Dataclasses Avro Schema Generator

Generate Avro Schemas from a Python class

Requirements

python 3.7+

Installation

with pip or poetry:

pip install dataclasses-avroschema or poetry install

Extras

• pydantic: pip install 'dataclasses-avroschema[pydantic]' or poetry install --extras "pydantic"
• faust-streaming: pip install 'dataclasses-avroschema[faust]' or poetry install --extras "faust"

Note: You can install all extra dependencies with pip install dataclasses-avroschema[faust, pydantic] or poetry install --extras "pydantic faust"

CLI

To add avro schemas cli install dc-avro

pip install 'dataclasses-avroschema[cli]' or poetry install --with cli

Documentation

https://marcosschroh.github.io/dataclasses-avroschema/

Usage

Generating the avro schema

from dataclasses import dataclass
import enum

import typing

from dataclasses_avroschema import AvroModel, types


class FavoriteColor(enum.Enum):
    BLUE = "BLUE"
    YELLOW = "YELLOW"
    GREEN = "GREEN"


@dataclass
class User(AvroModel):
    "An User"
    name: str
    age: int
    pets: typing.List[str]
    accounts: typing.Dict[str, int]
    favorite_colors: FavoriteColor
    country: str = "Argentina"
    address: str = None

    class Meta:
        namespace = "User.v1"
        aliases = ["user-v1", "super user"]

User.avro_schema()

'{
    "type": "record",
    "name": "User",
    "doc": "An User",
    "namespace": "User.v1",
    "aliases": ["user-v1", "super user"],
    "fields": [
        {"name": "name", "type": "string"},
        {"name": "age", "type": "long"},
        {"name": "pets", "type": "array", "items": "string"},
        {"name": "accounts", "type": "map", "values": "long"},
        {"name": "favorite_color", "type": {"type": "enum", "name": "FavoriteColor", "symbols": ["Blue", "Yellow", "Green"]}}
        {"name": "country", "type": "string", "default": "Argentina"},
        {"name": "address", "type": ["null", "string"], "default": null}
    ]
}'

User.avro_schema_to_python()

{
    "type": "record",
    "name": "User",
    "doc": "An User",
    "namespace": "User.v1",
    "aliases": ["user-v1", "super user"],
    "fields": [
        {"name": "name", "type": "string"},
        {"name": "age", "type": "long"},
        {"name": "pets", "type": {"type": "array", "items": "string", "name": "pet"}},
        {"name": "accounts", "type": {"type": "map", "values": "long", "name": "account"}},
        {"name": "favorite_colors", "type": {"type": "enum", "name": "FavoriteColor", "symbols": ["BLUE", "YELLOW", "GREEN"]}},
        {"name": "country", "type": "string", "default": "Argentina"},
        {"name": "address", "type": ["null", "string"], "default": None}
    ],
}

Serialization to avro or avro-json and json payload

For serialization is neccesary to use python class/dataclasses instance

from dataclasses import dataclass

import typing

from dataclasses_avroschema import AvroModel


@dataclass
class Address(AvroModel):
    "An Address"
    street: str
    street_number: int


@dataclass
class User(AvroModel):
    "User with multiple Address"
    name: str
    age: int
    addresses: typing.List[Address]

address_data = {
    "street": "test",
    "street_number": 10,
}

# create an Address instance
address = Address(**address_data)

data_user = {
    "name": "john",
    "age": 20,
    "addresses": [address],
}

# create an User instance
user = User(**data_user)

user.serialize()
# >>> b"\x08john(\x02\x08test\x14\x00"

user.serialize(serialization_type="avro-json")
# >>> b'{"name": "john", "age": 20, "addresses": [{"street": "test", "street_number": 10}]}'

# Get the json from the instance
user.to_json()
# >>> '{"name": "john", "age": 20, "addresses": [{"street": "test", "street_number": 10}]}'

# Get a python dict
user.to_dict()
# >>> {"name": "john", "age": 20, "addresses": [{"street": "test", "street_number": 10}]}

Deserialization

Deserialization could take place with an instance dataclass or the dataclass itself. Can return the dict representation or a new class instance

import typing
import dataclasses

from dataclasses_avroschema import AvroModel


@dataclasses.dataclass
class Address(AvroModel):
    "An Address"
    street: str
    street_number: int

@dataclasses.dataclass
class User(AvroModel):
    "User with multiple Address"
    name: str
    age: int
    addresses: typing.List[Address]

avro_binary = b"\x08john(\x02\x08test\x14\x00"
avro_json_binary = b'{"name": "john", "age": 20, "addresses": [{"street": "test", "street_number": 10}]}'

# return a new class instance!!
User.deserialize(avro_binary)
# >>>> User(name='john', age=20, addresses=[Address(street='test', street_number=10)])

# return a python dict
User.deserialize(avro_binary, create_instance=False)
# >>> {"name": "john", "age": 20, "addresses": [{"street": "test", "street_number": 10}]}

# return a new class instance!!
User.deserialize(avro_json_binary, serialization_type="avro-json")
# >>>> User(name='john', age=20, addresses=[Address(street='test', street_number=10)])

# return a python dict
User.deserialize(avro_json_binary, serialization_type="avro-json", create_instance=False)
# >>> {"name": "john", "age": 20, "addresses": [{"street": "test", "street_number": 10}]}

Pydantic integration

To add dataclasses-avroschema functionality to pydantic you only need to replace BaseModel by AvroBaseModel:

import typing
import enum
import dataclasses

from dataclasses_avroschema.avrodantic import AvroBaseModel

from pydantic import Field


class FavoriteColor(str, enum.Enum):
    BLUE = "BLUE"
    YELLOW = "YELLOW"
    GREEN = "GREEN"


