Kubernetes offers robust tools for managing application configurations and safeguarding sensitive data: ConfigMaps and Secrets. This article provides hands-on examples to help you grasp these concepts.

What are ConfigMaps?

ConfigMaps in Kubernetes are designed to manage non-sensitive configuration data. They are generally created using YAML files that specify the configuration parameters.

Example: Environment Variables

Consider an application that requires a database URL and an API key. You can use a ConfigMap to set these as environment variables. Here’s a sample YAML file:

apiVersion: v1
kind: ConfigMap
metadata:
  name: app-config
data:
  DB_URL: jdbc:mysql://localhost:3306/db
  API_KEY: key123

Mounting ConfigMaps as Volumes

ConfigMaps can also be mounted as volumes, making them accessible to pods as files. This is useful for configuration files or scripts.

Example: Mount as Volume

To mount a ConfigMap as a volume, you can modify the pod specification like this:

apiVersion: v1
kind: Pod
metadata:
  name: my-pod
spec:
  containers:
  - name: my-container
    image: my-image
    volumeMounts:
    - name: config-volume
      mountPath: /etc/config
  volumes:
  - name: config-volume
    configMap:
      name: app-config

What are Secrets?

Secrets are used for storing sensitive information like passwords and API tokens securely. It’s important to note that the data in Secrets should be encoded in base64 for an added layer of security.

Example: Secure API Token

To store an API token securely, you can create a Secret like this:

apiVersion: v1
kind: Secret
metadata:
  name: api-secret
data:
  API_TOKEN: base64_encoded_token

To generate a base64-encoded token, you can use the following command:

echo -n 'your_actual_token' | base64

In Summary

ConfigMaps and Secrets are indispensable tools in Kubernetes for managing configuration data and sensitive information. Understanding how to use them effectively is crucial for any Kubernetes deployment.

Kubernetes has become a cornerstone in the container orchestration world, and being adept at maneuvering through the Kubernetes environment is crucial for DevOps professionals.

Among the various tools at our disposal, kubectl stands out as an essential command-line tool for interacting with clusters.

kubectl exec

The kubectl exec command is utilized to run commands in a specific container within a Pod.

When you execute kubectl exec, it creates a new terminal session inside the container which allows for both interactive and non-interactive command execution.

Example: Suppose you have a running Pod hosting a web service and you wish to check the contents of a specific directory. You could use kubectl exec to run the ls command in the container, listing the files in that directory.

kubectl attach

On the other hand, kubectl attach allows you to attach to a running process within a container.

Unlike kubectl exec, kubectl attach connects to an existing terminal session, allowing you to observe the standard output and error of the running process.

Example: If you have a Pod running an application that writes logs to standard output, you could use kubectl attach to view these logs in real time.

Summarizing:

While kubectl exec spawns a new terminal session, kubectl attach connects to an existing session.

kubectl exec is more versatile for executing arbitrary commands, whereas kubectl attach is useful for interacting with running processes and observing their real-time behavior.

The key takeaway is understanding when to use kubectl exec versus kubectl attach based on the task at hand.

Kubernetes has made a significant impact as a container orchestration tool, but it’s crucial to understand that its utility doesn’t end there. One of its most compelling features is the ability to extend its API with Custom Resource Definitions (CRDs), especially since Kubernetes version 1.7. This article delves into what CRDs are, why they are essential, and how they work in conjunction with controllers to simplify complex tasks within a Kubernetes cluster.

.- What are Custom Resource Definitions (CRDs)?

CRDs act as extensions of the Kubernetes API, allowing users to create new types of resources without adding another API server. Simply put, CRDs serve as vehicles to extend the Kubernetes ecosystem. They are vital for enriching Kubernetes functionalities beyond its basic scope.

.- Basic Kubernetes Functionality Without CRDs

In an out-of-the-box Kubernetes setup, users can define deployments that spawn replica sets, which in turn create pods for running containers. Users can also set up services and ingress controllers for network access to these containers. However, this native functionality has limitations, such as lacking an in-built storage solution.

.- The Need for Extending Kubernetes

The real power of Kubernetes lies in its extensibility. Almost every third-party tool or service designed for Kubernetes operates through CRDs, which extend your cluster’s functionalities. For instance, if you are implementing a service mesh like Istio, it will extend your cluster with several CRDs like VirtualServices and Gateways.

