Pydantic V1 Integration

It is possible to use pydantic with dataclasses-avroschema making use of AvroBaseModel:

You must use use all the pydantic features and all dataclasses-avroschema functionality will be injected.

Note

With pydantic you do not have to use python dataclasses

Warning

The support of pydantic v1 will be deprecated in the future. We recommend to migrate to pydantic v2.

Avro and Json schemas

Basic usage

import typing
import enum
from dataclasses_avroschema.pydantic.v1 import AvroBaseModel

from pydantic.v1 import Field


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


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": "FavoriteColor", "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",
    "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"}}}'

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Note

You must use pydantic.Field instead of dataclasses.field

Avro schemas with pydantic types

Most of pydantic types are supported and from them it is possible to generate avro fields. Because pydantic types are not native python types the end result will contain extra metadata so the end users will have more context at the moment of using the schema. The extra metadata is specified using the key pydantic-class.

Supported fields

Avro Type	Pydantic Type
string	pydantic.FilePath
string	pydantic.DirectoryPath
string	pydantic.EmailStr
string	pydantic.NameEmail
string	pydantic.AnyUrl
string	pydantic.AnyHttpUrl
string	pydantic.HttpUrl
string	pydantic.FileUrl
string	pydantic.PostgresDsn
string	pydantic.CockroachDsn
string	pydantic.AmqpDsn
string	pydantic.RedisDsn
string	pydantic.MongoDsn
string	pydantic.KafkaDsn
string	pydantic.SecretStr
string	pydantic.IPvAnyAddress
string	pydantic.IPvAnyInterface
string	pydantic.IPvAnyNetwork
double	pydantic.NegativeFloat
double	pydantic.PositiveFloat
long	pydantic.NegativeInt
long	pydantic.PositiveIntstr
long	pydantic.ConstrainedInt (conint)

Avro Type	Logical type	Pydantic Type
string	uuid	pydantic.UUID1
string	uuid	pydantic.UUID3
string	uuid	pydantic.UUID4
string	uuid	pydantic.UUID5

from pydantic import v1 as pydantic
from dataclasses_avroschema.pydantic.v1 import AvroBaseModel


class Infrastructure(AvroBaseModel):
    email: pydantic.EmailStr
    postgres_dsn: pydantic.PostgresDsn
    cockroach_dsn: pydantic.CockroachDsn
    amqp_dsn: pydantic.AmqpDsn
    redis_dsn: pydantic.RedisDsn
    mongo_dsn: pydantic.MongoDsn
    kafka_url: pydantic.KafkaDsn
    total_nodes: pydantic.PositiveInt


Infrastructure.avro_schema()

{
    "type": "record",
    "name": "Infrastructure",
    "fields": [
        {"name": "email", "type": {"type": "string", "pydantic-class": "EmailStr"}},
        {"name": "postgres_dsn", "type": {"type": "string", "pydantic-class": "PostgresDsn"}},
        {"name": "cockroach_dsn", "type": {"type": "string", "pydantic-class": "CockroachDsn"}},
        {"name": "amqp_dsn", "type": {"type": "string", "pydantic-class": "AmqpDsn"}},
        {"name": "redis_dsn", "type": {"type": "string", "pydantic-class": "RedisDsn"}},
        {"name": "mongo_dsn", "type": {"type": "string", "pydantic-class": "MongoDsn"}},
        {"name": "kafka_url", "type": {"type": "string", "pydantic-class": "KafkaDsn"}},
        {"name": "total_nodes", "type": {"type": "long", "pydantic-class": "PositiveInt"}}
    ]
}

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Note

The key pydantic-class has been added as metadata to have more context when using the schema

Model generation

If is possible to generate pydantic models when pydantic types have been used. If a field has the matadata key pydantic-class then the proper pydantic types will be used.

Schema example:

from dataclasses_avroschema import ModelGenerator, BaseClassEnum

model_generator = ModelGenerator(base_class=BaseClassEnum.AVRO_DANTIC_MODEL.value)

schema = {
    "type": "record",
    "name": "Infrastructure",
    "fields": [
        {"name": "email", "type": {"type": "string", "pydantic-class": "EmailStr"}},
        {"name": "kafka_url", "type": {"type": "string", "pydantic-class": "KafkaDsn"}},
        {"name": "total_nodes", "type": {"type": "long", "pydantic-class": "PositiveInt"}},
        {"name": "event_id", "type": {"type": "string", "logicalType": "uuid", "pydantic-class": "UUID1"}},
        {"name": "landing_zone_nodes", "type": {"type": "array", "items": {"type": "long", "pydantic-class": "PositiveInt"}, "name": "landing_zone_node"}},
        {"name": "total_nodes_in_aws", "type": {"type": "long", "pydantic-class": "PositiveInt"}, "default": 10},
        {"name": "optional_kafka_url", "type": ["null", {"type": "string", "pydantic-class": "KafkaDsn"}], "default": None}
    ]
}

