Chapter 4. Developing Operators

4.1. About the Operator SDK

The Operator Framework is an open source toolkit to manage Kubernetes native applications, called Operators, in an effective, automated, and scalable way. Operators take advantage of Kubernetes extensibility to deliver the automation advantages of cloud services, like provisioning, scaling, and backup and restore, while being able to run anywhere that Kubernetes can run.

Operators make it easy to manage complex, stateful applications on top of Kubernetes. However, writing an Operator today can be difficult because of challenges such as using low-level APIs, writing boilerplate, and a lack of modularity, which leads to duplication.

The Operator SDK, a component of the Operator Framework, provides a command-line interface (CLI) tool that Operator developers can use to build, test, and deploy an Operator.

Why use the Operator SDK?

The Operator SDK simplifies this process of building Kubernetes-native applications, which can require deep, application-specific operational knowledge. The Operator SDK not only lowers that barrier, but it also helps reduce the amount of boilerplate code required for many common management capabilities, such as metering or monitoring.

The Operator SDK is a framework that uses the controller-runtime library to make writing Operators easier by providing the following features:

  • High-level APIs and abstractions to write the operational logic more intuitively
  • Tools for scaffolding and code generation to quickly bootstrap a new project
  • Integration with Operator Lifecycle Manager (OLM) to streamline packaging, installing, and running Operators on a cluster
  • Extensions to cover common Operator use cases
  • Metrics set up automatically in any generated Go-based Operator for use on clusters where the Prometheus Operator is deployed

Operator authors with cluster administrator access to a Kubernetes-based cluster (such as OpenShift Container Platform) can use the Operator SDK CLI to develop their own Operators based on Go, Ansible, or Helm. Kubebuilder is embedded into the Operator SDK as the scaffolding solution for Go-based Operators, which means existing Kubebuilder projects can be used as is with the Operator SDK and continue to work.

Note

OpenShift Container Platform 4.7 supports Operator SDK v1.3.0 or later.

4.1.1. What are Operators?

For an overview about basic Operator concepts and terminology, see Understanding Operators.

4.1.2. Development workflow

The Operator SDK provides the following workflow to develop a new Operator:

  1. Create an Operator project by using the Operator SDK command-line interface (CLI).
  2. Define new resource APIs by adding custom resource definitions (CRDs).
  3. Specify resources to watch by using the Operator SDK API.
  4. Define the Operator reconciling logic in a designated handler and use the Operator SDK API to interact with resources.
  5. Use the Operator SDK CLI to build and generate the Operator deployment manifests.

Figure 4.1. Operator SDK workflow

osdk workflow

At a high level, an Operator that uses the Operator SDK processes events for watched resources in an Operator author-defined handler and takes actions to reconcile the state of the application.

4.1.3. Additional resources

4.2. Installing the Operator SDK CLI

The Operator SDK provides a command-line interface (CLI) tool that Operator developers can use to build, test, and deploy an Operator. You can install the Operator SDK CLI on your workstation so that you are prepared to start authoring your own Operators.

Note

OpenShift Container Platform 4.7 supports Operator SDK v1.3.0.

4.2.1. Installing the Operator SDK CLI

You can install the OpenShift SDK CLI tool on Linux.

Prerequisites

  • Go v1.13+
  • docker v17.03+, podman v1.9.3+, or buildah v1.7+

Procedure

  1. Navigate to the OpenShift mirror site.
  2. From the 4.7.23 directory, download the latest version of the tarball for Linux.
  3. Unpack the archive:

    $ tar xvf operator-sdk-v1.3.0-ocp-linux-x86_64.tar.gz
  4. Make the file executable:

    $ chmod +x operator-sdk
  5. Move the extracted operator-sdk binary to a directory that is on your PATH.

    Tip

    To check your PATH:

    $ echo $PATH
    $ sudo mv ./operator-sdk /usr/local/bin/operator-sdk

Verification

  • After you install the Operator SDK CLI, verify that it is available:

    $ operator-sdk version

    Example output

    operator-sdk version: "v1.3.0-ocp", ...

4.3. Go-based Operators

4.3.1. Getting started with Operator SDK for Go-based Operators

To demonstrate the basics of setting up and running a Go-based Operator using tools and libraries provided by the Operator SDK, Operator developers can build an example Go-based Operator for Memcached, a distributed key-value store, and deploy it to a cluster.

4.3.1.1. Prerequisites

  • Operator SDK CLI installed
  • OpenShift CLI (oc) v4.7+ installed
  • Logged into an OpenShift Container Platform 4.7 cluster with oc with an account that has cluster-admin permissions
  • To allow the cluster pull the image, the repository where you push your image must be set as public, or you must configure an image pull secret.

4.3.1.2. Creating and deploying Go-based Operators

You can build and deploy a simple Go-based Operator for Memcached by using the Operator SDK.

Procedure

  1. Create a project.

    1. Create your project directory:

      $ mkdir memcached-operator
    2. Change into the project directory:

      $ cd memcached-operator
    3. Run the operator-sdk init command to initialize the project:

      $ operator-sdk init \
          --domain=example.com \
          --repo=github.com/example-inc/memcached-operator

      The command uses the Go plug-in by default.

    4. To enable your Go-based Operator to run on OpenShift Container Platform, edit the config/manager/manager.yaml file and replace the following line:

      runAsUser: 65532

      with:

      runAsNonRoot: true
      Note

      This step is a temporary workaround required for Go-based Operators only. For more information, see BZ#1914406.

  2. Create an API.

    Create a simple Memcached API:

    $ operator-sdk create api \
        --resource=true \
        --controller=true \
        --group cache \
        --version v1 \
        --kind Memcached
  3. Build and push the Operator image.

    Use the default Makefile targets to build and push your Operator. Set IMG with a pull spec for your image that uses a registry you can push to:

    $ make docker-build docker-push IMG=<registry>/<user>/<image_name>:<tag>
  4. Run the Operator.

    1. Install the CRD:

      $ make install
    2. Deploy the project to the cluster. Set IMG to the image that you pushed:

      $ make deploy IMG=<registry>/<user>/<image_name>:<tag>
  5. Create a sample custom resource (CR).

    1. Create a sample CR:

      $ oc apply -f config/samples/cache_v1_memcached.yaml \
          -n memcached-operator-system
    2. Watch for the CR to reconcile the Operator:

      $ oc logs deployment.apps/memcached-operator-controller-manager \
          -c manager \
          -n memcached-operator-system
  6. Clean up.

    Run the following command to clean up the resources that have been created as part of this procedure:

    $ make undeploy

4.3.1.3. Next steps

4.3.2. Operator SDK tutorial for Go-based Operators

Operator developers can take advantage of Go programming language support in the Operator SDK to build an example Go-based Operator for Memcached, a distributed key-value store, and manage its lifecycle.

This process is accomplished using two centerpieces of the Operator Framework:

Operator SDK
The operator-sdk CLI tool and controller-runtime library API
Operator Lifecycle Manager (OLM)
Installation, upgrade, and role-based access control (RBAC) of Operators on a cluster
Note

This tutorial goes into greater detail than Getting started with Operator SDK for Go-based Operators.

4.3.2.1. Prerequisites

  • Operator SDK CLI installed
  • OpenShift CLI (oc) v4.7+ installed
  • Logged into an OpenShift Container Platform 4.7 cluster with oc with an account that has cluster-admin permissions
  • To allow the cluster pull the image, the repository where you push your image must be set as public, or you must configure an image pull secret.

4.3.2.2. Creating a project

Use the Operator SDK CLI to create a project called memcached-operator.

Procedure

  1. Create a directory for the project:

    $ mkdir -p $HOME/projects/memcached-operator
  2. Change to the directory:

    $ cd $HOME/projects/memcached-operator
  3. Activate support for Go modules:

    $ export GO111MODULE=on
  4. Run the operator-sdk init command to initialize the project:

    $ operator-sdk init \
        --domain=example.com \
        --repo=github.com/example-inc/memcached-operator
    Note

    The operator-sdk init command uses the Go plug-in by default.

    The operator-sdk init command generates a go.mod file to be used with Go modules. The --repo flag is required when creating a project outside of $GOPATH/src/, because generated files require a valid module path.

  5. To enable your Go-based Operator to run on OpenShift Container Platform, edit the config/manager/manager.yaml file and replace the following line:

    runAsUser: 65532

    with:

    runAsNonRoot: true
    Note

    This step is a temporary workaround required for Go-based Operators only. For more information, see BZ#1914406.

4.3.2.2.1. PROJECT file

Among the files generated by the operator-sdk init command is a Kubebuilder PROJECT file. Subsequent operator-sdk commands, as well as help output, that are run from the project root read this file and are aware that the project type is Go. For example:

domain: example.com
layout: go.kubebuilder.io/v3
projectName: memcached-operator
repo: github.com/example-inc/memcached-operator
version: 3-alpha
plugins:
  manifests.sdk.operatorframework.io/v2: {}
  scorecard.sdk.operatorframework.io/v2: {}
4.3.2.2.2. About the Manager

The main program for the Operator is the main.go file, which initializes and runs the Manager. The Manager automatically registers the Scheme for all custom resource (CR) API definitions and sets up and runs controllers and webhooks.

The Manager can restrict the namespace that all controllers watch for resources:

mgr, err := ctrl.NewManager(cfg, manager.Options{Namespace: namespace})

By default, the Manager watches the namespace where the Operator runs. To watch all namespaces, you can leave the namespace option empty:

mgr, err := ctrl.NewManager(cfg, manager.Options{Namespace: ""})

You can also use the MultiNamespacedCacheBuilder function to watch a specific set of namespaces:

var namespaces []string 1
mgr, err := ctrl.NewManager(cfg, manager.Options{ 2
   NewCache: cache.MultiNamespacedCacheBuilder(namespaces),
})
1
List of namespaces.
2
Creates a Cmd struct to provide shared dependencies and start components.
4.3.2.2.3. About multi-group APIs

Before you create an API and controller, consider whether your Operator requires multiple API groups. This tutorial covers the default case of a single group API, but to change the layout of your project to support multi-group APIs, you can run the following command:

$ operator-sdk edit --multigroup=true

This command updates the PROJECT file, which should look like the following example:

domain: example.com
layout: go.kubebuilder.io/v3
multigroup: true
...

For multi-group projects, the API Go type files are created in the apis/<group>/<version>/ directory, and the controllers are created in the controllers/<group>/ directory. The Dockerfile is then updated accordingly.

Additional resource

4.3.2.3. Creating an API and controller

Use the Operator SDK CLI to create a custom resource definition (CRD) API and controller.

Procedure

  1. Run the following command to create an API with group cache, version, v1, and kind Memcached:

    $ operator-sdk create api \
        --group=cache \
        --version=v1 \
        --kind=Memcached
  2. When prompted, enter y for creating both the resource and controller:

    Create Resource [y/n]
    y
    Create Controller [y/n]
    y

    Example output

    Writing scaffold for you to edit...
    api/v1/memcached_types.go
    controllers/memcached_controller.go
    ...

This process generates the Memcached resource API at api/v1/memcached_types.go and the controller at controllers/memcached_controller.go.

4.3.2.3.1. Defining the API

Define the API for the Memcached custom resource (CR).

Procedure

  1. Modify the Go type definitions at api/v1/memcached_types.go to have the following spec and status:

    // MemcachedSpec defines the desired state of Memcached
    type MemcachedSpec struct {
    	// +kubebuilder:validation:Minimum=0
    	// Size is the size of the memcached deployment
    	Size int32 `json:"size"`
    }
    
    // MemcachedStatus defines the observed state of Memcached
    type MemcachedStatus struct {
    	// Nodes are the names of the memcached pods
    	Nodes []string `json:"nodes"`
    }
  2. Add the +kubebuilder:subresource:status marker to add a status subresource to the CRD manifest:

    // Memcached is the Schema for the memcacheds API
    // +kubebuilder:subresource:status 1
    type Memcached struct {
    	metav1.TypeMeta   `json:",inline"`
    	metav1.ObjectMeta `json:"metadata,omitempty"`
    
    	Spec   MemcachedSpec   `json:"spec,omitempty"`
    	Status MemcachedStatus `json:"status,omitempty"`
    }
    1
    Add this line.

    This enables the controller to update the CR status without changing the rest of the CR object.

  3. Update the generated code for the resource type:

    $ make generate
    Tip

    After you modify a *_types.go file, you must run the make generate command to update the generated code for that resource type.

    The above Makefile target invokes the controller-gen utility to update the api/v1/zz_generated.deepcopy.go file. This ensures your API Go type definitions implement the runtime.Object interface that all Kind types must implement.

4.3.2.3.2. Generating CRD manifests

After the API is defined with spec and status fields and custom resource definition (CRD) validation markers, you can generate CRD manifests.

Procedure

  • Run the following command to generate and update CRD manifests:

    $ make manifests

    This Makefile target invokes the controller-gen utility to generate the CRD manifests in the config/crd/bases/cache.example.com_memcacheds.yaml file.

4.3.2.3.2.1. About OpenAPI validation

OpenAPIv3 schemas are added to CRD manifests in the spec.validation block when the manifests are generated. This validation block allows Kubernetes to validate the properties in a Memcached custom resource (CR) when it is created or updated.

Markers, or annotations, are available to configure validations for your API. These markers always have a +kubebuilder:validation prefix.

Additional resources

4.3.2.4. Implementing the controller

After creating a new API and controller, you can implement the controller logic.

Procedure

  • For this example, replace the generated controller file controllers/memcached_controller.go with following example implementation:

    Example 4.1. Example memcached_controller.go

    /*
    Copyright 2020.
    
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at
    
        http://www.apache.org/licenses/LICENSE-2.0
    
    Unless required by applicable law or agreed to in writing, software
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.
    */
    
    package controllers
    
    import (
    	appsv1 "k8s.io/api/apps/v1"
    	corev1 "k8s.io/api/core/v1"
    	"k8s.io/apimachinery/pkg/api/errors"
    	metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
    	"k8s.io/apimachinery/pkg/types"
    	"reflect"
    
    	"context"
    
    	"github.com/go-logr/logr"
    	"k8s.io/apimachinery/pkg/runtime"
    	ctrl "sigs.k8s.io/controller-runtime"
    	"sigs.k8s.io/controller-runtime/pkg/client"
    
    	cachev1alpha1 "github.com/example/memcached-operator/api/v1alpha1"
    )
    
    // MemcachedReconciler reconciles a Memcached object
    type MemcachedReconciler struct {
    	client.Client
    	Log    logr.Logger
    	Scheme *runtime.Scheme
    }
    
    // +kubebuilder:rbac:groups=cache.example.com,resources=memcacheds,verbs=get;list;watch;create;update;patch;delete
    // +kubebuilder:rbac:groups=cache.example.com,resources=memcacheds/status,verbs=get;update;patch
    // +kubebuilder:rbac:groups=cache.example.com,resources=memcacheds/finalizers,verbs=update
    // +kubebuilder:rbac:groups=apps,resources=deployments,verbs=get;list;watch;create;update;patch;delete
    // +kubebuilder:rbac:groups=core,resources=pods,verbs=get;list;
    
    // Reconcile is part of the main kubernetes reconciliation loop which aims to
    // move the current state of the cluster closer to the desired state.
    // TODO(user): Modify the Reconcile function to compare the state specified by
    // the Memcached object against the actual cluster state, and then
    // perform operations to make the cluster state reflect the state specified by
    // the user.
    //
    // For more details, check Reconcile and its Result here:
    // - https://pkg.go.dev/sigs.k8s.io/controller-runtime@v0.7.0/pkg/reconcile
    func (r *MemcachedReconciler) Reconcile(ctx context.Context, req ctrl.Request) (ctrl.Result, error) {
    	log := r.Log.WithValues("memcached", req.NamespacedName)
    
    	// Fetch the Memcached instance
    	memcached := &cachev1alpha1.Memcached{}
    	err := r.Get(ctx, req.NamespacedName, memcached)
    	if err != nil {
    		if errors.IsNotFound(err) {
    			// Request object not found, could have been deleted after reconcile request.
    			// Owned objects are automatically garbage collected. For additional cleanup logic use finalizers.
    			// Return and don't requeue
    			log.Info("Memcached resource not found. Ignoring since object must be deleted")
    			return ctrl.Result{}, nil
    		}
    		// Error reading the object - requeue the request.
    		log.Error(err, "Failed to get Memcached")
    		return ctrl.Result{}, err
    	}
    
    	// Check if the deployment already exists, if not create a new one
    	found := &appsv1.Deployment{}
    	err = r.Get(ctx, types.NamespacedName{Name: memcached.Name, Namespace: memcached.Namespace}, found)
    	if err != nil && errors.IsNotFound(err) {
    		// Define a new deployment
    		dep := r.deploymentForMemcached(memcached)
    		log.Info("Creating a new Deployment", "Deployment.Namespace", dep.Namespace, "Deployment.Name", dep.Name)
    		err = r.Create(ctx, dep)
    		if err != nil {
    			log.Error(err, "Failed to create new Deployment", "Deployment.Namespace", dep.Namespace, "Deployment.Name", dep.Name)
    			return ctrl.Result{}, err
    		}
    		// Deployment created successfully - return and requeue
    		return ctrl.Result{Requeue: true}, nil
    	} else if err != nil {
    		log.Error(err, "Failed to get Deployment")
    		return ctrl.Result{}, err
    	}
    
    	// Ensure the deployment size is the same as the spec
    	size := memcached.Spec.Size
    	if *found.Spec.Replicas != size {
    		found.Spec.Replicas = &size
    		err = r.Update(ctx, found)
    		if err != nil {
    			log.Error(err, "Failed to update Deployment", "Deployment.Namespace", found.Namespace, "Deployment.Name", found.Name)
    			return ctrl.Result{}, err
    		}
    		// Spec updated - return and requeue
    		return ctrl.Result{Requeue: true}, nil
    	}
    
    	// Update the Memcached status with the pod names
    	// List the pods for this memcached's deployment
    	podList := &corev1.PodList{}
    	listOpts := []client.ListOption{
    		client.InNamespace(memcached.Namespace),
    		client.MatchingLabels(labelsForMemcached(memcached.Name)),
    	}
    	if err = r.List(ctx, podList, listOpts...); err != nil {
    		log.Error(err, "Failed to list pods", "Memcached.Namespace", memcached.Namespace, "Memcached.Name", memcached.Name)
    		return ctrl.Result{}, err
    	}
    	podNames := getPodNames(podList.Items)
    
    	// Update status.Nodes if needed
    	if !reflect.DeepEqual(podNames, memcached.Status.Nodes) {
    		memcached.Status.Nodes = podNames
    		err := r.Status().Update(ctx, memcached)
    		if err != nil {
    			log.Error(err, "Failed to update Memcached status")
    			return ctrl.Result{}, err
    		}
    	}
    
    	return ctrl.Result{}, nil
    }
    
    // deploymentForMemcached returns a memcached Deployment object
    func (r *MemcachedReconciler) deploymentForMemcached(m *cachev1alpha1.Memcached) *appsv1.Deployment {
    	ls := labelsForMemcached(m.Name)
    	replicas := m.Spec.Size
    
    	dep := &appsv1.Deployment{
    		ObjectMeta: metav1.ObjectMeta{
    			Name:      m.Name,
    			Namespace: m.Namespace,
    		},
    		Spec: appsv1.DeploymentSpec{
    			Replicas: &replicas,
    			Selector: &metav1.LabelSelector{
    				MatchLabels: ls,
    			},
    			Template: corev1.PodTemplateSpec{
    				ObjectMeta: metav1.ObjectMeta{
    					Labels: ls,
    				},
    				Spec: corev1.PodSpec{
    					Containers: []corev1.Container{{
    						Image:   "memcached:1.4.36-alpine",
    						Name:    "memcached",
    						Command: []string{"memcached", "-m=64", "-o", "modern", "-v"},
    						Ports: []corev1.ContainerPort{{
    							ContainerPort: 11211,
    							Name:          "memcached",
    						}},
    					}},
    				},
    			},
    		},
    	}
    	// Set Memcached instance as the owner and controller
    	ctrl.SetControllerReference(m, dep, r.Scheme)
    	return dep
    }
    
    // labelsForMemcached returns the labels for selecting the resources
    // belonging to the given memcached CR name.
    func labelsForMemcached(name string) map[string]string {
    	return map[string]string{"app": "memcached", "memcached_cr": name}
    }
    
    // getPodNames returns the pod names of the array of pods passed in
    func getPodNames(pods []corev1.Pod) []string {
    	var podNames []string
    	for _, pod := range pods {
    		podNames = append(podNames, pod.Name)
    	}
    	return podNames
    }
    
    // SetupWithManager sets up the controller with the Manager.
    func (r *MemcachedReconciler) SetupWithManager(mgr ctrl.Manager) error {
    	return ctrl.NewControllerManagedBy(mgr).
    		For(&cachev1alpha1.Memcached{}).
    		Owns(&appsv1.Deployment{}).
    		Complete(r)
    }

    The example controller runs the following reconciliation logic for each Memcached custom resource (CR):

    • Create a Memcached deployment if it does not exist.
    • Ensure that the deployment size is the same as specified by the Memcached CR spec.
    • Update the Memcached CR status with the names of the memcached pods.

The next subsections explain how the controller in the example implementation watches resources and how the reconcile loop is triggered. You can skip these subsections to go directly to Running the Operator.

