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 – 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 – Multi-Containers Design Pattern

A multi-container pod can be defined by the following structural design patterns:

  • init container
  • sidecar
  • ambassador
  • adapter

The are basically best practices and distributed system design patterns.

They contribute to achieving separation of concerns.

INIT CONTAINERS
Init containers are additional containers used to complete tasks before the regular containers are started in a pod.

For example:

spec:
  template:
    metadata:
      labels:
         app: flask
    spec:
      containers:
      - name: flask
        image: quay.io/kubernetes-for-developers/flask:0.2.0
        ports:
        - containerPort: 5000
        envFrom:
        - configMapRef:
           name: flask-config
        volumeMounts:
          - name: config
            mountPath: /etc/flask-config
            readOnly: true
      volumes:
        - name: config
          configMap:
            name: flask-config
      initContainers:
      - name: init-myservice
        image: busybox
        command: ['sh', '-c', 'until nslookup redis-master; do echo waiting for redis; sleep 2; done;']

SIDECAR

A sidecar container is meant to assist the main container with additional functionalities, like intercepting the inbound traffic, or aggregating logs.

The main application container is unaware of the sidecar container.

Let´s consider, for example, a central logging agent.

      containers:
      - name: cleaner-con
        image: bash:5.0.11
        args: ['bash', '-c', 'while true; do echo `date`: "remove random file" >> /var/log/cleaner/cleaner.log; sleep 1; done']
        volumeMounts:
        - name: logs
          mountPath: /var/log/cleaner
      - name: logger-con                                                
        image: busybox:1.31.0                                           
        command: ["sh", "-c", "tail -f /var/log/cleaner/cleaner.log"]  
        volumeMounts:                                                   
        - name: logs                                                    
          mountPath: /var/log/cleaner                                   

In the code about the sidecar container is called “logger-con”

The sidecar container will send the logs to a central logging service, for aggregation purporse.

This would have a huge benefit, as your changes to your central logging policy (for example, a new provider) will only affected the dedicates side container.

This would prevent you from breaking the application containers while performing logging updates.

ADAPTER

If the sidecar container is meant to adapt data for the container, it´s called an adapter.

The adapter pattern is about standardizing output from the main application container.

Consider the case of a service that is being rolled out incrementally: it may generate reports in a format that doesn’t conform to the previous version.

Other services and applications that consume that output haven’t been upgraded yet. An adapter container can be deployed in the same pod with the new application container and massage the output to match the old version until all consumers have been upgraded.

The adapter container shares the filesystem with the main application container, so it can watch the local filesystem, and whenever the new application writes something, it immediately adapts it.

AMBASSADOR

It´s a container calling other containers on behalf of the main container.

This allows to have several ambassadors according to protocols or different database types.

For example you might have a Database ambassador for MySql and another one for oracle-db.

It´s a specialization of a sidecar, responsible for hiding complexity and providing interfaces.

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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