load-shedding

Protect backend APIs from traffic spikes by implementing load shedding policies that enforce priority-based admission and graceful degradation, ensuring critical user flows survive while non-essential requests are dropped under heavy load.

Graceful degradation for overloaded services provides useful, degraded responses when dependencies fail, using adaptive shedding triggers based on latency and queue depth to prevent complete system failures during heavy load.

Prioritize critical requests when concurrency limits are reached by implementing priority-based admission, which evaluates traffic and preserves critical user flows while rejecting or degrading lower-priority background workers.

Yes, you can apply load shedding to background workers during latency alarms, using adaptive triggers based on queue depth and concurrency to enforce admission control and signal errors properly under heavy load.

Yes, this approach provides observability hooks specifically for tuning and alerting, allowing you to monitor load shedding triggers, degraded responses, and admission rates to optimize backend performance.

Implement adaptive load shedding triggers instead of static rate limiting when your backend services face unpredictable traffic spikes, requiring real-time adjustments based on latency, queue depth, and concurrency to maintain availability.