https://github.com/cloudcoil/models-kubernetes
Kubernetes models for cloudcoil
https://github.com/cloudcoil/models-kubernetes
cloudcoil cloudcoil-models
Last synced: over 1 year ago
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Kubernetes models for cloudcoil
- Host: GitHub
- URL: https://github.com/cloudcoil/models-kubernetes
- Owner: cloudcoil
- License: apache-2.0
- Created: 2025-01-03T19:30:25.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-26T08:17:43.000Z (over 1 year ago)
- Last Synced: 2025-02-26T09:20:08.320Z (over 1 year ago)
- Topics: cloudcoil, cloudcoil-models
- Language: Makefile
- Homepage:
- Size: 2.9 MB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# cloudcoil-models-kubernetes
Versioned kubernetes models for cloudcoil.
[](https://pypi.python.org/pypi/cloudcoil.models.kubernetes)
[](https://pepy.tech/project/cloudcoil.models.kubernetes)
[](https://opensource.org/license/apache-2-0/)
[](https://github.com/cloudcoil/models-kubernetes/actions/workflows/ci.yml)
> [!WARNING]
> This repository is auto-generated from the [cloudcoil repository](https://github.com/cloudcoil/cloudcoil/tree/main/models/kubernetes). Please do not submit pull requests here. Instead, submit them to the main repository at https://github.com/cloudcoil/cloudcoil.
## 🔧 Installation
> [!NOTE]
> For versioning information and compatibility, see the [Versioning Guide](https://github.com/cloudcoil/cloudcoil/blob/main/VERSIONING.md).
Using [uv](https://github.com/astral-sh/uv) (recommended):
```bash
# Install with Kubernetes support
uv add cloudcoil.models.kubernetes
```
Using pip:
```bash
pip install cloudcoil.models.kubernetes
```
## 💡 Examples
### Using Kubernetes Models
```python
from cloudcoil import apimachinery
import cloudcoil.models.kubernetes.core.v1 as k8score
import cloudcoil.models.kubernetes.apps.v1 as k8sapps
# Create a Deployment
deployment = k8sapps.Deployment(
metadata=apimachinery.ObjectMeta(name="nginx"),
spec=k8sapps.DeploymentSpec(
replicas=3,
selector=apimachinery.LabelSelector(
match_labels={"app": "nginx"}
),
template=k8score.PodTemplateSpec(
metadata=apimachinery.ObjectMeta(
labels={"app": "nginx"}
),
spec=k8score.PodSpec(
containers=[
k8score.Container(
name="nginx",
image="nginx:latest",
ports=[k8score.ContainerPort(container_port=80)]
)
]
)
)
)
).create()
# Create a Service
service = k8score.Service(
metadata=apimachinery.ObjectMeta(name="nginx"),
spec=k8score.ServiceSpec(
selector={"app": "nginx"},
ports=[k8score.ServicePort(port=80, target_port=80)]
)
).create()
# List Deployments
for deploy in k8sapps.Deployment.list():
print(f"Found deployment: {deploy.metadata.name}")
# Update a Deployment
deployment.spec.replicas = 5
deployment.save()
# Delete resources
k8score.Service.delete("nginx")
k8sapps.Deployment.delete("nginx")
```
### Using the Fluent Builder API
Cloudcoil provides a powerful fluent builder API for Kubernetes resources with full IDE support and rich autocomplete capabilities:
```python
from cloudcoil.models.kubernetes.apps.v1 import Deployment
from cloudcoil.models.kubernetes.core.v1 import Service
# Create a Deployment using the fluent builder
# The fluent style is great for one-liners and simple configurations
nginx_deployment = (
Deployment.builder()
# Metadata can be configured in a single chain for simple objects
.metadata(lambda metadata: metadata
.name("nginx")
.namespace("default")
)
# Complex nested structures can be built using nested lambda functions
.spec(lambda deployment_spec: deployment_spec
.replicas(3)
# Each level of nesting gets its own lambda for clarity
.selector(lambda label_selector: label_selector
.match_labels({"app": "nginx"})
)
.template(lambda pod_template: pod_template
.metadata(lambda pod_metadata: pod_metadata
.labels({"app": "nginx"})
)
.spec(lambda pod_spec: pod_spec
# Lists can be built using array literals with lambda items
.containers([
lambda container: container
.name("nginx")
.image("nginx:latest")
# Nested collections can use the add() helper
.ports(lambda port_list: port_list.add(
lambda port: port.container_port(80)
))
])
)
)
)
.build()
)
# Create a Service using the builder
service = (
Service.builder()
.metadata(lambda m: m
.name("nginx")
.namespace("default")
)
.spec(lambda s: s
.selector({"app": "nginx"})
.ports(lambda ports: ports.add(lambda p: p.container_port(80)))
)
.build()
)
```
The fluent builder provides:
- ✨ Full IDE support with detailed type information
- 🔍 Rich autocomplete for all fields and nested objects
