Kubernetes – Namespaces

You can create a single physical cluster as a set of virtual clusters by using namespaces.

After creating you first namespace, for example “my-space”, you can see that, as default, you also have a default namespace as well other others used by kubernetes under the hood:

Namespaces in Kubernetes allow you to group workloads and resources together.

It´s very useful if you have a lot of objects and you want to search or execute operations on some of them according to the purpose.

Namespace don´t provide isolation. By default, pods can access other pods and services in ohter namespaces, but you can isolate them by using network policies too. And also apply resource quotas to them.

You can´t assign nodes and persistent volumes to the same namespaces. This means, for example, that pods from different namespaces can you the same persistence storage.

If you omit the namespace (by using “-n myspace” or “–namespace myspace”), kubernetes will use the default one.

Make sure that users that can operate on a dedicated namespace don’t have access to the default namespace. Otherwise, every time they forget to specify a namespace, they’ll operate quietly on the default namespace.

The best way to avoid this situation is to “seal” the namespace and require different users and credentials for each namespace, like using users and root iwith sudo on your machine.

If you are planning to work with the same namespace for a while, you can defne a context, so you don’t have to keep typing --namespace=ns for every command:

$ kubectl config set-context dev-context --namespace=my-space --user=default --cluster=default
Context "dev-context" created.
$ kubectl config use-context dev-context
Switched to context "dev-context".

it´s good to split complex systems into smaller groups. For example, in a multi-tenant environment (prod-dev-test). And namespaces can help you!

Kubernetes – using the patch command for updates

What´s really cool about kubernetes is that you can update your workloads live.

One kubectl command that definetely might make you achieve things faster is the patch command.

Let´s say you want to add a container to the following deployment:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: dep-to-update
spec:
  replicas: 2
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: ngxinx-container
        image: nginx

To add another container you can create a patch file called “redis-patch.yaml”:

spec:
  template:
    spec:
      containers:
      - name: patch-demo-ctr-2
        image: redis

All you need to do then is executing a command like:

$ kubectl patch deployment dep-to-update --patch-file redis-patch.yaml

After running this command, kubernetes will redeploy the updated version.

If you run “k describe pod dep-to-update-3829238kdls” you will see that both containers are created:

If you access the dep-to-update deployment yaml file, you can now see both containers:

Don´t forget to put “spec > template > spec > containers” in the patch file.

The following file won´t be patched:

    spec:
      containers:
      - name: patch-demo-ctr-2
        image: redis

Kubernetes – Pods Security Context

A security context allows to control the privileges and access settings for a pod or container.

It allows you to define:

  • permissions to access an object (Discretionary Access Control)
  • security labels (Security Enhanced Linux)
  • privileged and unprivileged users
  • Linux capabilities
  • privilege escalation allowance
  • etc.

As default the containers run the processes as root. This is possible thanks to the container isolation principle.

However, in some circumstances, you might need to use specific rights according to your needs.

If you want to define a configuration for the whole pod, you can edit the security Context section under pod.spec.

In the container section, you also the opportunity to edit it under spec.containers, as you can see in the docs.

The following example shows you both the settings:


apiVersion: v1
kind: Pod
metadata:
  labels:
    run: nginx-secure
  name: nginx-secure
  namespace: default
spec:
  securityContext:
    runAsNonRoot: true
    runAsUser: 1000
    runAsGroup: 1001
    supplementalGroups:
    - 1002
    - 1003
  containers:
  - image: nginx
    name: nginx-secure
  - name: sec-ctx-demo
    image: busybox
    command: [ "sh", "-c", "sleep 1h" ]
    volumeMounts:
    - name: sec-ctx-vol
      mountPath: /data/demo
    securityContext:
      allowPrivilegeEscalation: false
      readOnlyRootFilesystem: true
      runAsNonRoot: false
  volumes:
  - name: sec-ctx-vol
    # This AWS EBS volume must already exist.
    awsElasticBlockStore:
      volumeID: "3232323"
      fsType: ext4

Kubernetes – Running from a custom Docker image

As I am getting ready for CKA exam, I will show you how to run a pod on Kubernetes starting from the a Dockerfile.

Let´s say we want to create a Node.Js simple server, a simpe app.js file.

const http = require('http');
const os = require('os');

console.log("My node js server is starting...");

var handler = function(request, response) {
  console.log("Received request from " + request.connection.remoteAddress);
  response.writeHead(200);
  response.end("You've hit " + os.hostname() + "\n");
};

var www = http.createServer(handler);
www.listen(8080);

Once we have the app.js file, we can create a Dockerfile too:

FROM node:7
ADD app.js /app.js
ENTRYPOINT ["node", "app.js"]

Then we can build the image:

docker build -t node-js-server-image .

Once we have created the image, we need to login to Docker Hub with the “docker login” command, then tag and push our image:

$ docker login

$ docker tag node-js-server-image lauraliparulo/node-js-server-image

$ docker push lauraliparulo/node-js-server-image

Then you can use the image to create a kubernetes pod

$ kubectl run nodejs --image=lauraliparulo/node-js-server-image --port=8080

With “kubectl describe pod node-js” we can find the IP of the exposed pod:

Then we can check the content with “curl”:

Kubernetes – Imperative Job creation

Let´s create a job the imperative way, by using the docker whalesay image:

kubectl create job whalesay --image=docker/whalesay --dry-run=client -o yaml > job.yaml -- cowsay I am going to ace CKAD!