4.3.2.4.1. Resources watched by the controller

The SetupWithManager() function in controllers/memcached_controller.go specifies how the controller is built to watch a CR and other resources that are owned and managed by that controller.

import (
	...
	appsv1 "k8s.io/api/apps/v1"
	...
)

func (r *MemcachedReconciler) SetupWithManager(mgr ctrl.Manager) error {
	return ctrl.NewControllerManagedBy(mgr).
		For(&cachev1.Memcached{}).
		Owns(&appsv1.Deployment{}).
		Complete(r)
}

NewControllerManagedBy() provides a controller builder that allows various controller configurations.

For(&cachev1.Memcached{}) specifies the Memcached type as the primary resource to watch. For each Add, Update, or Delete event for a Memcached type, the reconcile loop is sent a reconcile Request argument, which consists of a namespace and name key, for that Memcached object.

Owns(&appsv1.Deployment{}) specifies the Deployment type as the secondary resource to watch. For each Deployment type Add, Update, or Delete event, the event handler maps each event to a reconcile request for the owner of the deployment. In this case, the owner is the Memcached object for which the deployment was created.

4.3.2.4.2. Controller configurations

You can initialize a controller by using many other useful configurations. For example:

  • Set the maximum number of concurrent reconciles for the controller by using the MaxConcurrentReconciles option, which defaults to 1:

    func (r *MemcachedReconciler) SetupWithManager(mgr ctrl.Manager) error {
        return ctrl.NewControllerManagedBy(mgr).
            For(&cachev1.Memcached{}).
            Owns(&appsv1.Deployment{}).
            WithOptions(controller.Options{
                MaxConcurrentReconciles: 2,
            }).
            Complete(r)
    }
  • Filter watch events using predicates.
  • Choose the type of EventHandler to change how a watch event translates to reconcile requests for the reconcile loop. For Operator relationships that are more complex than primary and secondary resources, you can use the EnqueueRequestsFromMapFunc handler to transform a watch event into an arbitrary set of reconcile requests.

For more details on these and other configurations, see the upstream Builder and Controller GoDocs.

4.3.2.4.3. Reconcile loop

Every controller has a reconciler object with a Reconcile() method that implements the reconcile loop. The reconcile loop is passed the Request argument, which is a namespace and name key used to find the primary resource object, Memcached, from the cache:

import (
	ctrl "sigs.k8s.io/controller-runtime"

	cachev1 "github.com/example-inc/memcached-operator/api/v1"
	...
)

func (r *MemcachedReconciler) Reconcile(ctx context.Context, req ctrl.Request) (ctrl.Result, error) {
  // Lookup the Memcached instance for this reconcile request
  memcached := &cachev1.Memcached{}
  err := r.Get(ctx, req.NamespacedName, memcached)
  ...
}

Based on the return values, result, and error, the request might be requeued and the reconcile loop might be triggered again:

// Reconcile successful - don't requeue
return ctrl.Result{}, nil
// Reconcile failed due to error - requeue
return ctrl.Result{}, err
// Requeue for any reason other than an error
return ctrl.Result{Requeue: true}, nil

You can set the Result.RequeueAfter to requeue the request after a grace period as well:

import "time"

// Reconcile for any reason other than an error after 5 seconds
return ctrl.Result{RequeueAfter: time.Second*5}, nil
Note

You can return Result with RequeueAfter set to periodically reconcile a CR.

For more on reconcilers, clients, and interacting with resource events, see the Controller Runtime Client API documentation.

4.3.2.4.4. Permissions and RBAC manifests

The controller requires certain RBAC permissions to interact with the resources it manages. These are specified using RBAC markers, such as the following:

// +kubebuilder:rbac:groups=cache.example.com,resources=memcacheds,verbs=get;list;watch;create;update;patch;delete
// +kubebuilder:rbac:groups=cache.example.com,resources=memcacheds/status,verbs=get;update;patch
// +kubebuilder:rbac:groups=cache.example.com,resources=memcacheds/finalizers,verbs=update
// +kubebuilder:rbac:groups=apps,resources=deployments,verbs=get;list;watch;create;update;patch;delete
// +kubebuilder:rbac:groups=core,resources=pods,verbs=get;list;

func (r *MemcachedReconciler) Reconcile(ctx context.Context, req ctrl.Request) (ctrl.Result, error) {
  ...
}

The ClusterRole object manifest at config/rbac/role.yaml is generated from the previous markers by using the controller-gen utility whenever the make manifests command is run.

4.3.2.5. Running the Operator

There are three ways you can use the Operator SDK CLI to build and run your Operator:

  • Run locally outside the cluster as a Go program.
  • Run as a deployment on the cluster.
  • Bundle your Operator and use Operator Lifecycle Manager (OLM) to deploy on the cluster.
Note

Before running your Go-based Operator as either a deployment on OpenShift Container Platform or as a bundle that uses OLM, ensure that your project has been updated to use supported images.

4.3.2.5.1. Running locally outside the cluster

You can run your Operator project as a Go program outside of the cluster. This is useful for development purposes to speed up deployment and testing.

Procedure

  • Run the following command to install the custom resource definitions (CRDs) in the cluster configured in your ~/.kube/config file and run the Operator locally:

    $ make install run

    Example output

    ...
    2021-01-10T21:09:29.016-0700	INFO	controller-runtime.metrics	metrics server is starting to listen	{"addr": ":8080"}
    2021-01-10T21:09:29.017-0700	INFO	setup	starting manager
    2021-01-10T21:09:29.017-0700	INFO	controller-runtime.manager	starting metrics server	{"path": "/metrics"}
    2021-01-10T21:09:29.018-0700	INFO	controller-runtime.manager.controller.memcached	Starting EventSource	{"reconciler group": "cache.example.com", "reconciler kind": "Memcached", "source": "kind source: /, Kind="}
    2021-01-10T21:09:29.218-0700	INFO	controller-runtime.manager.controller.memcached	Starting Controller	{"reconciler group": "cache.example.com", "reconciler kind": "Memcached"}
    2021-01-10T21:09:29.218-0700	INFO	controller-runtime.manager.controller.memcached	Starting workers	{"reconciler group": "cache.example.com", "reconciler kind": "Memcached", "worker count": 1}

4.3.2.5.2. Preparing your Operator to use supported images

Before running your Go-based Operator on OpenShift Container Platform, update your project to use supported images.

Procedure

  1. Update the project root-level Dockerfile to use supported images. Change the default runner image reference from:

    FROM gcr.io/distroless/static:nonroot

    to:

    FROM registry.access.redhat.com/ubi8/ubi-minimal:latest
  2. Depending on the Go project version, your Dockerfile might contain a USER 65532:65532 or USER nonroot:nonroot directive. In either case, remove the line, as it is not required by the supported runner image.
  3. In the config/default/manager_auth_proxy_patch.yaml file, change the image value from:

    gcr.io/kubebuilder/kube-rbac-proxy:<tag>

    to use the supported image:

    registry.redhat.io/openshift4/ose-kube-rbac-proxy:v4.7
4.3.2.5.3. Running as a deployment on the cluster

You can run your Operator project as a deployment on your cluster.

Prerequisites

  • Prepared your Go-based Operator to run on OpenShift Container Platform by updating the project to use supported images

Procedure

  1. Run the following make commands to build and push the Operator image. Modify the IMG argument in the following steps to reference a repository that you have access to. You can obtain an account for storing containers at repository sites such as Quay.io.

    1. Build the image:

      $ make docker-build IMG=<registry>/<user>/<image_name>:<tag>
    2. Push the image to a repository:

      $ make docker-push IMG=<registry>/<user>/<image_name>:<tag>
      Note

      The name and tag of the image, for example IMG=<registry>/<user>/<image_name>:<tag>, in both the commands can also be set in your Makefile. Modify the IMG ?= controller:latest value to set your default image name.

  2. Run the following command to deploy the Operator:

    $ make deploy IMG=<registry>/<user>/<image_name>:<tag>

    By default, this command creates a namespace with the name of your Operator project in the form <project_name>-system and is used for the deployment. This command also installs the RBAC manifests from config/rbac.

  3. Verify that the Operator is running:

    $ oc get deployment -n <project_name>-system

    Example output

    NAME                                    READY   UP-TO-DATE   AVAILABLE   AGE
    <project_name>-controller-manager       1/1     1            1           8m

4.3.2.5.4. Bundling an Operator and deploying with Operator Lifecycle Manager

Operator Lifecycle Manager (OLM) helps you to install, update, and generally manage the lifecycle of Operators and their associated services on a Kubernetes cluster. OLM is installed by default on OpenShift Container Platform and runs as a Kubernetes extension so that you can use the web console and the OpenShift CLI (oc) for all Operator lifecycle management functions without any additional tools.

The Operator Bundle Format is the default packaging method for Operator SDK and OLM. You can get your Operator ready for OLM by using the Operator SDK to build, push, validate, and run a bundle image with OLM.

Prerequisites

  • Operator SDK CLI installed on a development workstation
  • OpenShift CLI (oc) v4.7+ installed
  • Operator Lifecycle Manager (OLM) installed on a Kubernetes-based cluster (v1.16.0 or later if you use apiextensions.k8s.io/v1 CRDs, for example OpenShift Container Platform 4.7)
  • Logged into the cluster with oc using an account with cluster-admin permissions
  • Operator project initialized by using the Operator SDK
  • If your Operator is Go-based, your project must have been updated to use supported images for running on OpenShift Container Platform

Procedure

  1. Run the following make commands in your Operator project directory to build and push your Operator image. Modify the IMG argument in the following steps to reference a repository that you have access to. You can obtain an account for storing containers at repository sites such as Quay.io.

    1. Build the image:

      $ make docker-build IMG=<registry>/<user>/<operator_image_name>:<tag>
    2. Push the image to a repository:

      $ make docker-push IMG=<registry>/<user>/<operator_image_name>:<tag>
  2. Update your Makefile by setting the IMG URL to your Operator image name and tag that you pushed:

    $ # Image URL to use all building/pushing image targets
    IMG ?= <registry>/<user>/<operator_image_name>:<tag>

    This value is used for subsequent operations.

  3. Create your Operator bundle manifest by running the make bundle command, which invokes several commands, including the Operator SDK generate bundle and bundle validate subcommands:

    $ make bundle

    Bundle manifests for an Operator describe how to display, create, and manage an application. The make bundle command creates the following files and directories in your Operator project:

    • A bundle manifests directory named bundle/manifests that contains a ClusterServiceVersion object
    • A bundle metadata directory named bundle/metadata
    • All custom resource definitions (CRDs) in a config/crd directory
    • A Dockerfile bundle.Dockerfile

    These files are then automatically validated by using operator-sdk bundle validate to ensure the on-disk bundle representation is correct.

  4. Build and push your bundle image by running the following commands. OLM consumes Operator bundles using an index image, which reference one or more bundle images.

    1. Build the bundle image. Set BUNDLE_IMAGE with the details for the registry, user namespace, and image tag where you intend to push the image:

      $ make bundle-build BUNDLE_IMG=<registry>/<user>/<bundle_image_name>:<tag>
    2. Push the bundle image:

      $ docker push <registry>/<user>/<bundle_image_name>:<tag>
  5. Check the status of OLM on your cluster by using the following Operator SDK command:

    $ operator-sdk olm status \
        --olm-namespace=openshift-operator-lifecycle-manager
  6. Run the Operator on your cluster by using the OLM integration in Operator SDK:

    $ operator-sdk run bundle \
        [-n <namespace>] \1
        <registry>/<user>/<bundle_image_name>:<tag>
    1
    By default, the command installs the Operator in the currently active project in your ~/.kube/config file. You can add the -n flag to set a different namespace scope for the installation.

    This command performs the following actions:

    • Create an index image with your bundle image injected.
    • Create a catalog source that points to your new index image, which enables OperatorHub to discover your Operator.
    • Deploy your Operator to your cluster by creating an Operator group, subscription, install plan, and all other required objects, including RBAC.

4.3.2.6. Creating a custom resource

After your Operator is installed, you can test it by creating a custom resource (CR) that is now provided on the cluster by the Operator.

Prerequisites

  • Example Memcached Operator, which provides the Memcached CR, installed on a cluster

Procedure

  1. Change to the namespace where your Operator is installed. For example, if you deployed the Operator using the make deploy command:

    $ oc project memcached-operator-system
  2. Edit the sample Memcached CR manifest at config/samples/cache_v1_memcached.yaml to contain the following specification:

    apiVersion: cache.example.com/v1
    kind: Memcached
    metadata:
      name: memcached-sample
    ...
    spec:
    ...
      size: 3
  3. Create the CR:

    $ oc apply -f config/samples/cache_v1_memcached.yaml
  4. Ensure that the Memcached Operator creates the deployment for the sample CR with the correct size:

    $ oc get deployments

    Example output

    NAME                                    READY   UP-TO-DATE   AVAILABLE   AGE
    memcached-operator-controller-manager   1/1     1            1           8m
    memcached-sample                        3/3     3            3           1m

  5. Check the pods and CR status to confirm the status is updated with the Memcached pod names.

    1. Check the pods:

      $ oc get pods

      Example output

      NAME                                  READY     STATUS    RESTARTS   AGE
      memcached-sample-6fd7c98d8-7dqdr      1/1       Running   0          1m
      memcached-sample-6fd7c98d8-g5k7v      1/1       Running   0          1m
      memcached-sample-6fd7c98d8-m7vn7      1/1       Running   0          1m

    2. Check the CR status:

      $ oc get memcached/memcached-sample -o yaml

      Example output

      apiVersion: cache.example.com/v1
      kind: Memcached
      metadata:
      ...
        name: memcached-sample
      ...
      spec:
        size: 3
      status:
        nodes:
        - memcached-sample-6fd7c98d8-7dqdr
        - memcached-sample-6fd7c98d8-g5k7v
        - memcached-sample-6fd7c98d8-m7vn7

  6. Update the deployment size.

    1. Update config/samples/cache_v1_memcached.yaml file to change the spec.size field in the Memcached CR from 3 to 5:

      $ oc patch memcached memcached-sample \
          -p '{"spec":{"size": 5}}' \
          --type=merge
    2. Confirm that the Operator changes the deployment size:

      $ oc get deployments

      Example output

      NAME                                    READY   UP-TO-DATE   AVAILABLE   AGE
      memcached-operator-controller-manager   1/1     1            1           10m
      memcached-sample                        5/5     5            5           3m

  7. Clean up the resources that have been created as part of this tutorial.

    • If you used the make deploy command to test the Operator, run the following command:

      $ make undeploy
    • If you used the operator-sdk run bundle command to test the Operator, run the following command:

      $ operator-sdk cleanup <project_name>

4.3.2.7. Additional resources

4.3.3. Project layout for Go-based Operators

The operator-sdk CLI can generate, or scaffold, a number of packages and files for each Operator project.

4.3.3.1. Go-based project layout

Go-based Operator projects, the default type, generated using the operator-sdk init command contain the following files and directories:

File or directoryPurpose

main.go

Main program of the Operator. This instantiates a new manager that registers all custom resource definitions (CRDs) in the apis/ directory and starts all controllers in the controllers/ directory.

apis/

Directory tree that defines the APIs of the CRDs. You must edit the apis/<version>/<kind>_types.go files to define the API for each resource type and import these packages in your controllers to watch for these resource types.

controllers/

Controller implementations. Edit the controller/<kind>_controller.go files to define the reconcile logic of the controller for handling a resource type of the specified kind.

config/

Kubernetes manifests used to deploy your controller on a cluster, including CRDs, RBAC, and certificates.

Makefile

Targets used to build and deploy your controller.

Dockerfile

Instructions used by a container engine to build your Operator.

manifests/

Kubernetes manifests for registering CRDs, setting up RBAC, and deploying the Operator as a deployment.

4.4. Ansible-based Operators

4.4.1. Getting started with Operator SDK for Ansible-based Operators

The Operator SDK includes options for generating an Operator project that leverages existing Ansible playbooks and modules to deploy Kubernetes resources as a unified application, without having to write any Go code.

To demonstrate the basics of setting up and running an Ansible-based Operator using tools and libraries provided by the Operator SDK, Operator developers can build an example Ansible-based Operator for Memcached, a distributed key-value store, and deploy it to a cluster.

4.4.1.1. Prerequisites

4.4.1.2. Creating and deploying Ansible-based Operators

You can build and deploy a simple Ansible-based Operator for Memcached by using the Operator SDK.

Procedure

  1. Create a project.

    1. Create your project directory:

      $ mkdir memcached-operator
    2. Change into the project directory:

      $ cd memcached-operator
    3. Run the operator-sdk init command with the ansible plug-in to initialize the project:

      $ operator-sdk init \
          --plugins=ansible \
          --domain=example.com
  2. Create an API.

    Create a simple Memcached API:

    $ operator-sdk create api \
        --group cache \
        --version v1 \
        --kind Memcached \
        --generate-role 1
    1
    Generates an Ansible role for the API.
  3. Build and push the Operator image.

    Use the default Makefile targets to build and push your Operator. Set IMG with a pull spec for your image that uses a registry you can push to:

    $ make docker-build docker-push IMG=<registry>/<user>/<image_name>:<tag>
  4. Run the Operator.

    1. Install the CRD:

      $ make install
    2. Deploy the project to the cluster. Set IMG to the image that you pushed:

      $ make deploy IMG=<registry>/<user>/<image_name>:<tag>
  5. Create a sample custom resource (CR).

    1. Create a sample CR:

      $ oc apply -f config/samples/cache_v1_memcached.yaml \
          -n memcached-operator-system
    2. Watch for the CR to reconcile the Operator:

      $ oc logs deployment.apps/memcached-operator-controller-manager \
          -c manager \
          -n memcached-operator-system

      Example output

      ...
      I0205 17:48:45.881666       7 leaderelection.go:253] successfully acquired lease memcached-operator-system/memcached-operator
      {"level":"info","ts":1612547325.8819902,"logger":"controller-runtime.manager.controller.memcached-controller","msg":"Starting EventSource","source":"kind source: cache.example.com/v1, Kind=Memcached"}
      {"level":"info","ts":1612547325.98242,"logger":"controller-runtime.manager.controller.memcached-controller","msg":"Starting Controller"}
      {"level":"info","ts":1612547325.9824686,"logger":"controller-runtime.manager.controller.memcached-controller","msg":"Starting workers","worker count":4}
      {"level":"info","ts":1612547348.8311093,"logger":"runner","msg":"Ansible-runner exited successfully","job":"4037200794235010051","name":"memcached-sample","namespace":"memcached-operator-system"}

  6. Clean up.

    Run the following command to clean up the resources that have been created as part of this procedure:

    $ make undeploy

4.4.1.3. Next steps

4.4.2. Operator SDK tutorial for Ansible-based Operators

Operator developers can take advantage of Ansible support in the Operator SDK to build an example Ansible-based Operator for Memcached, a distributed key-value store, and manage its lifecycle. This tutorial walks through the following process:

  • Create a Memcached deployment
  • Ensure that the deployment size is the same as specified by the Memcached custom resource (CR) spec
  • Update the Memcached CR status using the status writer with the names of the memcached pods

This process is accomplished by using two centerpieces of the Operator Framework:

Operator SDK
The operator-sdk CLI tool and controller-runtime library API
Operator Lifecycle Manager (OLM)
Installation, upgrade, and role-based access control (RBAC) of Operators on a cluster
Note

This tutorial goes into greater detail than Getting started with Operator SDK for Ansible-based Operators.

4.4.2.1. Prerequisites

4.4.2.2. Creating a project

Use the Operator SDK CLI to create a project called memcached-operator.

Procedure

  1. Create a directory for the project:

    $ mkdir -p $HOME/projects/memcached-operator
  2. Change to the directory:

    $ cd $HOME/projects/memcached-operator
  3. Run the operator-sdk init command with the ansible plug-in to initialize the project:

    $ operator-sdk init \
        --plugins=ansible \
        --domain=example.com
4.4.2.2.1. PROJECT file

Among the files generated by the operator-sdk init command is a Kubebuilder PROJECT file. Subsequent operator-sdk commands, as well as help output, that are run from the project root read this file and are aware that the project type is Ansible. For example:

domain: example.com
layout: ansible.sdk.operatorframework.io/v1
projectName: memcached-operator
version: 3-alpha

4.4.2.3. Creating an API

Use the Operator SDK CLI to create a Memcached API.

Procedure

  • Run the following command to create an API with group cache, version, v1, and kind Memcached:

    $ operator-sdk create api \
        --group cache \
        --version v1 \
        --kind Memcached \
        --generate-role 1
    1
    Generates an Ansible role for the API.

After creating the API, your Operator project updates with the following structure:

Memcached CRD
Includes a sample Memcached resource
Manager

Program that reconciles the state of the cluster to the desired state by using:

  • A reconciler, either an Ansible role or playbook
  • A watches.yaml file, which connects the Memcached resource to the memcached Ansible role

4.4.2.4. Modifying the manager

Update your Operator project to provide the reconcile logic, in the form of an Ansible role, which runs every time a Memcached resource is created, updated, or deleted.

Procedure

  1. Update the roles/memcached/tasks/main.yml file with the following structure:

    ---
    - name: start memcached
      community.kubernetes.k8s:
        definition:
          kind: Deployment
          apiVersion: apps/v1
          metadata:
            name: '{{ ansible_operator_meta.name }}-memcached'
            namespace: '{{ ansible_operator_meta.namespace }}'
          spec:
            replicas: "{{size}}"
            selector:
              matchLabels:
                app: memcached
            template:
              metadata:
                labels:
                  app: memcached
              spec:
                containers:
                - name: memcached
                  command:
                  - memcached
                  - -m=64
                  - -o
                  - modern
                  - -v
                  image: "docker.io/memcached:1.4.36-alpine"
                  ports:
                    - containerPort: 11211

    This memcached role ensures a memcached deployment exist and sets the deployment size.

