API Gateways and API Management Questions

Clarify requirements & constraints

• Public REST/gRPC API, target p95 latency < 100ms, throughput 50k rps typical, spikes to 200k rps; 99.9% availability; multi-AZ, global presence.

High-level architecture

• Edge CDN (CloudFront/Cloudflare) for static/edge caching + WAF.
• Global load balancer (AWS Route53 + Global Accelerator) -> Regional ingress (ALB/NLB).
• API Gateway (AWS API Gateway / Kong / Ambassador) handles auth, coarse rate-limiting, TLS termination.
• Service mesh / proxies (Envoy + Istio or App Mesh) between gateway and services for fine-grained traffic control, retries, circuit breaking, observability.
• Backend services in autoscaling groups (ECS/Fargate or k8s) behind Envoy sidecars.
• Message queue (Kafka/SQS) and worker pools for async/backpressure.
• Metrics/logs in Prometheus + Grafana, tracing in Jaeger/OTel.

Key mechanisms

• Edge caching: Cache idempotent GETs at CDN with short TTLs and stale-while-revalidate to keep p95 low during bursts.
• Rate limiting: Two-tier limits — global per-API key at API Gateway (token bucket), per-user per-route at Envoy using local token buckets with Redis as global token sync (or consistent hashing).
• Autoscaling: HPA based on request concurrency, CPU, and custom SLO-based metrics (queue length, p95 latency). Warm pools and predictive scaling using historical patterns.
• Backpressure: If downstream saturation, return 429 with Retry-After or enqueue to Kafka/SQS for async processing. Use client hints for backoff.
• Retries & Circuit Breakers: Envoy sidecar implements bounded retries with exponential backoff and jitter; circuit breakers per upstream (consecutive errors, error rate). Use bulkhead isolation per customer.
• SLA preservation during bursts: Prioritized traffic classes; graceful degradation — serve cached responses, reduce feature set, shed low-priority traffic; reserve capacity for high-tier customers.
• Observability & automation: SLOs alerting, auto-remediation playbooks (scale-up, failover), chaos testing.

Trade-offs

• Strong consistency vs. performance: prefer eventual consistency for async heavy paths.
• Cost vs latency: warm pools and global infra increase cost but preserve SLA.

This design ensures low-latency, high-throughput public API with robust protection against overload while remaining observable and operable in cloud environments.