The Purpose of Rate Limiting#

Rate limiting serves several key purposes for API providers:

• Resource Protection: Prevents a single client or a small group of clients from overwhelming the API backend, database, or other downstream services with excessive requests.
• Security: Mitigates various attacks, including Denial of Service (DoS) or Distributed Denial of Service (DDoS) attempts, brute-force login attempts, and spamming.
• Cost Management: Limits usage by free-tier users or enforces quotas for different subscription levels, managing infrastructure costs associated with high traffic.
• Fairness: Ensures equitable access to API resources across all legitimate users by preventing one user’s high volume from impacting others.

Rate Limiting Strategies#

Common rate limiting algorithms include:

• Fixed Window Counter: Counts requests within a fixed time interval (e.g., 60 seconds). Simple to implement but can allow a “burst” of requests at the window boundaries.
• Sliding Window Log: Stores a timestamp for each request. On a new request, it counts the number of timestamps within the current window (e.g., the last 60 seconds). This is more accurate in enforcing the rate but requires storing more data.
• Sliding Window Counter: A hybrid approach using counters weighted by the percentage of the previous window elapsed. Less accurate than the log but more memory efficient.

For a distributed API, the state (request counts or timestamps) must be stored externally from the application instance, typically in a shared data store like Redis.

Why Redis Streams for Rate Limiting?#

While Redis could be used for rate limiting with simple counters (INCR), sorted sets (ZADD, ZRANGEBYSCORE), or lists (LPUSH, LTRIM), Redis Streams offer specific advantages for implementing the sliding window log strategy:

• Append-Only Log: Streams are optimized for appending new entries, which aligns perfectly with recording incoming requests.
• Automatic Timestamps: Each stream entry automatically receives a unique ID, which by default is a timestamp derived from the Redis server’s time. This built-in timestamping is crucial for window-based counting.
• Range Queries: The XRANGE command allows querying entries within a specific range of IDs. Since IDs are time-based, this enables querying entries within a specific time window (e.g., all entries from 60 seconds ago to now).
• Length Calculation: XLEN quickly returns the total number of entries in a stream, useful for initial checks or alternative strategies.
• Trimming: XTRIM allows efficient removal of old entries from the stream, preventing unbounded memory growth.

Using Redis Streams simplifies the implementation of the sliding window log by leveraging its core features for timestamping and range queries.

Step-by-Step Walkthrough#

The process involves setting up the FastAPI application, connecting to Redis, and creating a dependency that executes the rate limiting logic before the request reaches the endpoint handler.

1. Set up the FastAPI Application: A standard FastAPI application structure is needed.

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   from fastapi import FastAPI, Request, HTTPException
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   import uvicorn
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   app = FastAPI()
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   @app.get("/")
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   async def read_root():
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       return {"message": "Welcome to the API"}
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   if __name__ == "__main__":
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       uvicorn.run(app, host="0.0.0.0", port=8000)
   

2. Connect to Redis: An asynchronous Redis client is required. redis-py (version 4.2+) supports async operations.

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   import redis
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   from redis.asyncio import Redis as AsyncRedis # Use AsyncRedis for async
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   redis_client: AsyncRedis = None
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   async def connect_redis():
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       global redis_client
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       # Configure your Redis connection details
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       redis_client = AsyncRedis(host="localhost", port=6379, db=0)
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       try:
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           await redis_client.ping()
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           print("Connected to Redis")
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       except redis.exceptions.ConnectionError as e:
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           print(f"Could not connect to Redis: {e}")
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           # Handle connection error appropriately in a real app
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   async def close_redis():
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       global redis_client
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       if redis_client:
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           await redis_client.close()
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           print("Closed Redis connection")
   

   Integrate connection management into the FastAPI app lifecycle:

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   # main.py (continued)
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   from fastapi import FastAPI, Request, HTTPException
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   import uvicorn
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   from redis_conf import connect_redis, close_redis, redis_client # Import the client
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   app = FastAPI()
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   @app.on_event("startup")
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   async def startup_event():
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       await connect_redis()
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   @app.on_event("shutdown")
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   async def shutdown_event():
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       await close_redis()
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   # ... (rest of the app)
   

3. Implement the Rate Limiting Logic: Create a function or class that acts as a FastAPI dependency. This dependency will receive the Request object, interact with Redis Streams, and raise an HTTPException if the limit is exceeded.

