D-operators define various declarative patterns to write kubernetes controllers. This uses metac under the hood. Users can create, delete, update, assert, patch, clone, & schedule one or more kubernetes resources (native as well as custom) using a yaml file. D-operators expose a bunch of kubernetes custom resources that provide the building blocks to implement higher order controller(s).
D-operators follow a pure intent based approach to writing specifications instead of having to deal with yamls that are cluttered with scripts, kubectl, loops, conditions, templating and so on.
apiVersion: dope.mayadata.io/v1
kind: Recipe
metadata:
name: crud-ops-on-pod
namespace: d-testing
spec:
tasks:
- name: apply-a-namespace
apply:
state:
kind: Namespace
apiVersion: v1
metadata:
name: my-ns
- name: create-a-pod
create:
state:
kind: Pod
apiVersion: v1
metadata:
name: my-pod
namespace: my-ns
spec:
containers:
- name: web
image: nginx
- name: delete-the-pod
delete:
state:
kind: Pod
apiVersion: v1
metadata:
name: my-pod
namespace: my-ns
- name: delete-the-namespace
delete:
state:
kind: Namespace
apiVersion: v1
metadata:
name: my-ns
It is important to understand that these declarative patterns are built upon programmatic ones. The low level constructs (read native Kubernetes resources & custom resources) might be implemented in programming language(s) of one's choice. Use d-controller's YAMLs to aggregate these low level resources in a particular way to build a completely new kubernetes controller.
D-operators is not meant to build complex controller logic like Deployment, StatefulSet or Pod in a declarative yaml. However, if one needs to use available Kubernetes resources to build new k8s controller(s) then d-operators should be considered to build one. D-operators helps implement the last mile automation needed to manage applications & infrastructure in Kubernetes clusters.
D-operators make use of its custom resource(s) to test its controllers. One can imagine these custom resources acting as the building blocks to implement a custom CI framework. One of the primary advantages with this approach, is to let custom resources remove the need to write code to implement test cases.
NOTE: One can make use of these YAMLs (kind: Recipe) to test any Kubernetes controllers declaratively
Navigate to test/declarative/experiments to learn more on these YAMLs.
# Following runs the declarative test suite
#
# NOTE: test/declarative/suite.sh does the following:
# - d-operators' image known as 'dope' is built
# - a docker container is started & acts as the image registry
# - dope image is pushed to this image registry
# - k3s is installed with above image registry
# - d-operators' manifests are applied
# - experiments _(i.e. test code written as YAMLs)_ are applied
# - experiments are asserted
# - if all experiments pass then this testing is a success else it failed
# - k3s is un-installed
# - local image registry is stopped
sudo make declarative-test-suite
D-operators also lets one to write testing Kubernetes controllers using Golang. This involves building the docker image (refer Dockerfile.testing) of the entire codebase and letting it run as a Kubernetes pod (refer test/integration/it.yaml). The setup required run these tests can be found at test/integration folder. Actual test logic are regular _test.go files found in respective packages. These _test.go files need to be tagged appropriately. These mode of testing has the additional advantage of getting the code coverage.
// +build integration
make integration-test-suite
- kind: Recipe
- kind: RecipeClass
- kind: RecipeGroupReport
- kind: RecipeDebug
- kind: Blueprint
- kind: Validation
- kind: CodeCov
- kind: HTTP
- kind: HTTPFlow
- kind: Command
- kind: DaemonJob
- kind: UberLoop