How Do I Build a Custom Kubernetes Operator?

Building a custom Kubernetes operator is about making a controller. This controller helps to manage complex applications on Kubernetes. Operators use the Kubernetes API. They help to make Kubernetes better by managing applications just like Kubernetes manages its own resources.

In this article, we will talk about how to build a custom Kubernetes operator from the beginning. We will look at what you need to start, how to set up your work space, the tools and frameworks you need, how to define custom resources, how to write controller logic, how to deploy your operator to a Kubernetes cluster, how to test your operator, and some real-life examples. Here is what we will cover:

  • How Can I Build a Custom Kubernetes Operator?
  • What Are the Prerequisites for Building a Custom Kubernetes Operator?
  • How Do I Set Up My Development Environment?
  • What Tools and Frameworks Do I Need for Building a Custom Operator?
  • How Do I Define the Custom Resource for My Operator?
  • How Do I Implement the Controller Logic?
  • What Are Some Real Life Use Cases for Custom Kubernetes Operators?
  • How Do I Deploy My Custom Operator to a Kubernetes Cluster?
  • How Can I Test My Custom Kubernetes Operator?
  • Frequently Asked Questions

For more information about Kubernetes and what it can do, we can check these articles: What Are Kubernetes Operators and How Do They Automate Tasks? and What Are Custom Resource Definitions (CRDs) in Kubernetes?.

What Are the Prerequisites for Building a Custom Kubernetes Operator?

To build a custom Kubernetes operator, we need to meet some important prerequisites. This will help us have a smooth development process. Here are the main requirements:

  1. Understanding of Kubernetes Concepts:
    • We should know how Kubernetes works. This includes Pods, Services, Deployments, and Custom Resource Definitions (CRDs).
    • We also need to be familiar with kubelet, etcd, and the Kubernetes API.
  2. Development Environment:
    • We need a local Kubernetes cluster, like Minikube, Kind, or a managed Kubernetes service to test our operator.
    • We must have access to the Kubernetes command-line tool, called kubectl.
  3. Programming Skills:
    • We should know Go because most Kubernetes operators are written in Go. It is important to be familiar with the Go programming language and its tools.
    • We also need a basic understanding of YAML because we use it to define Kubernetes resources.
  4. Operator Framework:
    • We should know about an operator framework, like the Operator SDK. This makes it easier to create and manage Kubernetes operators.
    • It helps if we are familiar with tools like Helm. They can help us package and deploy our custom operator.
  5. Version Control System:
    • We should be good with Git. We use Git for managing source code and working with others.
  6. Optional but Recommended:
    • It helps to understand CI/CD practices. These can automate testing and deployment of our operator.
    • Knowing about testing frameworks for Go, like Ginkgo and Gomega, is good. We can use them to write unit and integration tests for our operator.

By making sure we have these prerequisites, we will be ready to start building a custom Kubernetes operator. This operator can meet our specific needs. For more information on Kubernetes concepts and operator development, we can check this article on building a Kubernetes operator.

How Do We Set Up Our Development Environment?

To build a custom Kubernetes operator, we need to set up our development environment right. Here are the steps we can follow for a smooth process:

  1. Install Go: We usually write Kubernetes operators in Go. First, we need to install the latest version of Go from the official Go website.

    wget https://golang.org/dl/go1.19.4.linux-amd64.tar.gz
sudo tar -C /usr/local -xzf go1.19.4.linux-amd64.tar.gz

    We have to add Go to our PATH:

    echo 'export PATH=$PATH:/usr/local/go/bin' >> ~/.bashrc
source ~/.bashrc

  2. Set Up a Go Workspace: Next, we create a folder for our Go workspaces.

    mkdir -p $HOME/go/src
export GOPATH=$HOME/go
echo 'export GOPATH=$HOME/go' >> ~/.bashrc
source ~/.bashrc

  3. Install Kubernetes CLI (kubectl): Now we need the Kubernetes CLI to work with our Kubernetes cluster.

    curl -LO "https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/linux/amd64/kubectl"
chmod +x ./kubectl
sudo mv ./kubectl /usr/local/bin/kubectl

  4. Set Up Minikube: For local work, we can use Minikube to create a local Kubernetes cluster.

    curl -Lo minikube https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
chmod +x minikube
sudo mv minikube /usr/local/bin/

