


Caching in FastAPI: Unlocking High-Performance Development:
In der heutigen digitalen Welt ist jede Aktion – sei es das Wischen in einer Dating-App oder das Abschließen eines Kaufs – auf APIs angewiesen, die hinter den Kulissen effizient arbeiten. Als Back-End-Entwickler wissen wir, dass jede Millisekunde zählt. Aber wie können wir dafür sorgen, dass APIs schneller reagieren? Die Antwort liegt im Caching.
Caching ist eine Technik, die häufig aufgerufene Daten im Speicher speichert und es APIs ermöglicht, sofort zu reagieren, anstatt jedes Mal eine langsamere Datenbank abzufragen. Stellen Sie sich das so vor, als ob Sie wichtige Zutaten (Salz, Pfeffer, Öl) auf Ihrer Küchenarbeitsplatte aufbewahren, anstatt sie jedes Mal, wenn Sie kochen, aus der Speisekammer zu holen – das spart Zeit und macht den Prozess effizienter. Ebenso reduziert Caching die API-Antwortzeiten, indem häufig angeforderte Daten an einem schnellen, zugänglichen Ort wie Redis gespeichert werden.
Erforderliche Bibliotheken müssen installiert werden
Um eine Verbindung mit Redis Cache mit FastAPI herzustellen, müssen die folgenden Bibliotheken vorinstalliert sein.
pip install fastapi uvicorn aiocache pydantic
Pydantic dient zum Erstellen von Datenbanktabellen und -strukturen. aiocache führt asynchrone Vorgänge im Cache aus. uvicorn ist für den Serverbetrieb verantwortlich.
Redis-Einrichtung und -Verifizierung:
Eine direkte Einrichtung von Redis in einem Windows-System ist derzeit nicht möglich. Daher muss es im Windows-Subsystem für Linux eingerichtet und ausgeführt werden. Anweisungen zur Installation von WSL finden Sie unten
Post installing WSL, the following commands are required to install Redis
sudo apt update sudo apt install redis-server sudo systemctl start redis
To test Redis server connectivity, the following command is used
redis-cli
After this command, it will enter into a virtual terminal of port 6379. In that terminal, the redis commands can be typed and tested.
Setting Up the FastAPI Application
Let’s create a simple FastAPI app that retrieves user information and caches it for future requests. We will use Redis for storing cached responses.
Step 1: Define the Pydantic Model for User Data
We’ll use Pydantic to define our User model, which represents the structure of the API response.
from pydantic import BaseModel class User(BaseModel): id: int name: str email: str age: int
Step 2: Create a Caching Decorator
To avoid repeating the caching logic for each endpoint, we’ll create a reusable caching decorator using the aiocache library. This decorator will attempt to retrieve the response from Redis before calling the actual function.
import json from functools import wraps from aiocache import Cache from fastapi import HTTPException def cache_response(ttl: int = 60, namespace: str = "main"): """ Caching decorator for FastAPI endpoints. ttl: Time to live for the cache in seconds. namespace: Namespace for cache keys in Redis. """ def decorator(func): @wraps(func) async def wrapper(*args, **kwargs): user_id = kwargs.get('user_id') or args[0] # Assuming the user ID is the first argument cache_key = f"{namespace}:user:{user_id}" cache = Cache.REDIS(endpoint="localhost", port=6379, namespace=namespace) # Try to retrieve data from cache cached_value = await cache.get(cache_key) if cached_value: return json.loads(cached_value) # Return cached data # Call the actual function if cache is not hit response = await func(*args, **kwargs) try: # Store the response in Redis with a TTL await cache.set(cache_key, json.dumps(response), ttl=ttl) except Exception as e: raise HTTPException(status_code=500, detail=f"Error caching data: {e}") return response return wrapper return decorator
Step 3: Implement a FastAPI Route for User Details
We’ll now implement a FastAPI route that retrieves user information based on a user ID. The response will be cached using Redis for faster access in subsequent requests.
from fastapi import FastAPI app = FastAPI() # Sample data representing users in a database users_db = { 1: {"id": 1, "name": "Alice", "email": "alice@example.com", "age": 25}, 2: {"id": 2, "name": "Bob", "email": "bob@example.com", "age": 30}, 3: {"id": 3, "name": "Charlie", "email": "charlie@example.com", "age": 22}, } @app.get("/users/{user_id}") @cache_response(ttl=120, namespace="users") async def get_user_details(user_id: int): # Simulate a database call by retrieving data from users_db user = users_db.get(user_id) if not user: raise HTTPException(status_code=404, detail="User not found") return user
Step 4: Run the Application
Start your FastAPI application by running:
uvicorn main:app --reload
Now, you can test the API by fetching user details via:
http://127.0.0.1:8000/users/1
The first request will fetch the data from the users_db, but subsequent requests will retrieve the data from Redis.
Testing the Cache
You can verify the cache by inspecting the keys stored in Redis. Open the Redis CLI:
redis-cli KEYS *
You will get all keys that have been stored in the Redis till TTL.
How Caching Works in This Example
First Request
: When the user data is requested for the first time, the API fetches it from the database (users_db) and stores the result in Redis with a time-to-live (TTL) of 120 seconds.
Subsequent Requests:
Any subsequent requests for the same user within the TTL period are served directly from Redis, making the response faster and reducing the load on the database.
TTL (Time to Live):
After 120 seconds, the cache entry expires, and the data is fetched from the database again on the next request, refreshing the cache.
Conclusion
In this tutorial, we’ve demonstrated how to implement Redis caching in a FastAPI application using a simple user details example. By caching API responses, you can significantly improve the performance of your application, particularly for data that doesn't change frequently.
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