- ⚡ Compile-time validation of your configuration
- 🎯 Clear and chainable API that guides you through resource creation
### Using the Context Manager Builder API
For complex nested resources, Cloudcoil also provides a context manager-based builder pattern that can make the structure more clear:
```python
from cloudcoil.models.kubernetes.apps.v1 import Deployment
from cloudcoil.models.kubernetes.core.v1 import Service
# Create a deployment using context managers
# Context managers are ideal for deeply nested structures
with Deployment.new() as nginx_deployment:
# Each context creates a clear visual scope
with nginx_deployment.metadata() as deployment_metadata:
deployment_metadata.name("nginx")
deployment_metadata.namespace("default")
with nginx_deployment.spec() as deployment_spec:
# Simple fields can be set directly
deployment_spec.replicas(3)
# Each nested object gets its own context
with deployment_spec.selector() as label_selector:
label_selector.match_labels({"app": "nginx"})
with deployment_spec.template() as pod_template:
with pod_template.metadata() as pod_metadata:
pod_metadata.labels({"app": "nginx"})
with pod_template.spec() as pod_spec:
# Collections use a parent context for the list
with pod_spec.containers() as container_list:
# And child contexts for each item
with container_list.add() as nginx_container:
nginx_container.name("nginx")
nginx_container.image("nginx:latest")
# Ports can be added one by one
with nginx_container.add_port() as container_port:
container_port.container_port(80)
final_deployment = nginx_deployment.build()
# Create a service using context managers
with Service.new() as nginx_service:
# Context managers make the structure very clear
with nginx_service.metadata() as service_metadata:
service_metadata.name("nginx")
service_metadata.namespace("default")
with nginx_service.spec() as service_spec:
# Simple fields can still be set directly
service_spec.selector({"app": "nginx"})
# Port configuration is more readable with contexts
with service_spec.add_port() as service_port:
service_port.port(80)
service_port.target_port(80)
final_service = nginx_service.build()
```
The context manager builder provides:
- 🎭 Clear visual nesting of resource structure
- 🔒 Automatic resource cleanup
- 🎯 Familiar Python context manager pattern
- ✨ Same great IDE support as the fluent builder
### Mixing Builder Styles
CloudCoil's intelligent builder system automatically detects which style you're using and provides appropriate IDE support:
```python
from cloudcoil.models.kubernetes.apps.v1 import Deployment
from cloudcoil import apimachinery
# Mixing styles lets you choose the best approach for each part
# The IDE automatically adapts to your chosen style at each level
with Deployment.new() as nginx_deployment:
# Direct object initialization with full type checking
nginx_deployment.metadata(apimachinery.ObjectMeta(
name="nginx",
namespace="default",
labels={"app": "nginx"}
))
with nginx_deployment.spec() as deployment_spec:
# IDE shows all available fields with types
deployment_spec.replicas(3)
# Fluent style with rich autocomplete
deployment_spec.selector(lambda sel: sel.match_labels({"app": "nginx"}))
# Context manager style with full type hints
with deployment_spec.template() as pod_template:
# Mix and match freely - IDE adjusts automatically
pod_template.metadata(apimachinery.ObjectMeta(labels={"app": "nginx"}))
with pod_template.spec() as pod_spec:
with pod_spec.containers() as container_list:
with container_list.add() as nginx_container:
# Complete IDE support regardless of style
nginx_container.name("nginx")
nginx_container.image("nginx:latest")
# Switch styles any time
nginx_container.ports(lambda ports: ports
.add(lambda p: p.container_port(80))
.add(lambda p: p.container_port(443))
)
final_deployment = nginx_deployment.build()
```
This flexibility allows you to:
- 🔀 Choose the most appropriate style for each part of your configuration
- 📖 Maximize readability for both simple and complex structures
- 🎨 Format your code according to your team's preferences
- 🧠 Get full IDE support with automatic style detection
- ✨ Enjoy rich autocomplete in all styles
- ⚡ Benefit from type checking across mixed styles
- 🎯 Receive immediate feedback on type errors
- 🔍 See documentation for all fields regardless of style
## 📚 Documentation
For complete documentation, visit [cloudcoil.github.io/cloudcoil](https://cloudcoil.github.io/cloudcoil)
## 📜 License
Apache License, Version 2.0 - see [LICENSE](LICENSE)