SOLID Observability Demo
Relevant source files
The following files were used as context for generating this wiki page:
The SOLID Observability Demo is an advanced demonstration showcasing the application of SOLID design principles within the flext-observability module. It serves as a real-world implementation guide for integrating metrics, distributed tracing, health monitoring, and alerting using a clean, layered architecture that emphasizes single responsibility and dependency inversion.
This demo illustrates how the observability framework adheres to the FLEXT ecosystem standards, utilizing p.Result[T] patterns for railway-oriented programming (ROP) and the FlextContainer for dependency injection. By following these patterns, the system ensures high type safety and domain-driven validation across complex monitoring scenarios.
Sources: examples/02_solid_observability_demo.py:1-15, examples/README.md:21-30, src/README.md:15-25
Architecture and Design Principles
The demo is built upon a layered architecture consisting of Domain, Application, Interface, and Infrastructure layers. This structure ensures a clear separation of concerns, where core entities like FlextMetric and FlextTrace are decoupled from external adapters and platform orchestration logic.
The following table summarizes the mapping of SOLID principles to the observability implementation:
| Principle | Implementation in Demo |
|---|---|
| Single Responsibility (SRP) | Specialized services for metrics, tracing, alerts, and health checks. |
| Open/Closed (OCP) | Extensible monitoring patterns that allow adding new metrics without modifying core logic. |
| Liskov Substitution (LSP) | Polymorphic observability interfaces allowing standard result processing across different types. |
| Interface Segregation (ISP) | Specialized contracts for different observability concerns (e.g., FlextHealthService vs FlextMetricsService). |
| Dependency Inversion (DIP) | Use of FlextContainer to inject collaborators rather than hard-wiring logic. |
Sources: examples/README.md:21-30, src/README.md:27-35, AGENTS.md:150-165, src/flext_observability/README.md:20-50
Component Interaction Flow
The following diagram illustrates the flow from entity creation through the factory to result validation in a SOLID context.
The flow shows how the FlextContainer provides the context for the MasterFactory, which generates validated domain entities wrapped in result types.
Sources: examples/02_solid_observability_demo.py:53-65, src/flext_observability/README.md:55-65
Core Observability Features
Metrics Collection
The system demonstrates comprehensive metrics collection across various categories including performance (latency), system (CPU/Memory), and business counters. Metrics are created with specific names, values, and units, ensuring strict type safety.
def demonstrate_metrics_collection() -> None:
metrics = [
("api_requests_total", 1500.0, "count"),
("response_time_avg", 85.2, "milliseconds"),
("memory_usage", 68.5, "percent"),
]
for name, value, unit in metrics:
flext_metric(name, value, unit)
Sources: examples/02_solid_observability_demo.py:68-79
Distributed Tracing
Distributed tracing is showcased by correlating operations across services like api_gateway, auth_service, and database. The demo utilizes flext_trace to establish operation boundaries and context.
Health and Alerting
Health monitoring tracks the status of infrastructure components (Healthy, Degraded, Unhealthy). Alerting manages system notifications with varying severity levels.
- Health Statuses:
HEALTHY,DEGRADED,UNHEALTHY. - Alert Levels:
INFO,WARNING,ERROR,CRITICAL.
Sources: examples/02_solid_observability_demo.py:96-105, examples/02_solid_observability_demo.py:108-132
Factory and Container Integration
A key aspect of the SOLID demo is the usage of the FlextObservabilityMasterFactory. This factory is instantiated using a FlextContainer, which manages the lifecycle and dependency injection of observability services.
def demonstrate_factory_patterns() -> None:
container = FlextContainer()
factory = FlextObservability.FlextObservabilityMasterFactory(container)
factory.create_metric("global_metric", 42.0, "count")
The factory pattern ensures that entity creation is centralized and adheres to domain validation rules before returning a p.Result object.
Sources: examples/02_solid_observability_demo.py:141-147, src/flext_observability/README.md:88-95
Validation and Error Handling
Following the Railway-Oriented Programming (ROP) pattern, every observability operation returns a result object. This allows developers to handle success and failure paths explicitly without relying on traditional exception bubbling for business logic errors.
| Function | Validation Focus | Return Type |
|---|---|---|
flext_metric | Name format, unit consistency | p.Result[FlextMetric] |
flext_trace | Context propagation, name validity | p.Result[FlextTrace] |
flext_alert | Severity levels, message integrity | p.Result[FlextAlert] |
flext_health_check | Component name, status mapping | p.Result[FlextHealthCheck] |
Sources: examples/02_solid_observability_demo.py:150-166, tests/README.md:88-105
Conclusion
The SOLID Observability Demo successfully illustrates how to build a robust, enterprise-grade monitoring system within the FLEXT ecosystem. By leveraging Clean Architecture, the FlextContainer, and specialized factories, it provides a blueprint for developers to implement observability that is both scalable and maintainable, ensuring 100% type safety and consistent validation across all monitoring concerns.