  2. Set default values for variables used in your Ansible role by editing the roles/memcached/defaults/main.yml file:

    ---
    # defaults file for Memcached
    size: 1
  3. Update the Memcached sample resource in the config/samples/cache_v1_memcached.yaml file with the following structure:

    apiVersion: cache.example.com/v1
    kind: Memcached
    metadata:
      name: memcached-sample
    spec:
      size: 3

    The key-value pairs in the custom resource (CR) spec are passed to Ansible as extra variables.

Note

The names of all variables in the spec field are converted to snake case, meaning lowercase with an underscore, by the Operator before running Ansible. For example, serviceAccount in the spec becomes service_account in Ansible.

You can disable this case conversion by setting the snakeCaseParameters option to false in your watches.yaml file. It is recommended that you perform some type validation in Ansible on the variables to ensure that your application is receiving expected input.

4.4.2.5. Running the Operator

There are three ways you can use the Operator SDK CLI to build and run your Operator:

  • Run locally outside the cluster as a Go program.
  • Run as a deployment on the cluster.
  • Bundle your Operator and use Operator Lifecycle Manager (OLM) to deploy on the cluster.
4.4.2.5.1. Running locally outside the cluster

You can run your Operator project as a Go program outside of the cluster. This is useful for development purposes to speed up deployment and testing.

Procedure

  • Run the following command to install the custom resource definitions (CRDs) in the cluster configured in your ~/.kube/config file and run the Operator locally:

    $ make install run

    Example output

    ...
    {"level":"info","ts":1612589622.7888272,"logger":"ansible-controller","msg":"Watching resource","Options.Group":"cache.example.com","Options.Version":"v1","Options.Kind":"Memcached"}
    {"level":"info","ts":1612589622.7897573,"logger":"proxy","msg":"Starting to serve","Address":"127.0.0.1:8888"}
    {"level":"info","ts":1612589622.789971,"logger":"controller-runtime.manager","msg":"starting metrics server","path":"/metrics"}
    {"level":"info","ts":1612589622.7899997,"logger":"controller-runtime.manager.controller.memcached-controller","msg":"Starting EventSource","source":"kind source: cache.example.com/v1, Kind=Memcached"}
    {"level":"info","ts":1612589622.8904517,"logger":"controller-runtime.manager.controller.memcached-controller","msg":"Starting Controller"}
    {"level":"info","ts":1612589622.8905244,"logger":"controller-runtime.manager.controller.memcached-controller","msg":"Starting workers","worker count":8}

4.4.2.5.2. Preparing your Operator to use supported images

Before running your Ansible-based Operator on OpenShift Container Platform, update your project to use supported images.

Procedure

  1. Update the project root-level Dockerfile to use supported images. Change the default builder image reference from:

    FROM quay.io/operator-framework/ansible-operator:v1.3.0

    to:

    FROM registry.redhat.io/openshift4/ose-ansible-operator:v4.7
    Important

    Use the builder image version that matches your Operator SDK version. Failure to do so can result in problems due to project layout, or scaffolding, differences, particularly when mixing newer upstream versions of the Operator SDK with downstream OpenShift Container Platform builder images.

  2. In the config/default/manager_auth_proxy_patch.yaml file, change the image value from:

    gcr.io/kubebuilder/kube-rbac-proxy:<tag>

    to use the supported image:

    registry.redhat.io/openshift4/ose-kube-rbac-proxy:v4.7
4.4.2.5.3. Running as a deployment on the cluster

You can run your Operator project as a deployment on your cluster.

Procedure

  1. Run the following make commands to build and push the Operator image. Modify the IMG argument in the following steps to reference a repository that you have access to. You can obtain an account for storing containers at repository sites such as Quay.io.

    1. Build the image:

      $ make docker-build IMG=<registry>/<user>/<image_name>:<tag>
    2. Push the image to a repository:

      $ make docker-push IMG=<registry>/<user>/<image_name>:<tag>
      Note

      The name and tag of the image, for example IMG=<registry>/<user>/<image_name>:<tag>, in both the commands can also be set in your Makefile. Modify the IMG ?= controller:latest value to set your default image name.

  2. Run the following command to deploy the Operator:

    $ make deploy IMG=<registry>/<user>/<image_name>:<tag>

    By default, this command creates a namespace with the name of your Operator project in the form <project_name>-system and is used for the deployment. This command also installs the RBAC manifests from config/rbac.

  3. Verify that the Operator is running:

    $ oc get deployment -n <project_name>-system

    Example output

    NAME                                    READY   UP-TO-DATE   AVAILABLE   AGE
    <project_name>-controller-manager       1/1     1            1           8m

4.4.2.5.4. Bundling an Operator and deploying with Operator Lifecycle Manager

Operator Lifecycle Manager (OLM) helps you to install, update, and generally manage the lifecycle of Operators and their associated services on a Kubernetes cluster. OLM is installed by default on OpenShift Container Platform and runs as a Kubernetes extension so that you can use the web console and the OpenShift CLI (oc) for all Operator lifecycle management functions without any additional tools.

The Operator Bundle Format is the default packaging method for Operator SDK and OLM. You can get your Operator ready for OLM by using the Operator SDK to build, push, validate, and run a bundle image with OLM.

Prerequisites

  • Operator SDK CLI installed on a development workstation
  • OpenShift CLI (oc) v4.7+ installed
  • Operator Lifecycle Manager (OLM) installed on a Kubernetes-based cluster (v1.16.0 or later if you use apiextensions.k8s.io/v1 CRDs, for example OpenShift Container Platform 4.7)
  • Logged into the cluster with oc using an account with cluster-admin permissions
  • Operator project initialized by using the Operator SDK

Procedure

  1. Run the following make commands in your Operator project directory to build and push your Operator image. Modify the IMG argument in the following steps to reference a repository that you have access to. You can obtain an account for storing containers at repository sites such as Quay.io.

    1. Build the image:

      $ make docker-build IMG=<registry>/<user>/<operator_image_name>:<tag>
    2. Push the image to a repository:

      $ make docker-push IMG=<registry>/<user>/<operator_image_name>:<tag>
  2. Update your Makefile by setting the IMG URL to your Operator image name and tag that you pushed:

    $ # Image URL to use all building/pushing image targets
    IMG ?= <registry>/<user>/<operator_image_name>:<tag>

    This value is used for subsequent operations.

  3. Create your Operator bundle manifest by running the make bundle command, which invokes several commands, including the Operator SDK generate bundle and bundle validate subcommands:

    $ make bundle

    Bundle manifests for an Operator describe how to display, create, and manage an application. The make bundle command creates the following files and directories in your Operator project:

    • A bundle manifests directory named bundle/manifests that contains a ClusterServiceVersion object
    • A bundle metadata directory named bundle/metadata
    • All custom resource definitions (CRDs) in a config/crd directory
    • A Dockerfile bundle.Dockerfile

    These files are then automatically validated by using operator-sdk bundle validate to ensure the on-disk bundle representation is correct.

  4. Build and push your bundle image by running the following commands. OLM consumes Operator bundles using an index image, which reference one or more bundle images.

    1. Build the bundle image. Set BUNDLE_IMAGE with the details for the registry, user namespace, and image tag where you intend to push the image:

      $ make bundle-build BUNDLE_IMG=<registry>/<user>/<bundle_image_name>:<tag>
    2. Push the bundle image:

      $ docker push <registry>/<user>/<bundle_image_name>:<tag>
  5. Check the status of OLM on your cluster by using the following Operator SDK command:

    $ operator-sdk olm status \
        --olm-namespace=openshift-operator-lifecycle-manager
  6. Run the Operator on your cluster by using the OLM integration in Operator SDK:

    $ operator-sdk run bundle \
        [-n <namespace>] \1
        <registry>/<user>/<bundle_image_name>:<tag>
    1
    By default, the command installs the Operator in the currently active project in your ~/.kube/config file. You can add the -n flag to set a different namespace scope for the installation.

    This command performs the following actions:

    • Create an index image with your bundle image injected.
    • Create a catalog source that points to your new index image, which enables OperatorHub to discover your Operator.
    • Deploy your Operator to your cluster by creating an Operator group, subscription, install plan, and all other required objects, including RBAC.

4.4.2.6. Creating a custom resource

After your Operator is installed, you can test it by creating a custom resource (CR) that is now provided on the cluster by the Operator.

Prerequisites

  • Example Memcached Operator, which provides the Memcached CR, installed on a cluster

Procedure

  1. Change to the namespace where your Operator is installed. For example, if you deployed the Operator using the make deploy command:

    $ oc project memcached-operator-system
  2. Edit the sample Memcached CR manifest at config/samples/cache_v1_memcached.yaml to contain the following specification:

    apiVersion: cache.example.com/v1
    kind: Memcached
    metadata:
      name: memcached-sample
    ...
    spec:
    ...
      size: 3
  3. Create the CR:

    $ oc apply -f config/samples/cache_v1_memcached.yaml
  4. Ensure that the Memcached Operator creates the deployment for the sample CR with the correct size:

    $ oc get deployments

    Example output

    NAME                                    READY   UP-TO-DATE   AVAILABLE   AGE
    memcached-operator-controller-manager   1/1     1            1           8m
    memcached-sample                        3/3     3            3           1m

  5. Check the pods and CR status to confirm the status is updated with the Memcached pod names.

    1. Check the pods:

      $ oc get pods

      Example output

      NAME                                  READY     STATUS    RESTARTS   AGE
      memcached-sample-6fd7c98d8-7dqdr      1/1       Running   0          1m
      memcached-sample-6fd7c98d8-g5k7v      1/1       Running   0          1m
      memcached-sample-6fd7c98d8-m7vn7      1/1       Running   0          1m

    2. Check the CR status:

      $ oc get memcached/memcached-sample -o yaml

      Example output

      apiVersion: cache.example.com/v1
      kind: Memcached
      metadata:
      ...
        name: memcached-sample
      ...
      spec:
        size: 3
      status:
        nodes:
        - memcached-sample-6fd7c98d8-7dqdr
        - memcached-sample-6fd7c98d8-g5k7v
        - memcached-sample-6fd7c98d8-m7vn7

  6. Update the deployment size.

    1. Update config/samples/cache_v1_memcached.yaml file to change the spec.size field in the Memcached CR from 3 to 5:

      $ oc patch memcached memcached-sample \
          -p '{"spec":{"size": 5}}' \
          --type=merge
    2. Confirm that the Operator changes the deployment size:

      $ oc get deployments

      Example output

      NAME                                    READY   UP-TO-DATE   AVAILABLE   AGE
      memcached-operator-controller-manager   1/1     1            1           10m
      memcached-sample                        5/5     5            5           3m

  7. Clean up the resources that have been created as part of this tutorial.

    • If you used the make deploy command to test the Operator, run the following command:

      $ make undeploy
    • If you used the operator-sdk run bundle command to test the Operator, run the following command:

      $ operator-sdk cleanup <project_name>

4.4.2.7. Additional resources

4.4.3. Project layout for Ansible-based Operators

The operator-sdk CLI can generate, or scaffold, a number of packages and files for each Operator project.

4.4.3.1. Ansible-based project layout

Ansible-based Operator projects generated using the operator-sdk init --plugins ansible command contain the following directories and files:

File or directoryPurpose

Dockerfile

Dockerfile for building the container image for the Operator.

Makefile

Targets for building, publishing, deploying the container image that wraps the Operator binary, and targets for installing and uninstalling the custom resource definition (CRD).

PROJECT

YAML file containing metadata information for the Operator.

config/crd

Base CRD files and the kustomization.yaml file settings.

config/default

Collects all Operator manifests for deployment. Use by the make deploy command.

config/manager

Controller manager deployment.

config/prometheus

ServiceMonitor resource for monitoring the Operator.

config/rbac

Role and role binding for leader election and authentication proxy.

config/samples

Sample resources created for the CRDs.

config/testing

Sample configurations for testing.

playbooks/

A subdirectory for the playbooks to run.

roles/

Subdirectory for the roles tree to run.

watches.yaml

Group/version/kind (GVK) of the resources to watch, and the Ansible invocation method. New entries are added by using the create api command.

requirements.yml

YAML file containing the Ansible collections and role dependencies to install during a build.

molecule/

Molecule scenarios for end-to-end testing of your role and Operator.

4.4.4. Ansible support in Operator SDK

4.4.4.1. Custom resource files

Operators use the Kubernetes extension mechanism, custom resource definitions (CRDs), so your custom resource (CR) looks and acts just like the built-in, native Kubernetes objects.

The CR file format is a Kubernetes resource file. The object has mandatory and optional fields:

Table 4.1. Custom resource fields

FieldDescription

apiVersion

Version of the CR to be created.

kind

Kind of the CR to be created.

metadata

Kubernetes-specific metadata to be created.

spec (optional)

Key-value list of variables which are passed to Ansible. This field is empty by default.

status

Summarizes the current state of the object. For Ansible-based Operators, the status subresource is enabled for CRDs and managed by the operator_sdk.util.k8s_status Ansible module by default, which includes condition information to the CR status.

annotations

Kubernetes-specific annotations to be appended to the CR.

The following list of CR annotations modify the behavior of the Operator:

Table 4.2. Ansible-based Operator annotations

AnnotationDescription

ansible.operator-sdk/reconcile-period

Specifies the reconciliation interval for the CR. This value is parsed using the standard Golang package time. Specifically, ParseDuration is used which applies the default suffix of s, giving the value in seconds.

Example Ansible-based Operator annotation

apiVersion: "test1.example.com/v1alpha1"
kind: "Test1"
metadata:
  name: "example"
annotations:
  ansible.operator-sdk/reconcile-period: "30s"

4.4.4.2. watches.yaml file

A group/version/kind (GVK) is a unique identifier for a Kubernetes API. The watches.yaml file contains a list of mappings from custom resources (CRs), identified by its GVK, to an Ansible role or playbook. The Operator expects this mapping file in a predefined location at /opt/ansible/watches.yaml.

Table 4.3. watches.yaml file mappings

FieldDescription

group

Group of CR to watch.

version

Version of CR to watch.

kind

Kind of CR to watch

role (default)

Path to the Ansible role added to the container. For example, if your roles directory is at /opt/ansible/roles/ and your role is named busybox, this value would be /opt/ansible/roles/busybox. This field is mutually exclusive with the playbook field.

playbook

Path to the Ansible playbook added to the container. This playbook is expected to be a way to call roles. This field is mutually exclusive with the role field.

reconcilePeriod (optional)

The reconciliation interval, how often the role or playbook is run, for a given CR.

manageStatus (optional)

When set to true (default), the Operator manages the status of the CR generically. When set to false, the status of the CR is managed elsewhere, by the specified role or playbook or in a separate controller.

Example watches.yaml file

- version: v1alpha1 1
  group: test1.example.com
  kind: Test1
  role: /opt/ansible/roles/Test1

- version: v1alpha1 2
  group: test2.example.com
  kind: Test2
  playbook: /opt/ansible/playbook.yml

- version: v1alpha1 3
  group: test3.example.com
  kind: Test3
  playbook: /opt/ansible/test3.yml
  reconcilePeriod: 0
  manageStatus: false

1
Simple example mapping Test1 to the test1 role.
2
Simple example mapping Test2 to a playbook.
3
More complex example for the Test3 kind. Disables re-queuing and managing the CR status in the playbook.
4.4.4.2.1. Advanced options

Advanced features can be enabled by adding them to your watches.yaml file per GVK. They can go below the group, version, kind and playbook or role fields.

Some features can be overridden per resource using an annotation on that CR. The options that can be overridden have the annotation specified below.

Table 4.4. Advanced watches.yaml file options

FeatureYAML keyDescriptionAnnotation for overrideDefault value

Reconcile period

reconcilePeriod

Time between reconcile runs for a particular CR.

ansbile.operator-sdk/reconcile-period

1m

Manage status

manageStatus

Allows the Operator to manage the conditions section of each CR status section.

 

true

Watch dependent resources

watchDependentResources

Allows the Operator to dynamically watch resources that are created by Ansible.

 

true

Watch cluster-scoped resources

watchClusterScopedResources

Allows the Operator to watch cluster-scoped resources that are created by Ansible.

 

false

Max runner artifacts

maxRunnerArtifacts

Manages the number of artifact directories that Ansible Runner keeps in the Operator container for each individual resource.

ansible.operator-sdk/max-runner-artifacts

20

Example watches.yml file with advanced options

- version: v1alpha1
  group: app.example.com
  kind: AppService
  playbook: /opt/ansible/playbook.yml
  maxRunnerArtifacts: 30
  reconcilePeriod: 5s
  manageStatus: False
  watchDependentResources: False

4.4.4.3. Extra variables sent to Ansible

Extra variables can be sent to Ansible, which are then managed by the Operator. The spec section of the custom resource (CR) passes along the key-value pairs as extra variables. This is equivalent to extra variables passed in to the ansible-playbook command.

The Operator also passes along additional variables under the meta field for the name of the CR and the namespace of the CR.

For the following CR example:

apiVersion: "app.example.com/v1alpha1"
kind: "Database"
metadata:
  name: "example"
spec:
  message: "Hello world 2"
  newParameter: "newParam"

The structure passed to Ansible as extra variables is:

{ "meta": {
        "name": "<cr_name>",
        "namespace": "<cr_namespace>",
  },
  "message": "Hello world 2",
  "new_parameter": "newParam",
  "_app_example_com_database": {
     <full_crd>
   },
}

The message and newParameter fields are set in the top level as extra variables, and meta provides the relevant metadata for the CR as defined in the Operator. The meta fields can be accessed using dot notation in Ansible, for example:

---
- debug:
    msg: "name: {{ ansible_operator_meta.name }}, {{ ansible_operator_meta.namespace }}"

4.4.4.4. Ansible Runner directory

Ansible Runner keeps information about Ansible runs in the container. This is located at /tmp/ansible-operator/runner/<group>/<version>/<kind>/<namespace>/<name>.

Additional resources

4.4.5. Kubernetes Collection for Ansible

To manage the lifecycle of your application on Kubernetes using Ansible, you can use the Kubernetes Collection for Ansible. This collection of Ansible modules allows a developer to either leverage their existing Kubernetes resource files written in YAML or express the lifecycle management in native Ansible.

One of the biggest benefits of using Ansible in conjunction with existing Kubernetes resource files is the ability to use Jinja templating so that you can customize resources with the simplicity of a few variables in Ansible.

This section goes into detail on usage of the Kubernetes Collection. To get started, install the collection on your local workstation and test it using a playbook before moving on to using it within an Operator.

4.4.5.1. Installing the Kubernetes Collection for Ansible

You can install the Kubernetes Collection for Ansible on your local workstation.

Procedure

  1. Install Ansible 2.9+:

    $ sudo dnf install ansible
  2. Install the OpenShift python client package:

    $ pip3 install openshift
  3. Install the Kubernetes Collection using one of the following methods:

    • You can install the collection directly from Ansible Galaxy:

      $ ansible-galaxy collection install community.kubernetes
    • If you have already initialized your Operator, you might have a requirements.yml file at the top level of your project. This file specifies Ansible dependencies that must be installed for your Operator to function. By default, this file installs the community.kubernetes collection as well as the operator_sdk.util collection, which provides modules and plug-ins for Operator-specific fuctions.

      To install the dependent modules from the requirements.yml file:

      $ ansible-galaxy collection install -r requirements.yml

4.4.5.2. Testing the Kubernetes Collection locally

Operator developers can run the Ansible code from their local machine as opposed to running and rebuilding the Operator each time.

Prerequisites

  • Initialize an Ansible-based Operator project and create an API that has a generated Ansible role by using the Operator SDK
  • Install the Kubernetes Collection for Ansible

Procedure

  1. In your Ansible-based Operator project directory, modify the roles/<kind>/tasks/main.yml file with the Ansible logic that you want. The roles/<kind>/ directory is created when you use the --generate-role flag while creating an API. The <kind> replaceable matches the kind that you specified for the API.

    The following example creates and deletes a config map based on the value of a variable named state:

    ---
    - name: set ConfigMap example-config to {{ state }}
      community.kubernetes.k8s:
        api_version: v1
        kind: ConfigMap
        name: example-config
        namespace: default 1
        state: "{{ state }}"
      ignore_errors: true 2
    1
    Change this value if you want the config map to be created in a different namespace from default.
    2
    Setting ignore_errors: true ensures that deleting a nonexistent config map does not fail.
  2. Modify the roles/<kind>/defaults/main.yml file to set state to present by default:

    ---
    state: present
  3. Create an Ansible playbook by creating a playbook.yml file in the top-level of your project directory, and include your <kind> role:

    ---
    - hosts: localhost
      roles:
        - <kind>
  4. Run the playbook:

    $ ansible-playbook playbook.yml

    Example output

    [WARNING]: provided hosts list is empty, only localhost is available. Note that the implicit localhost does not match 'all'
    
    PLAY [localhost] ********************************************************************************
    
    TASK [Gathering Facts] ********************************************************************************
    ok: [localhost]
    
    TASK [memcached : set ConfigMap example-config to present] ********************************************************************************
    changed: [localhost]
    
    PLAY RECAP ********************************************************************************
    localhost                  : ok=2    changed=1    unreachable=0    failed=0    skipped=0    rescued=0    ignored=0

  5. Verify that the config map was created:

    $ oc get configmaps

    Example output

    NAME               DATA   AGE
    example-config     0      2m1s

  6. Rerun the playbook setting state to absent:

    $ ansible-playbook playbook.yml --extra-vars state=absent

    Example output

    [WARNING]: provided hosts list is empty, only localhost is available. Note that the implicit localhost does not match 'all'
    
    PLAY [localhost] ********************************************************************************
    
    TASK [Gathering Facts] ********************************************************************************
    ok: [localhost]
    
    TASK [memcached : set ConfigMap example-config to absent] ********************************************************************************
    changed: [localhost]
    
    PLAY RECAP ********************************************************************************
    localhost                  : ok=2    changed=1    unreachable=0    failed=0    skipped=0    rescued=0    ignored=0

  7. Verify that the config map was deleted:

    $ oc get configmaps

4.4.5.3. Next steps

4.4.6. Using Ansible inside an Operator

After you are familiar with using the Kubernetes Collection for Ansible locally, you can trigger the same Ansible logic inside of an Operator when a custom resource (CR) changes. This example maps an Ansible role to a specific Kubernetes resource that the Operator watches. This mapping is done in the watches.yaml file.