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   import time
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   from fastapi import Request, HTTPException
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   from redis.asyncio import Redis as AsyncRedis
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   from redis_conf import redis_client # Access the shared client
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   async def sliding_window_rate_limit(
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       request: Request,
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       limit: int = 10,       # Max requests
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       window_seconds: int = 60 # Time window
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   ):
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       """
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       FastAPI dependency for sliding window rate limiting using Redis Streams.
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       """
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       if redis_client is None:
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            # This should not happen if startup event works, but good practice
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           raise HTTPException(status_code=500, detail="Redis client not initialized")
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       # Use client IP as the key (or user ID if authenticated)
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       client_key = f"rate_limit:{request.client.host}"
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       stream_key = client_key
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       current_time_ms = int(time.time() * 1000)
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       window_start_time_ms = current_time_ms - (window_seconds * 1000)
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       # 1. Add the current request timestamp to the stream
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       # XADD stream_key * field value (using '*' auto-generates ID, which is timestamp-based)
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       # We just add a dummy field/value, the timestamp is in the ID
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       await redis_client.xadd(stream_key, {"request_time": current_time_ms})
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       # Optional: Trim old entries to prevent unbounded growth
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       # XTRIM stream_key MINID 0 ~ window_start_time_ms - small_buffer
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       # Using MINID and '~' makes it trim based on approximate ID (timestamp)
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       # Adding a small buffer to avoid edge cases with exact window start
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       await redis_client.xtrim(stream_key, "MINID", window_start_time_ms - 100, approximate=True)
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       # 2. Count requests within the window
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       # XRANGE stream_key start_id end_id
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       # Query entries from window_start_time_ms to current_time_ms
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       # Redis Stream IDs are typically "milliseconds_time-sequence_number"
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       # Using the millisecond timestamp as the ID range works effectively
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       start_id = str(window_start_time_ms) # Start ID inclusive
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       end_id = "+" # End ID inclusive (current time or later)
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       # Fetching all entries can be memory intensive for very high rates/windows
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       # A more efficient check is often to get the latest 'limit' entries
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       # and check the timestamp of the oldest one. But for clarity here, we fetch the range.
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       # For very high throughput, consider approximations or smaller windows.
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       # Alternative efficient check: Get last 'limit + 1' entries, check first timestamp
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       # latest_entries = await redis_client.xrevrange(stream_key, count=limit + 1)
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       # if len(latest_entries) > limit:
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       #     oldest_id_str = latest_entries[-1][0].decode('utf-8') # ID is bytes
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       #     oldest_timestamp_ms = int(oldest_id_str.split('-')[0])
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       #     if oldest_timestamp_ms >= window_start_time_ms:
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       #         raise HTTPException(status_code=429, detail="Too Many Requests")
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       # Counting with XRANGE (simpler to understand, less efficient for huge streams)
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       # Use COUNT option for XRANGE if supported by your redis-py version or Redis server
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       # entries = await redis_client.xrange(stream_key, minid=start_id, maxid=end_id)
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       # request_count = len(entries)
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       # A robust way: Use XLEN *after* trimming to MINID - this count is the exact number
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       # of requests currently within the window (or slightly before the window start due to trimming)
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       # Combined with the XTRIM MINID command using approximate=True, this provides a good balance.
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       request_count = await redis_client.xlen(stream_key)
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       # 3. Check the limit
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       if request_count > limit:
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           raise HTTPException(status_code=429, detail="Too Many Requests")
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       # If limit not exceeded, the request proceeds
   

4. Integrate the Dependency: Apply the sliding_window_rate_limit dependency to your FastAPI path operations.

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   # main.py (continued)
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   from fastapi import FastAPI, Request, HTTPException, Depends # Import Depends
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   import uvicorn
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   from redis_conf import connect_redis, close_redis, redis_client
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   from rate_limiter import sliding_window_rate_limit # Import the dependency
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   app = FastAPI()
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   @app.on_event("startup")
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   async def startup_event():
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       await connect_redis()
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   @app.on_event("shutdown")
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   async def shutdown_event():
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       await close_redis()
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   @app.get("/")
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   async def read_root():
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       return {"message": "Welcome to the API"}
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   # Apply the rate limiter dependency to an endpoint
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   @app.get("/protected", dependencies=[Depends(sliding_window_rate_limit)])
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   async def read_protected_item():
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       return {"message": "This is a protected endpoint"}
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   # Apply with custom limits
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   @app.get("/strict", dependencies=[Depends(sliding_window_rate_limit(limit=5, window_seconds=10))])
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   async def read_strict_item():
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       return {"message": "This endpoint has a strict rate limit"}
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   if __name__ == "__main__":
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       uvicorn.run(app, host="0.0.0.0", port=8000)
   

This setup demonstrates the core logic: recording requests in a Redis Stream and checking the number of entries within the defined time window using XLEN after trimming.

Practical Considerations and Optimizations#

• Client Identification: Using the client IP address is common but can be problematic behind proxies or load balancers. A more robust solution for authenticated users involves using a user ID or API key extracted from the request.
• Key Granularity: Choose the right key granularity for your streams (e.g., per IP, per user, per endpoint).
• Stream Trimming: Implementing XTRIM is crucial. Without it, the streams will grow indefinitely, consuming memory. Trimming using MINID with the approximate flag (approximate=True) provides efficient cleanup of old entries.
• Error Handling: Add robust error handling for Redis connection issues or other potential failures.
• Performance at Scale: For extremely high-throughput scenarios, simply counting XLEN after trimming might still involve significant Redis operations if the window is large or the limit is high. More advanced techniques might involve using Redis Lua scripts to combine operations atomically or exploring approximate counting structures if precision can be sacrificed. The presented XTRIM + XLEN approach offers a good balance of accuracy and performance for many use cases.
• Configuration: Use environment variables or a configuration file for Redis connection details, limits, and window sizes.