Patterns

Draft outline for expected content. Replace bullets with full content as you develop the docs.

Overview

• What are CaaS patterns?
• When to use patterns vs ad-hoc solutions

Core Runtime Patterns

• Request routing and operation dispatch
• Schema-driven validation and coercion
• Error handling and problem details

Data Access Patterns

• CRUD-by-contract
• Soft delete handling
• Query filtering, paging, and sorting

Extension Patterns

• Using OpenAPI extensions (x-*) to drive behavior
• Validation hooks (pre-processing)
• Change-event hooks (post-processing)

Integration Patterns

• Proxy/transform operations
• Aggregation and composition across services
• Handling legacy and third-party APIs

Security & Governance Patterns

• AuthN/AuthZ driven by contract
• Multi-tenant considerations
• Observability, logging, and auditing

Anti-Patterns

• Where CaaS is a bad fit
• Common design mistakes and how to avoid them

Examples

• 1–2 concrete pattern walkthroughs
• Links to sample contracts / repos (future)

Proxy/transform pattern

In many systems, some resources are not stored locally but must be retrieved from external or legacy services. CaaS supports this by allowing proxy/transform operations to be defined in the contract.

In this pattern:

• The contract declares that a given operation is implemented as a proxy call to another service.
• The runtime performs the outbound HTTP request (or other protocol call).
• Response data is mapped into the local schema defined in the contract.

This approach is particularly useful for:

• Integrating legacy systems that cannot be refactored immediately
• Wrapping third-party APIs in a stable, organization-specific model
• Gradually migrating from older architectures while keeping a consistent contract surface for consumers

Proxy/transform definitions keep transport details and data-shaping logic declarative and centralized, rather than scattering integration code across many bespoke services.