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        "author": "Sergey Vasilyev",
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        "description": "# Kubernetes Operator Pythonic Framework (Kopf)\n\n[![CI](https://github.com/nolar/kopf/workflows/Thorough%20tests/badge.svg)](https://github.com/nolar/kopf/actions/workflows/thorough.yaml)\n[![codecov](https://codecov.io/gh/nolar/kopf/branch/main/graph/badge.svg)](https://codecov.io/gh/nolar/kopf)\n[![Coverage Status](https://coveralls.io/repos/github/nolar/kopf/badge.svg?branch=main)](https://coveralls.io/github/nolar/kopf?branch=main)\n[![Total alerts](https://img.shields.io/lgtm/alerts/g/nolar/kopf.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/nolar/kopf/alerts/)\n[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/nolar/kopf.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/nolar/kopf/context:python)\n[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)\n\n**Kopf** \u2014Kubernetes Operator Pythonic Framework\u2014 is a framework and a library\nto make Kubernetes operators development easier, just in a few lines of Python code.\n\nThe main goal is to bring the Domain-Driven Design to the infrastructure level,\nwith Kubernetes being an orchestrator/database of the domain objects (custom resources),\nand the operators containing the domain logic (with no or minimal infrastructure logic).\n\nThe project was originally started as `zalando-incubator/kopf` in March 2019,\nand then forked as `nolar/kopf` in August 2020: but it is the same codebase,\nthe same packages, the same developer(s).\n\n\n## Documentation\n\n* https://kopf.readthedocs.io/\n\n\n## Features\n\n* Simple, but powerful:\n  * A full-featured operator in just 2 files: a `Dockerfile` + a Python file (*).\n  * Handling functions registered via decorators with a declarative approach.\n  * No infrastructure boilerplate code with K8s API communication.\n  * Both sync and async handlers, with sync ones being threaded under the hood.\n  * Detailed documentation with examples.\n* Intuitive mapping of Python concepts to Kubernetes concepts and back:\n  * Marshalling of resources' data to the handlers' kwargs.\n  * Marshalling of handlers' results to the resources' statuses.\n  * Publishing of logging messages as Kubernetes events linked to the resources.\n* Support anything that exists in K8s:\n  * Custom K8s resources.\n  * Builtin K8s resources (pods, namespaces, etc).\n  * Multiple resource types in one operator.\n  * Both cluster and namespaced operators.\n* All the ways of handling that a developer can wish for:\n  * Low-level handlers for events received from K8s APIs \"as is\" (an equivalent of _informers_).\n  * High-level handlers for detected causes of changes (creation, updates with diffs, deletion).\n  * Handling of selected fields only instead of the whole objects (if needed).\n  * Dynamically generated or conditional sub-handlers (an advanced feature).\n  * Timers that tick as long as the resource exists, optionally with a delay since the last change.\n  * Daemons that run as long as the resource exists (in threads or asyncio-tasks).\n  * Validating and mutating admission webhook (with dev-mode tunneling).\n  * Live in-memory indexing of resources or their excerpts.\n  * Filtering with stealth mode (no logging): by arbitrary filtering functions,\n    by labels/annotations with values, presence/absence, or dynamic callbacks.\n  * In-memory all-purpose containers to store non-serializable objects for individual resources.\n* Eventual consistency of handling:\n  * Retrying the handlers in case of arbitrary errors until they succeed.\n  * Special exceptions to request a special retry or to never retry again.\n  * Custom limits for the number of attempts or the time.\n  * Implicit persistence of the progress that survives the operator restarts.\n  * Tolerance to restarts and lengthy downtimes: handles the changes afterwards.\n* Awareness of other Kopf-based operators:\n  * Configurable identities for different Kopf-based operators for the same resource kinds.\n  * Avoiding double-processing due to cross-pod awareness of the same operator (\"peering\").\n  * Pausing of a deployed operator when a dev-mode operator runs outside of the cluster.\n* Extra toolkits and integrations:\n  * Some limited support for object hierarchies with name/labels propagation.\n  * Friendly to any K8s client libraries (and is client agnostic).\n  * Startup/cleanup operator-level handlers.\n  * Liveness probing endpoints and rudimentary metrics exports.\n  * Basic testing toolkit for in-memory per-test operator running.\n  * Embeddable into other Python applications.\n* Highly configurable (to some reasonable extent).\n\n(*) _Small font: two files of the operator itself, plus some amount of\ndeployment files like RBAC roles, bindings, service accounts, network policies\n\u2014 everything needed to deploy an application in your specific infrastructure._\n\n\n## Examples\n\nSee [examples](https://github.com/nolar/kopf/tree/main/examples)\nfor the examples of the typical use-cases.\n\nA minimalistic operator can look like this:\n\n```python\nimport kopf\n\n@kopf.on.create('kopfexamples')\ndef create_fn(spec, name, meta, status, **kwargs):\n    print(f\"And here we are! Created {name} with spec: {spec}\")\n```\n\nNumerous kwargs are available, such as `body`, `meta`, `spec`, `status`,\n`name`, `namespace`, `retry`, `diff`, `old`, `new`, `logger`, etc:\nsee [Arguments](https://kopf.readthedocs.io/en/latest/kwargs/)\n\nTo run a never-exiting function for every resource as long as it exists:\n\n```python\nimport time\nimport kopf\n\n@kopf.daemon('kopfexamples')\ndef my_daemon(spec, stopped, **kwargs):\n    while not stopped:\n        print(f\"Object's spec: {spec}\")\n        time.sleep(1)\n```\n\nOr the same with the timers:\n\n```python\nimport kopf\n\n@kopf.timer('kopfexamples', interval=1)\ndef my_timer(spec, **kwargs):\n    print(f\"Object's spec: {spec}\")\n```\n\nThat easy! For more features, see the [documentation](https://kopf.readthedocs.io/).\n\n\n## Usage\n\nPython 3.7+ is required:\n[CPython](https://www.python.org/) and [PyPy](https://www.pypy.org/)\nare officially supported and tested; other Python implementations can work too.\n\nWe assume that when the operator is executed in the cluster, it must be packaged\ninto a docker image with a CI/CD tool of your preference.\n\n```dockerfile\nFROM python:3.7\nADD . /src\nRUN pip install kopf\nCMD kopf run /src/handlers.py --verbose\n```\n\nWhere `handlers.py` is your Python script with the handlers\n(see `examples/*/example.py` for the examples).\n\nSee `kopf run --help` for other ways of attaching the handlers.\n\n\n## Contributing\n\nPlease read [CONTRIBUTING.md](https://github.com/nolar/kopf/blob/main/CONTRIBUTING.md)\nfor details on our process for submitting pull requests to us, and please ensure\nyou follow the [CODE_OF_CONDUCT.md](https://github.com/nolar/kopf/blob/main/CODE_OF_CONDUCT.md).\n\nTo install the environment for the local development,\nread [DEVELOPMENT.md](https://github.com/nolar/kopf/blob/main/DEVELOPMENT.md).\n\n\n## Versioning\n\nWe use [SemVer](http://semver.org/) for versioning. For the versions available,\nsee the [releases on this repository](https://github.com/nolar/kopf/releases).\n\n\n## License\n\nThis project is licensed under the MIT License \u2014\nsee the [LICENSE](https://github.com/nolar/kopf/blob/main/LICENSE) file for details.\n\n\n## Acknowledgments\n\n* Thanks to Zalando for starting this project in Zalando's Open-Source Incubator\n  in the first place.\n* Thanks to [@side8](https://github.com/side8) and their [k8s-operator](https://github.com/side8/k8s-operator)\n  for inspiration.\n\n\n",
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