Metadata-Version: 1.1
Name: kuber
Version: 1.0.0
Summary: High-level Kubernetes resource configuration API.
Home-page: https://github.com/sernst/kuber
Author: Scott Ernst
Author-email: swernst@gmail.com
License: MIT
Description: # Kuber
        
        A high-level Python client for Kubernetes. 
        
        ## tl;dr
        
        ```python
        import kuber
        from kuber.v1_13.apps import v1 as apps_v1
        
        # Create a bundle object to load and manage resource configurations
        resource_bundle = (
            kuber.create_bundle(kubernetes_version='1.13')
            .add_directory('app_configs')
            .add_file('secrets/app-secret.yaml')
        )
        
        # Modify the metadata labels on all resources in the bundle
        for resource in resource_bundle.resources:
            resource.metadata.labels.update(environment='production')
        
        # Update the replica count of the loaded deployment for production
        dep: apps_v1.Deployment = resource_bundle.get(name='my-app', kind='Deployment')
        dep.spec.replicas = 20
        
        # Print the combined YAML configuration for the bundle of resources
        print(resource_bundle.render_yaml_bundle())
        
        # Create all resources in the bundle
        resource_bundle.create()
        ```
        
        Or managing resources individually:
        
        ```python
        from kuber.v1_13.batch import v1 as batch_v1
        
        job = batch_v1.Job()
        
        # Populate metadata using context manager syntax for brevity
        with job.metadata as md:
            md.name = 'my-job'
            md.namespace = 'jobs'
            md.labels.update(component='backend-tasks', environment='production')
        
        # Add a container to the job spec
        job.spec.append_container(
            name='main',
            image='my-registry.com/projects/my-job:1.0.1',
            image_pull_policy='Always',
            env=[batch_v1.EnvVar('ENVIRONMENT', 'production')]
        )
        
        # Print the resulting YAML configuration for display
        print(job.to_yaml())
        
        # Create the resource in the currently configured cluster
        job.create_resource()
        ```
        
        kuber supports the basic CRUD behaviors using the locally installed and
        configured _kubectl_ as the intermediary. For more advanced and custom
        operations, the resource configurations can always be serialized to YAML
        or JSON and used in custom defined commands or just saved to disk for later
        application. The resource configurations also have a `to_dict()` function
        that serializes down to a Python dictionary that is compatible with the
        low-level _kubernetes_ library client APIs (passed into the _body_ parameter).
        
        ## Background
        
        The [Official Python Kubernetes client](https://github.com/kubernetes-client/python) 
        exists to provide low-level access to the Kubernetes API. However, low-level
        access can be clunky to use and require an additional barrier to entry for those
        of us that are not Kubernetes API experts. Generally, the lower-level API 
        functionality is abstracted away from us with tools like _kubectl_.
        
        _kuber_ is a higher-level abstraction designed to be compliant with the general
        usage level of someone comfortable working with Kubernetes configuration files
        and managing them with tools like _kubectl_ (and _helm_). _kuber_ was created 
        because we found that managing complex configurations through configuration 
        files alone was causing friction and that introducing tools like _helm_ that 
        rely on templating wasn't fully solving the problem.
        
        ## Configuring Resources
        
        _kuber_ allows Kubernetes resources to be defined entirely in Python code,
        or defined in configuration files and loaded and modified by code. Examples of
        the two approaches are shown below:
        
        ### The Pure Python Approach
        
        Here's an example of how a Deployment can be created with _kuber_:
        
        ```python
        from kuber.v1_13.apps import v1 as apps_v1
        
        # Create a deployment using Kubernetes version 1.13
        # from the apps/v1 API version.
        d = apps_v1.Deployment()
        
        with d.metadata as md:
            md.name = 'my-deployment'
            md.namespace = 'my-app'
            md.labels.update(app='foo', component='application')
        
        d.spec.selector.match_labels.update(app='foo')
        d.spec.template.metadata.labels.update(app='foo')
        
        d.append_container(
            name='app',
            image='my-app:1.0',
            ports=[apps_v1.ContainerPort(container_port=8080, host_port=80)],
            tty=True,
            image_pull_policy='Always',
            resources=apps_v1.ResourceRequirements(
                limits={'cpu': '1.5', 'memory': '1Gi'},
                requests={'cpu': '1.5', 'memory': '800Mi'},
            )
        )
        
        # Render the results to YAML
        print(d.to_yaml())
        ```
        
        The printed output of executing this would be:
        
        ```yaml
        apiVersion: apps/v1
        kind: Deployment
        metadata:
          labels:
            app: foo
            component: application
          name: my-deployment
          namespace: my-app
        spec:
          template:
            spec:
              containers:
              - image: my-app:1.0
                imagePullPolicy: Always
                name: app
                ports:
                - containerPort: 8080
                  hostPort: 80
                resources:
                  limits:
                    cpu: '1'
                    memory: 1Gi
                  requests:
                    cpu: '1'
                    memory: 800Mi
                tty: true
        ```
        
        ### The Hybrid Approach
        
        In many cases it is convenient to use standard Kubernetes configuration as a
        base template. The common approach in these cases used by projects like 
        [Helm](https://helm.sh/) is to introduce a templating language into the 
        configuration files that gets rendered prior to using the configuration. 
        However, this approach has a number of drawbacks. Instead _Kuber_ facilitates
        flexible modification and augmentation of resource configurations that have
        been loaded from configuration files.
        
Keywords: Kubernetes,kubectl
Platform: Linux
Platform: Mac OS X
Platform: Windows
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: System Administrators
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: System :: Systems Administration