    We can start Minikube with:

    minikube start

  5. Install Operator SDK: The Operator SDK helps us start a new operator project.

    curl -sSL https://github.com/operator-framework/operator-sdk/releases/download/v1.24.0/operator-sdk-v1.24.0-x86_64-linux-gnu -o /usr/local/bin/operator-sdk
chmod +x /usr/local/bin/operator-sdk

  6. Install Other Dependencies: Depending on what we need for our operator, we might need extra tools. If we want to use Helm, we can install it like this:

    curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash

  7. Verify Installation: Finally, we should check if all tools are installed correctly.

    go version
kubectl version --client
minikube status
operator-sdk version
helm version

By following these steps, we will have our development environment ready to build a custom Kubernetes operator. For more details on building Kubernetes operators, we can check the article How Do I Build a Kubernetes Operator.

What Tools and Frameworks Do We Need for Building a Custom Operator?

To build a custom Kubernetes operator, we need different tools and frameworks for development, testing, and deployment. Here is a list of tools and frameworks we recommend:

  1. Operator SDK:

    • This framework helps us build Kubernetes operators. It gives us tools to create operators in Go, Ansible, or Helm.

    • To install it, we can use this command:

      curl -sSL https://github.com/operator-framework/operator-sdk/releases/latest/download/operator-sdk-v$(curl -sSL https://github.com/operator-framework/operator-sdk/releases/latest | grep -oP '(?<=tag/v)[^"]*')-x86_64-linux-gnu -o /usr/local/bin/operator-sdk
chmod +x /usr/local/bin/operator-sdk
  2. Kubebuilder:

    • This is a framework for making Kubernetes APIs using custom resource definitions (CRDs). It makes developing operators easier.

    • To install it, we run:

      go install sigs.k8s.io/controller-tools/cmd/controller-gen@latest
  3. Kubernetes CLI (kubectl):
    • This command-line tool helps us interact with our Kubernetes cluster. It is very important for deploying and managing applications.
    • We can find installation instructions here.
  4. Go Programming Language:

    • Most operators use Go. We need to have Go installed because it is required for the Operator SDK and Kubebuilder.

    • We can install it by using:

      wget https://golang.org/dl/go1.18.linux-amd64.tar.gz
sudo tar -C /usr/local -xzf go1.18.linux-amd64.tar.gz
  5. Docker:
    • We use Docker to build and manage container images for our operator.
    • Installation instructions can be found here.
  6. Kustomize:

    • This tool helps us customize Kubernetes YAML files. It is useful for managing different deployment setups.

    • We can install it using:

      go install sigs.k8s.io/kustomize/kustomize/v4@latest
  7. Helm:
    • Helm is a package manager for Kubernetes. It makes it easier to deploy applications and works well with operators.
    • We can find installation instructions here.
  8. Kind (Kubernetes in Docker):

    • This tool helps us run local Kubernetes clusters using Docker containers. It is good for testing operators locally.

    • We can install it by running:

      go install sigs.k8s.io/kind@latest
  9. Prometheus and Grafana:
    • These are monitoring tools that we can connect with our operator. They help us see how well our operator is performing.
    • We can find installation instructions for Prometheus here.
  10. Git:
    • Git is a version control system. We use it to manage our operator’s source code.
    • Installation instructions can be found here.

These tools and frameworks help us build, test, and deploy custom Kubernetes operators. They make it easier for us to manage our applications well.

How Do We Define the Custom Resource for Our Operator?

To build a custom Kubernetes operator, we need to define the Custom Resource (CR). This step is very important. A Custom Resource Definition (CRD) helps us add new resource types in Kubernetes.

Defining a Custom Resource

  1. Create a CRD YAML file: This file tells about the schema, validation, and behavior of our custom resource.
apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: myresources.example.com
spec:
  group: example.com
  versions:
    - name: v1
      served: true
      storage: true
      schema:
        openAPIV3Schema:
          type: object
          properties:
            spec:
              type: object
              properties:
                replicas:
                  type: integer
                image:
                  type: string
  scope: Namespaced
  names:
    plural: myresources
    singular: myresource
    kind: MyResource
    shortNames:
      - mr
  1. Apply the CRD: We use kubectl to create the CRD in our Kubernetes cluster.
kubectl apply -f myresource-crd.yaml

Creating a Custom Resource Instance

After we define the CRD, we can create instances of our custom resource.

apiVersion: example.com/v1
kind: MyResource
metadata:
  name: my-resource-instance
spec:
  replicas: 3
  image: my-image:latest
  1. Apply the Custom Resource:
kubectl apply -f myresource-instance.yaml

Validation of Custom Resource

We should add validation rules in our CRD. This way, we can make sure the custom resource has the right structure. For example, we can say which fields are needed and what types they should be:

  validation:
    openAPIV3Schema:
      type: object
      properties:
        spec:
          type: object
          required:
            - replicas
            - image
          properties:
            replicas:
              type: integer
              minimum: 1
            image:
              type: string

Summary of Key Points

  • We define the CRD using YAML.
  • We use kubectl to put the CRD in the cluster.
  • We create instances of our custom resource with specific details.
  • We add validation rules in our CRD to keep data correct.

