Request-Response Model
The request-response model is the most basic observation unit for latency, error handling, data flow, and user experience in backend systems. A well-designed request path carries timeout, retry, idempotency, auth, and observability decisions together.
Quick Decision
| Situation | Approach | Watch Out |
|---|---|---|
| User expects an immediate answer | Synchronous HTTP/gRPC | Timeout and fast failure are required |
| Work is long-running or retryable | Async queue/event | Idempotency and status tracking are needed |
| Data depends on another service | Gateway or service call | Chain latency and partial failure grow |
| Reads are frequent and data changes slowly | Cache | Staleness and invalidation must be defined |
Production Checklist
- Problem: Is the request on the critical path, in the background, or running while the user waits?
- Solution: Are controller, service, repository, external call, and response mapping boundaries clear?
- Trade-off: Synchronous flow is easier to debug; asynchronous flow is more resilient but harder to observe.
- Failure mode: Timeout, retry, duplicate request, partial failure, and validation error behavior should be standard.
- Measurement: Track request count, p95/p99 latency, error rate, dependency latency, and trace coverage.
- Security/cost: Every request produces auth, rate-limit, and log cost; sensitive data must not be logged.
Request-Response Lifecycle in Spring Boot
DispatcherServlet
The heart of Spring MVC, routes incoming HTTP requests to appropriate controllers.
Handler Mapping
Maps URL patterns (@RequestMapping, @GetMapping) to controller methods.
Controller Layer
@RestControllerfor RESTful endpoints@RequestBody/@ResponseBodyfor JSON serialization/deserialization
Service Layer
@Serviceannotation for business logic@Transactionalfor transaction management
Repository Layer
- Spring Data JPA for ORM mapping
@Repositoryfor data access layer
Inter-Service Communication in Microservices
Synchronous Communication
- OpenFeign client for service-to-service HTTP calls
- Load balancing with Ribbon/Spring Cloud LoadBalancer
Asynchronous Communication
- Spring Cloud Stream for message-driven architecture
- RabbitMQ/Apache Kafka integration
Event-Driven Architecture
- Domain events for loose coupling
- Event sourcing pattern for eventual consistency
Timeout & Retry
@Retryableannotation for automatic retry- Circuit breaker pattern for fault tolerance
Performance Considerations
Connection Pooling
- HikariCP for database connection pooling
- Apache HttpClient for HTTP connection pooling
Caching
- Spring Cache abstraction (
@Cacheable) with Redis/Hazelcast integration
Async Processing
@Asyncannotation for non-blocking operations- CompletableFuture for async programming
API Gateway Pattern
Spring Cloud Gateway
- Route definitions
- Predicates
- Filters
Rate Limiting
- Redis-based rate limiting
- Token bucket algorithm
Circuit Breaking
- Resilience4j integration
- Fallback mechanisms
Request/Response Transformation
- Header manipulation
- Body transformation
Security
- JWT validation
- OAuth2 integration
- API key management
Load Balancing Strategies
Client-side Load Balancing
- Spring Cloud LoadBalancer
- Ribbon
Server-side Load Balancing
- Nginx
- HAProxy
- AWS ALB
Health Checks
- Spring Boot Actuator
- Custom health indicators
Service Discovery Integration
- Eureka
- Consul
- Kubernetes service discovery
Request Lifecycle Example
Performance Optimization
Request Optimization
- Keep-alive connections
- HTTP/2 multiplexing
- Request batching
- Compression (gzip)
Response Optimization
- Response caching
- Pagination
- Field filtering
- Data compression
Error Handling
- Circuit breaker pattern
- Retry mechanisms
- Graceful degradation
- Fallback responses
Monitoring & Observability
Request Tracing
- Distributed tracing
- Correlation IDs
- Request timing
- Error tracking
Metrics Collection
- Request count
- Response time
- Error rate
- Throughput
Logging Strategy
- Structured logging
- Log aggregation
- Request/response logging
- Security events