4.4.6.1. Custom resource files

Operators use the Kubernetes extension mechanism, custom resource definitions (CRDs), so your custom resource (CR) looks and acts just like the built-in, native Kubernetes objects.

The CR file format is a Kubernetes resource file. The object has mandatory and optional fields:

Table 4.5. Custom resource fields

FieldDescription

apiVersion

Version of the CR to be created.

kind

Kind of the CR to be created.

metadata

Kubernetes-specific metadata to be created.

spec (optional)

Key-value list of variables which are passed to Ansible. This field is empty by default.

status

Summarizes the current state of the object. For Ansible-based Operators, the status subresource is enabled for CRDs and managed by the operator_sdk.util.k8s_status Ansible module by default, which includes condition information to the CR status.

annotations

Kubernetes-specific annotations to be appended to the CR.

The following list of CR annotations modify the behavior of the Operator:

Table 4.6. Ansible-based Operator annotations

AnnotationDescription

ansible.operator-sdk/reconcile-period

Specifies the reconciliation interval for the CR. This value is parsed using the standard Golang package time. Specifically, ParseDuration is used which applies the default suffix of s, giving the value in seconds.

Example Ansible-based Operator annotation

apiVersion: "test1.example.com/v1alpha1"
kind: "Test1"
metadata:
  name: "example"
annotations:
  ansible.operator-sdk/reconcile-period: "30s"

4.4.6.2. Testing an Ansible-based Operator locally

You can test the logic inside of an Ansible-based Operator running locally by using the make run command from the top-level directory of your Operator project. The make run Makefile target runs the ansible-operator binary locally, which reads from the watches.yaml file and uses your ~/.kube/config file to communicate with a Kubernetes cluster just as the k8s modules do.

Note

You can customize the roles path by setting the environment variable ANSIBLE_ROLES_PATH or by using the ansible-roles-path flag. If the role is not found in the ANSIBLE_ROLES_PATH value, the Operator looks for it in {{current directory}}/roles.

Prerequisites

Procedure

  1. Install your custom resource definition (CRD) and proper role-based access control (RBAC) definitions for your custom resource (CR):

    $ make install

    Example output

    /usr/bin/kustomize build config/crd | kubectl apply -f -
    customresourcedefinition.apiextensions.k8s.io/memcacheds.cache.example.com created

  2. Run the make run command:

    $ make run

    Example output

    /home/user/memcached-operator/bin/ansible-operator run
    {"level":"info","ts":1612739145.2871568,"logger":"cmd","msg":"Version","Go Version":"go1.15.5","GOOS":"linux","GOARCH":"amd64","ansible-operator":"v1.3.0","commit":"1abf57985b43bf6a59dcd18147b3c574fa57d3f6"}
    ...
    {"level":"info","ts":1612739148.347306,"logger":"controller-runtime.metrics","msg":"metrics server is starting to listen","addr":":8080"}
    {"level":"info","ts":1612739148.3488882,"logger":"watches","msg":"Environment variable not set; using default value","envVar":"ANSIBLE_VERBOSITY_MEMCACHED_CACHE_EXAMPLE_COM","default":2}
    {"level":"info","ts":1612739148.3490262,"logger":"cmd","msg":"Environment variable not set; using default value","Namespace":"","envVar":"ANSIBLE_DEBUG_LOGS","ANSIBLE_DEBUG_LOGS":false}
    {"level":"info","ts":1612739148.3490646,"logger":"ansible-controller","msg":"Watching resource","Options.Group":"cache.example.com","Options.Version":"v1","Options.Kind":"Memcached"}
    {"level":"info","ts":1612739148.350217,"logger":"proxy","msg":"Starting to serve","Address":"127.0.0.1:8888"}
    {"level":"info","ts":1612739148.3506632,"logger":"controller-runtime.manager","msg":"starting metrics server","path":"/metrics"}
    {"level":"info","ts":1612739148.350784,"logger":"controller-runtime.manager.controller.memcached-controller","msg":"Starting EventSource","source":"kind source: cache.example.com/v1, Kind=Memcached"}
    {"level":"info","ts":1612739148.5511978,"logger":"controller-runtime.manager.controller.memcached-controller","msg":"Starting Controller"}
    {"level":"info","ts":1612739148.5512562,"logger":"controller-runtime.manager.controller.memcached-controller","msg":"Starting workers","worker count":8}

    With the Operator now watching your CR for events, the creation of a CR will trigger your Ansible role to run.

    Note

    Consider an example config/samples/<gvk>.yaml CR manifest:

    apiVersion: <group>.example.com/v1alpha1
    kind: <kind>
    metadata:
      name: "<kind>-sample"

    Because the spec field is not set, Ansible is invoked with no extra variables. Passing extra variables from a CR to Ansible is covered in another section. It is important to set reasonable defaults for the Operator.

  3. Create an instance of your CR with the default variable state set to present:

    $ oc apply -f config/samples/<gvk>.yaml
  4. Check that the example-config config map was created:

    $ oc get configmaps

    Example output

    NAME                    STATUS    AGE
    example-config          Active    3s

  5. Modify your config/samples/<gvk>.yaml file to set the state field to absent. For example:

    apiVersion: cache.example.com/v1
    kind: Memcached
    metadata:
      name: memcached-sample
    spec:
      state: absent
  6. Apply the changes:

    $ oc apply -f config/samples/<gvk>.yaml
  7. Confirm that the config map is deleted:

    $ oc get configmap

4.4.6.3. Testing an Ansible-based Operator on the cluster

After you have tested your custom Ansible logic locally inside of an Operator, you can test the Operator inside of a pod on an OpenShift Container Platform cluster, which is prefered for production use.

You can run your Operator project as a deployment on your cluster.

Procedure

  1. Run the following make commands to build and push the Operator image. Modify the IMG argument in the following steps to reference a repository that you have access to. You can obtain an account for storing containers at repository sites such as Quay.io.

    1. Build the image:

      $ make docker-build IMG=<registry>/<user>/<image_name>:<tag>
    2. Push the image to a repository:

      $ make docker-push IMG=<registry>/<user>/<image_name>:<tag>
      Note

      The name and tag of the image, for example IMG=<registry>/<user>/<image_name>:<tag>, in both the commands can also be set in your Makefile. Modify the IMG ?= controller:latest value to set your default image name.

  2. Run the following command to deploy the Operator:

    $ make deploy IMG=<registry>/<user>/<image_name>:<tag>

    By default, this command creates a namespace with the name of your Operator project in the form <project_name>-system and is used for the deployment. This command also installs the RBAC manifests from config/rbac.

  3. Verify that the Operator is running:

    $ oc get deployment -n <project_name>-system

    Example output

    NAME                                    READY   UP-TO-DATE   AVAILABLE   AGE
    <project_name>-controller-manager       1/1     1            1           8m

4.4.6.4. Ansible logs

Ansible-based Operators provide logs about the Ansible run, which can be useful for debugging your Ansible tasks. The logs can also contain detailed information about the internals of the Operator and its interactions with Kubernetes.

4.4.6.4.1. Viewing Ansible logs

Prerequisites

  • Ansible-based Operator running as a deployment on a cluster

Procedure

  • To view logs from an Ansible-based Operator, run the following command:

    $ oc logs deployment/<project_name>-controller-manager \
        -c manager \1
        -n <namespace> 2
    1
    View logs from the manager container.
    2
    If you used the make deploy command to run the Operator as a deployment, use the <project_name>-system namespace.

    Example output

    {"level":"info","ts":1612732105.0579333,"logger":"cmd","msg":"Version","Go Version":"go1.15.5","GOOS":"linux","GOARCH":"amd64","ansible-operator":"v1.3.0","commit":"1abf57985b43bf6a59dcd18147b3c574fa57d3f6"}
    {"level":"info","ts":1612732105.0587437,"logger":"cmd","msg":"WATCH_NAMESPACE environment variable not set. Watching all namespaces.","Namespace":""}
    I0207 21:08:26.110949       7 request.go:645] Throttling request took 1.035521578s, request: GET:https://172.30.0.1:443/apis/flowcontrol.apiserver.k8s.io/v1alpha1?timeout=32s
    {"level":"info","ts":1612732107.768025,"logger":"controller-runtime.metrics","msg":"metrics server is starting to listen","addr":"127.0.0.1:8080"}
    {"level":"info","ts":1612732107.768796,"logger":"watches","msg":"Environment variable not set; using default value","envVar":"ANSIBLE_VERBOSITY_MEMCACHED_CACHE_EXAMPLE_COM","default":2}
    {"level":"info","ts":1612732107.7688773,"logger":"cmd","msg":"Environment variable not set; using default value","Namespace":"","envVar":"ANSIBLE_DEBUG_LOGS","ANSIBLE_DEBUG_LOGS":false}
    {"level":"info","ts":1612732107.7688901,"logger":"ansible-controller","msg":"Watching resource","Options.Group":"cache.example.com","Options.Version":"v1","Options.Kind":"Memcached"}
    {"level":"info","ts":1612732107.770032,"logger":"proxy","msg":"Starting to serve","Address":"127.0.0.1:8888"}
    I0207 21:08:27.770185       7 leaderelection.go:243] attempting to acquire leader lease  memcached-operator-system/memcached-operator...
    {"level":"info","ts":1612732107.770202,"logger":"controller-runtime.manager","msg":"starting metrics server","path":"/metrics"}
    I0207 21:08:27.784854       7 leaderelection.go:253] successfully acquired lease memcached-operator-system/memcached-operator
    {"level":"info","ts":1612732107.7850506,"logger":"controller-runtime.manager.controller.memcached-controller","msg":"Starting EventSource","source":"kind source: cache.example.com/v1, Kind=Memcached"}
    {"level":"info","ts":1612732107.8853772,"logger":"controller-runtime.manager.controller.memcached-controller","msg":"Starting Controller"}
    {"level":"info","ts":1612732107.8854098,"logger":"controller-runtime.manager.controller.memcached-controller","msg":"Starting workers","worker count":4}

4.4.6.4.2. Enabling full Ansible results in logs

You can set the environment variable ANSIBLE_DEBUG_LOGS to True to enable checking the full Ansible result in logs, which can be helpful when debugging.

Procedure

  • Edit the config/manager/manager.yaml and config/default/manager_auth_proxy_patch.yaml files to include the following configuration:

          containers:
          - name: manager
            env:
            - name: ANSIBLE_DEBUG_LOGS
              value: "True"
4.4.6.4.3. Enabling verbose debugging in logs

While developing an Ansible-based Operator, it can be helpful to enable additional debugging in logs.

Procedure

  • Add the ansible.sdk.operatorframework.io/verbosity annotation to your custom resource to enable the verbosity level that you want. For example:

    apiVersion: "cache.example.com/v1alpha1"
    kind: "Memcached"
    metadata:
      name: "example-memcached"
      annotations:
        "ansible.sdk.operatorframework.io/verbosity": "4"
    spec:
      size: 4

4.4.7. Custom resource status management

4.4.7.1. About custom resource status in Ansible-based Operators

Ansible-based Operators automatically update custom resource (CR) status subresources with generic information about the previous Ansible run. This includes the number of successful and failed tasks and relevant error messages as shown:

status:
  conditions:
  - ansibleResult:
      changed: 3
      completion: 2018-12-03T13:45:57.13329
      failures: 1
      ok: 6
      skipped: 0
    lastTransitionTime: 2018-12-03T13:45:57Z
    message: 'Status code was -1 and not [200]: Request failed: <urlopen error [Errno
      113] No route to host>'
    reason: Failed
    status: "True"
    type: Failure
  - lastTransitionTime: 2018-12-03T13:46:13Z
    message: Running reconciliation
    reason: Running
    status: "True"
    type: Running

Ansible-based Operators also allow Operator authors to supply custom status values with the k8s_status Ansible module, which is included in the operator_sdk.util collection. This allows the author to update the status from within Ansible with any key-value pair as desired.

By default, Ansible-based Operators always include the generic Ansible run output as shown above. If you would prefer your application did not update the status with Ansible output, you can track the status manually from your application.

4.4.7.2. Tracking custom resource status manually

You can use the operator_sdk.util collection to modify your Ansible-based Operator to track custom resource (CR) status manually from your application.

Prerequisites

  • Ansible-based Operator project created by using the Operator SDK

Procedure

  1. Update the watches.yaml file with a manageStatus field set to false:

    - version: v1
      group: api.example.com
      kind: <kind>
      role: <role>
      manageStatus: false
  2. Use the operator_sdk.util.k8s_status Ansible module to update the subresource. For example, to update with key test and value data, operator_sdk.util can be used as shown:

    - operator_sdk.util.k8s_status:
        api_version: app.example.com/v1
        kind: <kind>
        name: "{{ ansible_operator_meta.name }}"
        namespace: "{{ ansible_operator_meta.namespace }}"
        status:
          test: data
  3. You can declare collections in the meta/main.yml file for the role, which is included for scaffolded Ansible-based Operators:

    collections:
      - operator_sdk.util
  4. After declaring collections in the role meta, you can invoke the k8s_status module directly:

    k8s_status:
      ...
      status:
        key1: value1

4.5. Helm-based Operators

4.5.1. Getting started with Operator SDK for Helm-based Operators

The Operator SDK includes options for generating an Operator project that leverages existing Helm charts to deploy Kubernetes resources as a unified application, without having to write any Go code.

To demonstrate the basics of setting up and running an Helm-based Operator using tools and libraries provided by the Operator SDK, Operator developers can build an example Helm-based Operator for Nginx and deploy it to a cluster.

4.5.1.1. Prerequisites

  • Operator SDK CLI installed
  • OpenShift CLI (oc) v4.7+ installed
  • Logged into an OpenShift Container Platform 4.7 cluster with oc with an account that has cluster-admin permissions
  • To allow the cluster pull the image, the repository where you push your image must be set as public, or you must configure an image pull secret.

4.5.1.2. Creating and deploying Helm-based Operators

You can build and deploy a simple Helm-based Operator for Nginx by using the Operator SDK.

Procedure

  1. Create a project.

    1. Create your project directory:

      $ mkdir nginx-operator
    2. Change into the project directory:

      $ cd nginx-operator
    3. Run the operator-sdk init command with the helm plug-in to initialize the project:

      $ operator-sdk init \
          --plugins=helm
  2. Create an API.

    Create a simple Nginx API:

    $ operator-sdk create api \
        --group demo \
        --version v1 \
        --kind Nginx

    This API uses the built-in Helm chart boilerplate from the helm create command.

  3. Build and push the Operator image.

    Use the default Makefile targets to build and push your Operator. Set IMG with a pull spec for your image that uses a registry you can push to:

    $ make docker-build docker-push IMG=<registry>/<user>/<image_name>:<tag>
  4. Run the Operator.

    1. Install the CRD:

      $ make install
    2. Deploy the project to the cluster. Set IMG to the image that you pushed:

      $ make deploy IMG=<registry>/<user>/<image_name>:<tag>
  5. Add a security context constraint (SCC).

    The Nginx service account requires privileged access to run in OpenShift Container Platform. Add the following SCC to the service account for the nginx-sample pod:

    $ oc adm policy add-scc-to-user \
        anyuid system:serviceaccount:nginx-operator-system:nginx-sample
  6. Create a sample custom resource (CR).

    1. Create a sample CR:

      $ oc apply -f config/samples/demo_v1_nginx.yaml \
          -n nginx-operator-system
    2. Watch for the CR to reconcile the Operator:

      $ oc logs deployment.apps/nginx-operator-controller-manager \
          -c manager \
          -n nginx-operator-system
  7. Clean up.

    Run the following command to clean up the resources that have been created as part of this procedure:

    $ make undeploy

4.5.1.3. Next steps

4.5.2. Operator SDK tutorial for Helm-based Operators

Operator developers can take advantage of Helm support in the Operator SDK to build an example Helm-based Operator for Nginx and manage its lifecycle. This tutorial walks through the following process:

  • Create a Nginx deployment
  • Ensure that the deployment size is the same as specified by the Nginx custom resource (CR) spec
  • Update the Nginx CR status using the status writer with the names of the nginx pods

This process is accomplished using two centerpieces of the Operator Framework:

Operator SDK
The operator-sdk CLI tool and controller-runtime library API
Operator Lifecycle Manager (OLM)
Installation, upgrade, and role-based access control (RBAC) of Operators on a cluster
Note

This tutorial goes into greater detail than Getting started with Operator SDK for Helm-based Operators.

4.5.2.1. Prerequisites

  • Operator SDK CLI installed
  • OpenShift CLI (oc) v4.7+ installed
  • Logged into an OpenShift Container Platform 4.7 cluster with oc with an account that has cluster-admin permissions
  • To allow the cluster pull the image, the repository where you push your image must be set as public, or you must configure an image pull secret.

4.5.2.2. Creating a project

Use the Operator SDK CLI to create a project called nginx-operator.

Procedure

  1. Create a directory for the project:

    $ mkdir -p $HOME/projects/nginx-operator
  2. Change to the directory:

    $ cd $HOME/projects/nginx-operator
  3. Run the operator-sdk init command with the helm plug-in to initialize the project:

    $ operator-sdk init \
        --plugins=helm \
        --domain=example.com \
        --group=demo \
        --version=v1 \
        --kind=Nginx
    Note

    By default, the helm plug-in initializes a project using a boilerplate Helm chart. You can use additional flags, such as the --helm-chart flag, to initialize a project using an existing Helm chart.

    The init command creates the nginx-operator project specifically for watching a resource with API version example.com/v1 and kind Nginx.

  4. For Helm-based projects, the init command generates the RBAC rules in the config/rbac/role.yaml file based on the resources that would be deployed by the default manifest for the chart. Verify that the rules generated in this file meet the permission requirements of the Operator.
4.5.2.2.1. Existing Helm charts

Instead of creating your project with a boilerplate Helm chart, you can alternatively use an existing chart, either from your local file system or a remote chart repository, by using the following flags:

  • --helm-chart
  • --helm-chart-repo
  • --helm-chart-version

If the --helm-chart flag is specified, the --group, --version, and --kind flags become optional. If left unset, the following default values are used:

FlagValue

--domain

my.domain

--group

charts

--version

v1

--kind

Deduced from the specified chart

If the --helm-chart flag specifies a local chart archive, for example example-chart-1.2.0.tgz, or directory, the chart is validated and unpacked or copied into the project. Otherwise, the Operator SDK attempts to fetch the chart from a remote repository.

If a custom repository URL is not specified by the --helm-chart-repo flag, the following chart reference formats are supported:

FormatDescription

<repo_name>/<chart_name>

Fetch the Helm chart named <chart_name> from the helm chart repository named <repo_name>, as specified in the $HELM_HOME/repositories/repositories.yaml file. Use the helm repo add command to configure this file.

<url>

Fetch the Helm chart archive at the specified URL.

If a custom repository URL is specified by --helm-chart-repo, the following chart reference format is supported:

FormatDescription

<chart_name>

Fetch the Helm chart named <chart_name> in the Helm chart repository specified by the --helm-chart-repo URL value.

If the --helm-chart-version flag is unset, the Operator SDK fetches the latest available version of the Helm chart. Otherwise, it fetches the specified version. The optional --helm-chart-version flag is not used when the chart specified with the --helm-chart flag refers to a specific version, for example when it is a local path or a URL.

For more details and examples, run:

$ operator-sdk init --plugins helm --help
4.5.2.2.2. PROJECT file

Among the files generated by the operator-sdk init command is a Kubebuilder PROJECT file. Subsequent operator-sdk commands, as well as help output, that are run from the project root read this file and are aware that the project type is Helm. For example:

domain: example.com
layout: helm.sdk.operatorframework.io/v1
projectName: helm-operator
resources:
- group: demo
  kind: Nginx
  version: v1
version: 3-alpha

4.5.2.3. Understanding the Operator logic

For this example, the nginx-operator project executes the following reconciliation logic for each Nginx custom resource (CR):

  • Create an Nginx deployment if it does not exist.
  • Create an Nginx service if it does not exist.
  • Create an Nginx ingress if it is enabled and does not exist.
  • Ensure that the deployment, service, and optional ingress match the desired configuration as specified by the Nginx CR, for example the replica count, image, and service type.

By default, the nginx-operator project watches Nginx resource events as shown in the watches.yaml file and executes Helm releases using the specified chart:

# Use the 'create api' subcommand to add watches to this file.
- group: demo
  version: v1
  kind: Nginx
  chart: helm-charts/nginx
# +kubebuilder:scaffold:watch
4.5.2.3.1. Sample Helm chart

When a Helm Operator project is created, the Operator SDK creates a sample Helm chart that contains a set of templates for a simple Nginx release.

