This document outlines alternative patterns and approaches that could be considered for future iterations of the template, but are not implemented in the current version.
- Structure: Feature-based folders (e.g.,
/Features/Users/,/Features/Products/) - Benefits: High cohesion, easier to understand and modify features
- Trade-offs: Potential code duplication, harder to enforce cross-cutting concerns
- When to Use: When features are highly independent and team prefers feature-centric organization
- Clean Architecture for core structure with vertical slices within Application layer
- Structure: Clean Architecture layers + feature-based organization within Application
- When to Use: Large teams with complex domains requiring both separation of concerns and feature focus
- Commands: Write operations with validation and business logic
- Queries: Read operations with optimized data fetching
- Benefits: Clear separation, optimized reads/writes, easier testing
- Trade-offs: Additional complexity, potential over-engineering
- When to Use: Complex business logic, different read/write models, high-scale applications
- Approach: DbContext injection with business logic in services
- Benefits: Simpler, less abstraction layers
- Trade-offs: Tighter coupling to EF, harder to test
- When to Use: Simple CRUD applications, rapid prototyping
- Benefits: Powerful server state management, automatic caching, background updates
- Trade-offs: More complex setup, learning curve
- When to Use: Heavy server interaction, complex caching requirements
- Benefits: Predictable state management, excellent DevTools, time-travel debugging
- Trade-offs: More boilerplate, steeper learning curve
- When to Use: Complex client state, need for state history, large teams
- Benefits: Simpler setup, good performance for basic scenarios
- Trade-offs: No message persistence, limited routing capabilities
- When to Use: Simple event broadcasting, already using Redis for caching
- Benefits: No external dependencies, excellent performance
- Trade-offs: No persistence across restarts, single-instance only
- When to Use: Simple background processing, development environments
- Benefits: Advanced orchestration, auto-scaling, service mesh capabilities
- Trade-offs: Significant complexity, resource overhead
- When to Use: Large-scale deployments, microservices, advanced deployment patterns
- Benefits: Simpler than Kubernetes, built into Docker
- Trade-offs: Less feature-rich than Kubernetes
- When to Use: Multi-node deployments without Kubernetes complexity
- Playwright: Better cross-browser support, faster execution
- Cypress: Better developer experience, easier debugging
- Decision factors: Browser support requirements, team preferences
- All are viable options for .NET testing
- Template uses: xUnit (most popular in modern .NET)
- Alternatives: NUnit (feature-rich), MSTest (Microsoft's framework)
- Self-hosted: Grafana + Prometheus (template choice)
- Managed: DataDog, New Relic, Application Insights
- Simple: Uptime Robot + basic logging
- PostgreSQL: Template choice (open-source, feature-rich)
- SQL Server: If already in Microsoft ecosystem
- SQLite: For simple applications or embedded scenarios
- NoSQL: MongoDB, CosmosDB for document-based data
The template is designed to evolve. Here's a suggested progression:
- Start: Clean Architecture + Repository/UoW (current template)
- Scale Up: Add CQRS when business logic becomes complex
- Optimize: Consider vertical slicing for specific features
- Advanced: Implement event sourcing for audit requirements
Each alternative approach should be evaluated based on:
- Team expertise
- Application complexity
- Performance requirements
- Maintenance overhead
- Business requirements