{
    "info": {
        "author": "Marcos Schroh",
        "author_email": "schrohm@gmail.com",
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            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
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            "Topic :: Software Development"
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        "description": "# Dataclasses Avro Schema Generator\n\nGenerate [Avro](https://avro.apache.org/docs/1.8.2/spec.html) Schemas from a Python class\n\n[![Build Status](https://img.shields.io/endpoint.svg?url=https%3A%2F%2Factions-badge.atrox.dev%2Fmarcosschroh%2Fdataclasses-avroschema%2Fbadge%3Fref%3Dmaster&style=flat)](https://actions-badge.atrox.dev/marcosschroh/dataclasses-avroschema/goto?ref=master)\n[![GitHub license](https://img.shields.io/github/license/marcosschroh/dataclasses-avroschema.svg)](https://github.com/marcosschroh/dataclasses-avroschema/blob/master/LICENSE)\n[![codecov](https://codecov.io/gh/marcosschroh/dataclasses-avroschema/branch/master/graph/badge.svg)](https://codecov.io/gh/marcosschroh/dataclasses-avroschema)\n![python version](https://img.shields.io/badge/python-3.7%2B-yellowgreen)\n\n## Requirements\n\n`python 3.7+`\n\n## Installation\n\n```bash\npip install dataclasses-avroschema\n```\n\nor with `pydantic` funcionalities\n\n```bash\npip install dataclasses-avroschema[pydantic]\n```\n\n## Documentation\n\nhttps://marcosschroh.github.io/dataclasses-avroschema/\n\n## Usage\n\n### Generating the avro schema\n\n```python\nfrom dataclasses import dataclass\nimport enum\n\nimport typing\n\nfrom dataclasses_avroschema import AvroModel, types\n\n\nclass FavoriteColor(enum.Enum):\n    BLUE = \"BLUE\"\n    YELLOW = \"YELLOW\"\n    GREEN = \"GREEN\"\n\n\n@dataclass\nclass User(AvroModel):\n    \"An User\"\n    name: str\n    age: int\n    pets: typing.List[str]\n    accounts: typing.Dict[str, int]\n    favorite_colors: FavoriteColor\n    country: str = \"Argentina\"\n    address: str = None\n\n    class Meta:\n        namespace = \"User.v1\"\n        aliases = [\"user-v1\", \"super user\"]\n\nUser.avro_schema()\n\n'{\n    \"type\": \"record\",\n    \"name\": \"User\",\n    \"doc\": \"An User\",\n    \"namespace\": \"User.v1\",\n    \"aliases\": [\"user-v1\", \"super user\"],\n    \"fields\": [\n        {\"name\": \"name\", \"type\": \"string\"},\n        {\"name\": \"age\", \"type\": \"long\"},\n        {\"name\": \"pets\", \"type\": \"array\", \"items\": \"string\"},\n        {\"name\": \"accounts\", \"type\": \"map\", \"values\": \"long\"},\n        {\"name\": \"favorite_colors\", \"type\": \"enum\", \"symbols\": [\"BLUE\", \"YELLOW\", \"GREEN\"]},\n        {\"name\": \"country\", \"type\": \"string\", \"default\": \"Argentina\"},\n        {\"name\": \"address\", \"type\": [\"null\", \"string\"], \"default\": null}\n    ]\n}'\n\nUser.avro_schema_to_python()\n\n{\n    \"type\": \"record\",\n    \"name\": \"User\",\n    \"doc\": \"An User\",\n    \"namespace\": \"User.v1\",\n    \"aliases\": [\"user-v1\", \"super user\"],\n    \"fields\": [\n        {\"name\": \"name\", \"type\": \"string\"},\n        {\"name\": \"age\", \"type\": \"long\"},\n        {\"name\": \"pets\", \"type\": {\"type\": \"array\", \"items\": \"string\", \"name\": \"pet\"}},\n        {\"name\": \"accounts\", \"type\": {\"type\": \"map\", \"values\": \"long\", \"name\": \"account\"}},\n        {\"name\": \"favorite_colors\", \"type\": {\"type\": \"enum\", \"name\": \"favorite_color\", \"symbols\": [\"BLUE\", \"YELLOW\", \"GREEN\"]}},\n        {\"name\": \"country\", \"type\": \"string\", \"default\": \"Argentina\"},\n        {\"name\": \"address\", \"type\": [\"null\", \"string\"], \"default\": None}\n    ],\n}\n```\n\n### Serialization to avro or avro-json and json payload\n\nFor serialization is neccesary to use python class/dataclasses instance\n\n```python\nfrom dataclasses import dataclass\n\nimport typing\n\nfrom dataclasses_avroschema import AvroModel\n\n\n@dataclass\nclass Address(AvroModel):\n    \"An