| Name | apischema JSON |
| Version |
0.18.1
JSON |
| download |
| home_page | https://github.com/wyfo/apischema |
| Summary | JSON (de)serialization, GraphQL and JSON schema generation using Python typing. |
| upload_time | 2023-10-10 08:15:13 |
| maintainer | |
| docs_url | None |
| author | Joseph Perez |
| requires_python | >=3.7 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# apischema
JSON (de)serialization, GraphQL and JSON schema generation using Python typing.
*apischema* makes your life easier when dealing with API data.
## Documentation
[https://wyfo.github.io/apischema/](https://wyfo.github.io/apischema/)
## Install
```shell
pip install apischema
```
It requires only Python 3.7+. *PyPy3* is also fully supported.
## Why another library?
(If you wonder how this differs from the *pydantic* library, see the [dedicated section of the documentation](https://wyfo.github.io/apischema/0.18/difference_with_pydantic) — there are many differences.)
This library fulfills the following goals:
- stay as close as possible to the standard library (dataclasses, typing, etc.) — as a consequence we do not need plugins for editors/linters/etc.;
- avoid object-oriented limitations — do not require a base class — thus handle easily every type (`Foo`, `list[Bar]`, `NewType(Id, int)`, etc.) the same way.
- be adaptable, provide tools to support any types (ORM, etc.);
- avoid dynamic things like using raw strings for attributes name - play nicely with your IDE.
No known alternative achieves all of this, and apischema is also [(a lot) faster](https://wyfo.github.io/apischema/0.18/optimizations_and_benchmark#benchmark) than all of them.
On top of that, because APIs are not only JSON, *apischema* is also a complete GraphQL library
> Actually, *apischema* is even adaptable enough to enable support of competitor libraries in a few dozens of line of code ([pydantic support example](https://wyfo.github.io/apischema/0.18/examples/pydantic_support) using [conversions feature](https://wyfo.github.io/apischema/0.18/conversions))
## Example
```python
from collections.abc import Collection
from dataclasses import dataclass, field
from uuid import UUID, uuid4
import pytest
from graphql import print_schema
from apischema import ValidationError, deserialize, serialize
from apischema.graphql import 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, resource) == data
# Validate during deserialization
with pytest.raises(ValidationError) as err: # pytest checks exception is raised
deserialize(Resource, {"id": "42", "name": "wyfo"})
assert err.value.errors == [
{"loc": ["id"], "err": "badly formed hexadecimal UUID string"}
]
# Generate JSON Schema
assert deserialization_schema(Resource) == {
"$schema": "http://json-schema.org/draft/2020-12/schema#",
"type": "object",
"properties": {
"id": {"type": "string", "format": "uuid"},
"name": {"type": "string"},
"tags": {
"type": "array",
"items": {"type": "string"},
"uniqueItems": True,
"default": [],
},
},
"required": ["id", "name"],
"additionalProperties": False,
}
# Define GraphQL operations
def resources(tags: Collection[str] | None = None) -> Collection[Resource] | None:
...
# 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.
> This example and further ones are using *pytest* API because they are in fact run as tests in the library CI
### Run the documentation examples
All documentation examples are written using the last Python minor version — currently 3.10 — in order to provide up-to-date documentation. Because Python 3.10 specificities (like [PEP 585](https://www.python.org/dev/peps/pep-0604/)) are used, this version is "mandatory" to execute the examples as-is.
In addition to *pytest*, some examples use third-party libraries like *SQLAlchemy* or *attrs*. All of this dependencies can be downloaded using the `examples` extra with
```shell
pip install apischema[examples]
```
Once dependencies are installed, you can simply copy-paste examples and execute them, using the proper Python version.
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