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When using this response class, json serialization is done using Pydantic's built-in json serialization (`dump_json(r)`) instead of generating an intermediate dict that is later serialized using a json library (`json.dumps(dump_python(r))`). In my testing this is 3-4x faster than using the standard json library (the default) and 50% faster than using orjson, without requiring any extra dependencies. This also allows configuring serialization behavior per model using Pydantic's model_config.pull/14299/head
Eugene Toder
8 months ago
5 changed files with 241 additions and 1 deletions
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import math |
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from typing import List, Optional |
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import pytest |
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from dirty_equals import IsFloatNan |
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from fastapi import FastAPI |
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from fastapi._compat import PYDANTIC_V2 |
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from fastapi.responses import PydanticJSONResponse |
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from fastapi.testclient import TestClient |
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from pydantic import BaseModel, Field |
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from .utils import needs_py_lt_314, needs_pydanticv2 |
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app = FastAPI(default_response_class=PydanticJSONResponse) |
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class CustomResponse(PydanticJSONResponse): |
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media_type = "application/x-custom" |
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class Item(BaseModel): |
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name: str |
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price: float |
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category: str = Field("food", alias="CAT") |
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tax: float = 8.875 |
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description: Optional[str] = None |
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@app.get("/response-model", response_model=Item) |
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@app.get( |
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"/response-model-include", |
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response_model=Item, |
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response_model_include={"name", "category"}, |
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) |
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@app.get( |
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"/response-model-exclude", |
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response_model=Item, |
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response_model_exclude={"tax", "description"}, |
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) |
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@app.get( |
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"/response-model-by-alias-false", |
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response_model=Item, |
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response_model_by_alias=False, |
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) |
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@app.get( |
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"/response-model-exclude-unset", |
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response_model=Item, |
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response_model_exclude_unset=True, |
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) |
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@app.get( |
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"/response-model-exclude-defaults", |
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response_model=Item, |
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response_model_exclude_defaults=True, |
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) |
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@app.get( |
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"/response-model-exclude-none", |
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response_model=Item, |
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response_model_exclude_none=True, |
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) |
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def get_response_model_params(): |
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return {"name": "cheese", "price": 1.23, "tax": 8.875, "description": None} |
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class FloatsNone(BaseModel): |
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# pydantic converts inf/nan to None by default |
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numbers: List[float] |
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class FloatsNum(FloatsNone): |
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model_config = {"ser_json_inf_nan": "constants"} |
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class FloatsStr(FloatsNone): |
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model_config = {"ser_json_inf_nan": "strings"} |
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@app.get("/floats-none", response_model=FloatsNone) |
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@app.get("/floats-num", response_model=FloatsNum) |
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@app.get("/floats-str", response_model=FloatsStr) |
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@app.get("/custom-class", response_class=CustomResponse, response_model=FloatsStr) |
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def get_floats(): |
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return {"numbers": [3.14, math.inf, math.nan, 2.72]} |
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client = TestClient(app) |
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@needs_pydanticv2 |
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@pytest.mark.parametrize( |
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"path,expected_response", |
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[ |
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( |
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"/response-model", |
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{ |
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"name": "cheese", |
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"price": 1.23, |
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"CAT": "food", |
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"tax": 8.875, |
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"description": None, |
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}, |
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), |
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("/response-model-include", {"name": "cheese", "CAT": "food"}), |
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("/response-model-exclude", {"name": "cheese", "price": 1.23, "CAT": "food"}), |
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( |
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"/response-model-by-alias-false", |
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{ |
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"name": "cheese", |
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"price": 1.23, |
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"category": "food", |
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"tax": 8.875, |
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"description": None, |
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}, |
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), |
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( |
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"/response-model-exclude-unset", |
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{ |
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"name": "cheese", |
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"price": 1.23, |
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"tax": 8.875, |
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"description": None, |
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}, |
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), |
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("/response-model-exclude-defaults", {"name": "cheese", "price": 1.23}), |
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( |
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"/response-model-exclude-none", |
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{ |
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"name": "cheese", |
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"price": 1.23, |
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"CAT": "food", |
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"tax": 8.875, |
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}, |
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), |
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], |
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) |
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def test_response_model_params(path: str, expected_response: dict): |
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response = client.get(path) |
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assert response.status_code == 200 |
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assert response.json() == expected_response |
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@needs_pydanticv2 |
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@pytest.mark.parametrize( |
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"path,expected_numbers", |
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[ |
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("/floats-none", [3.14, None, None, 2.72]), |
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("/floats-num", [3.14, math.inf, IsFloatNan, 2.72]), |
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("/floats-str", [3.14, "Infinity", "NaN", 2.72]), |
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], |
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) |
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def test_floats(path: str, expected_numbers: list): |
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response = client.get(path) |
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assert response.status_code == 200 |
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assert response.json() == {"numbers": expected_numbers} |
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@needs_pydanticv2 |
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def test_custom_response_class(): |
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response = client.get("/custom-class") |
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assert response.status_code == 200 |
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assert response.headers["content-type"] == "application/x-custom" |
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assert response.json() == {"numbers": [3.14, "Infinity", "NaN", 2.72]} |
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@needs_py_lt_314 |
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def test_requires_pydantic_v2_model(): |
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if PYDANTIC_V2: |
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from pydantic.v1 import BaseModel as BaseModelV1 |
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else: |
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from pydantic import BaseModel as BaseModelV1 |
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app = FastAPI(default_response_class=PydanticJSONResponse) |
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class ModelV1(BaseModelV1): |
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data: str |
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@app.get("/model-v1") |
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def get_model_v1() -> ModelV1: |
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return ModelV1(data="hello") |
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client = TestClient(app) |
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with pytest.raises(AssertionError, match="requires a pydantic v2 model"): |
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client.get("/model-v1") |
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