@dataclasses.dataclass
class UserAdvance(AvroBaseModel):
    name: str
    age: int
    pets: typing.List[str] = Field(default_factory=lambda: ["dog", "cat"])
    accounts: typing.Dict[str, int] = Field(default_factory=lambda: {"key": 1})
    has_car: bool = False
    favorite_colors: FavoriteColor = FavoriteColor.BLUE
    country: str = "Argentina"
    address: str = None

    class Meta:
        schema_doc = False


# Avro schema
UserAdvance.avro_schema()
'{
    "type": "record",
    "name": "UserAdvance",
    "fields": [
        {"name": "name", "type": "string"},
        {"name": "age", "type": "long"},
        {"name": "pets", "type": {"type": "array", "items": "string", "name": "pet"}, "default": ["dog", "cat"]},
        {"name": "accounts", "type": {"type": "map", "values": "long", "name": "account"}, "default": {"key": 1}},
        {"name": "has_car", "type": "boolean", "default": false},
        {"name": "favorite_colors", "type": {"type": "enum", "name": "favorite_color", "symbols": ["BLUE", "YELLOW", "GREEN"]}, "default": "BLUE"},
        {"name": "country", "type": "string", "default": "Argentina"},
        {"name": "address", "type": ["null", "string"], "default": null}
    ]
}'

# Json schema
UserAdvance.json_schema()

{
    "title": "UserAdvance",
    "description": "UserAdvance(*, name: str, age: int, pets: List[str] = None, ...",
    "type": "object",
    "properties": {
        "name": {"title": "Name", "type": "string"},
        "age": {"title": "Age", "type": "integer"},
        "pets": {"title": "Pets", "type": "array", "items": {"type": "string"}},
        "accounts": {"title": "Accounts", "type": "object", "additionalProperties": {"type": "integer"}},
        "has_car": {"title": "Has Car", "default": false, "type": "boolean"},
        "favorite_colors": {"default": "BLUE", "allOf": [{"$ref": "#/definitions/FavoriteColor"}]},
        "country": {"title": "Country", "default": "Argentina", "type": "string"},
        "address": {"title": "Address", "type": "string"}}, "required": ["name", "age"], "definitions": {"FavoriteColor": {"title": "FavoriteColor", "description": "An enumeration.", "enum": ["BLUE", "YELLOW", "GREEN"], "type": "string"}}
}

user = UserAdvance(name="bond", age=50)

# pydantic
user.dict()
# >>> {'name': 'bond', 'age': 50, 'pets': ['dog', 'cat'], 'accounts': {'key': 1}, 'has_car': False, 'favorite_colors': <FavoriteColor.BLUE: 'BLUE'>, 'country': 'Argentina', 'address': None}

# pydantic
user.json()
# >>> '{"name": "bond", "age": 50, "pets": ["dog", "cat"], "accounts": {"key": 1}, "has_car": false, "favorite_colors": "BLUE", "country": "Argentina", "address": null}'

# pydantic
user = UserAdvance(name="bond")

# ValidationError: 1 validation error for UserAdvance
# age
# field required (type=value_error.missing)


# dataclasses-avroschema
event = user.serialize()
print(event)
# >>> b'\x08bondd\x04\x06dog\x06cat\x00\x02\x06key\x02\x00\x00\x00\x12Argentina\x00'

UserAdvance.deserialize(data=event)
# >>> UserAdvance(name='bond', age=50, pets=['dog', 'cat'], accounts={'key': 1}, has_car=False, favorite_colors=<FavoriteColor.BLUE: 'BLUE'>, country='Argentina', address=None)

Examples with python streaming drivers (kafka and redis)

Under examples folder you can find 3 differents kafka examples, one with aiokafka (async) showing the simplest use case when a AvroModel instance is serialized and sent it thorught kafka, and the event is consumed. The other two examples are sync using the kafka-python driver, where the avro-json serialization and schema evolution (FULL compatibility) is shown. Also, there are two redis examples using redis streams with walrus and redisgears-py

Factory and fixtures

Dataclasses Avro Schema 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.

import typing
import dataclasses

from dataclasses_avroschema import AvroModel


@dataclasses.dataclass
class Address(AvroModel):
    "An Address"
    street: str
    street_number: int


@dataclasses.dataclass
class User(AvroModel):
    "User with multiple Address"
    name: str
    age: int
    addresses: typing.List[Address]


Address.fake()
# >>>> Address(street='PxZJILDRgbXyhWrrPWxQ', street_number=2067)

User.fake()
# >>>> User(name='VGSBbOGfSGjkMDnefHIZ', age=8974, addresses=[Address(street='vNpPYgesiHUwwzGcmMiS', street_number=4790)])

Features

• Primitive types: int, long, double, float, boolean, string and null support
• Complex types: enum, array, map, fixed, unions and records support
• typing.Annotated supported
• Logical Types: date, time (millis and micro), datetime (millis and micro), uuid support
• Schema relations (oneToOne, oneToMany)
• Recursive Schemas
• Generate Avro Schemas from faust.Record
• Instance serialization correspondent to avro schema generated
• Data deserialization. Return python dict or class instance
• Generate json from python class instance
• Case Schemas
• Generate models from avsc files
• Examples of integration with kafka drivers: aiokafka, kafka-python
• Example of integration with redis drivers: walrus and redisgears-py
• Factory instances
• Pydantic integration

Development

Poetry is needed to install the dependencies and develope locally

1. Install dependencies: poetry install
2. Code linting: ./scripts/format
3. Run tests: ./scripts/test

For commit messages we use commitizen in order to standardize a way of committing rules