In this tutorial, you will learn how to deploy a Prisma server on Kubernetes.
Kubernetes is a container orchestrator, that helps with deploying and scaling of your containerized applications.
The setup in this tutorial assumes that you have a running Kubernetes cluster in place. There are several providers out there that gives you the possibility to establish and maintain a production grade cluster. This tutorial aims to be provider agnostic, because Kubernetes is actually the abstraction layer. The only part which differs slightly is the mechanism for creating
persistent volumes. For demonstration purposes, we use the Kubernetes Engine on the Google Cloud Platform in this tutorial.
All Kubernetes definition files are also bundled in this repository.
If you haven't done that before, you need to fulfill the following prerequisites before you can deploy a Prisma cluster on Kubernetes. You need ...
- ... a running Kubernetes cluster (e.g. on the Google Cloud Platform)
- ... a local version of kubectl which is configured to communicate with your running Kubernetes cluster
You can go ahead now and create a new directory on your local machine – call it
kubernetes-demo. This will be the reference directory for our journey.
As you may know, Kubernetes comes with a primitive called
namespace. This allows you to group your workload logically. Before applying the actual namespace on the cluster, we have to write the definition file for it. Inside our project directory, create a file called
namespace.yml with the following content:
apiVersion: v1 kind: Namespace metadata: name: prisma
This definition will lead to a new namespace, called
prisma. Now, with the help of
kubectl, you can apply the namespace by executing:
kubectl apply -f namespace.yml
Afterwards, you can perform a
kubectl get namespaces in order to check if the actual namespace has been created. You should see the following on a fresh Kubernetes cluster:
❯ kubectl get namespaces NAME STATUS AGE default Active 1d kube-public Active 1d kube-system Active 1d prisma Active 2s
Prisma supports a good range of different database systems. Although we use MySQL for this tutorial, the steps can be easily adopted for a different database system, like PostgreSQL.
Now that we have a valid namespace in which we can rage, it is time to deploy MySQL. Kubernetes separates between stateless and stateful deployments. A database is by nature a stateful deployment and needs a disk to actually store the data. So how do we tell our cluster to create a new disk on the cluster? By using a PersistentVolumeClaim:
kind: PersistentVolumeClaim apiVersion: v1 metadata: name: database-disk namespace: prisma labels: stage: production name: database app: mysql spec: accessModes: - ReadWriteOnce resources: requests: storage: 20Gi
Here we request a disk with a storage capacity of 20 GB. You can apply this PVC by executing:
kubectl apply -f database/pvc.yml
You should see a new disk in the Disk Overview on the Google Cloud Platform after a couple of seconds.
Now where we have our disk for the database, it is time to create the actual deployment definition of our MySQL instance. A short reminder: Kubernetes comes with the primitives of
Pod is like a "virtual machine" in which a containerized application runs. It gets an own internal IP address and (if configured) disks attached to it. The
ReplicationController is responsible for scheduling your
Pod on cluster nodes and ensuring that they are running and scaled as configured.
In older releases of Kubernetes it was necessary to configure those separately. In recent versions, there is a new definition resource, called
Deployment. In such a configuration you define what kind of container image you want to use, how much replicas should be run and, in our case, which disk should be mounted.
The deployment definition of our MySQL database looks like:
apiVersion: extensions/v1beta1 kind: Deployment metadata: name: database namespace: prisma labels: stage: production name: database app: mysql spec: replicas: 1 strategy: type: Recreate template: metadata: labels: stage: production name: database app: mysql spec: containers: - name: mysql image: 'mysql:5.7' args: - --ignore-db-dir=lost+found env: - name: MYSQL_ROOT_PASSWORD value: 'prisma' ports: - name: mysql-3306 containerPort: 3306 volumeMounts: - name: database-disk readOnly: false mountPath: /var/lib/mysql volumes: - name: database-disk persistentVolumeClaim: claimName: database-disk
When applied, this definition schedules one Pod (
replicas: 1), with a running container based on the image
mysql:5.7, configures the environment (sets the password of the
root user to
prisma) and mounts the disk
database-disk to the path
To actually apply that definition, execute:
kubectl apply -f database/deployment.yml
You can check if the actual Pod has been scheduled by executing:
kubectl get pods --namespace prisma NAME READY STATUS RESTARTS AGE database-3199294884-93hw4 1/1 Running 0 1m
Before diving into this section, here's a short recap.
Our MySQL database pod is now running and available within the cluster internally. Remember, Kubernetes assigns a local IP address to the
Pod so that another application could access the database.
Now, imagine a scenario in which your database crashes. The cluster management system will take care of that situation and schedules the
Pod again. In this case, Kubernetes will assign a different IP address which results in crashes of your applications that are communicating with the database.
To avoid such a situation, the cluster manager provides an internal DNS resolution mechanism. You have to use a different primitive, called
Service, to benefit from this. A service is an internal load balancer that is reachable via the
service name. Its task is to forward the traffic to your
Pod(s) and make it reachable across the cluster by its name.