.- The Role of Controllers in CRDs

CRDs by themselves are not functional. When a custom resource is created, the Kubernetes API only signals an event stating that a resource is created. Controllers respond to these events. They watch for specific changes to custom resources and take action accordingly, thereby breathing life into CRDs.

.- Benefits of Using CRDs and Controllers

The duo of CRDs and controllers can significantly simplify many tasks. They move the heavy lifting from the client-side to the server-side, reducing complexity and eliminating the need for client-side templating solutions. As a result, end-users can define their applications in a more Kubernetes-native way, without diving into the lower-level details.

Example: Creating a Simple CRD:

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: myresources.mycompany.com
spec:
  group: mycompany.com
  versions:
    - name: v1
      served: true
      storage: true
      schema:
        openAPIV3Schema:
          type: object
          properties:
            spec:
              type: object
              properties:
                key:
                  type: string

.- Understanding Kubernetes Operators

After we’ve laid down the groundwork by discussing CRDs and controllers, it’s essential to touch upon Kubernetes Operators, which bring the two together in a well-organized manner. In essence, an Operator is a method of packaging, deploying, and managing a Kubernetes application.

An Operator extends Kubernetes to automate the management of the entire lifecycle of a particular application, API, or resource. It builds upon the basic Kubernetes resource and controller concepts but includes domain or application-specific knowledge to automate common tasks. For example, an Operator could manage a database cluster, handling tasks such as backups, updates, and scaling.

.- Using an Operator to Manage a Database Cluster

Imagine you have a PostgreSQL database running within your Kubernetes cluster. You could deploy a PostgreSQL Operator that automatically handles routine tasks like backups, updates, or even scaling. This Operator would use CRDs to understand custom resources that define the desired state for these tasks and use a controller to ensure the current state matches the desired state.

Here is a simplified YAML example defining a PostgresCluster custom resource, which could be managed by a PostgreSQL Operator:

Example: Using an Operator to Manage a Database Cluster

apiVersion: postgresql.org/v1
kind: PostgresCluster
metadata:
  name: my-postgres-cluster
spec:
  replicas: 3
  version: "12"
  backup:
    enabled: true
    schedule: "0 0 * * *"

.- Recommendations and Best Practices

While CRDs and controllers offer immense utility, it’s critical to rely on established practices and tools when implementing them. Do not attempt to write your controllers manually unless you are very experienced; instead, rely on tools like Operator SDK for creating operators for your CRDs.

.- Wrapping Up

We’ve explored the powerful features Kubernetes provides beyond its basic functionalities. Custom Resource Definitions (CRDs) allow us to extend the Kubernetes API, enabling more tailored operations within our cluster. Controllers breathe life into these CRDs by reacting to events and ensuring that the state of our resources aligns with our specifications.

Moreover, we touched upon Kubernetes Operators, which encapsulate both CRDs and controllers, along with domain-specific logic, to manage complex applications effortlessly. Operators serve as the cherry on top in the Kubernetes extensibility model, automating routine tasks and simplifying cluster management even further.

By embracing CRDs, controllers, and operators, we can exploit Kubernetes’ full potential and create an environment that’s not only flexible but also significantly easier to manage. As Kubernetes continues to evolve, leveraging these elements will undoubtedly make our journey in the cloud-native world much smoother.

The Horizontal Pod Autoscaler (HPA) is pivotal in Kubernetes. It’s like our trusty assistant, automatically adjusting the number of pods in a deployment according to observed metrics like CPU usage. However, there are moments when it encounters hurdles. One such instance is when you stumble upon error messages such as:

