132 lines
4.3 KiB
Markdown
132 lines
4.3 KiB
Markdown
# FastAPI Integration
|
|
|
|
## Introduction
|
|
|
|
This section includes a complete example showing how to integrate Redis OM with FastAPI.
|
|
|
|
Good news: Redis OM was **specifically designed to integrate with FastAPI**!
|
|
|
|
## Concepts
|
|
|
|
### Every Redis OM Model is also a Pydantic model
|
|
|
|
Every Redis OM model is also a Pydantic model, so you can define a model and then use the model class anywhere that FastAPI expects a Pydantic model.
|
|
|
|
This means a couple of things:
|
|
|
|
1. A Redis OM model can be used for request body validation
|
|
2. Redis OM models show up in the auto-generated API documentation
|
|
|
|
### Cache vs. Data
|
|
|
|
Redis works well as either a durable data store or a cache, but the optimal Redis configuration is often different between these two use cases.
|
|
|
|
You almost always want to use a Redis instance tuned for caching when you're caching and a separate Redis instance tuned for data durability for storing application state.
|
|
|
|
This example shows how to manage these two uses of Redis within the same application. The app uses a FastAPI caching framework and dedicated caching instance of Redis for caching, and a separate Redis instance tuned for durability for Redis OM models.
|
|
|
|
|
|
## Example app code
|
|
|
|
This is a complete example that you can run as-is:
|
|
|
|
```python
|
|
import datetime
|
|
from typing import Optional
|
|
|
|
import aioredis
|
|
|
|
from fastapi import FastAPI, HTTPException
|
|
from starlette.requests import Request
|
|
from starlette.responses import Response
|
|
|
|
from fastapi_cache import FastAPICache
|
|
from fastapi_cache.backends.redis import RedisBackend
|
|
from fastapi_cache.decorator import cache
|
|
|
|
from pydantic import EmailStr
|
|
|
|
from redis_om.model import HashModel, NotFoundError
|
|
from redis_om.connections import get_redis_connection
|
|
|
|
# This Redis instance is tuned for durability.
|
|
REDIS_DATA_URL = "redis://localhost:6380"
|
|
|
|
# This Redis instance is tuned for cache performance.
|
|
REDIS_CACHE_URL = "redis://localhost:6381"
|
|
|
|
|
|
class Customer(HashModel):
|
|
first_name: str
|
|
last_name: str
|
|
email: EmailStr
|
|
join_date: datetime.date
|
|
age: int
|
|
bio: Optional[str]
|
|
|
|
|
|
app = FastAPI()
|
|
|
|
|
|
@app.post("/customer")
|
|
async def save_customer(customer: Customer):
|
|
# We can save the model to Redis by calling `save()`:
|
|
return customer.save()
|
|
|
|
|
|
@app.get("/customers")
|
|
async def list_customers(request: Request, response: Response):
|
|
# To retrieve this customer with its primary key, we use `Customer.get()`:
|
|
return {"customers": Customer.all_pks()}
|
|
|
|
|
|
@app.get("/customer/{pk}")
|
|
@cache(expire=10)
|
|
async def get_customer(pk: str, request: Request, response: Response):
|
|
# To retrieve this customer with its primary key, we use `Customer.get()`:
|
|
try:
|
|
return Customer.get(pk)
|
|
except NotFoundError:
|
|
raise HTTPException(status_code=404, detail="Customer not found")
|
|
|
|
|
|
@app.on_event("startup")
|
|
async def startup():
|
|
r = aioredis.from_url(REDIS_CACHE_URL, encoding="utf8", decode_responses=True)
|
|
FastAPICache.init(RedisBackend(r), prefix="fastapi-cache")
|
|
|
|
# You can set the Redis OM URL using the REDIS_OM_URL environment
|
|
# variable, or by manually creating the connection using your model's
|
|
# Meta object.
|
|
Customer.Meta.database = get_redis_connection(url=REDIS_DATA_URL, decode_responses=True)
|
|
```
|
|
|
|
## Testing the app
|
|
|
|
You should install the app's dependencies first. This app uses Poetry, so you'll want to make sure you have that installed first:
|
|
|
|
$ pip install poetry
|
|
|
|
Then install the dependencies:
|
|
|
|
$ poetry install
|
|
|
|
Next, start the server:
|
|
|
|
$ poetry run uvicorn --reload main:test
|
|
|
|
Then, in another shell, create a customer:
|
|
|
|
$ curl -X POST "http://localhost:8000/customer" -H 'Content-Type: application/json' -d '{"first_name":"Andrew","last_name":"Brookins","email":"a@example.com","age":"38","join_date":"2020
|
|
-01-02"}'
|
|
{"pk":"01FM2G8EP38AVMH7PMTAJ123TA","first_name":"Andrew","last_name":"Brookins","email":"a@example.com","join_date":"2020-01-02","age":38,"bio":""}
|
|
|
|
Get a copy of the value for "pk" and make another request to get that customer:
|
|
|
|
$ curl "http://localhost:8000/customer/01FM2G8EP38AVMH7PMTAJ123TA"
|
|
{"pk":"01FM2G8EP38AVMH7PMTAJ123TA","first_name":"Andrew","last_name":"Brookins","email":"a@example.com","join_date":"2020-01-02","age":38,"bio":""}
|
|
|
|
You can also get a list of all customer PKs:
|
|
|
|
$ curl "http://localhost:8000/customers"
|
|
{"customers":["01FM2G8EP38AVMH7PMTAJ123TA"]} |