We are using the “dry-run” option to create a yaml manifest without creating the job.

In the last part of the command with add a command for the container, that will be put directly in the manifest.

Once the file is created, we can add parameters like completions, parallelism and backoffLimit under the spec.template section, like this:

Then we need to create the job, by running:

kubectl create -f job.yaml -n <your-namespace>

After a while we can see the pods have been run and completed:

If you inspect the log of one of the pod, you can see the funny whale comics:

kubectl logs whalesay-7h27f

MORE ABOUT JOBS

Specifying the restartPolicy is mandatory for a Job.

Notice that:

  • a job is persisted and survives cluster restarts
  • a completed job is kept for tracking purposes

Kubernetes – Resources limits

As containers might be consuming too many compressible resources, such as CPU or network bandwidth. And also incompressible resources, like memory.

Luckily, Kubernetes can access and control the linux cgroups CPU and memory limitations for each pod.

Kubernetes distinguish between “requests” and “limits”, very much like soft/hard limits in linux.

Requests specify the minimum amount of resources that are needed, whereas limits define the maximum amount the containers can grow up to. This means that limits are supposed to be larger than the requests.

The kubernetes schedules assings a pod to a node according to the requests value: only the nodes that can have enough capacity to accomodate the pods are considered for scheduling.

So, basically, the requests sections determines where a Pod will be scheduled.

HOW TO CONFIGURE RESOURCES

You can add the specification to your deployment or pod (?) directly with the “set” command:

$ kubectl set resources deployment nginx --limits=cpu=200m,memory=512Mi --requests=cpu=100m,memory=256Mi

This will awork as a live update and will assign the same values to each container in your deployment.

Your pods will be recreated with the new values.

Resources are always defined in the container section:

apiVersion: v1
kind: Pod
metadata:
  name: random-generator
spec:
  containers:
  - image: k8spatterns/random-generator:1.0
    name: random-generator
    resources:
      requests:                         
        cpu: 100m
        memory: 100Mi
      limits:                           
        cpu: 200m
        memory: 200Mi

If you omit the resources configuration, default values will be added.

In this case a best-effort strategy will be put in place, which means the pods will have the lowest priority and be killed first, where the node runs out of resources.

You won´t see any entry in the yaml manifest:

Kubernetes – updating strategies

The applications you deploy on Kubernetes will often need updates. Rather than deploying them from scratch again, you can take advantage of different update strategies.

A live update is not trivial, especially if you have interactions between different parts of the system, inter-dependecies among pods, etc.

in many cases you need to keep your applications running also while performing maintenance and upgrading tasks.  After all, it´s what Kubernetes is designed for: providing high availability and reliability.

There ae several updating strategies like:

  • rolling update
  • blue-green deployments
  • canary deployments

ROLLING UPDATE

With a rolling update strategy, Kubernetes creates a new ReplicaSet, replacing the Replicas one by one. the cluster will be running current and new components at the same time. If the components are backward-compatible, it´s a lot easier of course.

Two strategies possible:

– Recreate , which means killing all the pods before creating the new ones

– RollingUpdate – which guarantees the availability of the service during the update

RollingUpdate is the default strategy and can be tuned to guarantee a minimal and maximal amount of pods available during the update by using the options “maxSurge” and “maxUnavailable”.

The way an update is handled can be defined as a strategy in the spec.strategy section of the deployment manifest, like:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: random-generator
spec:
  replicas: 3                            
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1                        
      maxUnavailable: 1                  
    selector:
      matchLabels:
        app: random-generator
    template:
      metadata:
        labels:
          app: random-generator
      spec:
        containers:
        - image: k8spatterns/random-generator:1.0
            name: random-generator
          readinessProbe:                
            exec:
              command: [ "stat", "/random-generator-ready" ]

Notice that we can use both integer and percentages as value in the options. It means that the following is also valid:

strategy:

  rollingUpdate:

    maxSurge: 25%

    maxUnavailable: 50%

  type: RollingUpdate

You might have to create a temporary compatibility layer, while doing your updates. So Rolling updates is not the answer to more complex architectures.

BLUE-GREEN

It consists of preparing a full new version deployment for the whole production environment. So you have a blue old productive environment and a brand new green one ready to be put in place. If you have storage data to carry with you in the new version, you might need additional efforts.

The green deployment doesn´t serve any request, until the staff is confident that it will be working properly. That´s when the blue deployment will be killed and replaced.

Such strategy can be aided by extensions like a Service Mesh or Knative.

A drawback is that it takes double capacity.

CANARY DEPLOYMENT

A canary deployment is a new version deployment that is only submitted to a subset of users for testing purposes. Only when the subset of new instances will be satisfying, the whole deployment will be replaced.

This technique can be implemented by creating a new ReplicaSet for the new container version, by using a new deployment, with few replicas.


