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Omega supported an extensible object model, and @davidopp had proposed putting an API in front of the persistent store, as we later did in Kubernetes, but it wasn't declarative. Separate work on a common configuration store was discontinued as Google Cloud became the focus
GCP was comprised of independent services, with some common standards, such as the org hierarchy and authz. They used REST APIs, as the rest of the industry, and gRPC didn't exist yet. But, GCP’s APIs were not natively declarative, and Terraform didn’t exist, either
@jbeda proposed layering an aggregated config store/service with consistent, declarative CRUD REST APIs over underlying GCP and third-party service APIs. This sort of later evolved into Deployment Manager.
We folded learnings from these 5+ systems into the Kubernetes Resource Model, which now supports arbitrarily many built-in types, aggregated APIs, and centralized storage (CRDs), and can be used to configure 1st-party and 3rd-party services, including GCP:http://youtu.be/s_hiFuRDJSE
KRM is consistent and declarative. Metadata and verbs are uniform. Spec and status are distinctly separated. Resource identifiers, modeled closely after Borgmaster’s (http://issues.k8s.io/148 ), provide declarative names. Label selectors enable declarative sets.
For the most part, controllers know which fields to propagate from one resource instance to another and wait gracefully on declarative object (rather than field) references, without assuming referential integrity, which enables relaxed operation ordering.
There are some gaps in the model (e.g., http://issues.k8s.io/34363 , http://issues.k8s.io/30698 , http://issues.k8s.io/1698 , http://issues.k8s.io/22675 ), but for the most part it facilitates generic operations on arbitrary resource types.
In the next thread, I’ll cover more about configuration itself, such as the origin of kubectl apply
BTW, when I was digging through old docs/decks, I found a diagram from the Dec 2013 API proposal. Sunit->Pod, SunitPrototype->PodTemplate, Replicate->ReplicaSet, Autoscale->HorizontalPodAutoscaler.pic.twitter.com/oOd84Lzw3B
GCE was RESTful and, to some degree, was declarative. We didn't model all transitions as declarative though. We also didn't have a clear separation of the parameters that are set by the users and those that are status or set by the system.
This is why I introduced, in the first versions of the k8s API, the idea of desiredState and currentState. This late evolved into spec/status. The intent was to enable the simple decoupled control loops embodied with the ReplicationController vs. the monolothic Borg Job object.
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