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Introduction

In this tutorial, we’ll learn how to add rate limiting to a FastAPI application using Upstash Redis. Rate limiting is essential for controlling API usage and with Upstash Redis, you can easily implement rate limiting to protect your API resources. We’ll set up a simple FastAPI app and apply rate limiting to its endpoints. With Upstash Redis, we’ll configure a fixed window rate limiter that allows a specific number of requests per given time period.

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Environment Setup

First, install FastAPI, the Upstash Redis client, the Upstash rate limiting package, and an ASGI server: 
pip install fastapi upstash-redis upstash-ratelimit uvicorn[standard]

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Database Setup

Create a Redis database using the Upstash Console or Upstash CLI, and export the UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN to your environment: 
export UPSTASH_REDIS_REST_URL=<YOUR_URL>
export UPSTASH_REDIS_REST_TOKEN=<YOUR_TOKEN>
You can also use python-dotenv to load environment variables from your .env file.

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Application Setup

In this example, we will build an API endpoint that is rate-limited to a certain number of requests per time window. If the limit is exceeded (e.g., by making more than 10 requests in 10 seconds), the API will return an HTTP 429 error with the message “Rate limit exceeded. Please try again later.” Create main.py:
main.py
from fastapi import FastAPI, HTTPException
from upstash_ratelimit import Ratelimit, FixedWindow
from upstash_redis import Redis
from dotenv import load_dotenv
import requests

# Load environment variables from .env file
load_dotenv()

# Initialize the FastAPI app
app = FastAPI()

# Initialize Redis client
redis = Redis.from_env()

# Create a rate limiter that allows 10 requests per 10 seconds
ratelimit = Ratelimit(
    redis=redis,
    limiter=FixedWindow(max_requests=10, window=10),  # 10 requests per 10 seconds
    prefix="@upstash/ratelimit"
)

@app.get("/expensive_calculation")
def expensive_calculation():
    identifier = "api"  # Common identifier for rate limiting all users equally
    response = ratelimit.limit(identifier)

    if not response.allowed:
        raise HTTPException(status_code=429, detail="Rate limit exceeded. Please try again later.")
    
    # Placeholder for a resource-intensive operation
    result = do_expensive_calculation()
    return {"message": "Here is your result", "result": result}

# Simulated function for an expensive calculation
def do_expensive_calculation():
    return "Expensive calculation result"

# Test function to check rate limiting
def test_rate_limiting():
    url = "http://127.0.0.1:8000/expensive_calculation"
    success_count = 0
    fail_count = 0

    # Attempt 15 requests in quick succession
    for i in range(15):
        response = requests.get(url)
        
        if response.status_code == 200:
            success_count += 1
            print(f"Request {i+1}: Success - {response.json()['message']}")
        elif response.status_code == 429:
            fail_count += 1
            print(f"Request {i+1}: Failed - Rate limit exceeded")

        # Small delay to avoid flooding

    print("\nTest Summary:")
    print(f"Total Successful Requests: {success_count}")
    print(f"Total Failed Requests due to Rate Limit: {fail_count}")

if __name__ == "__main__":
    # Run the FastAPI app in a separate thread or terminal with:
    # uvicorn main:app --reload

    # To test rate limiting after the server is running
    test_rate_limiting()

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Running the Application

Run the FastAPI app with Uvicorn: 
uvicorn main:app --reload
Run the test function to check the rate limiting: 
python main.py

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Testing Rate Limiting

Here’s the output you should see when running the test function: 
Request 1: Success - Here is your result
Request 2: Success - Here is your result
Request 3: Success - Here is your result
Request 4: Success - Here is your result
Request 5: Success - Here is your result
Request 6: Success - Here is your result
Request 7: Success - Here is your result
Request 8: Success - Here is your result
Request 9: Success - Here is your result
Request 10: Success - Here is your result
Request 11: Failed - Rate limit exceeded
Request 12: Failed - Rate limit exceeded
Request 13: Failed - Rate limit exceeded
Request 14: Failed - Rate limit exceeded
Request 15: Failed - Rate limit exceeded

Test Summary:
Total Successful Requests: 10
Total Failed Requests due to Rate Limit: 5

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Code Breakdown

  1. Redis and Rate Limiter Setup:
    • We initialize a Redis client with Redis.from_env() using environment variables for configuration.
    • We create a rate limiter using Ratelimit with a FixedWindow limiter that allows 10 requests per 10 seconds. The prefix option is set to organize the Redis keys used by the rate limiter.
  2. Rate Limiting the Endpoint:
    • For the /expensive_calculation endpoint, the rate limiter is applied by calling ratelimit.limit(identifier).
    • The identifier variable uniquely identifies this rate limit. You could use user-specific identifiers (like user IDs) to implement per-user limits.
    • If the request exceeds the allowed limit, an HTTP 429 error is returned.
  3. Expensive Calculation Simulation:
    • The do_expensive_calculation function simulates a resource-intensive operation. In real scenarios, this could represent database queries, file processing, or other time-consuming tasks.

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Benefits of Rate Limiting with Redis

Using Redis for rate limiting helps control API usage across multiple instances of your app, making it highly scalable. Redis’s in-memory storage provides fast access to rate-limiting data, ensuring minimal performance impact on your API.