apischema 0.5.1

Another Python API schema handling and JSON (de)serialization through typing annotation; light, simple, powerful.

APISchema

Another Python API schema handling and JSON (de)serialization through typing annotation; light, simple, powerful.

Why another library

Because i'm not satisfied with existing ones. I would like to:

• stay closest as possible to the standard library (dataclasses, typing, etc.) to be the most accessible possible and as a consequency no need of plugin for editor/linter/etc.
• be able to tune the library and use my own types (or foreign libraries ones), instead of subclassing or having to do a PR for handling of bson.ObjectId
• have the least possible dynamic thing (like using string for attribute name)

And I simply want to enjoy myself coding this stuff (and trying to make it smaller and faster than its alternatives).

Getting Started

pip install apischema and follow examples (the only documentation for now)

Examples

Following example can be run 'as is' in Python 3.7 (all examples are valid tests of the library). quickstart.py

from dataclasses import dataclass, field
from enum import Enum
from typing import List, NewType
from uuid import UUID, uuid4

from pytest import raises

from apischema import (
    ValidationError,
    build_input_schema,
    build_output_schema,
    from_data,
    schema,
    to_data,
)

Tag = NewType("Tag", str)
schema(title="resource tag", max_len=20)(Tag)


class ResourceType(Enum):
    RESOURCE_A = "A"
    RESOURCE_B = "B"


@dataclass
class Resource:
    id: UUID
    type: ResourceType
    tags: List[Tag] = field(
        default_factory=list, metadata=schema(max_items=5, unique=True)
    )


def test_resource():
    uuid = uuid4()
    data = {"id": str(uuid), "type": "A", "tags": ["tag1"]}
    resource = from_data(Resource, data)
    assert resource == Resource(uuid, ResourceType.RESOURCE_A, [Tag("tag1")])
    assert to_data(resource) == data
    json_schema = build_input_schema(Resource)
    assert json_schema == build_output_schema(Resource)
    assert to_data(json_schema) == {
        "type": "object",
        "required": ["id", "type"],
        "additionalProperties": False,
        "properties": {
            "id": {"type": "string", "format": "uuid"},
            "type": {"type": "string", "enum": ["A", "B"]},
            "tags": {
                "type": "array",
                "maxItems": 5,
                "uniqueItems": True,
                "items": {"type": "string", "title": "resource tag", "maxLength": 20},
            },
        },
    }


def test_resource_error():
    with raises(ValidationError) as err:
        from_data(
            Resource,
            {
                "id": "uuid",
                "type": None,
                "tags": ["duplicate_tag", "duplicate_tag", "a_very_very_very_long_tag"],
            },
        )
    assert err.value == ValidationError(
        children={
            "id": ValidationError(["[ValueError]badly formed hexadecimal UUID string"]),
            "type": ValidationError(["None is not a valid ResourceType"]),
            "tags": ValidationError(
                ["duplicate items (uniqueItems)"],
                children={"2": ValidationError(["length greater than 20 (maxLength)"])},
            ),
        }
    )

See other examples; a suggested order:

• example_properties.py
• example_conversion.py
• example_validator.py
• example_coercion.py
• example_generic.py
• example_properties2.py
• example_conversion2.py
• example_validator2.py
• example_recursivity_and_pep563.py
• example_generic_conversion.py
• example_raw_conversion.py

Benchmark

Using Pydantic benchmark, using only CPython, apischema is faster than others libraries, including Pydantic, present in the benchmark.

Concerning Cython compilation, apischema is blocked for now by this Cython issue

Todo

• documentation (obviously)
• tests (coverage is not enough, and edge cases)