For this example, templates are available for deployment, service, and ingress resources, along with a NOTES.txt template, which Helm chart developers use to convey helpful information about a release.

If you are not already familiar with Helm charts, review the Helm developer documentation.

4.5.2.3.2. Modifying the custom resource spec

Helm uses a concept called values to provide customizations to the defaults of a Helm chart, which are defined in the values.yaml file.

You can override these defaults by setting the desired values in the custom resource (CR) spec. You can use the number of replicas as an example.

Procedure

  1. The helm-charts/nginx/values.yaml file has a value called replicaCount set to 1 by default. To have two Nginx instances in your deployment, your CR spec must contain replicaCount: 2.

    Edit the config/samples/demo_v1_nginx.yaml file to set replicaCount: 2:

    apiVersion: demo.example.com/v1
    kind: Nginx
    metadata:
      name: nginx-sample
    ...
    spec:
    ...
      replicaCount: 2
  2. Similarly, the default service port is set to 80. To use 8080, edit the config/samples/demo_v1_nginx.yaml file to set spec.port: 8080,which adds the service port override:

    apiVersion: demo.example.com/v1
    kind: Nginx
    metadata:
      name: nginx-sample
    spec:
      replicaCount: 2
      service:
        port: 8080

The Helm Operator applies the entire spec as if it was the contents of a values file, just like the helm install -f ./overrides.yaml command.

4.5.2.4. Running the Operator

There are three ways you can use the Operator SDK CLI to build and run your Operator:

  • Run locally outside the cluster as a Go program.
  • Run as a deployment on the cluster.
  • Bundle your Operator and use Operator Lifecycle Manager (OLM) to deploy on the cluster.
4.5.2.4.1. Running locally outside the cluster

You can run your Operator project as a Go program outside of the cluster. This is useful for development purposes to speed up deployment and testing.

Procedure

  • Run the following command to install the custom resource definitions (CRDs) in the cluster configured in your ~/.kube/config file and run the Operator locally:

    $ make install run

    Example output

    ...
    {"level":"info","ts":1612652419.9289865,"logger":"controller-runtime.metrics","msg":"metrics server is starting to listen","addr":":8080"}
    {"level":"info","ts":1612652419.9296563,"logger":"helm.controller","msg":"Watching resource","apiVersion":"demo.example.com/v1","kind":"Nginx","namespace":"","reconcilePeriod":"1m0s"}
    {"level":"info","ts":1612652419.929983,"logger":"controller-runtime.manager","msg":"starting metrics server","path":"/metrics"}
    {"level":"info","ts":1612652419.930015,"logger":"controller-runtime.manager.controller.nginx-controller","msg":"Starting EventSource","source":"kind source: demo.example.com/v1, Kind=Nginx"}
    {"level":"info","ts":1612652420.2307851,"logger":"controller-runtime.manager.controller.nginx-controller","msg":"Starting Controller"}
    {"level":"info","ts":1612652420.2309358,"logger":"controller-runtime.manager.controller.nginx-controller","msg":"Starting workers","worker count":8}

4.5.2.4.2. Preparing your Operator to use supported images

Before running your Helm-based Operator on OpenShift Container Platform, update your project to use supported images.

Procedure

  1. Update the project root-level Dockerfile to use supported images. Change the default builder image reference from:

    FROM quay.io/operator-framework/helm-operator:v1.3.0

    to:

    FROM registry.redhat.io/openshift4/ose-helm-operator:v4.7
    Important

    Use the builder image version that matches your Operator SDK version. Failure to do so can result in problems due to project layout, or scaffolding, differences, particularly when mixing newer upstream versions of the Operator SDK with downstream OpenShift Container Platform builder images.

  2. In the config/default/manager_auth_proxy_patch.yaml file, change the image value from:

    gcr.io/kubebuilder/kube-rbac-proxy:<tag>

    to use the supported image:

    registry.redhat.io/openshift4/ose-kube-rbac-proxy:v4.7
4.5.2.4.3. Running as a deployment on the cluster

You can run your Operator project as a deployment on your cluster.

Procedure

  1. Run the following make commands to build and push the Operator image. Modify the IMG argument in the following steps to reference a repository that you have access to. You can obtain an account for storing containers at repository sites such as Quay.io.

    1. Build the image:

      $ make docker-build IMG=<registry>/<user>/<image_name>:<tag>
    2. Push the image to a repository:

      $ make docker-push IMG=<registry>/<user>/<image_name>:<tag>
      Note

      The name and tag of the image, for example IMG=<registry>/<user>/<image_name>:<tag>, in both the commands can also be set in your Makefile. Modify the IMG ?= controller:latest value to set your default image name.

  2. Run the following command to deploy the Operator:

    $ make deploy IMG=<registry>/<user>/<image_name>:<tag>

    By default, this command creates a namespace with the name of your Operator project in the form <project_name>-system and is used for the deployment. This command also installs the RBAC manifests from config/rbac.

  3. Verify that the Operator is running:

    $ oc get deployment -n <project_name>-system

    Example output

    NAME                                    READY   UP-TO-DATE   AVAILABLE   AGE
    <project_name>-controller-manager       1/1     1            1           8m

4.5.2.4.4. Bundling an Operator and deploying with Operator Lifecycle Manager

Operator Lifecycle Manager (OLM) helps you to install, update, and generally manage the lifecycle of Operators and their associated services on a Kubernetes cluster. OLM is installed by default on OpenShift Container Platform and runs as a Kubernetes extension so that you can use the web console and the OpenShift CLI (oc) for all Operator lifecycle management functions without any additional tools.

The Operator Bundle Format is the default packaging method for Operator SDK and OLM. You can get your Operator ready for OLM by using the Operator SDK to build, push, validate, and run a bundle image with OLM.

Prerequisites

  • Operator SDK CLI installed on a development workstation
  • OpenShift CLI (oc) v4.7+ installed
  • Operator Lifecycle Manager (OLM) installed on a Kubernetes-based cluster (v1.16.0 or later if you use apiextensions.k8s.io/v1 CRDs, for example OpenShift Container Platform 4.7)
  • Logged into the cluster with oc using an account with cluster-admin permissions
  • Operator project initialized by using the Operator SDK

Procedure

  1. Run the following make commands in your Operator project directory to build and push your Operator image. Modify the IMG argument in the following steps to reference a repository that you have access to. You can obtain an account for storing containers at repository sites such as Quay.io.

    1. Build the image:

      $ make docker-build IMG=<registry>/<user>/<operator_image_name>:<tag>
    2. Push the image to a repository:

      $ make docker-push IMG=<registry>/<user>/<operator_image_name>:<tag>
  2. Update your Makefile by setting the IMG URL to your Operator image name and tag that you pushed:

    $ # Image URL to use all building/pushing image targets
    IMG ?= <registry>/<user>/<operator_image_name>:<tag>

    This value is used for subsequent operations.

  3. Create your Operator bundle manifest by running the make bundle command, which invokes several commands, including the Operator SDK generate bundle and bundle validate subcommands:

    $ make bundle

    Bundle manifests for an Operator describe how to display, create, and manage an application. The make bundle command creates the following files and directories in your Operator project:

    • A bundle manifests directory named bundle/manifests that contains a ClusterServiceVersion object
    • A bundle metadata directory named bundle/metadata
    • All custom resource definitions (CRDs) in a config/crd directory
    • A Dockerfile bundle.Dockerfile

    These files are then automatically validated by using operator-sdk bundle validate to ensure the on-disk bundle representation is correct.

  4. Build and push your bundle image by running the following commands. OLM consumes Operator bundles using an index image, which reference one or more bundle images.

    1. Build the bundle image. Set BUNDLE_IMAGE with the details for the registry, user namespace, and image tag where you intend to push the image:

      $ make bundle-build BUNDLE_IMG=<registry>/<user>/<bundle_image_name>:<tag>
    2. Push the bundle image:

      $ docker push <registry>/<user>/<bundle_image_name>:<tag>
  5. Check the status of OLM on your cluster by using the following Operator SDK command:

    $ operator-sdk olm status \
        --olm-namespace=openshift-operator-lifecycle-manager
  6. Run the Operator on your cluster by using the OLM integration in Operator SDK:

    $ operator-sdk run bundle \
        [-n <namespace>] \1
        <registry>/<user>/<bundle_image_name>:<tag>
    1
    By default, the command installs the Operator in the currently active project in your ~/.kube/config file. You can add the -n flag to set a different namespace scope for the installation.

    This command performs the following actions:

    • Create an index image with your bundle image injected.
    • Create a catalog source that points to your new index image, which enables OperatorHub to discover your Operator.
    • Deploy your Operator to your cluster by creating an Operator group, subscription, install plan, and all other required objects, including RBAC.

4.5.2.5. Creating a custom resource

After your Operator is installed, you can test it by creating a custom resource (CR) that is now provided on the cluster by the Operator.

Prerequisites

  • Example Nginx Operator, which provides the Nginx CR, installed on a cluster

Procedure

  1. Change to the namespace where your Operator is installed. For example, if you deployed the Operator using the make deploy command:

    $ oc project nginx-operator-system
  2. Edit the sample Nginx CR manifest at config/samples/demo_v1_nginx.yaml to contain the following specification:

    apiVersion: demo.example.com/v1
    kind: Nginx
    metadata:
      name: nginx-sample
    ...
    spec:
    ...
      replicaCount: 3
  3. The Nginx service account requires privileged access to run in OpenShift Container Platform. Add the following security context constraint (SCC) to the service account for the nginx-sample pod:

    $ oc adm policy add-scc-to-user \
        anyuid system:serviceaccount:nginx-operator-system:nginx-sample
  4. Create the CR:

    $ oc apply -f config/samples/demo_v1_nginx.yaml
  5. Ensure that the Nginx Operator creates the deployment for the sample CR with the correct size:

    $ oc get deployments

    Example output

    NAME                                    READY   UP-TO-DATE   AVAILABLE   AGE
    nginx-operator-controller-manager       1/1     1            1           8m
    nginx-sample                            3/3     3            3           1m

  6. Check the pods and CR status to confirm the status is updated with the Nginx pod names.

    1. Check the pods:

      $ oc get pods

      Example output

      NAME                                  READY     STATUS    RESTARTS   AGE
      nginx-sample-6fd7c98d8-7dqdr          1/1       Running   0          1m
      nginx-sample-6fd7c98d8-g5k7v          1/1       Running   0          1m
      nginx-sample-6fd7c98d8-m7vn7          1/1       Running   0          1m

    2. Check the CR status:

      $ oc get nginx/nginx-sample -o yaml

      Example output

      apiVersion: demo.example.com/v1
      kind: Nginx
      metadata:
      ...
        name: nginx-sample
      ...
      spec:
        replicaCount: 3
      status:
        nodes:
        - nginx-sample-6fd7c98d8-7dqdr
        - nginx-sample-6fd7c98d8-g5k7v
        - nginx-sample-6fd7c98d8-m7vn7

  7. Update the deployment size.

    1. Update config/samples/demo_v1_nginx.yaml file to change the spec.size field in the Nginx CR from 3 to 5:

      $ oc patch nginx nginx-sample \
          -p '{"spec":{"replicaCount": 5}}' \
          --type=merge
    2. Confirm that the Operator changes the deployment size:

      $ oc get deployments

      Example output

      NAME                                    READY   UP-TO-DATE   AVAILABLE   AGE
      nginx-operator-controller-manager       1/1     1            1           10m
      nginx-sample                            5/5     5            5           3m

  8. Clean up the resources that have been created as part of this tutorial.

    • If you used the make deploy command to test the Operator, run the following command:

      $ make undeploy
    • If you used the operator-sdk run bundle command to test the Operator, run the following command:

      $ operator-sdk cleanup <project_name>

4.5.2.6. Additional resources

4.5.3. Project layout for Helm-based Operators

The operator-sdk CLI can generate, or scaffold, a number of packages and files for each Operator project.

4.5.3.1. Helm-based project layout

Helm-based Operator projects generated using the operator-sdk init --plugins helm command contain the following directories and files:

File/foldersPurpose

config

Kustomize manifests for deploying the Operator on a Kubernetes cluster.

helm-charts/

Helm chart initialized with the operator-sdk create api command.

Dockerfile

Used to build the Operator image with the make docker-build command.

watches.yaml

Group/version/kind (GVK) and Helm chart location.

Makefile

Targets used to manage the project.

PROJECT

YAML file containing metadata information for the Operator.

4.5.4. Helm support in Operator SDK

4.5.4.1. Helm charts

One of the Operator SDK options for generating an Operator project includes leveraging an existing Helm chart to deploy Kubernetes resources as a unified application, without having to write any Go code. Such Helm-based Operators are designed to excel at stateless applications that require very little logic when rolled out, because changes should be applied to the Kubernetes objects that are generated as part of the chart. This may sound limiting, but can be sufficient for a surprising amount of use-cases as shown by the proliferation of Helm charts built by the Kubernetes community.

The main function of an Operator is to read from a custom object that represents your application instance and have its desired state match what is running. In the case of a Helm-based Operator, the spec field of the object is a list of configuration options that are typically described in the Helm values.yaml file. Instead of setting these values with flags using the Helm CLI (for example, helm install -f values.yaml), you can express them within a custom resource (CR), which, as a native Kubernetes object, enables the benefits of RBAC applied to it and an audit trail.

For an example of a simple CR called Tomcat:

apiVersion: apache.org/v1alpha1
kind: Tomcat
metadata:
  name: example-app
spec:
  replicaCount: 2

The replicaCount value, 2 in this case, is propagated into the template of the chart where the following is used:

{{ .Values.replicaCount }}

After an Operator is built and deployed, you can deploy a new instance of an app by creating a new instance of a CR, or list the different instances running in all environments using the oc command:

$ oc get Tomcats --all-namespaces

There is no requirement use the Helm CLI or install Tiller; Helm-based Operators import code from the Helm project. All you have to do is have an instance of the Operator running and register the CR with a custom resource definition (CRD). Because it obeys RBAC, you can more easily prevent production changes.

4.6. Defining cluster service versions (CSVs)

A cluster service version (CSV), defined by a ClusterServiceVersion object, is a YAML manifest created from Operator metadata that assists Operator Lifecycle Manager (OLM) in running the Operator in a cluster. It is the metadata that accompanies an Operator container image, used to populate user interfaces with information such as its logo, description, and version. It is also a source of technical information that is required to run the Operator, like the RBAC rules it requires and which custom resources (CRs) it manages or depends on.

The Operator SDK includes the CSV generator to generate a CSV for the current Operator project, customized using information contained in YAML manifests and Operator source files.

A CSV-generating command removes the responsibility of Operator authors having in-depth OLM knowledge in order for their Operator to interact with OLM or publish metadata to the Catalog Registry. Further, because the CSV spec will likely change over time as new Kubernetes and OLM features are implemented, the Operator SDK is equipped to easily extend its update system to handle new CSV features going forward.

4.6.1. How CSV generation works

Operator bundle manifests, which include cluster service versions (CSVs), describe how to display, create, and manage an application with Operator Lifecycle Manager (OLM). The CSV generator in the Operator SDK, called by the generate bundle subcommand, is the first step towards publishing your Operator to a catalog and deploying it with OLM. The subcommand requires certain input manifests to construct a CSV manifest; all inputs are read when the command is invoked, along with a CSV base, to idempotently generate or regenerate a CSV.

Typically, the generate kustomize manifests subcommand would be run first to generate the input Kustomize bases that are consumed by the generate bundle subcommand. However, the Operator SDK provides the make bundle command, which automates several tasks, including running the following subcommands in order:

  1. generate kustomize manifests
  2. generate bundle
  3. bundle validate

Additional resources

4.6.1.1. Generated files and resources

The make bundle command creates the following files and directories in your Operator project:

  • A bundle manifests directory named bundle/manifests that contains a ClusterServiceVersion (CSV) object
  • A bundle metadata directory named bundle/metadata
  • All custom resource definitions (CRDs) in a config/crd directory
  • A Dockerfile bundle.Dockerfile

The following resources are typically included in a CSV:

Role
Defines Operator permissions within a namespace.
ClusterRole
Defines cluster-wide Operator permissions.
Deployment
Defines how an Operand of an Operator is run in pods.
CustomResourceDefinition (CRD)
Defines custom resources that your Operator reconciles.
Custom resource examples
Examples of resources adhering to the spec of a particular CRD.

4.6.1.2. Version management

The --version flag for the generate bundle subcommand supplies a semantic version for your bundle when creating one for the first time and when upgrading an existing one.

By setting the VERSION variable in your Makefile, the --version flag is automatically invoked using that value when the generate bundle subcommand is run by the make bundle command. The CSV version is the same as the Operator version, and a new CSV is generated when upgrading Operator versions.

4.6.2. Manually-defined CSV fields

Many CSV fields cannot be populated using generated, generic manifests that are not specific to Operator SDK. These fields are mostly human-written metadata about the Operator and various custom resource definitions (CRDs).

Operator authors must directly modify their cluster service version (CSV) YAML file, adding personalized data to the following required fields. The Operator SDK gives a warning during CSV generation when a lack of data in any of the required fields is detected.

The following tables detail which manually-defined CSV fields are required and which are optional.

Table 4.7. Required

FieldDescription

metadata.name

A unique name for this CSV. Operator version should be included in the name to ensure uniqueness, for example app-operator.v0.1.1.

metadata.capabilities

The capability level according to the Operator maturity model. Options include Basic Install, Seamless Upgrades, Full Lifecycle, Deep Insights, and Auto Pilot.

spec.displayName

A public name to identify the Operator.

spec.description

A short description of the functionality of the Operator.

spec.keywords

Keywords describing the Operator.

spec.maintainers

Human or organizational entities maintaining the Operator, with a name and email.

spec.provider

The provider of the Operator (usually an organization), with a name.

spec.labels

Key-value pairs to be used by Operator internals.

spec.version

Semantic version of the Operator, for example 0.1.1.

spec.customresourcedefinitions

Any CRDs the Operator uses. This field is populated automatically by the Operator SDK if any CRD YAML files are present in deploy/. However, several fields not in the CRD manifest spec require user input:

  • description: description of the CRD.
  • resources: any Kubernetes resources leveraged by the CRD, for example Pod and StatefulSet objects.
  • specDescriptors: UI hints for inputs and outputs of the Operator.

Table 4.8. Optional

FieldDescription

spec.replaces

The name of the CSV being replaced by this CSV.

spec.links

URLs (for example, websites and documentation) pertaining to the Operator or application being managed, each with a name and url.

spec.selector

Selectors by which the Operator can pair resources in a cluster.

spec.icon

A base64-encoded icon unique to the Operator, set in a base64data field with a mediatype.

spec.maturity

The level of maturity the software has achieved at this version. Options include planning, pre-alpha, alpha, beta, stable, mature, inactive, and deprecated.

Further details on what data each field above should hold are found in the CSV spec.

Note

Several YAML fields currently requiring user intervention can potentially be parsed from Operator code.

Additional resources

4.6.2.1. Operator metadata annotations

Operator developers can manually define certain annotations in the metadata of a cluster service version (CSV) to enable features or highlight capabilities in user interfaces (UIs), such as OperatorHub.

The following table lists Operator metadata annotations that can be manually defined using metadata.annotations fields.

Table 4.9. Annotations

FieldDescription

alm-examples

Provide custom resource definition (CRD) templates with a minimum set of configuration. Compatible UIs pre-fill this template for users to further customize.

operatorframework.io/initialization-resource

Specify a single required custom resource that must be created at the time that the Operator is installed. Must include a template that contains a complete YAML definition.

operatorframework.io/suggested-namespace

Set a suggested namespace where the Operator should be deployed.

operators.openshift.io/infrastructure-features

Infrastructure features supported by the Operator. Users can view and filter by these features when discovering Operators through OperatorHub in the web console. Valid, case-sensitive values:

  • disconnected: Operator supports being mirrored into disconnected catalogs, including all dependencies, and does not require Internet access. All related images required for mirroring are listed by the Operator.
  • cnf: Operator provides a Cloud-native Network Functions (CNF) Kubernetes plug-in.
  • cni: Operator provides a Container Network Interface (CNI) Kubernetes plug-in.
  • csi: Operator provides a Container Storage Interface (CSI) Kubernetes plug-in.
  • fips: Operator accepts the FIPS mode of the underlying platform and works on nodes that are booted into FIPS mode.
Important

The use of FIPS Validated / Modules in Process cryptographic libraries is only supported on OpenShift Container Platform deployments on the x86_64 architecture.

  • proxy-aware: Operator supports running on a cluster behind a proxy. Operator accepts the standard proxy environment variables HTTP_PROXY and HTTPS_PROXY, which Operator Lifecycle Manager (OLM) provides to the Operator automatically when the cluster is configured to use a proxy. Required environment variables are passed down to Operands for managed workloads.

operators.openshift.io/valid-subscription

Free-form array for listing any specific subscriptions that are required to use the Operator. For example, '["3Scale Commercial License", "Red Hat Managed Integration"]'.

operators.operatorframework.io/internal-objects

Hides CRDs in the UI that are not meant for user manipulation.

Example use cases

Operator supports disconnected and proxy-aware

operators.openshift.io/infrastructure-features: '["disconnected", "proxy-aware"]'

Operator requires an OpenShift Container Platform license

operators.openshift.io/valid-subscription: '["OpenShift Container Platform"]'

Operator requires a 3scale license

operators.openshift.io/valid-subscription: '["3Scale Commercial License", "Red Hat Managed Integration"]'

Operator supports disconnected and proxy-aware, and requires an OpenShift Container Platform license

operators.openshift.io/infrastructure-features: '["disconnected", "proxy-aware"]'
operators.openshift.io/valid-subscription: '["OpenShift Container Platform"]'

4.6.3. Enabling your Operator for restricted network environments

As an Operator author, your Operator must meet additional requirements to run properly in a restricted network, or disconnected, environment.

