Node from_dict fails on required field port (in DeamonEndpoint)

Version 1.3.0

Hikaru is a collection of tools that allow you to work with Kubernetes resources from within Python in a variety of ways:

Hikaru provides type-annotated classes that model all of the Kubernetes resources in Python and supports CRUD operations on those classes to manage their lifecycle in your Kubernetes cluster.
Hikaru also provides tooling to shift formats for these objects, allowing you to turn K8s YAML into Python objects, JSON, or Python dicts, and vice-versa. It can also generate Python source code for K8s objects loaded from non-Python sources.
Hikaru also supports a number of features that aid in the management of your objects such as searching for specific fields or diffing two instances of a K8s resource.
Hikaru includes support for creating 'watches' on your objects, providing a means to monitor events on your provisioned K8s resources.
Hikaru provides support for creation of CRDs which support all the above features such as CRUD operations and watches.
Finally, Hikaru includes a facility to specify a collection of resources as an 'application', similar in spirit to a Helm chart, and provides the same CRUD, watch, and management capabilities on the entire application as it does on single resource objects (full format shifting support to come).

This hikaru package is a meta-package that has as dependencies:

hikaru-core for core Hikaru capabilities
hikaru-codegen for generating formatted Python source from Hikaru objects
The four most recent hikaru-model-* packages, currently:
- 25.x
- 26.x
- 27.x
- 28.x

See each package's specific PyPI page for any details on that package.

While you can continue to use this meta-package as before, we suggest you migrate to using the model versions that match the version of the Kubernetes API you need to interact with. This package just helps make that smoother.

From Python

Hikaru uses type-annotated Python dataclasses to represent each of the kinds of objects defined in the Kubernetes API, so when used with an IDE that understands Python type annotations, Hikaru enables the IDE to provide the user direct assistance as to what parameters are available, what type each parameter must be, and which parameters are optional. Assembled Hikaru object can be rendered into YAML that can be processed by regular Kubernetes tools.

From YAML

But you don’t have to start with authoring Python: you can use Hikaru to parse Kubernetes YAML into these same Python objects, at which point you can inspect the created objects, modify them and re-generate new YAML, or even have Hikaru emit Python source code that will re-create the same structure but from the Python interface.

From JSON

You can also process JSON or Python dict representations of Kubernetes configs into the corresponding Python objects

To YAML, Python, or JSON

Hikaru can output a Python Kubernetes object as Python source code, YAML, JSON, or a Python dict, and go back to any of these representations, allowing you to shift easily between representational formats for various purposes.

Supports multiple versions of Kubernetes

Hikaru allows you to use multiple releases of the Kubernetes client, providing appropriate bindings/methods/attributes for every object in each version of a release.

Direct Kubernetes via CRUD or low-level methods

You can use Hikaru objects to interact with a Kubernetes system. Hikaru wraps the Kubernetes Python client and maps API operations on to the Hikaru model they involve. For example, you can now create a Pod directly from a Pod object. Hikaru supports a higher-level CRUD-style set of methods as well as all the operations defined in the Swagger API specification.

Hikaru can work with any Kubernetes-compliant system such as K3s and minikube.

Monitor Kubernetes activities with watches

Hikaru provides an abstraction on over the K8s watch APIs, which allow you to easily create code that receives events for all activites carried out in your K8s cluster on a per-kind basis. Or, you can create a watch container that multiplexes the output from individual watches into a single stream.

Create Custom Resource Definitions

As of release 1.0.0, Hikaru supports the creation of CRDs that integrate with the rest of Hikaru. Automatically generate schema from a Hikaru class, define CRDs to Kubernetes, manage CRD instances with CRUD methods, and create watchers that allow you to build your own controllers for your CRDs.

Model entire applications

Hikaru provides an Application base class that can be derived from to allow the creation of sets of resources that comprise the infra for an application system. This Application model can be inspected, provisioned into a cluster, read from a cluster, and watched, just like with individual Hikaru K8s objects.

Integrate your own subclasses

You can not only in create your own subclasses of Hikaru document classes for your own use, but you can also register these classes with Hikaru and it will make instances of your classes when Hikaru encounters the relevant apiVersion and kind values. So for example, you can create your own MyPod subclass of Pod, and Hikaru will instantiate your subclass when reading Pod YAML.

Alternative to templating for customisation

Using Hikaru, you can assemble Kubernetes objects using previously defined libraries of objects in Python, craft replacements procedurally, or even tweak the values of an existing object and turn it back into YAML.

Build models for uses other than controlling systems

You can use Hikaru in the process of issuing instructions to Kubernetes, but the same Hikaru models can be used as high-fidelity replicas of the YAML for other processes as well.

Type checking, diffing, merging, and inspection

Hikaru supports a number of other operations on the Python objects it defines. For example, you can check the types of all attributes in a config against the defined types for each attribute, you can diff two configs to see where they aren't the same, and you can search through a config for specific values and contained objects.

API coverage

Hikaru supports all objects in the OpenAPI swagger spec for the Kubernetes API v 1.26, and has initial support for methods on those objects from the same swagger spec. Additionally, it defines some higher-level CRUD-style methods on top of these foundation methods.