For more details on Custom Resource Definitions, check what are Custom Resource Definitions (CRDs) in Kubernetes.

How Do We Implement the Controller Logic?

Implementing the controller logic for a custom Kubernetes operator means we define how our operator responds to changes in the custom resources (CRs) it manages. The controller keeps an eye on events about these resources. It takes the right actions based on their state.

1. Setup the Controller

We usually use a framework like the Operator SDK to create our controller. Here is a simple example in Go with the client-go library.

package main

import (
    "context"
    "fmt"
    "log"
    "sigs.k8s.io/controller-runtime/pkg/controller"
    "sigs.k8s.io/controller-runtime/pkg/controller/controllerutil"
    "sigs.k8s.io/controller-runtime/pkg/handler"
    "sigs.k8s.io/controller-runtime/pkg/manager"
    "sigs.k8s.io/controller-runtime/pkg/source"
)

func main() {
    mgr, err := manager.New(cfg, manager.Options{})
    if err != nil {
        log.Fatal(err)
    }
    
    c, err := controller.New("my-operator-controller", mgr, controller.Options{
        Reconciler: &ReconcileMyResource{client: mgr.GetClient()},
    })
    
    if err != nil {
        log.Fatal(err)
    }
    
    err = c.Watch(&source.Kind{Type: &MyResource{}}, &handler.EnqueueRequestForObject{})
    if err != nil {
        log.Fatal(err)
    }

    if err := mgr.Start(context.Background()); err != nil {
        log.Fatal(err)
    }
}

2. Define the Reconcile Logic

In the reconcile logic, we decide how to manage the state of our custom resource. This usually means checking the current state, comparing it with the desired state, and making changes as needed.

type ReconcileMyResource struct {
    client.Client
}

func (r *ReconcileMyResource) Reconcile(req ctrl.Request) (ctrl.Result, error) {
    ctx := context.Background()
    myResource := &MyResource{}

    err := r.Get(ctx, req.NamespacedName, myResource)
    if err != nil {
        return ctrl.Result{}, client.IgnoreNotFound(err)
    }

    // We need to manage the resource
    if myResource.Spec.DesiredState != myResource.Status.CurrentState {
        // Update status or take action
        myResource.Status.CurrentState = myResource.Spec.DesiredState
        if err := r.Status().Update(ctx, myResource); err != nil {
            return ctrl.Result{}, err
        }
    }

    return ctrl.Result{}, nil
}

3. Handle Events

Our controller should listen for events about our custom resources and act on them. We do this by using the Reconcile method, as we showed above. This method gets called whenever a change happens to the resources our operator manages.

4. Use Kubernetes Client Libraries

We can use client libraries like client-go or controller-runtime to work with the Kubernetes API. We can perform actions like creating, updating, or deleting resources.

5. Implement Custom Logic

In the Reconcile function, we can add any custom logic we need for our operator. This may include:

  • Creating dependent resources.
  • Updating statuses.
  • Handling errors and retries.

6. Test Your Controller

Before we deploy our operator, we need to test our controller logic well. We should consider different scenarios like resource creation, updates, and deletions.

For more details on how to build your custom Kubernetes operator and the main ideas, you can check this article on how to build a Kubernetes operator.

What Are Some Real Life Use Cases for Custom Kubernetes Operators?

Custom Kubernetes Operators help us automate the management of complex applications on Kubernetes. Let us look at some real-life examples of using custom operators.

  1. Database Management: Operators help us with the deployment, scaling, and management of databases like PostgreSQL, MySQL, or MongoDB. For example, an operator can handle database backups and restore processes. It can also scale the database based on workload.

    apiVersion: databases.example.com/v1
kind: Postgres
metadata:
  name: my-postgres
spec:
  size: 3
  version: "13"
  storage:
    size: 10Gi

  2. Machine Learning Workflows: Operators manage the lifecycle of machine learning models. They help with deployment, scaling, and versioning. For instance, an operator can automate the rollout of new model versions and manage canary testing.

