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| title |
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| Resource Model |
This documentation will explain the core resource model of KubeVela which is fully powered by Open Application Model (OAM).
Application
KubeVela introduces an Application CRD as its main API that captures a full application deployment. Every application is composed by multiple components with attachable operational behaviors (traits). For example:
apiVersion: core.oam.dev/v1beta1
kind: Application
metadata:
name: application-sample
spec:
components:
- name: foo
type: webservice
properties:
image: crccheck/hello-world
port: 8000
traits:
- type: ingress
properties:
domain: testsvc.example.com
http:
"/": 8000
- type: sidecar
properties:
name: "logging"
image: "fluentd"
- name: bar
type: aliyun-oss # cloud service
bucket: "my-bucket"
Though the application object doesn't have fixed schema, it is a composition object assembled by several programmable building blocks as shown below.
Component
The component model in KubeVela is designed to allow component providers to encapsulate deployable/provisionable entities by leveraging widely adopted tools such as CUE, Helm etc, and give a easier path to developers to deploy complicated microservices with ease.
Templates based encapsulation is probably the mostly widely used approach to enable efficient application deployment and exposes easier interfaces to end users. For example, many tools today encapsulate Kubernetes Deployment and Service into a Web Service module, and then instantiate this module by simply providing parameters such as image=foo and ports=80. This pattern can be found in cdk8s (e.g. web-service.ts ), CUE (e.g. kube.cue), and many widely used Helm charts (e.g. Web Service).
Hence, a components provider could be anyone who packages software components in form of Helm chart of CUE modules. Think about 3rd-party software distributor, DevOps team, or even your CI pipeline.
In above example, it describes an application composed with Kubernetes stateless workload (component foo) and a Alibaba Cloud OSS bucket (component bar) alongside.
How it Works?
In above example, type: worker means the specification of this component (claimed in following properties section) will be enforced by a ComponentDefinition object named worker as below:
apiVersion: core.oam.dev/v1beta1
kind: ComponentDefinition
metadata:
name: worker
annotations:
definition.oam.dev/description: "Describes long-running, scalable, containerized services that running at backend. They do NOT have network endpoint to receive external network traffic."
spec:
workload:
definition:
apiVersion: apps/v1
kind: Deployment
schematic:
cue:
template: |
output: {
apiVersion: "apps/v1"
kind: "Deployment"
spec: {
selector: matchLabels: {
"app.oam.dev/component": context.name
}
template: {
metadata: labels: {
"app.oam.dev/component": context.name
}
spec: {
containers: [{
name: context.name
image: parameter.image
if parameter["cmd"] != _|_ {
command: parameter.cmd
}
}]
}
}
}
}
parameter: {
image: string
cmd?: [...string]
}
Hence, the properties section of backend only exposes two parameters to fill: image and cmd, this is enforced by the parameter list of the .spec.template field of the definition.
Traits
Traits are operational features that can be attached to component per needs. Traits are normally considered as platform features and maintained by platform team. In the above example, type: autoscaler in frontend means the specification (i.e. properties section)
of this trait will be enforced by a TraitDefinition object named autoscaler as below:
apiVersion: core.oam.dev/v1beta1
kind: TraitDefinition
metadata:
annotations:
definition.oam.dev/description: "configure k8s HPA for Deployment"
name: hpa
spec:
appliesToWorkloads:
- webservice
- worker
schematic:
cue:
template: |
outputs: hpa: {
apiVersion: "autoscaling/v2beta2"
kind: "HorizontalPodAutoscaler"
metadata: name: context.name
spec: {
scaleTargetRef: {
apiVersion: "apps/v1"
kind: "Deployment"
name: context.name
}
minReplicas: parameter.min
maxReplicas: parameter.max
metrics: [{
type: "Resource"
resource: {
name: "cpu"
target: {
type: "Utilization"
averageUtilization: parameter.cpuUtil
}
}
}]
}
}
parameter: {
min: *1 | int
max: *10 | int
cpuUtil: *50 | int
}
The application also have a sidecar trait.
apiVersion: core.oam.dev/v1beta1
kind: TraitDefinition
metadata:
annotations:
definition.oam.dev/description: "add sidecar to the app"
name: sidecar
spec:
appliesToWorkloads:
- webservice
- worker
schematic:
cue:
template: |-
patch: {
// +patchKey=name
spec: template: spec: containers: [parameter]
}
parameter: {
name: string
image: string
command?: [...string]
}
Please note that the end users of KubeVela do NOT need to know about definition objects, they learn how to use a given capability with visualized forms (or the JSON schema of parameters if they prefer). Please check the Generate Forms from Definitions section about how this is achieved.
Standard Contract Behind The Abstractions
Once the application is deployed, KubeVela will index and manage the underlying instances with name, revisions, labels and selector etc in automatic approach. These metadata are shown as below.
| Label | Description |
|---|---|
workload.oam.dev/type=<component definition name> |
The name of its corresponding ComponentDefinition |
trait.oam.dev/type=<trait definition name> |
The name of its corresponding TraitDefinition |
app.oam.dev/name=<app name> |
The name of the application it belongs to |
app.oam.dev/component=<component name> |
The name of the component it belongs to |
trait.oam.dev/resource=<name of trait resource instance> |
The name of trait resource instance |
app.oam.dev/appRevision=<name of app revision> |
The name of the application revision it belongs to |
Consider these metadata as a standard contract for any "day 2" operation controller such as rollout controller to work on KubeVela deployed applications. This is the key to ensure the interoperability for KubeVela based platform as well.
No Configuration Drift
Despite the efficiency and extensibility in abstracting application deployment, IaC (Infrastructure-as-Code) tools may lead to an issue called Infrastructure/Configuration Drift, i.e. the generated component instances are not in line with the expected configuration. This could be caused by incomplete coverage, less-than-perfect processes or emergency changes. This makes them can be barely used as a platform level building block.
Hence, KubeVela is designed to maintain all these programmable capabilities with Kubernetes Control Loop and leverage Kubernetes control plane to eliminate the issue of configuration drifting, while still keeps the flexibly and velocity enabled by IaC.