- Overview
- Introduction to deployments
- Setup
- Learn about the deployment object
- Create a deployment
- Rolling update
- Canary deployments
- Blue-green deployments
- Congratulations!
- Finish your Quest
- Take your next lab
- Next steps / learn more
Dev Ops practices will regularly make use of multiple deployments to manage application deployment scenarios such as "Continuous Deployment", "Blue-Green Deployments," "Canary Deployments" and more. This lab is to provide practice in scaling and managing containers so you can accomplish these common scenarios where multiple heterogeneous deployments are being used.
- Practice with kubectl tool
- Create deployment yaml files
- Launch, update, and scale deployments
- Practice with updating deployments and deployment styles
- Before taking this lab, you should have taken at least the labs Introduction to Docker and Hello Node Kubernetes
- Linux System Administration skills
- Dev Ops theory: concepts of continuous deployment
Heterogeneous deployments typically involve connecting two or more distinct infrastructure environments or regions to address a specific technical or operational need. Heterogeneous deployments are called "hybrid", "multi-cloud", or "public-private", depending upon the specifics of the deployment. For the purposes of this lab, heterogeneous deployments include those that span regions within a single cloud environment, multiple public cloud environments (multi-cloud), or a combination of on-premises and public cloud environments (hybrid or public-private).
Various business and technical challenges can arise in deployments that are limited to a single environment or region:
- Maxed out resources: In any single environment, particularly in on-premises environments, you might not have the compute, networking, and storage resources to meet your production needs.
- Limited geographic reach: Deployments in a single environment require people who are geographically distant from one another to access one deployment. Their traffic might travel around the world to a central location.
- Limited availability: Web-scale traffic patterns challenge applications to remain fault-tolerant and resilient.
- Vendor lock-in: Vendor-level platform and infrastructure abstractions can prevent you from porting applications.
- Inflexible resources: Your resources might be limited to a particular set of compute, storage, or networking offerings.
Heterogeneous deployments can help address these challenges, but they must be architected using programmatic and deterministic processes and procedures. One-off or ad-hoc deployment procedures can cause deployments or processes to be brittle and intolerant of failures. Ad-hoc processes can lose data or drop traffic. Good deployment processes must be repeatable and use proven approaches for managing provisioning, configuration, and maintenance.
Three common scenarios for heterogeneous deployment are multi-cloud deployments, fronting on-premises data, and continuous integration/continuous delivery (CI/CD) processes.
The following exercises practice some common use cases for heterogeneous deployments, along with well-architected approaches using Kubernetes and other infrastructure resources to accomplish them.
gcloud is the command-line tool for Google Cloud Platform. It comes pre-installed on Cloud Shell and supports tab-completion.
You can list the active account name with this command:
gcloud auth list
You can list the project ID with this command:
gcloud config list project
export PROJECT_ID=qwiklabs-gcp-335fcfeb6710e0d4
gcloud config set project $PROJECT_ID
Set your working GCP zone by running the following command, substituting the local zone as us-central1-a:
gcloud config set compute/zone us-central1-a
Get the sample code for creating and running containers and deployments:
git clone https://github.com/Patechoc/orchestrate-with-kubernetes
cd orchestrate-with-kubernetes/kubernetes
Create a cluster with five n1-standard-1 nodes (this will take a few minutes to complete):
gcloud container clusters create bootcamp --num-nodes 5 --scopes "https://www.googleapis.com/auth/projecthosting,storage-rw"
Let's get started with Deployments. First let's take a look at the Deployment object. The explain command in kubectl can tell us about the Deployment object.
kubectl explain deployment
We can also see all of the fields using the --recursive option.
kubectl explain deployment --recursive
You can use the explain command as you go through the lab to help you understand the structure of a Deployment object and understand what the individual fields do.
_kubectl explain deployment.metadata.name_
Update the deployments/auth.yaml cs file:
nano deployments/auth.yaml
Change the image in the containers section of the Deployment to the following:
...
containers:
- name: auth
image: kelseyhightower/auth:1.0.0
...
Save the auth.yaml file.
Now let's create a simple deployment. Examine the deployment configuration file:
cat deployments/auth.yaml
(Output)
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: auth
spec:
replicas: 1
template:
metadata:
labels:
app: auth
track: stable
spec:
containers:
- name: auth
image: "kelseyhightower/auth:1.0.0"
ports:
- name: http
containerPort: 80
- name: health
containerPort: 81
...
Notice how the Deployment is creating one replica and it's using version 1.0.0 of the auth container.
When you run the kubectl create command to create the auth deployment, it will make one pod that conforms to the data in the Deployment manifest. This means we can scale the number of Pods by changing the number specified in the replicas field.