Address\"\n    street: str\n    street_number: int\n\n@dataclass\nclass User(AvroModel):\n    \"User with multiple Address\"\n    name: str\n    age: int\n    addresses: typing.List[Address]\n\naddress_data = {\n    \"street\": \"test\",\n    \"street_number\": 10,\n}\n\n# create an Address instance\naddress = Address(**address_data)\n\ndata_user = {\n    \"name\": \"john\",\n    \"age\": 20,\n    \"addresses\": [address],\n}\n\n# create an User instance\nuser = User(**data_user)\n\nuser.serialize()\n# >>> b\"\\x08john(\\x02\\x08test\\x14\\x00\"\n\nuser.serialize(serialization_type=\"avro-json\")\n# >>> b'{\"name\": \"john\", \"age\": 20, \"addresses\": [{\"street\": \"test\", \"street_number\": 10}]}'\n\n# Get the json from the instance\nuser.to_json()\n# >>> '{\"name\": \"john\", \"age\": 20, \"addresses\": [{\"street\": \"test\", \"street_number\": 10}]}'\n\n# Get a python dict\nuser.to_dict()\n# >>> {\"name\": \"john\", \"age\": 20, \"addresses\": [{\"street\": \"test\", \"street_number\": 10}]}\n\n```\n\n### Deserialization\n\nDeserialization could take place with an instance dataclass or the dataclass itself. Can return the dict representation or a new class instance\n\n```python\nimport typing\n\nfrom dataclasses_avroschema import AvroModel\n\n\nclass Address(AvroModel):\n    \"An Address\"\n    street: str\n    street_number: int\n\nclass User(AvroModel):\n    \"User with multiple Address\"\n    name: str\n    age: int\n    addresses: typing.List[Address]\n\navro_binary = b\"\\x08john(\\x02\\x08test\\x14\\x00\"\navro_json_binary = b'{\"name\": \"john\", \"age\": 20, \"addresses\": [{\"street\": \"test\", \"street_number\": 10}]}'\n\n# return a new class instance!!\nUser.deserialize(avro_binary)\n# >>>> User(name='john', age=20, addresses=[Address(street='test', street_number=10)])\n\n# return a python dict\nUser.deserialize(avro_binary, create_instance=False)\n# >>> {\"name\": \"john\", \"age\": 20, \"addresses\": [{\"street\": \"test\", \"street_number\": 10}]}\n\n# return a new class instance!!\nUser.deserialize(avro_json_binary, serialization_type=\"avro-json\")\n# >>>> User(name='john', age=20, addresses=[Address(street='test', street_number=10)])\n\n# return a python dict\nUser.deserialize(avro_json_binary, serialization_type=\"avro-json\", create_instance=False)\n# >>> {\"name\": \"john\", \"age\": 20, \"addresses\": [{\"street\": \"test\", \"street_number\": 10}]}\n```\n\n## Pydantic integration\n\nTo add `dataclasses-avroschema` functionality to `pydantic` you only need to replace `BaseModel` by `AvroBaseModel`:\n\n```python\nimport typing\nimport enum\nfrom dataclasses_avroschema.avrodantic import AvroBaseModel\n\nfrom pydantic import Field\n\n\nclass FavoriteColor(str, enum.Enum):\n    BLUE = \"BLUE\"\n    YELLOW = \"YELLOW\"\n    GREEN = \"GREEN\"\n\n\nclass UserAdvance(AvroBaseModel):\n    name: str\n    age: int\n    pets: typing.List[str] = Field(default_factory=lambda: [\"dog\", \"cat\"])\n    accounts: typing.Dict[str, int] = Field(default_factory=lambda: {\"key\": 1})\n    has_car: bool = False\n    favorite_colors: FavoriteColor = FavoriteColor.BLUE\n    country: str = \"Argentina\"\n    address: str = None\n\n    class Meta:\n        schema_doc = False\n\n\n# Avro schema\nUserAdvance.avro_schema()\n'{\n    \"type\": \"record\",\n    \"name\": \"UserAdvance\",\n    \"fields\": [\n        {\"name\": \"name\", \"type\": \"string\"},\n        {\"name\": \"age\", \"type\": \"long\"},\n        {\"name\": \"pets\", \"type\": {\"type\": \"array\", \"items\": \"string\", \"name\": \"pet\"}, \"default\": [\"dog\", \"cat\"]},\n        {\"name\": \"accounts\", \"type\": {\"type\": \"map\", \"values\": \"long\", \"name\": \"account\"}, \"default\": {\"key\": 1}},\n        {\"name\": \"has_car\", \"type\": \"boolean\", \"default\": false},\n        {\"name\": \"favorite_colors\", \"type\": {\"type\": \"enum\", \"name\": \"favorite_color\", \"symbols\": [\"BLUE\", \"YELLOW\", \"GREEN\"]}, \"default\": \"BLUE\"},\n        {\"name\": \"country\", \"type\": \"string\", \"default\": \"Argentina\"},\n        {\"name\": \"address\", \"type\": [\"null\", \"string\"], \"default\": null}\n    ]\n}'\n\nuser = UserAdvance(name=\"bond\", age=50)\n\n# pydantic\nuser.dict()\n# >>> {'name': 'bond', 'age': 50, 'pets': ['dog', 'cat'], 'accounts': {'key': 1}, 'has_car': False, 'favorite_colors': <FavoriteColor.BLUE: 'BLUE'>, 'country': 'Argentina', 'address': None}\n\n# pydantic\nuser.json()\n# >>> '{\"name\": \"bond\", \"age\": 50, \"pets\": [\"dog\", \"cat\"], \"accounts\": {\"key\": 1}, \"has_car\": false, \"favorite_colors\": \"BLUE\", \"country\": \"Argentina\", \"address\": null}'\n\n# pydantic\nuser = UserAdvance(name=\"bond\")\n\n# ValidationError: 1 validation error for UserAdvance\n# age\n# field required (type=value_error.missing)\n\n\n# dataclasses-avroschema\nevent = user.serialize()\nprint(event)\n# >>> b'\\x08bondd\\x04\\x06dog\\x06cat\\x00\\x02\\x06key\\x02\\x00\\x00\\x00\\x12Argentina\\x00'\n\nUserAdvance.deserialize(data=event)\n# >>> UserAdvance(name='bond', age=50, pets=['dog', 'cat'], accounts={'key': 1}, has_car=False, favorite_colors=<FavoriteColor.BLUE: 'BLUE'>, country='Argentina', address=None)\n```\n\n## Examples with python streaming drivers (kafka and redis)\n\nUnder [examples](https://github.com/marcosschroh/dataclasses-avroschema/tree/master/examples) folder you can find 3 differents kafka examples, one with [aiokafka](https://github.com/aio-libs/aiokafka) (`async`) showing the simplest use case when a `AvroModel` instance is serialized and sent it thorught kafka, and the event is consumed.\nThe other two examples are `sync` using the [kafka-python](https://github.com/dpkp/kafka-python) driver, where the `avro-json` serialization and `schema evolution` (`FULL` compatibility) is shown.\nAlso, there are two `redis` examples using `redis streams` with [walrus](https://github.com/coleifer/walrus) and [redisgears-py](https://github.com/RedisGears/redisgears-py)\n\n## Factory and fixtures\n\n[Dataclasses Avro Schema](https://github.com/marcosschroh/dataclasses-avroschema) also includes a `factory` feature, so you can generate `fast` python instances and use them, for example, to test your data streaming pipelines. Instances can be genrated using the `fake` method.\n\n```python\nimport typing\n\nfrom dataclasses_avroschema import AvroModel\n\n\nclass Address(AvroModel):\n    \"An Address\"\n    street: str\n    street_number: int\n\nclass User(AvroModel):\n    \"User with multiple Address\"\n    name: str\n    age: int\n    addresses: typing.List[Address]\n\n\nAddress.fake()\n# >>>> Address(street='PxZJILDRgbXyhWrrPWxQ', street_number=2067)\n\nUser.fake()\n# >>>> User(name='VGSBbOGfSGjkMDnefHIZ', age=8974, addresses=[Address(street='vNpPYgesiHUwwzGcmMiS', street_number=4790)])\n```\n\n## Features\n\n* [X] Primitive types: int, long, double, float, boolean, string and null support\n* [X] Complex types: enum, array, map, fixed, unions and records support\n* [x] Logical Types: date, time, datetime, uuid support\n* [X] Schema relations (oneToOne, oneToMany)\n* [X] Recursive Schemas\n* [X] Generate Avro Schemas from `faust.Record`\n* [X] Instance serialization correspondent to `avro schema` generated\n* [X] Data deserialization. Return python dict or class instance\n* [X] Generate json from python class instance\n* [X] Case Schemas\n* [X] Examples of integration with `kafka` drivers: [aiokafka](https://github.com/aio-libs/aiokafka), [kafka-python](https://github.com/dpkp/kafka-python)\n* [X] Example of integration  with `redis` drivers: [walrus](https://github.com/coleifer/walrus) and [redisgears-py](https://github.com/RedisGears/redisgears-py)\n* [X] Factory instances\n* [X] [Pydantic](https://pydantic-docs.helpmanual.io/) integration\n\n## Development\n\n1. Create a `virtualenv`: `python3.7 -m venv venv && source venv/bin/activate`\n2. Install requirements: `pip install -r requirements.txt`\n3. Code linting: `./scripts/lint`\n4. Run tests: `./scripts/test`\n\n\n",
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