Argo | Functional JSON parsing library for Swift | iOS library

Because each fetch produces its own packfile and one packfile is more efficient than multiple packfiles. A lot more efficient. How?

First, the checkouts are a red herring. They don't affect the size of the .git/ directory.

Second, in the first example only the first git fetch origin does anything. The rest will fetch nothing (unless something changed on origin).

Compression works by finding common long sequences within the data and reducing them to very short sequences. If

We can see in the example below that the first packfile is 113M, the second is 161M, the third is 177M, and the final fetch is 209M. The size of the final packfile is roughly equal to the size of the single garbage compacted packfile.

git fetch is very efficient. It will only fetch objects you not already have. Sending individual object files is inefficient. A smart Git server will send them as a single packfile.

When you do a single git fetch on a fresh repository, Git asks the server for every object. The remote sends it a packfile of every object.

When you do git fetch ABC and then git fetch DEFs, Git tells the server "I already have everything up to ABC, give me all the objects up to DEF", so the server makes a new packfile of everything from ABC to DEF and sends it.

Eventually your repository will do an automatic garbage collection and repack these into a single packfile.

We can reduce the examples. I'm going to use Rails to illustrate because it has clearly defined tags to fetch.

There's an open PR to add support for arbitrary YAML in the values field. If merged, I would expect it to be available in 2.4. Reviews/testing are appreciated if you have time!

One workaround is to use the parameters field and set parameters individually. It's not ideal, but maybe could help until 2.4 is released.

The result input to the sayHello template must be passed explicitly from the third step of the main template.

steps.addTenToResult.outputs.result has no meaning in the sayHello template definition. Variables starting with steps. only have meaning in steps templates (i.e. templates with the steps field populated, like in main).

A very similar workflow from the Argo developers/maintainers can be found here:

https://github.com/argoproj/argo-workflows/blob/master/examples/README.md#artifacts

Argo Workflows automatically adds the name of the Cron Workflow as a label on the workflow. That label is accessible as a variable.

The problem is mentioned in Argo's useraccounts docs:

When you create local users, each of those users will need additional RBAC rules set up, otherwise they will fall back to the default policy specified by policy.default field of the argocd-rbac-cm ConfigMap.

But these additional RBAC rules could be setup the simplest using ArgoCD Projects. And with such a AppProject you don't even need to create a user like tekton in the ConfigMap argocd-cm. ArgoCD projects have the ability to define Project roles:

Projects include a feature called roles that enable automated access to a project's applications. These can be used to give a CI pipeline a restricted set of permissions. For example, a CI system may only be able to sync a single app (but not change its source or destination).

There are 2 solutions how to configure the AppProject, role & permissions incl. role token:

1. using argocd CLI
2. using a manifest YAML file

So let's get our hands dirty and create a ArgoCD AppProject using the argocd CLI called apps2deploy:

A namespace install allows Workflows to run only in the namespace where Argo Workflows is installed.

A managed namespace install allows Workflows to run only in one namespace besides the one where Argo Workflows is installed.

Using a managed namespace install might make sense if you want some users/processes to be able to run Workflows without granting them any privileges in the namespace where Argo Workflows is installed.

For example, if I only run CI/CD-related Workflows that are maintained by the same team that manages the Argo Workflows installation, it's probably reasonable to use a namespace install. But if all the Workflows are run by a separate data science team, it probably makes sense to give them a data-science-workflows namespace and run a "managed namespace install" of Argo Workflows from another namespace.

To configure a managed namespace install, edit the workflow-controller and argo-server Deployments to pass the --managed-namespace argument.

You can currently only configure one managed namespace, but in the future it may be possible to manage more than one.

Something like this should work:

The argo CLI manages two API clients. The first client connects to the Argo Workflows API server. The second connects to the Kubernetes API. Depending on what you're doing, the CLI might connect just to one API or the other.

To connect to the Kubernetes API, the CLI just uses your kube config.

To connect to the Argo server, the CLI first checks for an ARGO_TOKEN environment variable. If it's not available, the CLI falls back to using the kube config.

ARGO_TOKEN is only necessary when the Argo Server is configured to require client auth and then only if you're doing things which require access to the Argo API instead of just the Kubernetes API.