How do you design a highly available and scalable microservices architecture?

Designing a highly available and scalable microservices architecture requires addressing availability, scalability, resilience, and observability at each layer.

Availability Patterns

Multiple instances: Run at least 3 replicas of each service across multiple availability zones. Health checks: Liveness probes restart unhealthy containers; readiness probes stop traffic to unready instances. Circuit breakers: Prevent cascade failures by stopping calls to failing services (e.g., Hystrix, Resilience4j). Load balancing: Distribute traffic evenly across instances at layer 7 with health-aware routing.

Scalability Patterns

Horizontal scaling: Add more instances rather than bigger machines. Auto-scaling based on CPU, memory, or custom metrics (e.g., queue depth). Stateless services: Store state in external data stores (Redis, databases) so any instance can handle any request. Database sharding and read replicas to scale data layer. CDN and caching layers to reduce backend load.

Resilience Patterns

Bulkhead pattern: Isolate failures to prevent one service from exhausting shared resources. Retry with exponential backoff: Handle transient failures gracefully. Timeout configuration: Prevent slow services from blocking the entire request chain. Graceful degradation: Return partial results or cached data when a dependency fails.

Service Communication

Synchronous: REST or gRPC for real-time request/response. Use API Gateway for external traffic routing, auth, and rate limiting. Asynchronous: Message queues (Kafka, RabbitMQ, SQS) for event-driven communication, decoupling producers from consumers.

Observability

Distributed tracing with correlation IDs. Centralized logging with structured log format. Per-service metrics with SLO-based alerting. Service mesh (Istio, Linkerd) for traffic management, mTLS, and observability.