#microservices

Design scalable serverless architecture on AWS. Components: 1. API Gateway for request routing and throttling. 2. AWS Lambda for business logic execution. 3. Amazon DynamoDB for NoSQL data storage. 4. Amazon Cognito for user authentication. 5. AWS Step Functions for workflow orchestration. 6. Amazon SQS/SNS for event-driven messaging. 7. CloudWatch for monitoring and logging. 8. CI/CD with AWS CodePipeline. Include cost estimation and disaster recovery strategy.

Deploy Istio service mesh on Kubernetes. Features: 1. Automatic sidecar injection for traffic management. 2. Mutual TLS for service-to-service encryption. 3. Traffic routing (canary deployments, A/B testing). 4. Circuit breaking and retry policies. 5. Distributed tracing with Jaeger. 6. Service-level metrics and dashboards. 7. Ingress gateway for external traffic. Configure virtual services and destination rules. Use Kiali for visualization. Include performance impact analysis and troubleshooting guide.

Design microservices architecture effectively. Principles: 1. Single Responsibility (one service, one business capability). 2. Decentralized Data (each service owns its database). 3. API Gateway (single entry point). 4. Service Discovery (Consul, Eureka). 5. Asynchronous Communication (message queues, events). 6. Circuit Breaker (fault tolerance). 7. Containerization (Docker, Kubernetes). Challenges: distributed tracing, data consistency, testing. Use API versioning. Implement health checks. Centralized logging (ELK). Monitoring (Prometheus, Grafana). Start with monolith, extract services gradually. Not always the right choice - consider team size and complexity.

Deploy and manage API gateways with rate limiting, authentication, and security controls for microservices architecture. API Gateway features: 1. Request routing: path-based routing, host headers, query parameters, weighted routing for A/B testing. 2. Protocol translation: REST to GraphQL, HTTP to gRPC, WebSocket support. 3. Response transformation: data format conversion, header modification, CORS handling. 4. Caching: response caching (5-minute TTL), cache invalidation, edge caching integration. Rate limiting strategies: 1. Throttling levels: per-API key (1000 req/min), per-IP (100 req/min), global limits. 2. Rate limiting algorithms: token bucket, sliding window, fixed window approaches. 3. Burst handling: temporary burst allowance, graceful degradation during spikes. Authentication methods: 1. API key management: key rotation, expiration policies, usage analytics. 2. OAuth 2.0/JWT: token validation, scope-based authorization, refresh token handling. 3. mTLS: certificate-based authentication, client certificate validation. Security controls: 1. Input validation: request size limits (10MB), content type validation, schema enforcement. 2. WAF integration: SQL injection prevention, XSS protection, bot detection. 3. DDoS protection: rate limiting, IP blocking, geographic restrictions. Monitoring and analytics: 1. Request metrics: latency percentiles (P50, P95, P99), error rates, throughput tracking. 2. API usage: top consumers, endpoint popularity, quota utilization. Load balancing: upstream health checks, circuit breaker pattern, retry mechanisms with exponential backoff.

Master Docker containerization for microservices with optimization and security best practices. Dockerfile optimization: 1. Multi-stage builds: separate build and runtime environments, reduce image size by 70-80%. 2. Base image selection: Alpine Linux for minimal footprint, distroless for security. 3. Layer caching: order instructions from least to most frequently changing. 4. Security practices: non-root user, minimal packages, vulnerability scanning. Container orchestration: 1. Docker Compose: local development, service dependencies, network configuration. 2. Production considerations: resource limits (CPU: 1 core, Memory: 512MB typical), health checks every 30 seconds. Image management: 1. Registry strategy: private registries for proprietary code, image tagging conventions (semantic versioning). 2. Security scanning: Trivy, Clair for vulnerability detection, policy enforcement. 3. Image optimization: .dockerignore files, multi-arch builds (AMD64, ARM64). Microservices patterns: 1. Service mesh: Istio/Linkerd for inter-service communication, observability. 2. API gateway: rate limiting, authentication, request routing. Monitoring: container metrics (CPU, memory, disk I/O), log aggregation, distributed tracing with Jaeger/Zipkin.

Deploy and manage microservices communication using Istio service mesh for traffic management, security, and observability. Istio architecture: 1. Data plane: Envoy proxy sidecars, automatic injection, traffic interception. 2. Control plane: Pilot (traffic management), Citadel (security), Galley (configuration). 3. Ingress/egress gateways: external traffic management, TLS termination, rate limiting. Traffic management: 1. Virtual services: request routing, traffic splitting (10% canary, 90% stable), fault injection. 2. Destination rules: load balancing (round robin, least connection), circuit breaker configuration. 3. Gateways: external traffic entry points, protocol configuration, host-based routing. Security features: 1. mTLS: automatic mutual TLS between services, certificate management, encryption at service level. 2. Authorization policies: RBAC for service-to-service communication, JWT validation. 3. Security policies: network policies, ingress/egress controls, threat detection. Observability: 1. Distributed tracing: Jaeger integration, request flow visualization, latency analysis. 2. Metrics collection: Prometheus integration, service-level indicators, golden signals. 3. Access logging: comprehensive request logging, audit trails, compliance support. Performance optimization: 1. Sidecar configuration: resource limits (CPU: 100m, Memory: 128Mi), proxy protocols. 2. Traffic policies: timeout configuration (30s), retry policies (3 attempts), connection pooling. Canary deployments: 1. Traffic splitting: gradual rollout (5% → 10% → 50% → 100%), automated rollback. 2. Success criteria: error rate <0.1%, latency increase <10%, business metrics validation.

Compare and contrast the microservices architecture with the monolithic architecture. Discuss the pros and cons of each in terms of development, deployment, scalability, and complexity. For a new, small-scale e-commerce startup, which architecture would you recommend and why?

Design microservices effectively. Patterns: 1. Service per business capability. 2. API Gateway for routing. 3. Service discovery (Consul, Eureka). 4. Circuit breaker for resilience. 5. Event-driven communication. 6. Database per service. 7. Saga pattern for distributed transactions. 8. CQRS for read/write separation. Use Docker and Kubernetes. Implement observability from start.