Usage examples

To create Python objects from a Kubernetes YAML source, use load_full_yaml():

from hikaru import load_full_yaml  # or just 'from hikaru import *'

docs = load_full_yaml(stream=open("test.yaml", "r"))
p = docs[0]

load_full_yaml() loads every Kubernetes YAML document in a YAML file and returns a list of the resulting Hikaru objects found. You can then use the YAML property names to navigate the resulting object. If you assert that an object is of a known object type, your IDE can provide you assistance in navigation:

from hikaru.model.rel_1_16 import Pod
assert isinstance(p, Pod)
print(p.metadata.labels["lab2"])
print(p.spec.containers[0].ports[0].containerPort)
for k, v in p.metadata.labels.items():
    print(f"key:{k} value:{v}")

You can create Hikaru representations of Kubernetes objects in Python:

from hikaru.model.rel_1_16 import Pod, PodSpec, Container, ObjectMeta
x = Pod(apiVersion='v1', kind='Pod',
        metadata=ObjectMeta(name='hello-kiamol-3'),
        spec=PodSpec(
            containers=[Container(name='web', image='kiamol/ch02-hello-kiamol') ]
             )
    )

…and then render it in YAML:

from hikaru import get_yaml
print(get_yaml(x))

…which yields:

---
apiVersion: v1
kind: Pod
metadata:
  name: hello-kiamol-3
spec:
  containers:
    - name: web
      image: kiamol/ch02-hello-kiamol

If you use Hikaru to parse this back in as Python objects, you can then ask Hikaru to output Python source code that will re-create it (thus providing a migration path):

from hikaru import get_python_source, load_full_yaml
docs = load_full_yaml(path="to/the/above.yaml")
print(get_python_source(docs[0], assign_to='x', style="black"))

...which results in:

x = Pod(
    apiVersion="v1",
    kind="Pod",
    metadata=ObjectMeta(name="hello-kiamol-3"),
    spec=PodSpec(containers=[Container(name="web", image="kiamol/ch02-hello-kiamol")]),
)

...and then turn it into real Kubernetes resources using the CRUD methods:

x.create(namespace='my-namespace')

...or read an existing object back in:

p = Pod().read(name='hello-kiamol-3', namespace='my-namespace')

...or use a Hikaru object as a context manager to automatically perform updates:

with Pod().read(name='hello-kiamol-3', namespace='my-namespace') as p:
        p.metadata.labels["new-label"] = 'some-value'
        # and other changes

# when the 'with' ends, the context manager sends an update()

It is entirely possible to load YAML into Python, tailor it, and then send it back to YAML; Hikaru can round-trip YAML through Python and then back to the equivalent YAML.

The pieces of complex objects can be created separately and even stored in a standard components library module for assembly later, or returned as the value of a factory function, as opposed to using a templating system to piece text files together:

from component_lib import web_container, lb_container
from hikaru.model.rel_1_16 import Pod, ObjectMeta, PodSpec
# make an ObjectMeta instance here called "om"
p = Pod(apiVersion="v1", kind="Pod",
        metadata=om,
        spec=PodSpec(containers=[web_container, lb_container])
        )

You can also transform Hikaru objects into Python dicts:

from pprint import pprint
pprint(get_clean_dict(x))

...which yields:

{'apiVersion': 'v1',
 'kind': 'Pod',
 'metadata': {'name': 'hello-kiamol-3'},
 'spec': {'containers': [{'image': 'kiamol/ch02-hello-kiamol', 'name': 'web'}]}}

...and go back into Hikaru objects. You can also render Hikaru objects as JSON:

from hikaru import *
print(get_json(x))

...which outputs the similar:

{"apiVersion": "v1", "kind": "Pod", "metadata": {"name": "hello-kiamol-3"}, "spec": {"containers": [{"name": "web", "image": "kiamol/ch02-hello-kiamol"}]}}

Hikaru lets you go from JSON back to Hikaru objects as well.

Hikaru objects can be tested for equivalence with ‘==’, and you can also easily create deep copies of entire object structures with dup(). This latter is useful in cases where you have a component that you want to use multiple times in a model but need it slightly tweaked in each use; a shared instance can’t have different values at each use, so it’s easy to make a copy that can be customised in isolation.

Finally, every Hikaru object that holds other properties and objects have methods that allow you to search the entire collection of objects. This lets you find various objects of interest for review and checking against policies and conventions. For example, if we had a Pod ‘p’ that was pulled in with load_full_yaml(), we could examine all of the Container objects with:

containers = p.find_by_name("containers")
for c in containers:
    # check what you want...

Or you can get all of the ExecAction object (the value of ‘exec’ properties) that are part the second container’s lifecycle’s httpGet property like so:

execs = p.find_by_name("exec", following='containers.1.lifecycle.httpGet')

These queries result in a list of CatalogEntry objects, which are named tuples that provide the path to the found element. You can acquire the actual element for inspection with the object_at_path() method:

o = p.object_at_path(execs[0].path)

This makes it easy to scan for specific items in a config under automated control.

Future work

With basic support of managing Kubernetes resources in place, other directions are being considered such as event/watch support and bringing in support for additional releases of Kubernetes.

About

Hikaru is Mr. Sulu’s first name, a famed fictional helmsman.

	for f in fields(self.__class__):
	k8s_name = f.name.strip("_")
	k8s_name = (h2kc_translate(self.__class__, k8s_name)
	if translate
	else k8s_name)

haxsaw / hikaru Goto Github PK

hikaru's Introduction

From Python

From YAML

From JSON

To YAML, Python, or JSON

Supports multiple versions of Kubernetes

Direct Kubernetes via CRUD or low-level methods

Monitor Kubernetes activities with watches

Create Custom Resource Definitions

Model entire applications

Integrate your own subclasses

Alternative to templating for customisation

Build models for uses other than controlling systems

Type checking, diffing, merging, and inspection

API coverage

Usage examples

Future work

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