result = model_generator.render(schema=schema)

with open("models.py", mode="+w") as f:
    f.write(result)

and then render the result:

from dataclasses_avroschema.pydantic.v1 import AvroBaseModel
from pydantic import v1 as pydantic
import typing


class Infrastructure(AvroBaseModel):
    email: pydantic.EmailStr
    kafka_url: pydantic.KafkaDsn
    total_nodes: pydantic.PositiveInt
    event_id: pydantic.UUID1
    landing_zone_nodes: typing.List[pydantic.PositiveInt]
    total_nodes_in_aws: pydantic.PositiveInt = 10
    optional_kafka_url: typing.Optional[pydantic.KafkaDsn] = None

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Note

In order to render the pydantic types the base class must be AVRO_BASE_MODEL or PYDANTIC_MODEL

Mapping `avro fields` to `pydantic types`

Avro Type	Metadata	Pydantic Type
string	"pydantic-class": "DirectoryPath"	pydantic.FilePath
string	"pydantic-class": "DirectoryPath"	pydantic.DirectoryPath
string	"pydantic-class": "EmailStr"	pydantic.EmailStr
string	"pydantic-class": "NameEmail"	pydantic.NameEmail
string	"pydantic-class": "AnyUrl"	pydantic.AnyUrl
string	"pydantic-class": "AnyHttpUrl"	pydantic.AnyHttpUrl
string	"pydantic-class": "HttpUrl"	pydantic.HttpUrl
string	"pydantic-class": "FileUrl"	pydantic.FileUrl
string	"pydantic-class": "PostgresDsn"	pydantic.PostgresDsn
string	"pydantic-class": "CockroachDsn	pydantic.CockroachDsn
string	"pydantic-class": "AmqpDsn"	pydantic.AmqpDsn
string	"pydantic-class": "RedisDsn"	pydantic.RedisDsn
string	"pydantic-class": "MongoDsn"	pydantic.MongoDsn
string	"pydantic-class": "KafkaDsn"	pydantic.KafkaDsn
string	"pydantic-class": "SecretStr"	pydantic.SecretStr
string	"pydantic-class": "IPvAnyAddress"	pydantic.IPvAnyAddress
string	"pydantic-class": "IPvAnyInterface"	pydantic.IPvAnyInterface
string	"pydantic-class": "IPvAnyNetwork"	pydantic.IPvAnyNetwork
double	"pydantic-class": "NegativeFloat"	pydantic.NegativeFloat
double	"pydantic-class": "PositiveFloat"	pydantic.PositiveFloat
long	"pydantic-class": "NegativeInt"	pydantic.NegativeInt
long	"pydantic-class": "PositiveInt"	pydantic.PositiveInt
long	"pydantic-class": ConstrainedInt"	pydantic.ConstrainedInt

Avro Type	Logical Type	Metadata	Pydantic Type
string	uuid	"pydantic-class": "UUID1"	pydantic.UUID1
string	uuid	"pydantic-class": "UUID3"	pydantic.UUID3
string	uuid	"pydantic-class": "UUID4"	pydantic.UUID4
string	uuid	"pydantic-class": "UUID5"	pydantic.UUID5

Pydantic and dataclasses_avroschema batteries

To dict, to json and serialization

getting dict and json

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

# to_json from dataclasses-avroschema is the same that json from pydantic
assert user.to_json() == user.json()

# to_dict from dataclasses-avroschema is the same that dict from pydantic
assert user.to_dict() == user.dict()

serialization

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)

Parsing Objects

parse_obj usage

import typing

from dataclasses_avroschema.pydantic.v1 import AvroBaseModel


class Address(AvroBaseModel):
    "An Address"
    street: str
    street_number: int


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

data_user = {
    "name": "john",
    "age": 20,
    "addresses": [{
        "street": "test",
        "street_number": 10,
        }],
    }

user = User.parse_obj(data=data_user)
assert type(user.addresses[0]) is Address

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parse_obj_as usage

from typing import List

from pydantic import parse_obj_as

from dataclasses_avroschema.pydantic.v1 import AvroBaseModel


class User(AvroBaseModel):
    "User with multiple Address"
    name: str
    age: int


data = [{"name": "bond", "age": 50}, {"name": "bond2", "age": 60}]
users = parse_obj_as(List[User], data)

users[0].avro_schema()
# '{"type": "record", "name": "User", "fields": [{"name": "name", "type": "string"}, {"name": "age", "type": "long"}], "doc": "User with multiple Address"}'

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Fake

It is also possible to create fake instances with pydantic models:

import typing
import datetime
from pydantic.v1 import Field
from dataclasses_avroschema.pydantic.v1 import AvroBaseModel


class User(AvroBaseModel):
    name: str
    age: int
    birthday: datetime.date
    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

print(User.fake())
# >>> User(name='qWTLkqcIVmSBxpWMpFyR', age=2608, birthday=datetime.date(1982, 3, 30), pets=['wqoEXcJRYjcnJmnIvtiI'], accounts={'JueNdHdzIhHIDsjlHJLc': 779}, has_car=True)

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Note

All pydantic supported fields can be used with fake

Excluding fields

Pydantic Fields can be excluded when dict, json or copy methods are called. This meaans that the exclusion is only for exporting models but not excluded in the instance creations, then the avro serialization will include all the class attributes.

import typing
from pydantic.v1 import Field
from dataclasses_avroschema.pydantic.v1 import AvroBaseModel


class User(AvroBaseModel):
    name: str
    age: int
    pets: typing.List[str] = Field(default_factory=lambda: ["dog", "cat"], exclude=True)
    accounts: typing.Dict[str, int] = Field(default_factory=lambda: {"key": 1}, exclude=True)
    has_car: bool = False

user = User(name="bond", age=50, has_car=True)
print(user)
# >>> User(name='bond', age=50, pets=['dog', 'cat'], accounts={'key': 1}, has_car=True)

print(user.dict())
# >>> {'name': 'bond', 'age': 50, 'has_car': True} Excludes pets and accounts !!!

event = user.serialize()
assert user == User.deserialize(event)

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Model Config

With AvroBaseModel you can use the same Model Config that pydantic provides, for example:

Not use Enum valuesUse Enum values

import enum
from dataclasses_avroschema.pydantic import AvroBaseModel

class Color(str, enum.Enum):
    BLUE = "BLUE"
    RED = "RED"


class Bus(AvroBaseModel):
    driver: str
    color: Color

bus =  Bus(driver="bond", color=Color.RED)
print(bus.dict())
# >>> {'driver': 'bond', 'color': <Color.RED: 'RED'>}

import enum
from dataclasses_avroschema.pydantic import AvroBaseModel

class Color(str, enum.Enum):
    BLUE = "BLUE"
    RED = "RED"


class Bus(AvroBaseModel):
    driver: str
    color: Color

    class Config:
        use_enum_values = True

bus =  Bus(driver="bond", color=Color.RED)
print(bus.dict())
# >>> {'driver': 'bond', 'color': 'RED'}

Adding Custom Field-level Attributes

To add custom field attributes the metadata attribute must be set in pydantic.Field. For more info check adding-custom-field-level-attributes section for dataclasses.

Note

Make sure that pydantic.Field is used and NOT dataclasses.field

Custom Data Types as Fields

If needed, you can annotate fields with custom classes that define validators.

Classes with `__get_validators__`

These classes are defined by pydantic as Python classes that define the validate and __get_validators__ methods.

Note

The conversion mapping of a custom class to its supported type must be defined in the model's json_encoders config.

Warning

Generating models from avro schemas that were generated by classes containing Custom Class fields is not supported.

from dataclasses_avroschema.pydantic.v1 import AvroBaseModel


class CustomClass:
    def __init__(self, value: str) -> None:
        self.value = value

    @classmethod
    def __get_validators__(cls):
        yield cls.validate

    @classmethod
    def validate(cls, value):
        if isinstance(value, CustomClass):
            return value
        elif not isinstance(value, str):
            raise ValueError(f"Value must be a string or CustomClass - not {type(value)}")

        return cls(value)

    def __str__(self) -> str:
        return f"{self.value}"


class MyModel(AvroBaseModel):
    my_id: CustomClass

    class Config:
        json_encoders = {CustomClass: str}


print(MyModel.avro_schema_to_python())
"""
{
  "type": "record",
  "name": "MyModel",
  "fields": [
    {
      "name": "my_id",
      "type": "string"
    }
  ]
}
"""

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Pydantic V1 Integration

Avro and Json schemas

Avro schemas with pydantic types

Supported fields

Model generation

Mapping avro fields to pydantic types

Pydantic and dataclasses_avroschema batteries

To dict, to json and serialization

Parsing Objects

Fake

Excluding fields

Model Config

Adding Custom Field-level Attributes

Custom Data Types as Fields

Classes with __get_validators__

Mapping `avro fields` to `pydantic types`

Classes with `__get_validators__`