Architecture Patterns
System architecture patterns provide proven templates for structuring applications and aligning software design with organizational scaling goals. Choosing the right pattern requires balancing development speed, system complexity, and organizational structure.
Here is a high-level overview of key architectural patterns used in modern software design:
1. Micro Frontends
Extends the concept of microservices to frontend development. The user interface is split into independent, domain-owned applications that are composed at runtime or compile-time.
- Trade-offs: Enables independent team deployments and technology stack agility, but increases orchestration complexity and can impact performance (bundle sizes) and UX consistency.
- Resources:
- Martin Fowler on Micro Frontends (External)
- Micro Frontends Community Portal (External)
- Design Systems (Internal)
2. Strangler Fig Application
An incremental migration strategy where a legacy monolithic system is gradually replaced by routing specific API paths or frontend routes to new modern services, eventually deprecating the legacy system entirely.
- Trade-offs: Minimizes migration risk by avoiding "big bang" rewrites and delivers business value continuously, but introduces routing/proxy complexity and data synchronization overhead.
- Resources:
- Strangler Fig Application (Martin Fowler) (External)
3. Command Query Responsibility Segregation (CQRS)
Separates write operations (commands) from read operations (queries) into distinct data models, allowing each side to scale and optimize independently.
- Trade-offs: Optimizes read/write performance and simplifies domain logic on the write side, but introduces eventual consistency challenges and increased infrastructure complexity.
- Resources:
- CQRS Architecture (Internal)
- CAP Theorem (Internal)
4. Microservices
Decomposes an application into a collection of small, loosely coupled services organized around business domains. Each service is fully autonomous and communicates via lightweight APIs.
- Trade-offs: Strong team autonomy, independent scaling, and high deployment frequency, but introduces operational complexity, distributed tracing challenges, and network latency.
- Resources:
- Conway's Law (Internal)
- Architectural Decision Records (Internal)