GitOps
As we have seen, our Continous Integration pipelines are not the best place to perform a rollout of our application.
GitOps is a term coined by WeaveWorks, so let's see what is their definition:
GitOps is a way to do Kubernetes cluster management and application delivery. It works by using Git as a single source of truth for declarative infrastructure and applications. With Git at the center of your delivery pipelines, developers can make pull requests to accelerate and simplify application deployments and operations tasks to Kubernetes.
Let's list the principles they name:
- The entire system described declaratively
- The canonical desired system state versioned in Git
- Approved changes can be automatically applied to the system
- Software agents to ensure correctness and alert on divergence
While we are very used to 1-3, 4 is something that sounds new. Let's see that one more in detail:
Once the state of your system is declared and kept under version control, software agents can inform you whenever reality doesn’t match your expectations. The use of agents also ensures that your entire system is self-healing. And by self-healing, we don’t just mean when nodes or pods fail—those are handled by Kubernetes—but in a broader sense, like in the case of human error. In this case, software agents act as the feedback and control loop for your operations.
Now, this is what feels different as a Continuous Delivery procedure. Let's replicate the workflow they claim:
- A pull request for a new feature is pushed to GitHub for review.
- The code is reviewed and approved by a colleague. After the code is revised, and re-approved it is merged to Git.
- The Git merge triggers the CI and build pipeline, runs a series of tests and then eventually builds a new image and deposits to the new image to a registry.
- The Weave Cloud ‘Deployment Automator’ watches the image registry, notices the image, pulls the new image from the registry and updates its YAML in the config repo.
- The Weave Cloud ‘Deployment Synchronizer’ (installed to the cluster), detects that the cluster is out of date. It pulls the changed manifests from the config repo and deploys the new feature to production.
And an obligatory diagram:
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And how a GitOps pipeline would look like:
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It's very clear now that we have now a set of explicit components driving the rollout phase. It is the responsibility of this component to ensure that the deployment is driven to success, and alert otherwise.
Solutions like Argo CD allow for an implementation of Continuous Delivery pipelines as well.