kyma VS argo

Although I had the project on my radar, I never took a closer look at it. However, now it was the time to give it a spin ... or try. All I needed was a Kubernetes environment. As I am working in the SAP space the choice of Kubernetes is kind of predefined, namely Kyma in its managed version on the SAP Business Technology Platform.

News from the SAP side: the clear focus is here SAP Business Technology Platform especially Kyma

I could only find https://github.com/kyma-project/kyma as something close to being real-world. But my google fu is failing here. Any help would be appreciated :)

I am using Kyma as opinionated stack on top of Kubernetes (to be precise on a Gardener cluster) for this exercise. The setup should be similar for vanilla stacks, but the API gateway must be adjusted accordingly depending on what you use.

So you can install Kyma 1.x without own domain, but you have no access to the Kyma console UI. An issue is open for this (https://github.com/kyma-project/kyma/issues/11924) and according to the Kyma slack channel the situation will improve with the upcoming release of Kyma 2.x, but until then you must provide an own domain to get access to the Kyma console UI. From my point of view this lifts the entry barrier of trying out open-source Kyma, but currently this is the way.

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)