Rate limiting is essential for managing traffic, controlling costs, and providing a smooth user experience for APIs. It involves setting request limits per time period to handle traffic loads efficiently. Key rate limiting policies like fixed window, sliding window, token bucket, and leaky bucket have specific use cases and trade-offs. Implementation can be done at proxy or application levels, providing different degrees of control. The article walks through setting up a rate limiting service using Go, Redis, and the leaky bucket policy, emphasizing practical tips and the importance of monitoring and adjustments.
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