Design and Implement Microservices Architectures

Microservices Patterns covers architecture patterns for service boundaries, inter-service communication, distributed data management, and resilience when building distributed systems. It gives three service decomposition strategies (by business capability, by DDD subdomain with bounded contexts, and the strangler fig pattern for gradually extracting a monolith), two communication styles (synchronous REST/gRPC/GraphQL and asynchronous event streaming via Kafka or message queues like RabbitMQ/SQS), the database-per-service data-ownership pattern, and the saga pattern for distributed transactions with compensating actions. It includes working Python code for: business-capability decomposition with OrderService, PaymentService, and InventoryService publishing domain events; an API gateway built on FastAPI that aggregates parallel calls to backend services behind a circuit breaker and handles partial failures; a synchronous HTTP service client with automatic retries and exponential backoff via tenacity; an asynchronous event bus built on aiokafka for publishing and subscribing to domain events, illustrated with an OrderCreated event consumed by an inventory service; a saga orchestrator that runs ordered steps (create order, reserve inventory, process payment, confirm order) with compensating actions run in reverse order on failure; and a circuit breaker implementation with closed, open, and half-open states, configurable failure and success thresholds, and a recovery timeout. Two further resilience patterns are named but not coded: retry with exponential backoff for transient faults, and the bulkhead pattern for isolating resources so one failure doesn't spread. It closes with eight best practices - business-capability-aligned boundaries, database per service, versioned API contracts, preferring async, circuit breakers, distributed tracing, service registry, health checks - and eight common pitfalls, including the distributed monolith, chatty services, shared databases, missing circuit breakers, all-synchronous communication, premature adoption of microservices, ignoring network failures, and missing compensation logic.

Use it when decomposing monoliths into microservices, designing service boundaries and contracts, implementing inter-service communication, managing distributed data and transactions, building resilient distributed systems, implementing service discovery and load balancing, or designing event-driven architectures. Do not use it when the system is small enough for a modular monolith, a quick prototype without distributed complexity is needed, or there is no operational support for distributed systems.

Input is a system to decompose or an inter-service communication problem to solve, along with the target services' domain boundaries. Output is service decomposition guidance, communication client code (synchronous with retries, or asynchronous via an event bus), saga orchestration for distributed transactions, and resilience components like a circuit breaker, plus a migration plan and operational guardrails for moving off a monolith.

Examples run on FastAPI with httpx for HTTP calls, tenacity for retry and backoff, a circuit breaker implementation, and aiokafka for Kafka-based event streaming; RabbitMQ and SQS are named as alternative message-queue options for asynchronous communication. The API gateway's circuit breaker specifically imports the circuitbreaker package's @circuit decorator, a separate implementation from the hand-rolled CircuitBreaker class shown in the resilience patterns section.

Backend and platform engineers decomposing a monolith into microservices, or designing inter-service communication, distributed transactions, and resilience patterns for an existing distributed system.