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concept#Integration#Architecture#Platform#Software Engineering

Frontend–Backend Integration

Conceptual guidance for designing reliable interfaces and communication between frontend and backend systems.

Frontend–Backend integration outlines concepts and patterns for reliable communication between user interfaces and backend systems.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

API gateways (e.g., Kong, Traefik)Identity provider / OAuth2 (e.g., Keycloak)Observability stacks (Prometheus, Grafana, OpenTelemetry)

Principles & goals

Clear API contracts (versioning)Loose coupling between client and serverDesign for fault tolerance and observable telemetry
Build
Domain, Team

Use cases & scenarios

Compromises

  • Breaking API contracts can cause production outages
  • Insecure implementation of authentication/authorization
  • Poor caching strategies cause inconsistent user experiences
  • Use-case-driven API designs and small, well-considered endpoints
  • Introduce contract tests and automated integration tests
  • Centralize security mechanisms (tokens, scopes)

I/O & resources

  • API design documentation (OpenAPI/GraphQL schema)
  • Auth and security requirements
  • Test data and integration stubs
  • Stable API contracts and versioning
  • Monitoring and observability metrics
  • Documented integration workflows

Description

Frontend–Backend integration outlines concepts and patterns for reliable communication between user interfaces and backend systems. It covers API design, data models, authentication, error handling and performance aspects. The goal is loose coupling, clear contracts and scalable communication to improve maintainability, security and observability.

✔

  • Improved maintainability through well-defined interfaces
  • Independent development and deployment of frontend and backend
  • Better scalability and performance optimization

✖

  • Increased coordination effort for API changes
  • Latency and network dependence for real-time needs
  • Complexity for data-intensive aggregations

  • API latency (p95)

    Measure of 95th percentile latency for typical API calls; important for UX.

  • Error rate (4xx/5xx)

    Proportion of erroneous responses; indicator for stability and compatibility.

  • Throughput (requests/s)

    Number of processed requests per second; relevant for scaling planning.

E-commerce webshop

Frontend consumes REST services for catalog, cart and checkout; clear API contracts and contract tests were introduced.

Internal management dashboard

Dashboard uses GraphQL backend for aggregated data; schema federation enables modular growth.

Mobile app with offline capability

Client syncs locally modified data with backend; conflict rules and backoff strategies are implemented.

1

Create API specification (OpenAPI/GraphQL)

2

Provide mock server and develop frontend against mock

3

Implement backend, introduce contract tests and integrate CI

4

Define monitoring, logging and SLOs

⚠️ Technical debt & bottlenecks

  • Ad-hoc endpoints without tests
  • Outdated API versions without deprecation plan
  • No automation for API contracts
Network latencyData aggregationAuth performance
  • Frontend implements business logic instead of backend, leading to duplication
  • Published public API without rate limiting
  • Using caching without invalidation for dynamic data
  • Unclear responsibilities between teams for API changes
  • Lack of observability hinders root cause analysis
  • Integrating security aspects too late
API design and documentationNetworking and security fundamentalsKnowledge of testing and contract testing
Scalability of interfacesSecurity and access controlMaintainability and deployability
  • • Existing legacy APIs constrain changes
  • • Regulatory requirements for data security
  • • Limited bandwidth in mobile contexts