Operator requirements for supporting disconnected mode

  • In the cluster service version (CSV) of your Operator:

    • List any related images, or other container images that your Operator might require to perform their functions.
    • Reference all specified images by a digest (SHA) and not by a tag.
  • All dependencies of your Operator must also support running in a disconnected mode.
  • Your Operator must not require any off-cluster resources.

For the CSV requirements, you can make the following changes as the Operator author.

Prerequisites

  • An Operator project with a CSV.

Procedure

  1. Use SHA references to related images in two places in the CSV for your Operator:

    1. Update spec.relatedImages:

      ...
      spec:
        relatedImages: 1
          - name: etcd-operator 2
            image: quay.io/etcd-operator/operator@sha256:d134a9865524c29fcf75bbc4469013bc38d8a15cb5f41acfddb6b9e492f556e4 3
          - name: etcd-image
            image: quay.io/etcd-operator/etcd@sha256:13348c15263bd8838ec1d5fc4550ede9860fcbb0f843e48cbccec07810eebb68
      ...
      1
      Create a relatedImages section and set the list of related images.
      2
      Specify a unique identifier for the image.
      3
      Specify each image by a digest (SHA), not by an image tag.
    2. Update the env section in the deployment when declaring environment variables that inject the image that the Operator should use:

      spec:
        install:
          spec:
            deployments:
            - name: etcd-operator-v3.1.1
              spec:
                replicas: 1
                selector:
                  matchLabels:
                    name: etcd-operator
                strategy:
                  type: Recreate
                template:
                  metadata:
                    labels:
                      name: etcd-operator
                  spec:
                    containers:
                    - args:
                      - /opt/etcd/bin/etcd_operator_run.sh
                      env:
                      - name: WATCH_NAMESPACE
                        valueFrom:
                          fieldRef:
                            fieldPath: metadata.annotations['olm.targetNamespaces']
                      - name: ETCD_OPERATOR_DEFAULT_ETCD_IMAGE 1
                        value: quay.io/etcd-operator/etcd@sha256:13348c15263bd8838ec1d5fc4550ede9860fcbb0f843e48cbccec07810eebb68 2
                      - name: ETCD_LOG_LEVEL
                        value: INFO
                      image: quay.io/etcd-operator/operator@sha256:d134a9865524c29fcf75bbc4469013bc38d8a15cb5f41acfddb6b9e492f556e4 3
                      imagePullPolicy: IfNotPresent
                      livenessProbe:
                        httpGet:
                          path: /healthy
                          port: 8080
                        initialDelaySeconds: 10
                        periodSeconds: 30
                      name: etcd-operator
                      readinessProbe:
                        httpGet:
                          path: /ready
                          port: 8080
                        initialDelaySeconds: 10
                        periodSeconds: 30
                      resources: {}
                    serviceAccountName: etcd-operator
          strategy: deployment
      1
      Inject the images referenced by the Operator by using environment variables.
      2
      Specify each image by a digest (SHA), not by an image tag.
      3
      Also reference the Operator container image by a digest (SHA), not by an image tag.
      Note

      When configuring probes, the timeoutSeconds value must be lower than the periodSeconds value. The timeoutSeconds default value is 1. The periodSeconds default value is 10.

  2. Add the disconnected annotation, which indicates that the Operator works in a disconnected environment:

    metadata:
      annotations:
        operators.openshift.io/infrastructure-features: '["disconnected"]'

    Operators can be filtered in OperatorHub by this infrastructure feature.

4.6.4. Enabling your Operator for multiple architectures and operating systems

Operator Lifecycle Manager (OLM) assumes that all Operators run on Linux hosts. However, as an Operator author, you can specify whether your Operator supports managing workloads on other architectures, if worker nodes are available in the OpenShift Container Platform cluster.

If your Operator supports variants other than AMD64 and Linux, you can add labels to the cluster service version (CSV) that provides the Operator to list the supported variants. Labels indicating supported architectures and operating systems are defined by the following:

labels:
    operatorframework.io/arch.<arch>: supported 1
    operatorframework.io/os.<os>: supported 2
1
Set <arch> to a supported string.
2
Set <os> to a supported string.
Note

Only the labels on the channel head of the default channel are considered for filtering package manifests by label. This means, for example, that providing an additional architecture for an Operator in the non-default channel is possible, but that architecture is not available for filtering in the PackageManifest API.

If a CSV does not include an os label, it is treated as if it has the following Linux support label by default:

labels:
    operatorframework.io/os.linux: supported

If a CSV does not include an arch label, it is treated as if it has the following AMD64 support label by default:

labels:
    operatorframework.io/arch.amd64: supported

If an Operator supports multiple node architectures or operating systems, you can add multiple labels, as well.

Prerequisites

  • An Operator project with a CSV.
  • To support listing multiple architectures and operating systems, your Operator image referenced in the CSV must be a manifest list image.
  • For the Operator to work properly in restricted network, or disconnected, environments, the image referenced must also be specified using a digest (SHA) and not by a tag.

Procedure

  • Add a label in the metadata.labels of your CSV for each supported architecture and operating system that your Operator supports:

    labels:
      operatorframework.io/arch.s390x: supported
      operatorframework.io/os.zos: supported
      operatorframework.io/os.linux: supported 1
      operatorframework.io/arch.amd64: supported 2
    1 2
    After you add a new architecture or operating system, you must also now include the default os.linux and arch.amd64 variants explicitly.

Additional resources

4.6.4.1. Architecture and operating system support for Operators

The following strings are supported in Operator Lifecycle Manager (OLM) on OpenShift Container Platform when labeling or filtering Operators that support multiple architectures and operating systems:

Table 4.10. Architectures supported on OpenShift Container Platform

ArchitectureString

AMD64

amd64

64-bit PowerPC little-endian

ppc64le

IBM Z

s390x

Table 4.11. Operating systems supported on OpenShift Container Platform

Operating systemString

Linux

linux

z/OS

zos

Note

Different versions of OpenShift Container Platform and other Kubernetes-based distributions might support a different set of architectures and operating systems.

4.6.5. Setting a suggested namespace

Some Operators must be deployed in a specific namespace, or with ancillary resources in specific namespaces, to work properly. If resolved from a subscription, Operator Lifecycle Manager (OLM) defaults the namespaced resources of an Operator to the namespace of its subscription.

As an Operator author, you can instead express a desired target namespace as part of your cluster service version (CSV) to maintain control over the final namespaces of the resources installed for their Operators. When adding the Operator to a cluster using OperatorHub, this enables the web console to autopopulate the suggested namespace for the cluster administrator during the installation process.

Procedure

  • In your CSV, set the operatorframework.io/suggested-namespace annotation to your suggested namespace:

    metadata:
      annotations:
        operatorframework.io/suggested-namespace: <namespace> 1
    1
    Set your suggested namespace.

4.6.6. Enabling Operator conditions

Operator Lifecycle Manager (OLM) provides Operators with a channel to communicate complex states that influence OLM behavior while managing the Operator. By default, OLM creates an OperatorCondition custom resource definition (CRD) when it installs an Operator. Based on the conditions set in the OperatorCondition custom resource (CR), the behavior of OLM changes accordingly.

To support Operator conditions, an Operator must be able to read the OperatorCondition CR created by OLM and have the ability to:

  • Get the specific condition.
  • Set the status of a specific condition.

This can be accomplished by using the operator-lib library. An Operator author can provide a controller-runtime client in their Operator for the library to access the OperatorCondition CR owned by the Operator in the cluster.

The library provides a generic Conditions interface, which has the following methods to Get and Set a conditionType in the OperatorCondition CR:

Get
To get the specific condition, the library uses the client.Get function from controller-runtime, which requires an ObjectKey of type types.NamespacedName present in conditionAccessor.
Set
To update the status of the specific condition, the library uses the client.Update function from controller-runtime. An error occurs if the conditionType is not present in the CRD.

The Operator is allowed to modify only the status subresource of the CR. Operators can either delete or update the status.conditions array to include the condition. For more details on the format and description of the fields present in the conditions, see the upstream Condition GoDocs.

Note

Operator SDK v1.3.0 supports operator-lib v0.3.0.

Prerequisites

  • An Operator project generated using the Operator SDK.

Procedure

To enable Operator conditions in your Operator project:

  1. In the go.mod file of your Operator project, add operator-framework/operator-lib as a required library:

    module github.com/example-inc/memcached-operator
    
    go 1.15
    
    require (
      k8s.io/apimachinery v0.19.2
      k8s.io/client-go v0.19.2
      sigs.k8s.io/controller-runtime v0.7.0
      operator-framework/operator-lib v0.3.0
    )
  2. Write your own constructor in your Operator logic that:

    • Accepts a controller-runtime client.
    • Accepts a conditionType.
    • Returns a Condition interface to update or add conditions.

    Because OLM currently supports the Upgradeable condition, you can create an interface that has methods to access the Upgradeable condition. For example:

    import (
      ...
      apiv1 "github.com/operator-framework/api/pkg/operators/v1"
    )
    
    func NewUpgradeable(cl client.Client) (Condition, error) {
      return NewCondition(cl, "apiv1.OperatorUpgradeable")
    }
    
    cond, err := NewUpgradeable(cl);

    In this example, the NewUpgradeable constructor is further used to create a variable cond of type Condition. The cond variable would in turn have Get and Set methods, which can be used for handling the OLM Upgradeable condition.

Additional resources

4.6.7. Defining webhooks

Webhooks allow Operator authors to intercept, modify, and accept or reject resources before they are saved to the object store and handled by the Operator controller. Operator Lifecycle Manager (OLM) can manage the lifecycle of these webhooks when they are shipped alongside your Operator.

The cluster service version (CSV) resource of an Operator can include a webhookdefinitions section to define the following types of webhooks:

  • Admission webhooks (validating and mutating)
  • Conversion webhooks

Procedure

  • Add a webhookdefinitions section to the spec section of the CSV of your Operator and include any webhook definitions using a type of ValidatingAdmissionWebhook, MutatingAdmissionWebhook, or ConversionWebhook. The following example contains all three types of webhooks:

    CSV containing webhooks

      apiVersion: operators.coreos.com/v1alpha1
      kind: ClusterServiceVersion
      metadata:
        name: webhook-operator.v0.0.1
      spec:
        customresourcedefinitions:
          owned:
          - kind: WebhookTest
            name: webhooktests.webhook.operators.coreos.io 1
            version: v1
        install:
          spec:
            deployments:
            - name: webhook-operator-webhook
              ...
              ...
              ...
          strategy: deployment
        installModes:
        - supported: false
          type: OwnNamespace
        - supported: false
          type: SingleNamespace
        - supported: false
          type: MultiNamespace
        - supported: true
          type: AllNamespaces
        webhookdefinitions:
        - type: ValidatingAdmissionWebhook 2
          admissionReviewVersions:
          - v1beta1
          - v1
          containerPort: 443
          targetPort: 4343
          deploymentName: webhook-operator-webhook
          failurePolicy: Fail
          generateName: vwebhooktest.kb.io
          rules:
          - apiGroups:
            - webhook.operators.coreos.io
            apiVersions:
            - v1
            operations:
            - CREATE
            - UPDATE
            resources:
            - webhooktests
          sideEffects: None
          webhookPath: /validate-webhook-operators-coreos-io-v1-webhooktest
        - type: MutatingAdmissionWebhook 3
          admissionReviewVersions:
          - v1beta1
          - v1
          containerPort: 443
          targetPort: 4343
          deploymentName: webhook-operator-webhook
          failurePolicy: Fail
          generateName: mwebhooktest.kb.io
          rules:
          - apiGroups:
            - webhook.operators.coreos.io
            apiVersions:
            - v1
            operations:
            - CREATE
            - UPDATE
            resources:
            - webhooktests
          sideEffects: None
          webhookPath: /mutate-webhook-operators-coreos-io-v1-webhooktest
        - type: ConversionWebhook 4
          admissionReviewVersions:
          - v1beta1
          - v1
          containerPort: 443
          targetPort: 4343
          deploymentName: webhook-operator-webhook
          generateName: cwebhooktest.kb.io
          sideEffects: None
          webhookPath: /convert
          conversionCRDs:
          - webhooktests.webhook.operators.coreos.io 5
    ...

    1
    The CRDs targeted by the conversion webhook must exist here.
    2
    A validating admission webhook.
    3
    A mutating admission webhook.
    4
    A conversion webhook.
    5
    The spec.PreserveUnknownFields property of each CRD must be set to false or nil.

4.6.7.1. Webhook considerations for OLM

When deploying an Operator with webhooks using Operator Lifecycle Manager (OLM), you must define the following:

  • The type field must be set to either ValidatingAdmissionWebhook, MutatingAdmissionWebhook, or ConversionWebhook, or the CSV will be placed in a failed phase.
  • The CSV must contain a deployment whose name is equivalent to the value supplied in the deploymentName field of the webhookdefinition.

When the webhook is created, OLM ensures that the webhook only acts upon namespaces that match the Operator group that the Operator is deployed in.

Certificate authority constraints

OLM is configured to provide each deployment with a single certificate authority (CA). The logic that generates and mounts the CA into the deployment was originally used by the API service lifecycle logic. As a result:

  • The TLS certificate file is mounted to the deployment at /apiserver.local.config/certificates/apiserver.crt.
  • The TLS key file is mounted to the deployment at /apiserver.local.config/certificates/apiserver.key.
Admission webhook rules constraints

To prevent an Operator from configuring the cluster into an unrecoverable state, OLM places the CSV in the failed phase if the rules defined in an admission webhook intercept any of the following requests:

  • Requests that target all groups
  • Requests that target the operators.coreos.com group
  • Requests that target the ValidatingWebhookConfigurations or MutatingWebhookConfigurations resources
Conversion webhook constraints

OLM places the CSV in the failed phase if a conversion webhook definition does not adhere to the following constraints:

  • CSVs featuring a conversion webhook can only support the AllNamespaces install mode.
  • The CRD targeted by the conversion webhook must have its spec.preserveUnknownFields field set to false or nil.
  • The conversion webhook defined in the CSV must target an owned CRD.
  • There can only be one conversion webhook on the entire cluster for a given CRD.

4.6.8. Understanding your custom resource definitions (CRDs)

There are two types of custom resource definitions (CRDs) that your Operator can use: ones that are owned by it and ones that it depends on, which are required.

4.6.8.1. Owned CRDs

The custom resource definitions (CRDs) owned by your Operator are the most important part of your CSV. This establishes the link between your Operator and the required RBAC rules, dependency management, and other Kubernetes concepts.

It is common for your Operator to use multiple CRDs to link together concepts, such as top-level database configuration in one object and a representation of replica sets in another. Each one should be listed out in the CSV file.

Table 4.12. Owned CRD fields

FieldDescriptionRequired/optional

Name

The full name of your CRD.

Required

Version

The version of that object API.

Required

Kind

The machine readable name of your CRD.

Required

DisplayName

A human readable version of your CRD name, for example MongoDB Standalone.

Required

Description

A short description of how this CRD is used by the Operator or a description of the functionality provided by the CRD.

Required

Group

The API group that this CRD belongs to, for example database.example.com.

Optional

Resources

Your CRDs own one or more types of Kubernetes objects. These are listed in the resources section to inform your users of the objects they might need to troubleshoot or how to connect to the application, such as the service or ingress rule that exposes a database.

It is recommended to only list out the objects that are important to a human, not an exhaustive list of everything you orchestrate. For example, do not list config maps that store internal state that are not meant to be modified by a user.

Optional

SpecDescriptors, StatusDescriptors, and ActionDescriptors

These descriptors are a way to hint UIs with certain inputs or outputs of your Operator that are most important to an end user. If your CRD contains the name of a secret or config map that the user must provide, you can specify that here. These items are linked and highlighted in compatible UIs.

There are three types of descriptors:

  • SpecDescriptors: A reference to fields in the spec block of an object.
  • StatusDescriptors: A reference to fields in the status block of an object.
  • ActionDescriptors: A reference to actions that can be performed on an object.

All descriptors accept the following fields:

  • DisplayName: A human readable name for the Spec, Status, or Action.
  • Description: A short description of the Spec, Status, or Action and how it is used by the Operator.
  • Path: A dot-delimited path of the field on the object that this descriptor describes.
  • X-Descriptors: Used to determine which "capabilities" this descriptor has and which UI component to use. See the openshift/console project for a canonical list of React UI X-Descriptors for OpenShift Container Platform.

Also see the openshift/console project for more information on Descriptors in general.

Optional

The following example depicts a MongoDB Standalone CRD that requires some user input in the form of a secret and config map, and orchestrates services, stateful sets, pods and config maps:

Example owned CRD

      - displayName: MongoDB Standalone
        group: mongodb.com
        kind: MongoDbStandalone
        name: mongodbstandalones.mongodb.com
        resources:
          - kind: Service
            name: ''
            version: v1
          - kind: StatefulSet
            name: ''
            version: v1beta2
          - kind: Pod
            name: ''
            version: v1
          - kind: ConfigMap
            name: ''
            version: v1
        specDescriptors:
          - description: Credentials for Ops Manager or Cloud Manager.
            displayName: Credentials
            path: credentials
            x-descriptors:
              - 'urn:alm:descriptor:com.tectonic.ui:selector:core:v1:Secret'
          - description: Project this deployment belongs to.
            displayName: Project
            path: project
            x-descriptors:
              - 'urn:alm:descriptor:com.tectonic.ui:selector:core:v1:ConfigMap'
          - description: MongoDB version to be installed.
            displayName: Version
            path: version
            x-descriptors:
              - 'urn:alm:descriptor:com.tectonic.ui:label'
        statusDescriptors:
          - description: The status of each of the pods for the MongoDB cluster.
            displayName: Pod Status
            path: pods
            x-descriptors:
              - 'urn:alm:descriptor:com.tectonic.ui:podStatuses'
        version: v1
        description: >-
          MongoDB Deployment consisting of only one host. No replication of
          data.

4.6.8.2. Required CRDs

Relying on other required CRDs is completely optional and only exists to reduce the scope of individual Operators and provide a way to compose multiple Operators together to solve an end-to-end use case.

An example of this is an Operator that might set up an application and install an etcd cluster (from an etcd Operator) to use for distributed locking and a Postgres database (from a Postgres Operator) for data storage.

Operator Lifecycle Manager (OLM) checks against the available CRDs and Operators in the cluster to fulfill these requirements. If suitable versions are found, the Operators are started within the desired namespace and a service account created for each Operator to create, watch, and modify the Kubernetes resources required.

Table 4.13. Required CRD fields

FieldDescriptionRequired/optional

Name

The full name of the CRD you require.

Required

Version

The version of that object API.

Required

Kind

The Kubernetes object kind.

Required

DisplayName

A human readable version of the CRD.

Required

Description

A summary of how the component fits in your larger architecture.

Required

Example required CRD

    required:
    - name: etcdclusters.etcd.database.coreos.com
      version: v1beta2
      kind: EtcdCluster
      displayName: etcd Cluster
      description: Represents a cluster of etcd nodes.

4.6.8.3. CRD upgrades

OLM upgrades a custom resource definition (CRD) immediately if it is owned by a singular cluster service version (CSV). If a CRD is owned by multiple CSVs, then the CRD is upgraded when it has satisfied all of the following backward compatible conditions:

  • All existing serving versions in the current CRD are present in the new CRD.
  • All existing instances, or custom resources, that are associated with the serving versions of the CRD are valid when validated against the validation schema of the new CRD.
4.6.8.3.1. Adding a new CRD version

Procedure

To add a new version of a CRD to your Operator:

  1. Add a new entry in the CRD resource under the versions section of your CSV.

    For example, if the current CRD has a version v1alpha1 and you want to add a new version v1beta1 and mark it as the new storage version, add a new entry for v1beta1:

    versions:
      - name: v1alpha1
        served: true
        storage: false
      - name: v1beta1 1
        served: true
        storage: true
    1
    New entry.
  2. Ensure the referencing version of the CRD in the owned section of your CSV is updated if the CSV intends to use the new version:

    customresourcedefinitions:
      owned:
      - name: cluster.example.com
        version: v1beta1 1
        kind: cluster
        displayName: Cluster
    1
    Update the version.
  3. Push the updated CRD and CSV to your bundle.
4.6.8.3.2. Deprecating or removing a CRD version

Operator Lifecycle Manager (OLM) does not allow a serving version of a custom resource definition (CRD) to be removed right away. Instead, a deprecated version of the CRD must be first disabled by setting the served field in the CRD to false. Then, the non-serving version can be removed on the subsequent CRD upgrade.

Procedure

To deprecate and remove a specific version of a CRD:

  1. Mark the deprecated version as non-serving to indicate this version is no longer in use and may be removed in a subsequent upgrade. For example:

    versions:
      - name: v1alpha1
        served: false 1
        storage: true
    1
    Set to false.
  2. Switch the storage version to a serving version if the version to be deprecated is currently the storage version. For example:

    versions:
      - name: v1alpha1
        served: false
        storage: false 1
      - name: v1beta1
        served: true
        storage: true 2
    1 2
    Update the storage fields accordingly.
    Note

    To remove a specific version that is or was the storage version from a CRD, that version must be removed from the storedVersion in the status of the CRD. OLM will attempt to do this for you if it detects a stored version no longer exists in the new CRD.

