{
    "info": {
        "author": "Marcos Schroh",
        "author_email": "schrohm@gmail.com",
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        "classifiers": [
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3 :: Only",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development"
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        "description": "# Python Rest Client Schema Registry\n\n[![Build Status](https://img.shields.io/endpoint.svg?url=https%3A%2F%2Factions-badge.atrox.dev%2Fmarcosschroh%2Fpython-schema-registry-client%2Fbadge%3Fref%3Dmaster&style=flat)](https://actions-badge.atrox.dev/marcosschroh/python-schema-registry-client/goto?ref=master)\n[![GitHub license](https://img.shields.io/github/license/marcosschroh/python-schema-registry-client.svg)](https://github.com/marcosschroh/python-schema-registry-client/blob/master/LICENSE)\n[![codecov](https://codecov.io/gh/marcosschroh/python-schema-registry-client/branch/master/graph/badge.svg)](https://codecov.io/gh/marcosschroh/python-schema-registry-client)\n[![Python Version](https://img.shields.io/badge/python-3.7+-blue.svg)](https://img.shields.io/badge/python-3.7+-blue.svg)\n\nPython Rest Client to interact against [schema-registry](https://docs.confluent.io/current/schema-registry/index.html) confluent server to manage [Avro](https://docs.oracle.com/database/nosql-12.1.3.1/GettingStartedGuide/avroschemas.html) and [JSON](https://json-schema.org/) schemas resources.\n\n## Requirements\n\npython 3.7+\n\n## Installation\n\n```bash\npip install python-schema-registry-client\n```\n\nIf you want the `Faust` functionality:\n\n```bash\npip install python-schema-registry-client[faust]\n```\n\nNote that this will automatically add a dependency on the [faust-streaming](https://github.com/faust-streaming/faust) fork of faust. If you want to use the\nold faust version, simply install it manually and then install `python-schema-registry-client` without the `faust` extra enabled, the functionality will\nbe the same.\n\n## Client API, Serializer, Faust Integration and Schema Server description\n\n**Documentation**: [https://marcosschroh.github.io/python-schema-registry-client.io](https://marcosschroh.github.io/python-schema-registry-client)\n\n## Avro Schema Usage\n\n```python\nfrom schema_registry.client import SchemaRegistryClient, schema\n\nclient = SchemaRegistryClient(url=\"http://127.0.0.1:8081\")\n\ndeployment_schema = {\n    \"type\": \"record\",\n    \"namespace\": \"com.kubertenes\",\n    \"name\": \"AvroDeployment\",\n    \"fields\": [\n        {\"name\": \"image\", \"type\": \"string\"},\n        {\"name\": \"replicas\", \"type\": \"int\"},\n        {\"name\": \"port\", \"type\": \"int\"},\n    ],\n}\n\navro_schema = schema.AvroSchema(deployment_schema)\n\nschema_id = client.register(\"test-deployment\", avro_schema)\n```\n\nor async\n\n```python\nfrom schema_registry.client import AsyncSchemaRegistryClient, schema\n\nasync_client = AsyncSchemaRegistryClient(url=\"http://127.0.0.1:8081\")\n\ndeployment_schema = {\n    \"type\": \"record\",\n    \"namespace\": \"com.kubertenes\",\n    \"name\": \"AvroDeployment\",\n    \"fields\": [\n        {\"name\": \"image\", \"type\": \"string\"},\n        {\"name\": \"replicas\", \"type\": \"int\"},\n        {\"name\": \"port\", \"type\": \"int\"},\n    ],\n}\n\navro_schema = schema.AvroSchema(deployment_schema)\n\nschema_id = await async_client.register(\"test-deployment\", avro_schema)\n```\n\n## JSON Schema Usage\n\n```python\nfrom schema_registry.client import SchemaRegistryClient, schema\n\nclient = SchemaRegistryClient(url=\"http://127.0.0.1:8081\")\n\ndeployment_schema = {\n    \"definitions\" : {\n        \"JsonDeployment\" : {\n            \"type\" : \"object\",\n            \"required\" : [\"image\", \"replicas\", \"port\"],\n            \"properties\" : {\n                \"image\" :       {\"type\" : \"string\"},\n                \"replicas\" :    {\"type\" : \"integer\"},\n                \"port\" :        {\"type\" : \"integer\"}\n            }\n        }\n    },\n    \"$ref\" : \"#/definitions/JsonDeployment\"\n}\n\njson_schema = schema.JsonSchema(deployment_schema)\n\nschema_id = client.register(\"test-deployment\", json_schema)\n```\n\nor async\n\n```python\nfrom schema_registry.client import AsyncSchemaRegistryClient, schema\n\nasync_client = AsyncSchemaRegistryClient(url=\"http://127.0.0.1:8081\")\n\ndeployment_schema = {\n    \"definitions\" : {\n        \"JsonDeployment\" : {\n            \"type\" : \"object\",\n            \"required\" : [\"image\", \"replicas\", \"port\"],\n            \"properties\" : {\n                \"image\" :       {\"type\" : \"string\"},\n                \"replicas\" :    {\"type\" : \"integer\"},\n                \"port\" :        {\"type\" : \"integer\"}\n            }\n        }\n    },\n    \"$ref\" : \"#/definitions/JsonDeployment\"\n}\n\njson_schema = schema.JsonSchema(deployment_schema)\n\nschema_id = await async_client.register(\"test-deployment\", json_schema)\n```\n\n## Usage with dataclasses-avroschema for avro schemas\n\nYou can generate the `avro schema` directely from a python class using [dataclasses-avroschema](https://github.com/marcosschroh/dataclasses-avroschema)\nand use it in the API for `register schemas`, `check versions` and `test compatibility`:\n\n```python\nimport dataclasses\n\nfrom dataclasses_avroschema import AvroModel, types\n\nfrom schema_registry.client import SchemaRegistryClient\n\nclient = SchemaRegistryClient(url=\"http://127.0.0.1:8081\")\n\n\n@dataclasses.dataclass\nclass UserAdvance(AvroModel):\n    name: str\n    age: int\n    pets: typing.List[str] = dataclasses.field(default_factory=lambda: [\"dog\", \"cat\"])\n    accounts: typing.Dict[str, int] = dataclasses.field(default_factory=lambda: {\"key\": 1})\n    has_car: bool = False\n    favorite_colors: types.Enum = types.Enum([\"BLUE\", \"YELLOW\", \"GREEN\"], default=\"BLUE\")\n    country: str = \"Argentina\"\n    address: str = None\n\n# register the schema\nschema_id = client.register(subject, UserAdvance.avro_schema())\n\nprint(schema_id)\n# >>> 12\n\nresult = client.check_version(subject, UserAdvance.avro_schema())\nprint(result)\n# >>> SchemaVersion(subject='dataclasses-avroschema-subject-2', schema_id=12, schema=1, version={\"type\":\"record\" ...')\n\ncompatibility = client.test_compatibility(subject, UserAdvance.avro_schema())\nprint(compatibility)\n\n# >>> True\n```\n\n### Usage with pydantic for json schemas\nYou can generate the json schema directely from a python class using pydantic and use it in the API for register schemas, check versions and test compatibility:\n\n```python\nimport typing\n\nfrom enum import Enum\n\nfrom pydantic import BaseModel\n\nfrom schema_registry.client import SchemaRegistryClient\n\nclient = SchemaRegistryClient(url=\"http://127.0.0.1:8081\")\n\nclass ColorEnum(str, Enum):\n  BLUE = \"BLUE\"\n  YELLOW = \"YELLOW\"\n  GREEN = \"GREEN\"\n\n\nclass UserAdvance(BaseModel):\n    name: str\n    age: int\n    pets: typing.List[str] = [\"dog\", \"cat\"]\n    accounts: typing.Dict[str, int] = {\"key\": 1}\n    has_car: bool = False\n    favorite_colors: ColorEnum = ColorEnum.BLUE\n    country: str = \"Argentina\"\n    address: str = None\n\n# register the schema\nschema_id = client.register(subject, UserAdvance.schema_json(), schema_type=\"JSON\")\n\nprint(schema_id)\n# >>> 12\n\nresult = client.check_version(subject, UserAdvance.schema_json(), schema_type=\"JSON\")\nprint(result)\n# >>> SchemaVersion(subject='pydantic-jsonschema-subject', schema_id=12, schema=1, version=<schema_registry.client.schema.JsonSchema object at 0x7f40354550a0>)\n\ncompatibility = client.test_compatibility(subject, UserAdvance.schema_json(), schema_type=\"JSON\")\nprint(compatibility)\n\n# >>> True\n```\n\n## Serializers\n\nYou can use `AvroMessageSerializer` to encode/decode messages in `avro`\n\n```python\nfrom