This documentation outlines the step-by-step guide for deploying the Kubernetes Python client in a production environment. Ensure that all prerequisite knowledge about Kubernetes and its components is understood before proceeding.

1. Dockerfile Creation

The first step involves creating a Dockerfile that will containerize your Python application using the Kubernetes client.

FROM nbgallery/jupyter-alpine:latest 

RUN pip install git+https://github.com/kubernetes-client/python.git

ENTRYPOINT ["/sbin/tini", "--"]
CMD ["jupyter", "notebook", "--ip=0.0.0.0"]

• The Dockerfile above uses an Alpine image and installs the Kubernetes Python client directly from the GitHub repository.
• The ENTRYPOINT and CMD directives ensure that a Jupyter notebook is started when the container launches.

2. Release Preparation

Before deploying, a stable release of your application needs to be created. This involves the following steps:

2.1 Setting Environment Variables

export GITHUB_TOKEN=t  # github-personal-access-token
export MINOR_VERSION=x
export PATCH_VERSION=y  # The latest patch version for the minor version, not required for snapshot.

2.2 Creating a Stable Release

Make a stable release with the following command:

$ KUBERNETES_BRANCH=release-1.${MINOR_VERSION} CLIENT_VERSION=${MINOR_VERSION}.${PATCH_VERSION}.0 DEVELOPMENT_STATUS="5 - Production/Stable" scripts/release.sh

This command creates a release branch that can later be used for adjustments and deployment.

3. Deployment Configuration

Configure your deployment in Kubernetes using the Python client. Two primary objects for deployment are Deployment and DeploymentList. You need to set the desired specifications in your deployment YAML or through Python code.

Example Deployment Code

from kubernetes import client, config

config.load_kube_config()  # Load kube config from default location

# Define deployment spec with desired replicas
deployment_spec = client.V1DeploymentSpec(
    replicas=3,
    selector={'matchLabels': {'app': 'nginx'}},
    template=client.V1PodTemplateSpec(
        metadata=client.V1ObjectMeta(labels={'app': 'nginx'}),
        spec=client.V1PodSpec(containers=[client.V1Container(
            name='nginx',
            image='nginx:latest',
            ports=[client.V1ContainerPort(container_port=80)]
        )])
    )
)

# Create deployment
deployment = client.V1Deployment(
    api_version='apps/v1',
    kind='Deployment',
    metadata=client.V1ObjectMeta(name='nginx-deployment'),
    spec=deployment_spec
)

apps_v1 = client.AppsV1Api()
apps_v1.create_namespaced_deployment(namespace='default', body=deployment)

• The code snippet defines a deployment for an Nginx application with three replicas.
• It uses the Kubernetes Python client API to create deployments programmatically.

4. Monitoring Deployment Status

To ensure that the deployment is proceeding as expected, monitor the deployment status using the following code snippet:

status = apps_v1.read_namespaced_deployment_status(name='nginx-deployment', namespace='default')
print(status)

You can inspect attributes like status.conditions to determine if the deployment was successful or if it encountered issues.

5. Rollback Strategy

Define a rollback strategy by specifying the revision_history_limit. This sets the number of old ReplicaSets to retain:

deployment_spec.revision_history_limit = 10  # Retain the last 10 revisions

6. Handling Deployment Conditions

Be vigilant about deployment conditions to catch potential issues early in the deployment lifecycle:

if status.conditions:
    for condition in status.conditions:
        print(f"Type: {condition.type}, Status: {condition.status}, Reason: {condition.reason}")

Conclusion

Successfully deploying the Kubernetes client in Python requires setting up a Docker container, preparing releases, configuring Kubernetes deployments, and monitoring the deployment status. Careful error-checking and fallback strategies will ensure smooth functionality.

Sources:

• Kubernetes Client Python Release Documentation (devel/release.md)
• Kubernetes Deployment Specification (kubernetes/docs/V1DeploymentSpec.md)
• Kubernetes Deployment Status Documentation (kubernetes/docs/V1DeploymentStatus.md)