  3. Custom Application Management: Some businesses have specific applications. They can create operators to manage the deployment lifecycle of these applications. For example, an operator can make sure a microservices application runs the correct version of each service.

  4. CI/CD Integration: Operators support continuous integration and continuous deployment workflows. They can automatically deploy new versions of applications when changes are pushed to a repository.

  5. Monitoring and Alerting: Custom operators work with monitoring tools like Prometheus. They can automatically set up alerts based on application metrics. They also scale resources when needed.

  6. Infrastructure Management: Operators can manage cloud resources. They can provision and deprovision resources based on application demand. This helps us optimize costs and performance.

  7. Stateful Applications: Operators make it easier to deploy and manage stateful applications. They ensure that data stays consistent and persistent when scaling and updating.

  8. Multi-Cluster Management: Custom operators can monitor and manage applications across many Kubernetes clusters. They give us a clear view and control of distributed systems.

  9. Event-Driven Applications: Operators can handle events from different sources. They can trigger actions based on specific conditions or changes in the environment. This includes auto-scaling or reallocating resources.

  10. Backup and Disaster Recovery: Operators can automate backups for applications and databases. They manage schedules and ensure data integrity through consistent snapshots.

These use cases show how useful custom Kubernetes operators are. They help us automate complex workflows and manage application lifecycles well. If you want to read more about Kubernetes operators, check this article on what are Kubernetes operators and how do they automate tasks.

How Do We Deploy Our Custom Operator to a Kubernetes Cluster?

To deploy our custom Kubernetes operator to a cluster, we can follow these steps:

  1. Build Our Operator Image: We need to use Docker to make a container image of our operator. We have to make sure our Dockerfile is set up right.

    FROM golang:1.16 AS builder
WORKDIR /app
COPY . .
RUN go build -o my-operator .

FROM gcr.io/distroless/base
COPY --from=builder /app/my-operator /my-operator
ENTRYPOINT ["/my-operator"]

    Now we build the image:

    docker build -t my-operator:latest .

  2. Push Our Image to a Registry: After we build the image, we push it to a container registry like Docker Hub, GCR, or ECR.

    docker tag my-operator:latest <your-registry>/my-operator:latest
docker push <your-registry>/my-operator:latest

  3. Create a Custom Resource Definition (CRD): We need to define our CRD in a YAML file. This tells what custom resource our operator will manage.

    apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: myresources.mygroup.example.com
spec:
  group: mygroup.example.com
  names:
    kind: MyResource
    listKind: MyResourceList
    plural: myresources
    singular: myresource
  scope: Namespaced
  versions:
    - name: v1
      served: true
      storage: true
      schema:
        openAPIV3Schema:
          type: object
          properties:
            spec:
              type: object
              properties:
                foo:
                  type: string

    We apply the CRD to the cluster:

    kubectl apply -f myresource-crd.yaml

  4. Deploy the Operator: We create a deployment for our operator using a Kubernetes deployment YAML file.

    apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-operator
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: my-operator
  template:
    metadata:
      labels:
        app: my-operator
    spec:
      containers:
        - name: my-operator
          image: <your-registry>/my-operator:latest
          imagePullPolicy: Always

    Then we deploy the operator:

    kubectl apply -f my-operator-deployment.yaml

  5. Verify Deployment: We check the status of our operator deployment to make sure it runs good.

    kubectl get pods -l app=my-operator
kubectl logs <pod-name>

  6. Create Custom Resources: After we deploy our operator, we can create instances of the custom resource we defined in our CRD.

    apiVersion: mygroup.example.com/v1
kind: MyResource
metadata:
  name: example-myresource
  namespace: default
spec:
  foo: "bar"

    We apply our custom resource:

    kubectl apply -f myresource-instance.yaml

By following these steps, our custom Kubernetes operator will be deployed. It will be ready to manage the custom resources we defined in our cluster. For more information on building Kubernetes operators, we can check out this guide.

How Can We Test Our Custom Kubernetes Operator?

Testing our custom Kubernetes Operator is very important. It helps us make sure it works well and is reliable. Here are some simple ways and tools that we can use to test our Operator.

Unit Testing

  1. Framework: We can use Go’s testing package to write unit tests for our controller logic.
  2. Mocking: We should use libraries like gomock or testify to create mock Kubernetes clients and resources.
package controller

import (
    "testing"
    "github.com/stretchr/testify/assert"
)

func TestReconcile(t *testing.T) {
    // Setup test environment and objects
    req := reconcile.Request{NamespacedName: types.NamespacedName{Name: "test", Namespace: "default"}}
    result, err := Reconcile(req)

    assert.NoError(t, err)
    assert.Equal(t, reconcile.Result{}, result)
}

Integration Testing

  1. Framework: We can use envtest from the controller-runtime to create a test environment.
  2. Setup: We need to start a local Kubernetes cluster. We can use Kind or Minikube to deploy our Operator and test how it works.
# Start Kind cluster
kind create cluster

# Deploy our operator
kubectl apply -f config/manager/manager.yaml

# Test our operator with real Kubernetes objects
kubectl apply -f config/samples/example_v1alpha1_myresource.yaml

End-to-End Testing

  1. Tools: We should use tools like Helm and Kube-bench to check our Operator in a full application scenario.
  2. Scripts: We can automate our deployment and validation scripts. This will help us see if the Operator works as we expect.
# Install Helm chart for our operator
helm install my-operator ./my-operator-chart

# Validate deployment
kubectl get pods --namespace default

Monitoring and Logging

  1. Tools: We can add monitoring tools like Prometheus and logging tools like Fluentd. This helps us watch how our Operator behaves in production.
  2. Metrics: We can show metrics with Prometheus to check the health and performance of our Operator.
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
    scrape_configs:
      - job_name: 'my-operator'
        static_configs:
          - targets: ['my-operator:8080']

Continuous Integration

  1. Tools: We can set up CI/CD pipelines with tools like GitHub Actions or Jenkins.
  2. Automation: We should automate our testing on pull requests. This way, we can make sure new changes do not break what already works.
name: CI Pipeline
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v2
      - name: Run tests
        run: go test ./...

Testing Custom Resources

  1. CRDs: We must create test cases for our Custom Resource Definitions (CRDs). This helps us check that our Operator handles them correctly.
  2. Validation: We should check that our CRDs enforce the right rules and behavior.
apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: myresources.example.com
spec:
  group: example.com
  names:
    kind: MyResource
    listKind: MyResourceList
    plural: myresources
    singular: myresource
  scope: Namespaced
  versions:
    - name: v1alpha1
      served: true
      storage: true
      schema:
        openAPIV3Schema:
          type: object
          properties:
            spec:
              type: object

By following these testing ways, we can make sure our custom Kubernetes Operator is strong and reliable. For more information on building and managing Kubernetes Operators, we can check out articles on Kubernetes Operators and Custom Resource Definitions.

Frequently Asked Questions

1. What is a Kubernetes Operator and why would we create one?

A Kubernetes Operator is a way to package, deploy, and manage a Kubernetes application. It helps us manage complex applications and how they work over time. When we build a custom Kubernetes Operator, we can automate tasks that are special for our application. This helps us manage it better and scale it easily. If we want to improve our Kubernetes setup, we should learn how to build a Kubernetes Operator.

2. What are Custom Resource Definitions (CRDs) in Kubernetes?

Custom Resource Definitions (CRDs) let us add new resource types to Kubernetes API. When we create a custom Kubernetes Operator, defining CRDs is very important. They show the desired state of our application’s parts. Learning about CRDs is a key step in understanding how to build a custom Kubernetes Operator well.

3. How can we test our custom Kubernetes Operator?

We can test our custom Kubernetes Operator by running unit tests and integration tests. We can use tools like Kubebuilder or Operator SDK that help us write tests. Testing makes sure our Operator works as we want when it manages the lifecycle of our custom resources. For more details, we can check how to test our custom Kubernetes Operator.

4. What tools are important for building a Kubernetes Operator?

When we build a Kubernetes Operator, we need tools like Kubebuilder, Operator SDK, and kustomize. These tools make the development process easier. They give us a structure, help generate code, and assist with deployment. To find out more about the tools we need, we can read this guide on tools for Kubernetes development.

5. How do we deploy our custom Operator to a Kubernetes cluster?

To deploy our custom Kubernetes Operator to a cluster, we need to create a deployment YAML file. Then we apply it using kubectl and make sure our CRDs are registered. This lets Kubernetes manage our Operator with other resources. For a step-by-step guide, we can look at this resource on deploying a Kubernetes Operator.

By answering these common questions, we can understand better how to build a custom Kubernetes Operator and use its features for our applications.