG, ELK, ELF, TICK, Jaeger, Pyroscope), service mesh (Linkerd, Istio, NSM, Consul Connect, Cillium), and progressive delivery (ArgoCD, FluxCD, Spinnaker).

lol! Wham! Third choice! https://github.com/argoproj/argo-cd

If you mean for each app, I don't think it's listed anywhere though you may find it in `repo-server` logs. Like so