Kubernetes – Logging

The standard error (stderr) and output (stdout) are stored by Kubernetes for every container and can be visualized with the “kubectl logs” command.

Let´s consider for example a simple pod like:

apiVersion: v1
kind: Pod
metadata:
  name: now
spec:
  containers:
    - name: date-pod
      image: g1g1/py-kube:0.2
      command: ["/bin/bash", "-c", "while true; do sleep 20; date; done"]

If you run “kubectl logs now” you will see the date:

if you pod is a multicontainer, you need to specify which container do you want to inspect:

For example if you want to check the logs of a container called “background” in  a pod called “webapp”, using the “-c” option:

$ kubectl logs webapp -c background

If your pod is in a deployment:

$ kubectl logs deployment/flask

If you want to stream the log, add the “-f” option:

$ kubectl logs deployment/flask -f

You can as well redirect it in a file.. .also with “tee” for both screen and file:

$ kubectl logs now | tee log.txt

You can check the logs of the previous container with the “-p” options:

$ kubectl logs -p podname

You can also add the timestamp. For example:

$ k run text-pod --image=busybox --labels="tier=msg,review=none" --env VAR1=hello --env VAR2=world -o yaml > p.yaml --command -- sh -c "while true; do echo this is a logging text; sleep 2; done"

Then run:

$ kubectl logs text-pod --timestamps

Kubernetes – labels, selectors and annotations

When you create a deployment or simply are new pod with Kubernetes, you are expected to add some metadata, like semantic information in form of labels or deployment annotations. That´s because the amount of pods you need to run might grow a lot and you need a way to search through them.

It´s a mechanism for organizing the dozens of resources you are going to have.

LABELS

Labels are key-value pairs used mainly for grouping and selecting purposes.

For instance, you can use them to add information about the environment, the team or area of responsibility involved, the version, etc.

labels are not the same as “selectors”, but you can use the “-l” options For both, if you want to  retrieve a set of resources:

$ kubectl get pods -l app=flask

You get:
Name: flask
Namespace: default
CreationTimestamp: Sat, 16 Sep 2017 08:31:00 -0700
labels: pod-template-hash=866287979
        run=flask
Annotations: deployment.kubernetes.io/revision=1
kubectl.kubernetes.io/last-applied-configuration={"apiVersion":"apps/v1beta1","kind":"deployment","metadata":{"annotations":{},"labels":{"run":"flask"},"name":"flask","namespace":"default"},"spec":{"t...
Selector: app=flask
Replicas: 1 desired | 1 updated | 1 total | 1 available | 0 unavailable
StrategyType: RollingUpdate
MinReadySeconds: 0
RollingUpdateStrategy: 25% max unavailable, 25% max surge
pod Template:
 labels: app=flask
 etc.
 


If you create a deployment using “kubectl run”, the level “run=flask” is added automatically.
the command assigns the keys run, pod-template-hash, and app For specific meanings.

To get the label values in the information view, use “–show-labels”.

$ kubectl get pods --show-lables

To query labeled pods:

$ kubectl get pods -L run,pod-template-hash
$ kubectl get po -l creation_method=manual
$ kubectl get po -l '!env'

Notice that also the “not” operator (!) can be used.

You can add a label to an existing pod:

$ kubectl label po kubia-manual creation_method=manual

Or you can overwrite an existing one:

$ kubectl label po kubia-manual-v2 env=debug --overwrite

Assign a label by searching two or possible values:

$ kubectl label pod -l "type in (worker,runner)" protected=true


SELECTORS

Labels can be used in selectors:

$ kubectl get deployments.app --selector nl=spook

For example, to find all the deployments that have the label “app” set to “nginx”:

$ kubectl get all --selector="app=nginx" -o wide

FIELD SELECTORS

all The fields you see in the yaml files can be used for querying with the “field-selector” options like:

$ kubectl get pods --field-selector status.phase=Running
$ kubectl get pods --field-selector metadata.namespace!=jupiter
$ kubectl get pods --field-selector=status.phase!=Running,spec.restartPolicy=Always

You can also use the metadata as environment variables with “fieldRef” in the manifest file:


    spec:
      containers:
      - image: nginx
        name: nginx
        env:
        - name: POD_NAME
          valueFrom:
            fieldRef:
                fieldPath: metadata.name 

ANNOTATIONS

Annotations are used to provide additional information related to an instance. They are intended as descriptions and are very useful when it comes to checking a rollout history.

For example:

$ kubectl annotate pod redis description="this is doc"

This will be added to the metadata of your yaml file.

To set an annotation for the deployment history:

$ kubectl annotate deployment flask kubernetes.io/change-cause='deploying image 0.1.1'
deployment "flask" annotated

Now, if we look at the history, you will see the following displayed:

kubectl rollout history deployment/flask
deployments "flask"
REVISION  CHANGE-CAUSE
1         <none>
2         deploying image 0.1.1

The second revision now has a “change-cause” entry.

It´s important to know that you annotate a group of resources that have the same labels, especially if you have a lot of pods:

$ kubectl annotate pod -l protected=true protected="do not delete this pod".
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