Home Backend Development Python Tutorial Caching in FastAPI: Unlocking High-Performance Development:

Caching in FastAPI: Unlocking High-Performance Development:

Oct 18, 2024 am 11:39 AM

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
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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

Caching in FastAPI: Unlocking High-Performance Development:

WSL installieren | Microsoft Learn

Installieren Sie das Windows-Subsystem für Linux mit dem Befehl wsl --install. Verwenden Sie ein Bash-Terminal auf Ihrem Windows-Computer, auf dem Ihre bevorzugte Linux-Distribution ausgeführt wird – Ubuntu, Debian, SUSE, Kali, Fedora, Pengwin, Alpine und mehr sind verfügbar.

learn.microsoft.com

Post installing WSL, the following commands are required to install Redis

sudo apt update
sudo apt install redis-server
sudo systemctl start redis
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To test Redis server connectivity, the following command is used

redis-cli
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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
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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
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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

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Step 4: Run the Application

Start your FastAPI application by running:

uvicorn main:app --reload
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Now, you can test the API by fetching user details via:

http://127.0.0.1:8000/users/1
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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 *
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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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