Context and Problem
When two or more systems are trying to pass data each other but have different data schemas. This happens when you have a legacy systemt hat you are trying to sunset, but still need to use the functionality in that system.This happens when you have a legacy systemt hat you are trying to sunset, but still need to use the functionality in that system. When integrating with legacy systems or external services that use different data models, direct integration can contaminate your domain model with external concepts. This is especially problematic when:
- Two or more systems require to communicate with each other with different data schemas
- You are prohibited to making modifications to the legacy system to adapt to the new schema.
- You need to protect your clean domain model from external influences.
Solution
Create a translation layer between different subsystems that don't share the same semantics. This will allow you to convert the from one schema to the other. This communication API layer might have rules to apply based on values from the payload. This layer:
- Translates requests between newer and legacy/external systems
- Transforms data between different models and schemas
- Isolates the complexity of communication with external systems
- Acts as a facade that provides clean interfaces to your application
- Prevents coupling between your domain model and external systems
Benefits
- Domain Model Protection
- Prevents contamination of your domain model with concepts from external systems
- Controlled Integration
- Provides a controlled way to communicate with external services or legacy systems
- Simplified Migration
- Makes it easier to gradually replace legacy systems without disrupting functionality
- Reduced Coupling
- Isolates changes in external systems from affecting your application core
- Better Testability
- Allows mocking of external systems for testing purposes
- Encapsulated Complexity
- Hides the complexity of dealing with legacy or complex external APIs
Trade-offs
- Additional Layer
- Introduces an extra component that needs to be maintained
- Translation Overhead
- Creates performance overhead due to data transformation (latency!)
- Increased Complexity
- Adds complexity to the overall system architecture
- Development Effort
- Requires additional effort to implement and maintain translations
- Potential Inconsistency
- Translations might lose some semantic meaning if not carefully implemented
Issues and Considerations
- Versioning Strategy
- How to handle changes in either system's data schemas
- Error Handling
- How to translate and propagate errors between systems
- Performance Impact
- Evaluate the performance impact of data translations
- Testing Strategy
- Determine how to test the translation logic thoroughly
- Transaction Boundaries
- Define how transactions span across the layer
- Caching Strategy
- Consider caching translated data to improve performance
When to Use This Pattern
- During legacy system migration or modernization efforts
- When integrating with external systems that have different data models
- When protecting a clean domain model in a Domain-Driven Design approach
- When building a new system that must interface with legacy components
- When you need a clear separation between bounded contexts
- When you want to shield your application from changes in external services
- When migrating from monolithic to microservices architecture