schema_registry.client import SchemaRegistryClient, schema\nfrom schema_registry.serializers import AvroMessageSerializer\n\n\nclient = SchemaRegistryClient(\"http://127.0.0.1:8081\")\navro_message_serializer = AvroMessageSerializer(client)\n\navro_user_schema = schema.AvroSchema({\n    \"type\": \"record\",\n    \"namespace\": \"com.example\",\n    \"name\": \"AvroUsers\",\n    \"fields\": [\n        {\"name\": \"first_name\", \"type\": \"string\"},\n        {\"name\": \"last_name\", \"type\": \"string\"},\n        {\"name\": \"age\", \"type\": \"int\"},\n\n    ],\n})\n\n# We want to encode the user_record with avro_user_schema\nuser_record = {\n    \"first_name\": \"my_first_name\",\n    \"last_name\": \"my_last_name\",\n    \"age\": 20,\n}\n\n# Encode the record\nmessage_encoded = avro_message_serializer.encode_record_with_schema(\n    \"user\", avro_user_schema, user_record)\n\nprint(message_encoded)\n# >>> b'\\x00\\x00\\x00\\x00\\x01\\x1amy_first_name\\x18my_last_name('\n```\n\nor with `json schemas`\n\n```python\nfrom schema_registry.client import SchemaRegistryClient, schema\nfrom schema_registry.serializers import JsonMessageSerializer\n\n\nclient = SchemaRegistryClient(\"http://127.0.0.1:8081\")\njson_message_serializer = JsonMessageSerializer(client)\n\njson_schema = schema.JsonSchema({\n  \"definitions\" : {\n    \"record:python.test.basic.basic\" : {\n      \"description\" : \"basic schema for tests\",\n      \"type\" : \"object\",\n      \"required\" : [ \"number\", \"name\" ],\n      \"properties\" : {\n        \"number\" : {\n          \"oneOf\" : [ {\n            \"type\" : \"integer\"\n          }, {\n            \"type\" : \"null\"\n          } ]\n        },\n        \"name\" : {\n          \"oneOf\" : [ {\n            \"type\" : \"string\"\n          } ]\n        }\n      }\n    }\n  },\n  \"$ref\" : \"#/definitions/record:python.test.basic.basic\"\n})\n\n# Encode the record\nbasic_record = {\n    \"number\": 10,\n    \"name\": \"a_name\",\n}\n\nmessage_encoded = json_message_serializer.encode_record_with_schema(\n    \"basic\", json_schema, basic_record)\n\nprint(message_encoded)\n# >>> b'\\x00\\x00\\x00\\x00\\x02{\"number\": 10, \"name\": \"a_name\"}'\n```\n\n## When use this library\n\nUsually, we have a situation like this:\n\n![Confluent Architecture](docs/img/confluent_architecture.png)\n\nSo, our producers/consumers have to serialize/deserialize messages every time that they send/receive from Kafka topics. In this picture, we can imagine a `Faust` application receiving messages (encoded with an Avro schema) and we want to deserialize them, so we can ask the `schema server` to do that for us. In this scenario, the `MessageSerializer` is perfect.\n\nAlso, could be a use case that we would like to have an Application only to administrate `Avro Schemas` (register, update compatibilities, delete old schemas, etc.), so the `SchemaRegistryClient` is perfect.\n\n## Development\n\nInstall the project and development utilities in edit mode:\n\n```bash\npip3 install -e \".[tests,docs,faust]\"\n```\n\nThe tests are run against the `Schema Server` using `docker compose`, so you will need\n`Docker` and `Docker Compose` installed.\n\n```bash\n./scripts/test\n```\n\nYou can run tests with arbitrary python version by:\n\n```bash\n./scripts/test --python-version 3.x\n```\n\nAll additional args will be passed to pytest, for example:\n\n```bash\n./scripts/test ./tests/client/ --maxfail=1 \n```\n\nRun code linting:\n\n```bash\n./scripts/lint\n```\n\nTo perform tests using the python shell you can execute `docker-compose up` and the `schema registry server` \nwill run on `http://127.0.0.1:8081`, then you can interact against it using the `SchemaRegistryClient`:\n\n```python\nfrom schema_registry.client import SchemaRegistryClient, schema\n\nclient = SchemaRegistryClient(url=\"http://127.0.0.1:8081\")\n\n# do some operations with the client...\n```",
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