A service definition for our MySQL database would look like:
apiVersion: v1 kind: Service metadata: name: database namespace: prisma spec: ports: - port: 3306 targetPort: 3306 protocol: TCP selector: stage: production name: database app: mysql
The definition would create an internal load balancer with the name
database. The service is then reachable by this name within the
prisma namespace. A little explanation about the
- ports: Here you map the service port to the actual container port. In this case the mapping is
- selector: Kind of a query. The load balancer identifies
Podsby selecting the ones with the specified labels.
After creating this file, you can apply it with:
kubectl apply -f database/service.yml
To verify that the service is up, execute:
kubectl get services --namespace prisma NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE database ClusterIP 10.3.241.165 <none> 3306/TCP 1m
Okay, fair enough, the database is deployed. Next up: Deploying the actual Prisma server which is responsible for serving as an endpoint for the Prisma CLI.
This application communicates with the already deployed
database service and uses it as the storage backend. Therefore, the Prisma server is a stateless application because it doesn't need any additional disk storage.
The Prisma server needs some configuration, like the database connection information and which connector Prisma should use. We will deploy this configuration as a so-called
ConfigMap which acts like an ordinary configuration file, but whose content can be injected into an environment variable:
apiVersion: v1 kind: ConfigMap metadata: name: prisma-configmap namespace: prisma labels: stage: production name: prisma app: prisma data: PRISMA_CONFIG: | port: 4466 # uncomment the next line and provide the env var PRISMA_MANAGEMENT_API_SECRET=my-secret to activate cluster security # managementApiSecret: my-secret databases: default: connector: mysql host: database port: 3306 user: root password: prisma migrations: true
After defining the file, you can apply it via:
kubectl apply -f prisma/configmap.yml
Deploying the actual Prisma server to run in a Pod is pretty straightforward. First of all you have to define the deployment definition:
apiVersion: extensions/v1beta1 kind: Deployment metadata: name: prisma namespace: prisma labels: stage: production name: prisma app: prisma spec: replicas: 1 strategy: type: Recreate template: metadata: labels: stage: production name: prisma app: prisma spec: containers: - name: prisma image: prismagraphql/prisma:1.31 ports: - name: prisma-4466 containerPort: 4466 env: - name: PRISMA_CONFIG valueFrom: configMapKeyRef: name: prisma-configmap key: PRISMA_CONFIG
This configuration looks similar to the deployment configuration of the MySQL database. We tell Kubernetes that it should schedule one replica of the server and define the environment variable by using the previously deployed
Afterwards, we are ready to apply that deployment definition:
kubectl apply -f prisma/deployment.yml
As in the previous sections: In order to check that the Prisma server has been scheduled on the Kubernetes cluster, execute:
kubectl get pods --namespace prisma NAME READY STATUS RESTARTS AGE database-3199294884-93hw4 1/1 Running 0 5m prisma-1733176504-zlphg 1/1 Running 0 1m
Yay! The Prisma server is running! Off to our next and last step:
Okay, cool, the database
Pod is running and has an internal load balancer in front of it, the Prisma server
Pod is also running, but is missing the load balancer a.k.a.
Service. Let's fix that:
apiVersion: v1 kind: Service metadata: name: prisma namespace: prisma spec: ports: - port: 4466 targetPort: 4466 protocol: TCP selector: stage: production name: prisma app: prisma
Apply it via:
kubectl apply -f prisma/service.yml
Okay, done! The Prisma server is now reachable within the Kubernetes cluster via its name
That's all. Prisma is running on Kubernetes!
The last step is to configure your local
Prisma CLI so that you can communicate with the instance on the Kubernetes Cluster.
The upcoming last step is also necessary if you want to integrate
prisma deploy into your CI/CD process.
The Prisma server is running on the Kubernetes cluster and has an internal load balancer. This is a sane security default, because you won't expose the Prisma server to the public directly. Instead, you would develop a GraphQL API and deploy it to the Kubernetes cluster as well.
You may ask: "Okay, but how do I execute
prisma deploy in order to populate my data model when I'm not able to communicate with the Prisma server directly?". That is indeed a very good question!
kubectl comes with a mechanism that allows forwarding a local port to an application that lives on the Kubernetes cluster.
So every time you want to communicate with your Prisma server on the Kubernetes cluster, you have to perform the following steps:
kubectl get pods --namespace prismato identify the pod name
kubectl port-forward --namespace prisma <the-pod-name> 4467:4466– This will forward from
The Prisma server is now reachable via
http://localhost:4467. This is the actual
endpoint you have to specify in your
prisma.yml. So when your service should have the name
myservice and you want to deploy to stage
production, your endpoint URL would look like:
prisma.yml could look like:
endpoint: http://localhost:4467/myservice/production datamodel: datamodel.graphql
With this in place, you can deploy the Prisma service via the Prisma CLI (
prisma deploy) as long as your port forwarding to the cluster is active.
Okay, you made it! Congratulations, you have successfully deployed a Prisma server to a production Kubernetes cluster environment.