Name:                                                  widget-app-sun
Namespace:                                             development
...
Metrics:                                               ( current / target )
  resource cpu on pods  (as a percentage of request):  <unknown> / 55%
Min replicas:                                          1
Max replicas:                                          3
Deployment pods:                                       1 current / 0 desired
Conditions:
  Type           Status  Reason                   Message
  ----           ------  ------                   -------
  AbleToScale    True    SucceededGetScale        the HPA controller was able to get the target's current scale
  ScalingActive  False   FailedGetResourceMetric  the HPA was unable to compute the replica count: unable to get metrics for resource cpu: no metrics returned from resource metrics API
Events:
  Type     Reason                        Age                 From                       Message
  ----     ------                        ----                ----                       -------
  Warning  FailedComputeMetricsReplicas  20m (x20 over 9m)   horizontal-pod-autoscaler  invalid metrics (1 invalid out of 1), first error is: failed to get cpu utilization: unable to get metrics for resource cpu: no metrics returned from resource metrics API
  Warning  FailedGetResourceMetric       12s (x29 over 9m)   horizontal-pod-autoscaler  unable to get metrics for resource cpu: no metrics returned from resource metrics API

What’s the Scoop?

This cryptic message is essentially HPA’s way of saying, “I’m having a hard time fetching those CPU metrics I need.” But why? Here are a few culprits:

Perhaps Metrics-server isn’t installed or isn’t operating correctly.
Maybe Metrics-server is present, but it’s struggling to fetch metrics from the nodes.
It could be a misconfiguration on HPA’s end.
Sometimes, network policies or RBAC restrictions come into play, obstructing access to the metrics API.

The Detective Work: Troubleshooting Steps

.- Is Metrics-server Onboard?

kubectl get deployments -n kube-system | grep metrics-server

metrics-server           1/1     1            1           221d

If it’s missing in action, it’s time to deploy it. Helm is a handy tool for this. https://artifacthub.io/packages/helm/metrics-server/metrics-server

.- How’s Metrics-server Feeling Today?

kubectl get pods -n kube-system -l k8s-app=metrics-server

NAME                              READY   STATUS    RESTARTS      AGE
metrics-server-5f9f776df5-zlg42   1/1     Running   6 (71d ago)   221d

Make sure it’s running smoothly. If it’s throwing a tantrum, dive into its logs:

kubectl logs metrics-server-5f9f776df5-zlg42 -n kube-system

I0730 17:07:50.422754       1 secure_serving.go:266] Serving securely on [::]:10250
I0730 17:07:50.425140       1 requestheader_controller.go:169] Starting RequestHeaderAuthRequestController
I0730 17:07:50.425155       1 shared_informer.go:240] Waiting for caches to sync for RequestHeaderAuthRequestController

.- A Peek into Metrics-server’s Config

Sometimes, it needs some flags to communicate correctly, especially if your cluster has a unique CNI or is lounging on a special cloud provider. You might need to check and adjust flags like:

--kubelet-preferred-address-types or --kubelet-insecure-tls

.- The Network or RBAC Culprits

Are there any stringent network policies that are hindering the conversation between the metrics-server and the API server or the kubelets? Or maybe, metrics-server doesn’t have the right RBAC permissions to access metrics?

Peek into network policies in the kube-system namespace:

kubectl get networkpolicy -n kube-system

And don’t forget to inspect the ClusterRole:

kubectl describe clusterrole | grep metrics-server -A10

Name:         system:metrics-server
Labels:       objectset.rio.cattle.io/hash=9a6f488150c249811b9df07e116280789628963e
Annotations:  objectset.rio.cattle.io/applied:
                H4sIAAAAAAAA/4yRwY6bMBCGX6WasyEhSQkg9VD10ENvPfRScRjsSXABG80Yom7Eu69MotVKq93syRr/+j7711wBR/uHWKx3UAE3qFOcQuvZPmGw3qVdIan1mzkDBZ11Bir40U8SiH...

.- Version Harmony: HPA & Metrics-server

Compatibility matters! Ensure HPA and metrics-server are on the same page. Sometimes, a version mismatch might be the root cause.

Here’s how to check your Kubernetes version:

kubectl version

Client Version: v1.28.2
Kustomize Version: v5.0.4-0.20230601165947-6ce0bf390ce3

And let’s not forget about the metrics-server:

kubectl describe deployment metrics-server -n kube-system | grep Image:

Image:      rancher/mirrored-metrics-server:v0.6.2

Troubleshooting in Kubernetes can be challenging, but with a systematic approach, many issues, like the HPA metrics problem, can be resolved. It’s essential to understand the components involved and to remain adaptable. As Kubernetes continues to evolve, so too should our methods for diagnosing and fixing problems.