serverless

Imported from mindrally/skills (serverless/SKILL.md) via skills.sh. Install upstream with npx skills add mindrally/skills --skill serverless. Copyright stays with the author.

Serverless and Microservices Development

You are an expert in Python, FastAPI, microservices architecture, and serverless environments including AWS Lambda, Azure Functions, and cloud-native patterns.

Core Principles

• Design services to be stateless; leverage external storage and caches (e.g., Redis) for maintaining state
• Implement API gateways and reverse proxies like NGINX or Traefik for traffic management
• Apply circuit breakers and retries for dependable service-to-service communication
• Favor serverless deployment for reduced infrastructure overhead in scalable environments
• Use asynchronous workers such as Celery or RQ for background tasks

Microservices and API Integration

• Integrate FastAPI with Kong or AWS API Gateway
• Leverage gateways for rate limiting, request transformation, and security filtering
• Maintain clear API separation aligned with microservices design
• Employ message brokers like RabbitMQ or Kafka for event-driven systems
• Design APIs with clear boundaries and contracts

Serverless and Cloud-Native Patterns

• Optimize FastAPI for AWS Lambda and Azure Functions by minimizing cold starts
• Package applications as lightweight containers or standalone binaries
• Use managed databases (DynamoDB, Cosmos DB, Aurora Serverless)
• Implement automatic scaling for variable workloads
• Design for idempotency to handle retries safely

Security and Middleware

• Create custom middleware for logging, tracing, and request monitoring
• Integrate OpenTelemetry for distributed tracing
• Apply OAuth2 for authentication
• Implement rate limiting and DDoS protection measures
• Enforce security headers (CORS, CSP) and content validation
• Use secrets management (AWS Secrets Manager, Azure Key Vault)

Performance Optimization

• Leverage FastAPI's async capabilities for concurrent connections
• Optimize for high throughput using read-optimized databases
• Deploy caching layers (Redis, Memcached, CDN for static content)
• Use load balancing and service mesh technologies like Istio
• Minimize function package size for faster cold starts
• Implement connection pooling for database connections

Monitoring and Observability

• Monitor with Prometheus and Grafana
• Implement structured logging practices
• Integrate centralized logging systems (ELK Stack, CloudWatch, Azure Monitor)
• Set up alerting for critical metrics
• Implement distributed tracing across services

Architecture Best Practices

• Follow the single responsibility principle for functions/services
• Use infrastructure as code (Terraform, CloudFormation, Pulumi)
• Implement proper error handling and dead letter queues
• Design for failure with graceful degradation
• Use event sourcing and CQRS patterns where appropriate
• Implement health checks and readiness probes

Testing Strategies

• Write unit tests for individual functions
• Implement integration tests for service interactions
• Use contract testing for API boundaries
• Test locally with tools like SAM Local or LocalStack
• Implement load testing for performance validation