Software architecture: Getting Started and Scaling
Importance of Architecture
Software architecture is the fundamental organization of a system—its components, their relationships, and the principles governing its design and evolution.
Key benefits
Communication and Shared Understanding: Architecture provides a common language for stakeholders—developers, managers, clients—to discuss the system at a high level without getting lost in implementation details.
Early Design Decisions: It forces teams to make critical decisions early about technology choices, integration patterns, scalability approaches, and trade-offs. These decisions are expensive to change later.
Quality Attributes: Architecture directly impacts non-functional requirements like performance, security, maintainability, scalability, and reliability. Good architecture makes these qualities achievable; poor architecture makes them nearly impossible.
Risk Reduction: By identifying potential issues early—bottlenecks, security vulnerabilities, integration challenges—architecture helps mitigate risks before they become expensive problems.
Team Coordination: Clear architectural boundaries allow multiple teams to work in parallel on different components without stepping on each other’s toes.
Long-term Maintainability: A well-documented architecture helps new team members understand the system quickly and guides future modifications consistently.
Risks from lack of architecture
Technical Debt Accumulation Without architectural guidelines, developers make inconsistent decisions, creating a patchwork system that becomes increasingly difficult and expensive to maintain.
Scalability Failures Systems may work fine initially but collapse under load because scaling concerns weren’t considered in the design.
Integration Nightmares Components built without architectural coordination often can’t communicate effectively, requiring costly rewrites or complex workarounds.
Security Vulnerabilities Ad-hoc designs frequently overlook security patterns, leaving systems exposed to threats that would have been prevented by architectural planning.
Increased Costs Fixing architectural problems after implementation costs 10-100x more than addressing them during design. Major refactoring or complete rewrites may become necessary.
Team Inefficiency Developers waste time figuring out inconsistent patterns, redoing work, or dealing with conflicts from unclear boundaries.
Business Agility Loss Systems without good architecture become rigid and fragile. Adding features or adapting to market changes becomes slow and risky, creating competitive disadvantages.
Knowledge Silos Without documentation, critical knowledge exists only in individuals’ heads, creating key-person dependencies and making staff transitions painful.
Right architecture - best match for project needs
Architecture should match the problem complexity. Over-architecting wastes time; under-architecting creates chaos.
| App Capability | Simple | Medium Complexity | Complex |
|---|---|---|---|
| App Characteristics | 3-5 screens, stable requirements | 5-15 screens, moderate changes | 15+ screens, high change frequency |
| UI Pattern | View and ViewModel | MVVM or MVVM-C | MVVM-C |
| Layer Complexity | Basic layers (View + ViewModel + Model) | Clean Architecture | Clean Architecture with advanced DDD concepts when business logic requires |
| Navigation | Direct segues/NavigationLink | Coordinator pattern for some screens | Coordinator pattern |
| Dependency Injection | None (direct instantiation) | Initializer injection | DI Container |
| Business Logic | In ViewModels or Views | In ViewModels and Services | Entities and Services in Domain layer |
| Data Access | Direct API/database calls | Repository pattern (basic) | Repository + multiple data sources + caching strategy |
| State Management | @State, @Published | @Published, Combine | Stores for a single source of truth and Managers to coordinate changes |
| Testing Strategy | Manual QA, minimal unit tests | Unit tests for ViewModels and key services | Comprehensive unit, integration, and UI tests |
| Modularity | Single target | Feature-based folders | Feature modules (Swift Packages/Frameworks) |
| Persistence | UserDefaults | UserDefaults + simple Core-Data/SwiftData | Repository abstraction + Core Data/SwiftData/SQLite + Offline Sync |
| Networking | URLSession directly in VM | NetworkService protocol | NetworkService + API client abstraction |
| Change Adaptation | Rewrite sections as needed | Refactor with some pain | Isolated changes, minimal ripple effects |
Evolution Path: Simple → Medium → Complex
The Simple-to-Medium Transition occurs when direct coupling becomes painful—when changing one screen breaks another, or when testing requires running the entire app. The key shift is introducing boundaries through protocols and services, separating “what” (business operations) from “how” (implementation details). This transition is triggered by team growth or feature complexity, not arbitrary milestones.
The Medium-to-Complex Transition happens when coordinating changes across features becomes the bottleneck—when adding a feature requires touching many files, or when multiple developers block each other. The essential change is moving from feature-based organization to domain-based architecture, where business concepts (Entities, UseCases) become first-class citizens independent of UI or data concerns. This shift enables parallel teamwork and reduces the blast radius of changes.
The Critical Principle across all transitions is that architecture evolves in response to specific pain points, not preemptively. You migrate when the current structure actively slows development, not because you anticipate future complexity. Each level adds overhead that only pays for itself when the problems it solves are already present and measurable.