  3. Upgrade the CRD with the above changes.
  4. In subsequent upgrade cycles, the non-serving version can be removed completely from the CRD. For example:

    versions:
      - name: v1beta1
        served: true
        storage: true
  5. Ensure the referencing CRD version in the owned section of your CSV is updated accordingly if that version is removed from the CRD.

4.6.8.4. CRD templates

Users of your Operator must be made aware of which options are required versus optional. You can provide templates for each of your custom resource definitions (CRDs) with a minimum set of configuration as an annotation named alm-examples. Compatible UIs will pre-fill this template for users to further customize.

The annotation consists of a list of the kind, for example, the CRD name and the corresponding metadata and spec of the Kubernetes object.

The following full example provides templates for EtcdCluster, EtcdBackup and EtcdRestore:

metadata:
  annotations:
    alm-examples: >-
      [{"apiVersion":"etcd.database.coreos.com/v1beta2","kind":"EtcdCluster","metadata":{"name":"example","namespace":"default"},"spec":{"size":3,"version":"3.2.13"}},{"apiVersion":"etcd.database.coreos.com/v1beta2","kind":"EtcdRestore","metadata":{"name":"example-etcd-cluster"},"spec":{"etcdCluster":{"name":"example-etcd-cluster"},"backupStorageType":"S3","s3":{"path":"<full-s3-path>","awsSecret":"<aws-secret>"}}},{"apiVersion":"etcd.database.coreos.com/v1beta2","kind":"EtcdBackup","metadata":{"name":"example-etcd-cluster-backup"},"spec":{"etcdEndpoints":["<etcd-cluster-endpoints>"],"storageType":"S3","s3":{"path":"<full-s3-path>","awsSecret":"<aws-secret>"}}}]

4.6.8.5. Hiding internal objects

It is common practice for Operators to use custom resource definitions (CRDs) internally to accomplish a task. These objects are not meant for users to manipulate and can be confusing to users of the Operator. For example, a database Operator might have a Replication CRD that is created whenever a user creates a Database object with replication: true.

As an Operator author, you can hide any CRDs in the user interface that are not meant for user manipulation by adding the operators.operatorframework.io/internal-objects annotation to the cluster service version (CSV) of your Operator.

Procedure

  1. Before marking one of your CRDs as internal, ensure that any debugging information or configuration that might be required to manage the application is reflected on the status or spec block of your CR, if applicable to your Operator.
  2. Add the operators.operatorframework.io/internal-objects annotation to the CSV of your Operator to specify any internal objects to hide in the user interface:

    Internal object annotation

    apiVersion: operators.coreos.com/v1alpha1
    kind: ClusterServiceVersion
    metadata:
      name: my-operator-v1.2.3
      annotations:
        operators.operatorframework.io/internal-objects: '["my.internal.crd1.io","my.internal.crd2.io"]' 1
    ...

    1
    Set any internal CRDs as an array of strings.

4.6.8.6. Initializing required custom resources

An Operator might require the user to instantiate a custom resource before the Operator can be fully functional. However, it can be challenging for a user to determine what is required or how to define the resource.

As an Operator developer, you can specify a single required custom resource that must be created at the time that the Operator is installed by adding the operatorframework.io/initialization-resource annotation to the cluster service version (CSV). The annotation must include a template that contains a complete YAML definition that is required to initialize the resource during installation.

If this annotation is defined, after installing the Operator from the OpenShift Container Platform web console, the user is prompted to create the resource using the template provided in the CSV.

Procedure

  • Add the operatorframework.io/initialization-resource annotation to the CSV of your Operator to specify a required custom resource. For example, the following annotation requires the creation of a StorageCluster resource and provides a full YAML definition:

    Initialization resource annotation

    apiVersion: operators.coreos.com/v1alpha1
    kind: ClusterServiceVersion
    metadata:
      name: my-operator-v1.2.3
      annotations:
        operatorframework.io/initialization-resource: |-
            {
                "apiVersion": "ocs.openshift.io/v1",
                "kind": "StorageCluster",
                "metadata": {
                    "name": "example-storagecluster"
                },
                "spec": {
                    "manageNodes": false,
                    "monPVCTemplate": {
                        "spec": {
                            "accessModes": [
                                "ReadWriteOnce"
                            ],
                            "resources": {
                                "requests": {
                                    "storage": "10Gi"
                                }
                            },
                            "storageClassName": "gp2"
                        }
                    },
                    "storageDeviceSets": [
                        {
                            "count": 3,
                            "dataPVCTemplate": {
                                "spec": {
                                    "accessModes": [
                                        "ReadWriteOnce"
                                    ],
                                    "resources": {
                                        "requests": {
                                            "storage": "1Ti"
                                        }
                                    },
                                    "storageClassName": "gp2",
                                    "volumeMode": "Block"
                                }
                            },
                            "name": "example-deviceset",
                            "placement": {},
                            "portable": true,
                            "resources": {}
                        }
                    ]
                }
            }
    ...

4.6.9. Understanding your API services

As with CRDs, there are two types of API services that your Operator may use: owned and required.

4.6.9.1. Owned API services

When a CSV owns an API service, it is responsible for describing the deployment of the extension api-server that backs it and the group/version/kind (GVK) it provides.

An API service is uniquely identified by the group/version it provides and can be listed multiple times to denote the different kinds it is expected to provide.

Table 4.14. Owned API service fields

FieldDescriptionRequired/optional

Group

Group that the API service provides, for example database.example.com.

Required

Version

Version of the API service, for example v1alpha1.

Required

Kind

A kind that the API service is expected to provide.

Required

Name

The plural name for the API service provided.

Required

DeploymentName

Name of the deployment defined by your CSV that corresponds to your API service (required for owned API services). During the CSV pending phase, the OLM Operator searches the InstallStrategy of your CSV for a Deployment spec with a matching name, and if not found, does not transition the CSV to the "Install Ready" phase.

Required

DisplayName

A human readable version of your API service name, for example MongoDB Standalone.

Required

Description

A short description of how this API service is used by the Operator or a description of the functionality provided by the API service.

Required

Resources

Your API services own one or more types of Kubernetes objects. These are listed in the resources section to inform your users of the objects they might need to troubleshoot or how to connect to the application, such as the service or ingress rule that exposes a database.

It is recommended to only list out the objects that are important to a human, not an exhaustive list of everything you orchestrate. For example, do not list config maps that store internal state that are not meant to be modified by a user.

Optional

SpecDescriptors, StatusDescriptors, and ActionDescriptors

Essentially the same as for owned CRDs.

Optional

4.6.9.1.1. API service resource creation

Operator Lifecycle Manager (OLM) is responsible for creating or replacing the service and API service resources for each unique owned API service:

  • Service pod selectors are copied from the CSV deployment matching the DeploymentName field of the API service description.
  • A new CA key/certificate pair is generated for each installation and the base64-encoded CA bundle is embedded in the respective API service resource.
4.6.9.1.2. API service serving certificates

OLM handles generating a serving key/certificate pair whenever an owned API service is being installed. The serving certificate has a common name (CN) containing the hostname of the generated Service resource and is signed by the private key of the CA bundle embedded in the corresponding API service resource.

The certificate is stored as a type kubernetes.io/tls secret in the deployment namespace, and a volume named apiservice-cert is automatically appended to the volumes section of the deployment in the CSV matching the DeploymentName field of the API service description.

If one does not already exist, a volume mount with a matching name is also appended to all containers of that deployment. This allows users to define a volume mount with the expected name to accommodate any custom path requirements. The path of the generated volume mount defaults to /apiserver.local.config/certificates and any existing volume mounts with the same path are replaced.

4.6.9.2. Required API services

OLM ensures all required CSVs have an API service that is available and all expected GVKs are discoverable before attempting installation. This allows a CSV to rely on specific kinds provided by API services it does not own.

Table 4.15. Required API service fields

FieldDescriptionRequired/optional

Group

Group that the API service provides, for example database.example.com.

Required

Version

Version of the API service, for example v1alpha1.

Required

Kind

A kind that the API service is expected to provide.

Required

DisplayName

A human readable version of your API service name, for example MongoDB Standalone.

Required

Description

A short description of how this API service is used by the Operator or a description of the functionality provided by the API service.

Required

4.7. Working with bundle images

You can use the Operator SDK to package, deploy, and upgrade Operators in the Bundle Format on Operator Lifecycle Manager (OLM).

4.7.1. Bundling an Operator and deploying with Operator Lifecycle Manager

Operator Lifecycle Manager (OLM) helps you to install, update, and generally manage the lifecycle of Operators and their associated services on a Kubernetes cluster. OLM is installed by default on OpenShift Container Platform and runs as a Kubernetes extension so that you can use the web console and the OpenShift CLI (oc) for all Operator lifecycle management functions without any additional tools.

The Operator Bundle Format is the default packaging method for Operator SDK and OLM. You can get your Operator ready for OLM by using the Operator SDK to build, push, validate, and run a bundle image with OLM.

Prerequisites

  • Operator SDK CLI installed on a development workstation
  • OpenShift CLI (oc) v4.7+ installed
  • Operator Lifecycle Manager (OLM) installed on a Kubernetes-based cluster (v1.16.0 or later if you use apiextensions.k8s.io/v1 CRDs, for example OpenShift Container Platform 4.7)
  • Logged into the cluster with oc using an account with cluster-admin permissions
  • Operator project initialized by using the Operator SDK
  • If your Operator is Go-based, your project must have been updated to use supported images for running on OpenShift Container Platform

Procedure

  1. Run the following make commands in your Operator project directory to build and push your Operator image. Modify the IMG argument in the following steps to reference a repository that you have access to. You can obtain an account for storing containers at repository sites such as Quay.io.

    1. Build the image:

      $ make docker-build IMG=<registry>/<user>/<operator_image_name>:<tag>
    2. Push the image to a repository:

      $ make docker-push IMG=<registry>/<user>/<operator_image_name>:<tag>
  2. Update your Makefile by setting the IMG URL to your Operator image name and tag that you pushed:

    $ # Image URL to use all building/pushing image targets
    IMG ?= <registry>/<user>/<operator_image_name>:<tag>

    This value is used for subsequent operations.

  3. Create your Operator bundle manifest by running the make bundle command, which invokes several commands, including the Operator SDK generate bundle and bundle validate subcommands:

    $ make bundle

    Bundle manifests for an Operator describe how to display, create, and manage an application. The make bundle command creates the following files and directories in your Operator project:

    • A bundle manifests directory named bundle/manifests that contains a ClusterServiceVersion object
    • A bundle metadata directory named bundle/metadata
    • All custom resource definitions (CRDs) in a config/crd directory
    • A Dockerfile bundle.Dockerfile

    These files are then automatically validated by using operator-sdk bundle validate to ensure the on-disk bundle representation is correct.

  4. Build and push your bundle image by running the following commands. OLM consumes Operator bundles using an index image, which reference one or more bundle images.

    1. Build the bundle image. Set BUNDLE_IMAGE with the details for the registry, user namespace, and image tag where you intend to push the image:

      $ make bundle-build BUNDLE_IMG=<registry>/<user>/<bundle_image_name>:<tag>
    2. Push the bundle image:

      $ docker push <registry>/<user>/<bundle_image_name>:<tag>
  5. Check the status of OLM on your cluster by using the following Operator SDK command:

    $ operator-sdk olm status \
        --olm-namespace=openshift-operator-lifecycle-manager
  6. Run the Operator on your cluster by using the OLM integration in Operator SDK:

    $ operator-sdk run bundle \
        [-n <namespace>] \1
        <registry>/<user>/<bundle_image_name>:<tag>
    1
    By default, the command installs the Operator in the currently active project in your ~/.kube/config file. You can add the -n flag to set a different namespace scope for the installation.

    This command performs the following actions:

    • Create an index image with your bundle image injected.
    • Create a catalog source that points to your new index image, which enables OperatorHub to discover your Operator.
    • Deploy your Operator to your cluster by creating an Operator group, subscription, install plan, and all other required objects, including RBAC.

4.7.2. Testing an Operator upgrade on Operator Lifecycle Manager

You can quickly test upgrading your Operator by using Operator Lifecycle Manager (OLM) integration in the Operator SDK, without requiring you to manually manage index images and catalog sources.

The run bundle-upgrade subcommand automates triggering an installed Operator to upgrade to a later version by specifying a bundle image for the later version.

Prerequisites

  • Operator installed with OLM by using the run bundle subcommand
  • A bundle image that represents a later version of the installed Operator

Procedure

  1. If your Operator has not already been installed on OLM with the run bundle subcommand, install the earlier version of your Operator by specifying the bundle image. For example, for a Memcached Operator:

    $ operator-sdk run bundle <registry>/<user>/memcached-operator:v0.0.1

    Example output

    INFO[0009] Successfully created registry pod: quay-io-demo-memcached-operator-v0-0-1
    INFO[0009] Created CatalogSource: memcached-operator-catalog
    INFO[0010] OperatorGroup "operator-sdk-og" created
    INFO[0010] Created Subscription: memcached-operator-v0-0-1-sub
    INFO[0013] Approved InstallPlan install-bqggr for the Subscription: memcached-operator-v0-0-1-sub
    INFO[0013] Waiting for ClusterServiceVersion "my-project/memcached-operator.v0.0.1" to reach 'Succeeded' phase
    INFO[0013]   Waiting for ClusterServiceVersion "my-project/memcached-operator.v0.0.1" to appear
    INFO[0019]   Found ClusterServiceVersion "my-project/memcached-operator.v0.0.1" phase: Succeeded

  2. Upgrade the installed Operator by specifying the bundle image for the later Operator version:

    $ operator-sdk run bundle-upgrade <registry>/<user>/memcached-operator:v0.0.2

    Example output

    INFO[0002] Found existing subscription with name memcached-operator-v0-0-1-sub and namespace my-project
    INFO[0002] Found existing catalog source with name memcached-operator-catalog and namespace my-project
    INFO[0009] Successfully created registry pod: quay-io-demo-memcached-operator-v0-0-2
    INFO[0009] Updated catalog source memcached-operator-catalog with address and annotations
    INFO[0010] Deleted previous registry pod with name "quay-io-demo-memcached-operator-v0-0-1"
    INFO[0041] Approved InstallPlan install-gvcjh for the Subscription: memcached-operator-v0-0-1-sub
    INFO[0042] Waiting for ClusterServiceVersion "my-project/memcached-operator.v0.0.2" to reach 'Succeeded' phase
    INFO[0042]   Found ClusterServiceVersion "my-project/memcached-operator.v0.0.2" phase: InstallReady
    INFO[0043]   Found ClusterServiceVersion "my-project/memcached-operator.v0.0.2" phase: Installing
    INFO[0044]   Found ClusterServiceVersion "my-project/memcached-operator.v0.0.2" phase: Succeeded
    INFO[0044] Successfully upgraded to "memcached-operator.v0.0.2"

  3. Clean up the installed Operators:

    $ operator-sdk cleanup memcached-operator

4.7.3. Additional resources

4.8. Validating Operators using the scorecard tool

As an Operator author, you can use the scorecard tool in the Operator SDK to do the following tasks:

  • Validate that your Operator project is free of syntax errors and packaged correctly
  • Review suggestions about ways you can improve your Operator

4.8.1. About the scorecard tool

While the Operator SDK bundle validate subcommand can validate local bundle directories and remote bundle images for content and structure, you can use the scorecard command to run tests on your Operator based on a configuration file and test images. These tests are implemented within test images that are configured and constructed to be executed by the scorecard.

The scorecard assumes it is run with access to a configured Kubernetes cluster, such as OpenShift Container Platform. The scorecard runs each test within a pod, from which pod logs are aggregated and test results are sent to the console. The scorecard has built-in basic and Operator Lifecycle Manager (OLM) tests and also provides a means to execute custom test definitions.

Scorecard workflow

  1. Create all resources required by any related custom resources (CRs) and the Operator
  2. Create a proxy container in the deployment of the Operator to record calls to the API server and run tests
  3. Examine parameters in the CRs

The scorecard tests make no assumptions as to the state of the Operator being tested. Creating Operators and CRs for an Operators are beyond the scope of the scorecard itself. Scorecard tests can, however, create whatever resources they require if the tests are designed for resource creation.

scorecard command syntax

$ operator-sdk scorecard <bundle_dir_or_image> [flags]

The scorecard requires a positional argument for either the on-disk path to your Operator bundle or the name of a bundle image.

For further information about the flags, run:

$ operator-sdk scorecard -h

4.8.2. Scorecard configuration

The scorecard tool uses a configuration that allows you to configure internal plug-ins, as well as several global configuration options. Tests are driven by a configuration file named config.yaml, which is generated by the make bundle command, located in your bundle/ directory:

./bundle
...
└── tests
    └── scorecard
        └── config.yaml

Example scorecard configuration file

kind: Configuration
apiversion: scorecard.operatorframework.io/v1alpha3
metadata:
  name: config
stages:
- parallel: true
  tests:
  - image: quay.io/operator-framework/scorecard-test:v1.3.0
    entrypoint:
    - scorecard-test
    - basic-check-spec
    labels:
      suite: basic
      test: basic-check-spec-test
  - image: quay.io/operator-framework/scorecard-test:v1.3.0
    entrypoint:
    - scorecard-test
    - olm-bundle-validation
    labels:
      suite: olm
      test: olm-bundle-validation-test

The configuration file defines each test that scorecard can execute. The following fields of the scorecard configuration file define the test as follows:

Configuration fieldDescription

image

Test container image name that implements a test

entrypoint

Command and arguments that are invoked in the test image to execute a test

labels

Scorecard-defined or custom labels that select which tests to run

4.8.3. Built-in scorecard tests

The scorecard ships with pre-defined tests that are arranged into suites: the basic test suite and the Operator Lifecycle Manager (OLM) suite.

Table 4.16. Basic test suite

TestDescriptionShort name

Spec Block Exists

This test checks the custom resource (CR) created in the cluster to make sure that all CRs have a spec block.

basic-check-spec-test

Table 4.17. OLM test suite

TestDescriptionShort name

Bundle Validation

This test validates the bundle manifests found in the bundle that is passed into scorecard. If the bundle contents contain errors, then the test result output includes the validator log as well as error messages from the validation library.

olm-bundle-validation-test

Provided APIs Have Validation

This test verifies that the custom resource definitions (CRDs) for the provided CRs contain a validation section and that there is validation for each spec and status field detected in the CR.

olm-crds-have-validation-test

Owned CRDs Have Resources Listed

This test makes sure that the CRDs for each CR provided via the cr-manifest option have a resources subsection in the owned CRDs section of the ClusterServiceVersion (CSV). If the test detects used resources that are not listed in the resources section, it lists them in the suggestions at the end of the test. Users are required to fill out the resources section after initial code generation for this test to pass.

olm-crds-have-resources-test

Spec Fields With Descriptors

This test verifies that every field in the CRs spec sections has a corresponding descriptor listed in the CSV.

olm-spec-descriptors-test

Status Fields With Descriptors

This test verifies that every field in the CRs status sections have a corresponding descriptor listed in the CSV.

olm-status-descriptors-test

4.8.4. Running the scorecard tool

A default set of Kustomize files are generated by the Operator SDK after running the init command. The default bundle/tests/scorecard/config.yaml file that is generated can be immediately used to run the scorecard tool against your Operator, or you can modify this file to your test specifications.

Prerequisites

  • Operator project generated by using the Operator SDK

Procedure

  1. Generate or regenerate your bundle manifests and metadata for your Operator:

    $ make bundle

    This command automatically adds scorecard annotations to your bundle metadata, which is used by the scorecard command to run tests.

  2. Run the scorecard against the on-disk path to your Operator bundle or the name of a bundle image:

    $ operator-sdk scorecard <bundle_dir_or_image>

4.8.5. Scorecard output

The --output flag for the scorecard command specifies the scorecard results output format: either text or json.

Example 4.2. Example JSON output snippet

{
  "apiVersion": "scorecard.operatorframework.io/v1alpha3",
  "kind": "TestList",
  "items": [
    {
      "kind": "Test",
      "apiVersion": "scorecard.operatorframework.io/v1alpha3",
      "spec": {
        "image": "quay.io/operator-framework/scorecard-test:v1.3.0",
        "entrypoint": [
          "scorecard-test",
          "olm-bundle-validation"
        ],
        "labels": {
          "suite": "olm",
          "test": "olm-bundle-validation-test"
        }
      },
      "status": {
        "results": [
          {
            "name": "olm-bundle-validation",
            "log": "time=\"2020-06-10T19:02:49Z\" level=debug msg=\"Found manifests directory\" name=bundle-test\ntime=\"2020-06-10T19:02:49Z\" level=debug msg=\"Found metadata directory\" name=bundle-test\ntime=\"2020-06-10T19:02:49Z\" level=debug msg=\"Getting mediaType info from manifests directory\" name=bundle-test\ntime=\"2020-06-10T19:02:49Z\" level=info msg=\"Found annotations file\" name=bundle-test\ntime=\"2020-06-10T19:02:49Z\" level=info msg=\"Could not find optional dependencies file\" name=bundle-test\n",
            "state": "pass"
          }
        ]
      }
    }
  ]
}

Example 4.3. Example text output snippet

--------------------------------------------------------------------------------
Image:      quay.io/operator-framework/scorecard-test:v1.3.0
Entrypoint: [scorecard-test olm-bundle-validation]
Labels:
	"suite":"olm"
	"test":"olm-bundle-validation-test"
Results:
	Name: olm-bundle-validation
	State: pass
	Log:
		time="2020-07-15T03:19:02Z" level=debug msg="Found manifests directory" name=bundle-test
		time="2020-07-15T03:19:02Z" level=debug msg="Found metadata directory" name=bundle-test
		time="2020-07-15T03:19:02Z" level=debug msg="Getting mediaType info from manifests directory" name=bundle-test
		time="2020-07-15T03:19:02Z" level=info msg="Found annotations file" name=bundle-test
		time="2020-07-15T03:19:02Z" level=info msg="Could not find optional dependencies file" name=bundle-test
Note

The output format spec matches the Test type layout.