Go ahead and create your deployment object using kubectl create:
kubectl create -f deployments/auth.yaml
Once you have created the Deployment, you can verify that it was created.
kubectl get deployments
Once the deployment is created, Kubernetes will create a ReplicaSet for the Deployment. We can verify that a ReplicaSet was created for our Deployment:
kubectl get replicasets
We should see a ReplicaSet with a name like auth-xxxxxxx
Finally, we can view the Pods that were created as part of our Deployment. The single Pod is created by the Kubernetes when the ReplicaSet is created.
kubectl get pods
It's time to create a service for our auth deployment. You've already seen service manifest files, so we won't go into the details here. Use the kubectl create command to create the auth service.
kubectl create -f services/auth.yaml
Now, do the same thing to create and expose the hello Deployment.
kubectl create -f deployments/hello.yaml
kubectl create -f services/hello.yaml
And one more time to create and expose the frontend Deployment.
kubectl create secret generic tls-certs --from-file tls/
kubectl create configmap nginx-frontend-conf --from-file=nginx/frontend.conf
kubectl create -f deployments/frontend.yaml
kubectl create -f services/frontend.yaml
Note: You created a ConfigMap for the frontend.
Interact with the frontend by grabbing its external IP and then curling to it.
kubectl get services frontend
curl -ks https://<EXTERNAL-IP>
And you get the hello response back.
You can also use the output templating feature of kubectl to use curl as a one-liner:
curl -ks https://kubectl get svc frontend -o=jsonpath={.status.loadBalancer.ingress[0].ip}"``
Now that we have a Deployment created, we can scale it. Do this by updating the spec.replicas field. You can look at an explanation of this field using the kubectl explain command again.
kubectl explain deployment.spec.replicas
The replicas field can be most easily updated using the kubectl scale command:
kubectl scale deployment hello --replicas=5
Note: It may take a minute or so for all the new pods to start up.
After the Deployment is updated, Kubernetes will automatically update the associated ReplicaSet and start new Pods to make the total number of Pods equal 5.
Verify that there are now 5 hello Pods running:
kubectl get pods | grep hello- | wc -l
Now scale back the application:
kubectl scale deployment hello --replicas=3
Again, verify that you have the correct number of Pods.
kubectl get pods | grep hello- | wc -l
You learned about Kubernetes deployments and how to manage & scale a group of Pods.
Deployments support updating images to a new version through a rolling update mechanism. When a Deployment is updated with a new version, it creates a new ReplicaSet and slowly increases the number of replicas in the new ReplicaSet as it decreases the replicas in the old ReplicaSet.
To update your Deployment, run the following command:
kubectl edit deployment hello
Change the image in the containers section of the Deployment to the following:
...
containers:
- name: hello
image: kelseyhightower/hello:2.0.0
...
Save and exit.
Once you save out of the editor, the updated Deployment will be saved to your cluster and Kubernetes will begin a rolling update.
See the new ReplicaSet that Kubernetes creates.:
kubectl get replicaset
You can also see a new entry in the rollout history:
kubectl rollout history deployment/hello
If you detect problems with a running rollout, pause it to stop the update. Give that a try now:
kubectl rollout pause deployment/hello
Verify the current state of the rollout:
kubectl rollout status deployment/hello
You can also verify this on the Pods directly:
kubectl get pods -o jsonpath --template='{range .items[*]}{.metadata.name}{"\t"}{"\t"}{.spec.containers[0].image}{"\n"}{end}'
The rollout is paused which means that some pods are at the new version and some pods are at the older version. We can continue the rollout using the resume command.
kubectl rollout resume deployment/hello
When the rollout is complete, you should see the following when running the status command.
kubectl rollout status deployment/hello
(Output)
deployment "hello" successfully rolled out
Assume that a bug was detected in your new version. Since the new version is presumed to have problems, any users connected to the new Pods will experience those issues.
You will want to roll back to the previous version so you can investigate and then release a version that is fixed properly.
Use the rollout command to roll back to the previous version:
kubectl rollout undo deployment/hello
Verify the roll back in the history:
kubectl rollout history deployment/hello
Finally, verify that all the Pods have rolled back to their previous versions:
kubectl get pods -o jsonpath --template='{range .items[*]}{.metadata.name}{"\t"}{"\t"}{.spec.containers[0].image}{"\n"}{end}'
Great! You learned about rolling updates for Kubernetes deployments and how to update applications without downtime.
When you want to test a new deployment in production with a subset of your users, use a canary deployment. Canary deployments allow you to release a change to a small subset of your users to mitigate risk associated with new releases.
A canary deployment consists of a separate deployment with your new version and a service that targets both your normal, stable deployment as well as your canary deployment.
First, create a new canary deployment for the new version:
cat deployments/hello-canary.yaml
(Output)
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: hello-canary
spec:
replicas: 1
template:
metadata:
labels:
app: hello
track: canary
# Use ver 1.0.0 so it matches version on service selector
version: 1.0.0
spec:
containers:
- name: hello
image: kelseyhightower/hello:2.0.0
ports:
- name: http
containerPort: 80
- name: health
containerPort: 81
...
Make sure to update version to 1.0.0 (if your version is pointing to any other)
Now create the canary deployment:
kubectl create -f deployments/hello-canary.yaml
After the canary deployment is created, you should have two deployments, hello and hello-canary. Verify it with this kubectl command:
kubectl get deployments
On the hello service, the selector uses the app:hello selector which will match pods in both the prod deployment and canary deployment. However, because the canary deployment has a fewer number of pods, it will be visible to fewer users.
