rate-limiting-and-traffic-control

Rate limiting protects APIs by enforcing quotas, burst handling, and backpressure across public APIs, partner integrations, and login flows. You define limit dimensions like tenant, user, or API key, then apply algorithms such as token bucket or sliding window to ensure service reliability under pressure.

Token bucket and leaky bucket algorithms handle burst traffic while ensuring fairness across tenants. Token bucket allows accumulated capacity for sudden bursts, whereas leaky bucket smooths request flow. Sliding window algorithms provide precise quota tracking to prevent abuse and maintain predictable throughput.

Configure concurrency limits and queue depth limits for event streams and batch processing by defining operation types and priority classes. Apply adaptive throttling to apply backpressure, which protects downstream resources and enables graceful degradation when systems face variable demand.

Traffic control can be applied across multiple dimensions including tenant, user, API key, IP, partner, endpoint, operation type, cost unit, and priority class. This multi-dimensional throttling ensures fairness, security, and predictability across diverse API integrations and workflows.

HTTP responses when a rate limit threshold is exceeded should include an explicit rejection status and Retry-After guidance. Designing response behavior with proper observability and override procedures ensures clients receive clear feedback to manage traffic predictably.

Adaptive throttling should be used instead of fixed quotas when system demand varies unpredictably and downstream resources require dynamic protection. It enables graceful degradation by adjusting limits in real-time based on observed load, ensuring reliability over strict static enforcement.