Components and Tooling

1. Component Boundaries

Interface components

  • OrderController: HTTP endpoint adapter for command/query operations
  • DTOs: request/response transport contracts and validation shape
  • GlobalExceptionHandler: maps internal exceptions to stable API error schema

Application components

  • OrderService: command-side orchestration and transactional write behavior
  • OrderQueryService: read-side cache-aside orchestration
  • OutboxService: persistence of integration events for async publication
  • ports: repository/cache/publisher abstractions for infrastructure substitution

Domain components

  • Order: aggregate root with lifecycle behavior
  • OrderState hierarchy: transition policy per lifecycle state
  • OrderStatus and OrderItem: domain value representations

Infrastructure components

  • persistence adapters: JPA-backed implementations of application ports
  • messaging pipeline: outbox scheduler/fetcher/processor/retry + producer/consumer
  • resilience/crosscutting: failover manager, multi-region health indicator (OUT_OF_SERVICE when passive), global idempotency coordinator
  • web/security: request context, rate limiting, JWT claim conversion

2. Why These Components Exist

  • Keep transport, business rules, and technical concerns independently changeable
  • Maximize write correctness without sacrificing async throughput
  • Preserve read availability under cache degradation
  • Make failure handling explicit and observable

3. Component Interaction Patterns

3.1 Command side pattern

  • Controller -> OrderService -> repository/outbox ports
  • idempotency check + regional gating before durable write
  • cache eviction after successful mutation

3.2 Event publication pattern

  • write transaction persists outbox row
  • scheduled workers publish asynchronously to Kafka
  • retry policy managed by outbox components, not controllers/services

3.3 Event consumption pattern

  • consumer validates payload
  • dedupe check + processed marker in one transaction (no aggregate lifecycle transition; PENDING→PROCESSING is the scheduler)
  • retries and DLT separation for transient vs terminal failures

4. Tooling and Rationale

Runtime and framework

  • Spring Boot: dependency management, lifecycle, actuator integration
  • Spring Web: HTTP routing and validation integration
  • Spring Security/OAuth2 Resource Server: JWT-based authN/authZ

Persistence and messaging

  • Spring Data JPA + Hibernate: relational persistence, optimistic locking, query abstraction
  • Spring Kafka: producer/consumer plumbing, retry-topic and DLT support
  • Spring Data Redis (Lettuce): distributed cache/rate-limiter state and resilience controls

Observability and operations

  • Micrometer + Prometheus: application and infra metrics
  • OpenTelemetry bridge: trace emission for distributed diagnostics

Build and test

  • Maven: build lifecycle and dependency management
  • JUnit 5 + Mockito + Spring test stack: layered unit/integration testing

5. Component-Level Failure Behavior

Component Failure scenario Expected behavior
RedisCacheProvider Redis unavailable fail-soft cache miss, DB fallback
RateLimitingFilter Redis eval failure fail-open request path
KafkaEventPublisher broker send errors async failure surfaced to outbox retry flow
OrderCreatedConsumer duplicate delivery dedupe marker prevents repeated processing; scheduled job owns PENDING→PROCESSING
RegionalFailoverManager sustained unhealthy deps switch passive, block writes
OrderService stale version update conflict response, no lost update