Rate limiting protects APIs from abuse and ensures fair resource usage. Different algorithms offer different trade-offs between accuracy, memory, and performance.
Rate Limiting Algorithms#
Token Bucket:
- Tokens added at fixed rate
- Requests consume tokens
- Allows bursts up to bucket size
Sliding Window Log:
- Stores timestamp of each request
- Counts requests in sliding window
- Most accurate, highest memory
Sliding Window Counter:
- Combines fixed and sliding windows
- Good accuracy with less memory
- Best overall choice
Fixed Window Counter:
- Counts requests per time window
- Simple but allows boundary bursts
- Simplest implementation
Token Bucket Implementation#
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Sliding Window Counter#
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Fixed Window Counter#
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Tiered Rate Limiting#
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Distributed Rate Limiting#
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Middleware Integration#
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Best Practices#
Algorithm Selection:
✓ Token bucket for bursty traffic
✓ Sliding window for accuracy
✓ Fixed window for simplicity
Implementation:
✓ Use Redis for distributed systems
✓ Return rate limit headers
✓ Provide clear error messages
✓ Allow for bursts when appropriate
Operations:
✓ Monitor rate limit hits
✓ Alert on sustained limit hits
✓ Provide upgrade paths
✓ Document limits clearly
Conclusion#
Choose the right rate limiting algorithm based on your needs. Token bucket allows bursts, sliding window is most accurate, and fixed window is simplest. Use Redis for distributed systems and always return informative headers to clients.