You can verify the hello version being served by the request:
curl -ks https://kubectl get svc frontend -o=jsonpath="{.status.loadBalancer.ingress[0].ip}"/version
Run this several times and you should see that some of the requests are served by hello 1.0.0 and a small subset (1/4 = 25%) are served by 2.0.0.
In this lab, each request sent to the Nginx service had a chance to be served by the canary deployment. But what if you wanted to ensure that a user didn't get served by the Canary deployment? A use case could be that the UI for an application changed, and you don't want to confuse the user. In a case like this, you want the user to "stick" to one deployment or the other.
You can do this by creating a service with session affinity. This way the same user will always be served from the same version. In the example below the service is the same as before, but a new sessionAffinity field has been added, and set to ClientIP. All clients with the same IP address will have their requests sent to the same version of the hello application.
kind: Service
apiVersion: v1
metadata:
name: "hello"
spec:
sessionAffinity: ClientIP
selector:
app: "hello"
ports:
- protocol: "TCP"
port: 80
targetPort: 80
Due to it being difficult to set up an environment to test this, you don't need to here, but you may want to use sessionAffinity for canary deployments in production.
Rolling updates are ideal because they allow you to deploy an application slowly with minimal overhead, minimal performance impact, and minimal downtime. There are instances where it is beneficial to modify the load balancers to point to that new version only after it has been fully deployed. In this case, blue-green deployments are the way to go.
Kubernetes achieves this by creating two separate deployments; one for the old "blue" version and one for the new "green" version. Use your existing hello deployment for the "blue" version. The deployments will be accessed via a Service which will act as the router. Once the new "green" version is up and running, you'll switch over to using that version by updating the Service.
A major downside of blue-green deployments is that you will need to have at least 2x the resources in your cluster necessary to host your application. Make sure you have enough resources in your cluster before deploying both versions of the application at once.
Use the existing hello service, but update it so that it has a selector app:hello, version: 1.0.0. The selector will match the existing "blue" deployment. But it will not match the "green" deployment because it will use a different version.
First update the service:
kubectl apply -f services/hello-blue.yaml
In order to support a blue-green deployment style, we will create a new "green" deployment for our new version. The green deployment updates the version label and the image path.
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: hello-green
spec:
replicas: 3
template:
metadata:
labels:
app: hello
track: stable
version: 2.0.0
spec:
containers:
- name: hello
image: kelseyhightower/hello:2.0.0
ports:
- name: http
containerPort: 80
- name: health
containerPort: 81
resources:
limits:
cpu: 0.2
memory: 10Mi
livenessProbe:
httpGet:
path: /healthz
port: 81
scheme: HTTP
initialDelaySeconds: 5
periodSeconds: 15
timeoutSeconds: 5
readinessProbe:
httpGet:
path: /readiness
port: 81
scheme: HTTP
initialDelaySeconds: 5
timeoutSeconds: 1
Create the green deployment:
kubectl create -f deployments/hello-green.yaml
Once you have a green deployment and it has started up properly, verify that the current version of 1.0.0 is still being used:
curl -ks https://kubectl get svc frontend -o=jsonpath="{.status.loadBalancer.ingress[0].ip}"/version
Now, update the service to point to the new version:
kubectl apply -f services/hello-green.yaml
With the service is updated, the "green" deployment will be used immediately. You can now verify that the new version is always being used.
curl -ks https://kubectl get svc frontend -o=jsonpath="{.status.loadBalancer.ingress[0].ip}"/version
If necessary, you can roll back to the old version in the same way. While the "blue" deployment is still running, just update the service back to the old version.
kubectl apply -f services/hello-blue.yaml
Once you have updated the service, your rollback will have been successful. Again, verify that the right version is now being used:
curl -ks https://kubectl get svc frontend -o=jsonpath="{.status.loadBalancer.ingress[0].ip}"/version
You did it! You learned about blue-green deployments and how to deploy updates to applications that need to switch versions all at once.
This concludes this hands-on lab practicing deployment management with Kubernetes. In this lab you've had the opportunity to work more with the kubectl command-line tool, and many styles of deployment configurations set up in YAML files to launch, update, and scale your deployments. With this foundation of practice you should feel comfortable applying these skills to your own DevOps practice.
This self-paced lab is part of the Qwiklabs Kubernetes in the Google Cloud, Kubernetes Solutions, Cloud Engineering, and DevOps Essentials Quests. A Quest is a series of related labs that form a learning path. Completing this Quest earns you the badge above, to recognize your achievement. You can make your badge public and link to them in your online resume or social media account. Enroll in a Quest and get immediate completion credit if you've taken this lab. See other available Qwiklabs Quests.
Continue your Quest with Build a Slack Bot with Node.js on Kubernetes, or check out these suggestions:
- Continuous Delivery with Jenkins in Kubernetes Engine
- Using Kubernetes Engine to Deploy Apps with Regional Persistent Disks
- Read further on Deployment Patterns with Kubernetes.
- Read further on DevOps Solutions and DevOps Guides in the Google Cloud documentation
- Connect with the Kubernetes Community!