4.8.6. Selecting tests

Scorecard tests are selected by setting the --selector CLI flag to a set of label strings. If a selector flag is not supplied, then all the tests within the scorecard configuration file are run.

Tests are run serially with test results being aggregated by the scorecard and written to standard output, or stdout.

Procedure

  1. To select a single test, for example basic-check-spec-test, specify the test by using the --selector flag:

    $ operator-sdk scorecard <bundle_dir_or_image> \
        -o text \
        --selector=test=basic-check-spec-test
  2. To select a suite of tests, for example olm, specify a label that is used by all of the OLM tests:

    $ operator-sdk scorecard <bundle_dir_or_image> \
        -o text \
        --selector=suite=olm
  3. To select multiple tests, specify the test names by using the selector flag using the following syntax:

    $ operator-sdk scorecard <bundle_dir_or_image> \
        -o text \
        --selector='test in (basic-check-spec-test,olm-bundle-validation-test)'

4.8.7. Enabling parallel testing

As an Operator author, you can define separate stages for your tests using the scorecard configuration file. Stages run sequentially in the order they are defined in the configuration file. A stage contains a list of tests and a configurable parallel setting.

By default, or when a stage explicitly sets parallel to false, tests in a stage are run sequentially in the order they are defined in the configuration file. Running tests one at a time is helpful to guarantee that no two tests interact and conflict with each other.

However, if tests are designed to be fully isolated, they can be parallelized.

Procedure

  • To run a set of isolated tests in parallel, include them in the same stage and set parallel to true:

    apiVersion: scorecard.operatorframework.io/v1alpha3
    kind: Configuration
    metadata:
      name: config
    stages:
    - parallel: true 1
      tests:
      - entrypoint:
        - scorecard-test
        - basic-check-spec
        image: quay.io/operator-framework/scorecard-test:v1.3.0
        labels:
          suite: basic
          test: basic-check-spec-test
      - entrypoint:
        - scorecard-test
        - olm-bundle-validation
        image: quay.io/operator-framework/scorecard-test:v1.3.0
        labels:
          suite: olm
          test: olm-bundle-validation-test
    1
    Enables parallel testing

    All tests in a parallel stage are executed simultaneously, and scorecard waits for all of them to finish before proceding to the next stage. This can make your tests run much faster.

4.8.8. Custom scorecard tests

The scorecard tool can run custom tests that follow these mandated conventions:

  • Tests are implemented within a container image
  • Tests accept an entrypoint which include a command and arguments
  • Tests produce v1alpha3 scorecard output in JSON format with no extraneous logging in the test output
  • Tests can obtain the bundle contents at a shared mount point of /bundle
  • Tests can access the Kubernetes API using an in-cluster client connection

Writing custom tests in other programming languages is possible if the test image follows the above guidelines.

The following example shows of a custom test image written in Go:

Example 4.4. Example custom scorecard test

// Copyright 2020 The Operator-SDK Authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

package main

import (
	"encoding/json"
	"fmt"
	"log"
	"os"

	scapiv1alpha3 "github.com/operator-framework/api/pkg/apis/scorecard/v1alpha3"
	apimanifests "github.com/operator-framework/api/pkg/manifests"
)

// This is the custom scorecard test example binary
// As with the Redhat scorecard test image, the bundle that is under
// test is expected to be mounted so that tests can inspect the
// bundle contents as part of their test implementations.
// The actual test is to be run is named and that name is passed
// as an argument to this binary.  This argument mechanism allows
// this binary to run various tests all from within a single
// test image.

const PodBundleRoot = "/bundle"

func main() {
	entrypoint := os.Args[1:]
	if len(entrypoint) == 0 {
		log.Fatal("Test name argument is required")
	}

	// Read the pod's untar'd bundle from a well-known path.
	cfg, err := apimanifests.GetBundleFromDir(PodBundleRoot)
	if err != nil {
		log.Fatal(err.Error())
	}

	var result scapiv1alpha3.TestStatus

	// Names of the custom tests which would be passed in the
	// `operator-sdk` command.
	switch entrypoint[0] {
	case CustomTest1Name:
		result = CustomTest1(cfg)
	case CustomTest2Name:
		result = CustomTest2(cfg)
	default:
		result = printValidTests()
	}

	// Convert scapiv1alpha3.TestResult to json.
	prettyJSON, err := json.MarshalIndent(result, "", "    ")
	if err != nil {
		log.Fatal("Failed to generate json", err)
	}
	fmt.Printf("%s\n", string(prettyJSON))

}

// printValidTests will print out full list of test names to give a hint to the end user on what the valid tests are.
func printValidTests() scapiv1alpha3.TestStatus {
	result := scapiv1alpha3.TestResult{}
	result.State = scapiv1alpha3.FailState
	result.Errors = make([]string, 0)
	result.Suggestions = make([]string, 0)

	str := fmt.Sprintf("Valid tests for this image include: %s %s",
		CustomTest1Name,
		CustomTest2Name)
	result.Errors = append(result.Errors, str)
	return scapiv1alpha3.TestStatus{
		Results: []scapiv1alpha3.TestResult{result},
	}
}

const (
	CustomTest1Name = "customtest1"
	CustomTest2Name = "customtest2"
)

// Define any operator specific custom tests here.
// CustomTest1 and CustomTest2 are example test functions. Relevant operator specific
// test logic is to be implemented in similarly.

func CustomTest1(bundle *apimanifests.Bundle) scapiv1alpha3.TestStatus {
	r := scapiv1alpha3.TestResult{}
	r.Name = CustomTest1Name
	r.State = scapiv1alpha3.PassState
	r.Errors = make([]string, 0)
	r.Suggestions = make([]string, 0)
	almExamples := bundle.CSV.GetAnnotations()["alm-examples"]
	if almExamples == "" {
		fmt.Println("no alm-examples in the bundle CSV")
	}

	return wrapResult(r)
}

func CustomTest2(bundle *apimanifests.Bundle) scapiv1alpha3.TestStatus {
	r := scapiv1alpha3.TestResult{}
	r.Name = CustomTest2Name
	r.State = scapiv1alpha3.PassState
	r.Errors = make([]string, 0)
	r.Suggestions = make([]string, 0)
	almExamples := bundle.CSV.GetAnnotations()["alm-examples"]
	if almExamples == "" {
		fmt.Println("no alm-examples in the bundle CSV")
	}
	return wrapResult(r)
}

func wrapResult(r scapiv1alpha3.TestResult) scapiv1alpha3.TestStatus {
	return scapiv1alpha3.TestStatus{
		Results: []scapiv1alpha3.TestResult{r},
	}
}

4.9. Configuring built-in monitoring with Prometheus

This guide describes the built-in monitoring support provided by the Operator SDK using the Prometheus Operator and details usage for Operator authors.

4.9.1. Prometheus Operator support

Prometheus is an open-source systems monitoring and alerting toolkit. The Prometheus Operator creates, configures, and manages Prometheus clusters running on Kubernetes-based clusters, such as OpenShift Container Platform.

Helper functions exist in the Operator SDK by default to automatically set up metrics in any generated Go-based Operator for use on clusters where the Prometheus Operator is deployed.

4.9.2. Metrics helper

In Go-based Operators generated using the Operator SDK, the following function exposes general metrics about the running program:

func ExposeMetricsPort(ctx context.Context, port int32) (*v1.Service, error)

These metrics are inherited from the controller-runtime library API. By default, the metrics are served on 0.0.0.0:8383/metrics.

A Service object is created with the metrics port exposed, which can be then accessed by Prometheus. The Service object is garbage collected when the leader pod’s root owner is deleted.

The following example is present in the cmd/manager/main.go file in all Operators generated using the Operator SDK:

import(
    "github.com/operator-framework/operator-sdk/pkg/metrics"
    "machine.openshift.io/controller-runtime/pkg/manager"
)

var (
    // Change the below variables to serve metrics on a different host or port.
    metricsHost       = "0.0.0.0" 1
    metricsPort int32 = 8383 2
)
...
func main() {
    ...
    // Pass metrics address to controller-runtime manager
    mgr, err := manager.New(cfg, manager.Options{
        Namespace:          namespace,
        MetricsBindAddress: fmt.Sprintf("%s:%d", metricsHost, metricsPort),
    })

    ...
    // Create Service object to expose the metrics port.
    _, err = metrics.ExposeMetricsPort(ctx, metricsPort)
    if err != nil {
        // handle error
        log.Info(err.Error())
    }
    ...
}
1
The host that the metrics are exposed on.
2
The port that the metrics are exposed on.

4.9.2.1. Modifying the metrics port

Operator authors can modify the port that metrics are exposed on.

Prerequisites

  • Go-based Operator generated using the Operator SDK
  • Kubernetes-based cluster with the Prometheus Operator deployed

Procedure

  • In the cmd/manager/main.go file of the generated Operator, change the value of metricsPort in the following line:

    var metricsPort int32 = 8383

4.9.3. Service monitors

A ServiceMonitor is a custom resource provided by the Prometheus Operator that discovers the Endpoints in Service objects and configures Prometheus to monitor those pods.

In Go-based Operators generated using the Operator SDK, the GenerateServiceMonitor() helper function can take a Service object and generate a ServiceMonitor object based on it.

Additional resources

4.9.3.1. Creating service monitors

Operator authors can add service target discovery of created monitoring services using the metrics.CreateServiceMonitor() helper function, which accepts the newly created service.

Prerequisites

  • Go-based Operator generated using the Operator SDK
  • Kubernetes-based cluster with the Prometheus Operator deployed

Procedure

  • Add the metrics.CreateServiceMonitor() helper function to your Operator code:

    import(
        "k8s.io/api/core/v1"
        "github.com/operator-framework/operator-sdk/pkg/metrics"
        "machine.openshift.io/controller-runtime/pkg/client/config"
    )
    func main() {
    
        ...
        // Populate below with the Service(s) for which you want to create ServiceMonitors.
        services := []*v1.Service{}
        // Create one ServiceMonitor per application per namespace.
        // Change the below value to name of the Namespace you want the ServiceMonitor to be created in.
        ns := "default"
        // restConfig is used for talking to the Kubernetes apiserver
        restConfig := config.GetConfig()
    
        // Pass the Service(s) to the helper function, which in turn returns the array of ServiceMonitor objects.
        serviceMonitors, err := metrics.CreateServiceMonitors(restConfig, ns, services)
        if err != nil {
            // Handle errors here.
        }
        ...
    }

4.10. Configuring leader election

During the lifecycle of an Operator, it is possible that there may be more than one instance running at any given time, for example when rolling out an upgrade for the Operator. In such a scenario, it is necessary to avoid contention between multiple Operator instances using leader election. This ensures only one leader instance handles the reconciliation while the other instances are inactive but ready to take over when the leader steps down.

There are two different leader election implementations to choose from, each with its own trade-off:

Leader-for-life
The leader pod only gives up leadership, using garbage collection, when it is deleted. This implementation precludes the possibility of two instances mistakenly running as leaders, a state also known as split brain. However, this method can be subject to a delay in electing a new leader. For example, when the leader pod is on an unresponsive or partitioned node, the pod-eviction-timeout dictates long how it takes for the leader pod to be deleted from the node and step down, with a default of 5m. See the Leader-for-life Go documentation for more.
Leader-with-lease
The leader pod periodically renews the leader lease and gives up leadership when it cannot renew the lease. This implementation allows for a faster transition to a new leader when the existing leader is isolated, but there is a possibility of split brain in certain situations. See the Leader-with-lease Go documentation for more.

By default, the Operator SDK enables the Leader-for-life implementation. Consult the related Go documentation for both approaches to consider the trade-offs that make sense for your use case.

4.10.1. Operator leader election examples

The following examples illustrate how to use the two leader election options for an Operator, Leader-for-life and Leader-with-lease.

4.10.1.1. Leader-for-life election

With the Leader-for-life election implementation, a call to leader.Become() blocks the Operator as it retries until it can become the leader by creating the config map named memcached-operator-lock:

import (
  ...
  "github.com/operator-framework/operator-sdk/pkg/leader"
)

func main() {
  ...
  err = leader.Become(context.TODO(), "memcached-operator-lock")
  if err != nil {
    log.Error(err, "Failed to retry for leader lock")
    os.Exit(1)
  }
  ...
}

If the Operator is not running inside a cluster, leader.Become() simply returns without error to skip the leader election since it cannot detect the name of the Operator.

4.10.1.2. Leader-with-lease election

The Leader-with-lease implementation can be enabled using the Manager Options for leader election:

import (
  ...
  "sigs.k8s.io/controller-runtime/pkg/manager"
)

func main() {
  ...
  opts := manager.Options{
    ...
    LeaderElection: true,
    LeaderElectionID: "memcached-operator-lock"
  }
  mgr, err := manager.New(cfg, opts)
  ...
}

When the Operator is not running in a cluster, the Manager returns an error when starting because it cannot detect the namespace of the Operator to create the config map for leader election. You can override this namespace by setting the LeaderElectionNamespace option for the Manager.

4.11. Operator SDK CLI reference

The Operator SDK command-line interface (CLI) is a development kit designed to make writing Operators easier.

Operator SDK CLI syntax

$ operator-sdk <command> [<subcommand>] [<argument>] [<flags>]

Operator authors with cluster administrator access to a Kubernetes-based cluster (such as OpenShift Container Platform) can use the Operator SDK CLI to develop their own Operators based on Go, Ansible, or Helm. Kubebuilder is embedded into the Operator SDK as the scaffolding solution for Go-based Operators, which means existing Kubebuilder projects can be used as is with the Operator SDK and continue to work.

4.11.1. bundle

The operator-sdk bundle command manages Operator bundle metadata.

4.11.1.1. validate

The bundle validate subcommand validates an Operator bundle.

Table 4.18. bundle validate flags

FlagDescription

-h, --help

Help output for the bundle validate subcommand.

--index-builder (string)

Tool to pull and unpack bundle images. Only used when validating a bundle image. Available options are docker, which is the default, podman, or none.

--list-optional

List all optional validators available. When set, no validators are run.

--select-optional (string)

Label selector to select optional validators to run. When run with the --list-optional flag, lists available optional validators.

4.11.2. cleanup

The operator-sdk cleanup command destroys and removes resources that were created for an Operator that was deployed with the run command.

Table 4.19. cleanup flags

FlagDescription

-h, --help

Help output for the run bundle subcommand.

--kubeconfig (string)

Path to the kubeconfig file to use for CLI requests.

n, --namespace (string)

If present, namespace in which to run the CLI request.

--timeout <duration>

Time to wait for the command to complete before failing. The default value is 2m0s.

4.11.3. completion

The operator-sdk completion command generates shell completions to make issuing CLI commands quicker and easier.

Table 4.20. completion subcommands

SubcommandDescription

bash

Generate bash completions.

zsh

Generate zsh completions.

Table 4.21. completion flags

FlagDescription

-h, --help

Usage help output.

For example:

$ operator-sdk completion bash

Example output

# bash completion for operator-sdk                         -*- shell-script -*-
...
# ex: ts=4 sw=4 et filetype=sh

4.11.4. create

The operator-sdk create command is used to create, or scaffold, a Kubernetes API.

4.11.4.1. api

The create api subcommand scaffolds a Kubernetes API. The subcommand must be run in a project that was initialized with the init command.

Table 4.22. create api flags

FlagDescription

-h, --help

Help output for the run bundle subcommand.

4.11.5. generate

The operator-sdk generate command invokes a specific generator to generate code or manifests.

4.11.5.1. bundle

The generate bundle subcommand generates a set of bundle manifests, metadata, and a bundle.Dockerfile file for your Operator project.

Note

Typically, you run the generate kustomize manifests subcommand first to generate the input Kustomize bases that are used by the generate bundle subcommand. However, you can use the make bundle command in an initialized project to automate running these commands in sequence.

Table 4.23. generate bundle flags

FlagDescription

--channels (string)

Comma-separated list of channels to which the bundle belongs. The default value is alpha.

--crds-dir (string)

Root directory for CustomResoureDefinition manifests.

--default-channel (string)

The default channel for the bundle.

--deploy-dir (string)

Root directory for Operator manifests, such as deployments and RBAC. This directory is different from the directory passed to the --input-dir flag.

-h, --help

Help for generate bundle

--input-dir (string)

Directory from which to read an existing bundle. This directory is the parent of your bundle manifests directory and is different from the --deploy-dir directory.

--kustomize-dir (string)

Directory containing Kustomize bases and a kustomization.yaml file for bundle manifests. The default path is config/manifests.

--manifests

Generate bundle manifests.

--metadata

Generate bundle metadata and Dockerfile.

--output-dir (string)

Directory to write the bundle to.

--overwrite

Overwrite the bundle metadata and Dockerfile if they exist. The default value is true.

--package (string)

Package name for the bundle.

-q, --quiet

Run in quiet mode.

--stdout

Write bundle manifest to standard out.

--version (string)

Semantic version of the Operator in the generated bundle. Set only when creating a new bundle or upgrading the Operator.

Additional resources

4.11.5.2. kustomize

The generate kustomize subcommand contains subcommands that generate Kustomize data for the Operator.

4.11.5.2.1. manifests

The generate kustomize manifests subcommand generates or regenerates Kustomize bases and a kustomization.yaml file in the config/manifests directory, which are used to build bundle manifests by other Operator SDK commands. This command interactively asks for UI metadata, an important component of manifest bases, by default unless a base already exists or you set the --interactive=false flag.

Table 4.24. generate kustomize manifests flags

FlagDescription

--apis-dir (string)

Root directory for API type definitions.

-h, --help

Help for generate kustomize manifests.

--input-dir (string)

Directory containing existing Kustomize files.

--interactive

When set to false, if no Kustomize base exists, an interactive command prompt is presented to accept custom metadata.

--output-dir (string)

Directory where to write Kustomize files.

--package (string)

Package name.

-q, --quiet

Run in quiet mode.

4.11.6. init

The operator-sdk init command initializes a Operator project and generates, or scaffolds, a default project directory layout for the given plug-in.

This command writes the following files:

  • Boilerplate license file
  • PROJECT file with the domain and repository
  • Makefile to build the project
  • go.mod file with project dependencies
  • kustomization.yaml file for customizing manifests
  • Patch file for customizing images for manager manifests
  • Patch file for enabling Prometheus metrics
  • main.go file to run

Table 4.25. init flags

FlagDescription

--help, -h

Help output for the init command.

--plugins (string)

Name and optionally version of the plug-in to initialize the project with. Available plug-ins are ansible.sdk.operatorframework.io/v1, go.kubebuilder.io/v2, go.kubebuilder.io/v3, and helm.sdk.operatorframework.io/v1.

--project-version

Project version. Available values are 2 and 3-alpha, which is the default.

4.11.7. run

The operator-sdk run command provides options that can launch the Operator in various environments.

4.11.7.1. bundle

The run bundle subcommand deploys an Operator in the bundle format with Operator Lifecycle Manager (OLM).

Table 4.26. run bundle flags

FlagDescription

--index-image (string)

Index image in which to inject a bundle. The default image is quay.io/operator-framework/upstream-opm-builder:latest.

--install-mode <install_mode_value>

Install mode supported by the cluster service version (CSV) of the Operator, for example AllNamespaces or SingleNamespace.

--timeout <duration>

Install timeout. The default value is 2m0s.

--kubeconfig (string)

Path to the kubeconfig file to use for CLI requests.

n, --namespace (string)

If present, namespace in which to run the CLI request.

-h, --help

Help output for the run bundle subcommand.

Additional resources

4.11.7.2. bundle-upgrade

The run bundle-upgrade subcommand upgrades an Operator that was previously installed in the bundle format with Operator Lifecycle Manager (OLM).

Table 4.27. run bundle-upgrade flags

FlagDescription

--timeout <duration>

Upgrade timeout. The default value is 2m0s.

--kubeconfig (string)

Path to the kubeconfig file to use for CLI requests.

n, --namespace (string)

If present, namespace in which to run the CLI request.

-h, --help

Help output for the run bundle subcommand.

4.11.8. scorecard

The operator-sdk scorecard command runs the scorecard tool to validate an Operator bundle and provide suggestions for improvements. The command takes one argument, either a bundle image or directory containing manifests and metadata. If the argument holds an image tag, the image must be present remotely.

Table 4.28. scorecard flags

FlagDescription

-c, --config (string)

Path to scorecard configuration file. The default path is bundle/tests/scorecard/config.yaml.

-h, --help

Help output for the scorecard command.

--kubeconfig (string)

Path to kubeconfig file.

-L, --list

List which tests are available to run.

-n, --namespace (string)

Namespace in which to run the test images.

-o, --output (string)

Output format for results. Available values are text, which is the default, and json.

-l, --selector (string)

Label selector to determine which tests are run.

-s, --service-account (string)

Service account to use for tests. The default value is default.

-x, --skip-cleanup

Disable resource cleanup after tests are run.

-w, --wait-time <duration>

Seconds to wait for tests to complete, for example 35s. The default value is 30s.

Additional resources