Test your Python deployment

Table of contents

Prerequisites

Overview

In this section, you'll learn how to use Docker Desktop to deploy your application to a fully-featured Kubernetes environment on your development machine. This allows you to test and debug your workloads on Kubernetes locally before deploying.

Create a Kubernetes YAML file

In your python-docker-dev-example directory, create a file named docker-postgres-kubernetes.yaml. Open the file in an IDE or text editor and add the following contents.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: postgres
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: postgres
  template:
    metadata:
      labels:
        app: postgres
    spec:
      containers:
        - name: postgres
          image: postgres
          ports:
            - containerPort: 5432
          env:
            - name: POSTGRES_DB
              value: example
            - name: POSTGRES_USER
              value: postgres
            - name: POSTGRES_PASSWORD
              valueFrom:
                secretKeyRef:
                  name: postgres-secret
                  key: POSTGRES_PASSWORD
          volumeMounts:
            - name: postgres-data
              mountPath: /var/lib/postgresql/data
      volumes:
        - name: postgres-data
          persistentVolumeClaim:
            claimName: postgres-pvc
---
apiVersion: v1
kind: Service
metadata:
  name: postgres
  namespace: default
spec:
  ports:
    - port: 5432
  selector:
    app: postgres
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: postgres-pvc
  namespace: default
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 1Gi
---
apiVersion: v1
kind: Secret
metadata:
  name: postgres-secret
  namespace: default
type: Opaque
data:
  POSTGRES_PASSWORD: cG9zdGdyZXNfcGFzc3dvcmQ= # Base64 encoded password (e.g., 'postgres_password')

In your python-docker-dev-example directory, create a file named docker-python-kubernetes.yaml.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: docker-python-demo
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      service: fastapi
  template:
    metadata:
      labels:
        service: fastapi
    spec:
      containers:
        - name: fastapi-service
          image: DOCKER_USERNAME/REPO_NAME
          imagePullPolicy: Always
          env:
            - name: POSTGRES_PASSWORD
              valueFrom:
                secretKeyRef:
                  name: postgres-secret
                  key: POSTGRES_PASSWORD
            - name: POSTGRES_USER
              value: postgres
            - name: POSTGRES_DB
              value: example
            - name: POSTGRES_SERVER
              value: postgres
            - name: POSTGRES_PORT
              value: "5432"
          ports:
            - containerPort: 8001
---
apiVersion: v1
kind: Service
metadata:
  name: service-entrypoint
  namespace: default
spec:
  type: NodePort
  selector:
    service: fastapi
  ports:
    - port: 8001
      targetPort: 8001
      nodePort: 30001

In these Kubernetes YAML file, there are various objects, separated by the ---:

  • A Deployment, describing a scalable group of identical pods. In this case, you'll get just one replica, or copy of your pod. That pod, which is described under template, has just one container in it. The container is created from the image built by GitHub Actions in Configure CI/CD for your Python application.
  • A Service, which will define how the ports are mapped in the containers.
  • A PersistentVolumeClaim, to define a storage that will be persistent through restarts for the database.
  • A Secret, Keeping the database password as a example using secret kubernetes resource.
  • A NodePort service, which will route traffic from port 30001 on your host to port 8001 inside the pods it routes to, allowing you to reach your app from the network.

To learn more about Kubernetes objects, see the Kubernetes documentation.

Note

  • The NodePort service is good for development/testing purposes. For production you should implement an ingress-controller.

Deploy and check your application

  1. In a terminal, navigate to python-docker-dev-example and deploy your database to Kubernetes.

    $ kubectl apply -f docker-postgres-kubernetes.yaml

    You should see output that looks like the following, indicating your Kubernetes objects were created successfully.

    deployment.apps/postgres created
service/postgres created
persistentvolumeclaim/postgres-pvc created
secret/postgres-secret created

    Now, deploy your python application.

    kubectl apply -f docker-python-kubernetes.yaml

    You should see output that looks like the following, indicating your Kubernetes objects were created successfully.

    deployment.apps/docker-python-demo created
service/service-entrypoint created

  2. Make sure everything worked by listing your deployments.

    $ kubectl get deployments

    Your deployment should be listed as follows:

    NAME                 READY   UP-TO-DATE   AVAILABLE   AGE
docker-python-demo   1/1     1            1           48s
postgres             1/1     1            1           2m39s

    This indicates all one of the pods you asked for in your YAML are up and running. Do the same check for your services.

    $ kubectl get services

    You should get output like the following.

    NAME                 TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)          AGE
kubernetes           ClusterIP   10.43.0.1      <none>        443/TCP          13h
postgres             ClusterIP   10.43.209.25   <none>        5432/TCP         3m10s
service-entrypoint   NodePort    10.43.67.120   <none>        8001:30001/TCP   79s

    In addition to the default kubernetes service, you can see your service-entrypoint service, accepting traffic on port 30001/TCP and the internal ClusterIP postgres with the port 5432 open to accept connections from you python app.

  3. In a terminal, curl the service. Note that a database was not deployed in this example.

    $ curl http://localhost:30001/
Hello, Docker!!!

  4. Run the following commands to tear down your application.

    $ kubectl delete -f docker-python-kubernetes.yaml
$ kubectl delete -f docker-postgres-kubernetes.yaml

Summary

In this section, you learned how to use Docker Desktop to deploy your application to a fully-featured Kubernetes environment on your development machine.

Related information: