apischema 0.14.3

JSON (de)serialization + GraphQL and JSON schema generation through python typing, with a spoonful of sugar.

Apischema

Makes your life easier when it comes to python API.

JSON (de)serialization + GraphQL and JSON schema generation through python typing, with a spoonful of sugar.

Documentation

https://wyfo.github.io/apischema/

Install

pip install apischema

It requires only Python 3.6+ (and dataclasses official backport for version 3.6 only)

PyPy3 is fully supported.

Why another library?

(If you wonder what the difference is with pydantic library, see the dedicated section of the documentation, you will find a lot of difference.)

This library fulfills the following goals:

• stay as close as possible to the standard library (dataclasses, typing, etc.) — as a consequence do not need plugins for editors/linters/etc.;
• be additive and tunable, be able to work with user own types as well as foreign libraries ones; do not need a PR for handling new types like bson.ObjectId or collection.deque;
• avoid dynamic things like using string for attribute name;
• support GraphQL;
• (Bonus) be the fastest.

No known alternative achieves all of this.

Example

from collections.abc import Collection
from dataclasses import dataclass, field
from typing import Optional
from uuid import UUID, uuid4

from graphql import print_schema
from pytest import raises

from apischema import ValidationError, deserialize, serialize, graphql_schema
from apischema.json_schema import deserialization_schema


# Define a schema with standard dataclasses
@dataclass
class Resource:
    id: UUID
    name: str
    tags: set[str] = field(default_factory=set)


# Get some data
uuid = uuid4()
data = {"id": str(uuid), "name": "wyfo", "tags": ["some_tag"]}
# Deserialize data
resource = deserialize(Resource, data)
assert resource == Resource(uuid, "wyfo", {"some_tag"})
# Serialize objects
assert serialize(resource) == data
# Validate during deserialization
with raises(ValidationError) as err:  # pytest check exception is raised
    deserialize(Resource, {"id": "42", "name": "wyfo"})
assert serialize(err.value) == [  # ValidationError is serializable
    {"loc": ["id"], "err": ["badly formed hexadecimal UUID string"]}
]
# Generate JSON Schema
assert deserialization_schema(Resource) == {
    "$schema": "http://json-schema.org/draft/2019-09/schema#",
    "type": "object",
    "properties": {
        "id": {"type": "string", "format": "uuid"},
        "name": {"type": "string"},
        "tags": {"type": "array", "items": {"type": "string"}, "uniqueItems": True},
    },
    "required": ["id", "name"],
    "additionalProperties": False,
}

# Define GraphQL operations
def resources(tags: Optional[Collection[str]] = None) -> Optional[Collection[Resource]]:
    ...


# Generate GraphQL schema
schema = graphql_schema(query=[resources], id_types={UUID})
schema_str = """\
type Query {
  resources(tags: [String!]): [Resource!]
}

type Resource {
  id: ID!
  name: String!
  tags: [String!]!
}
"""
assert print_schema(schema) == schema_str

Apischema works out of the box with your data model.

Let's start the Apischema tour.

Changelog

See releases