Architecture Overview
Relevant source files
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The FLEXT Observability system is an enterprise-grade monitoring, metrics, and telemetry library designed for the FLEXT ecosystem. It provides a standardized way to implement observability patterns across multiple projects, ensuring type safety, domain validation, and consistent error handling through Railway-Oriented Programming (ROP).
The architecture follows Clean Architecture and Domain-Driven Design (DDD) principles, separating the system into distinct layers: Domain, Application, Interface, and Infrastructure. This structure facilitates the collection of metrics, distributed traces, alerts, and health checks while integrating with industry standards like OpenTelemetry and Prometheus.
Sources: src/README.md:14-16, src/flext_observability/README.md:21-23, README.md:7-9
Layered Architecture
The system is organized into four primary layers to ensure a clear separation of concerns and maintainability.
Layer Definitions
| Layer | Responsibility | Key Components |
|---|---|---|
| Domain Layer | Core entities and business validation rules. | FlextMetric, FlextTrace, FlextAlert, FlextHealthCheck |
| Application Layer | Service coordination and business operations. | FlextMetricsService, FlextTracingService, FlextHealthService |
| Interface Layer | APIs, factories, and external adapters. | FlextObservabilityMasterFactory, flext_simple.py, @flext_monitor_function |
| Infrastructure Layer | Data access patterns and low-level utilities. | FlextObservabilityRepository, HealthChecker, MetricsCollector |
Sources: src/README.md:25-30, src/flext_observability/README.md:27-77
Component Relationship Diagram
The following diagram illustrates the flow from the Interface layer through the Application layer to the underlying Domain entities.
Core Service Components
The functionality of the system is encapsulated in specialized services that manage specific observability concerns.
Metrics and Tracing
The system supports advanced metrics collection and distributed tracing. Metrics include attributes like name, value, unit, and tags, while traces focus on span context propagation.
- FlextMetricsService: Manages the collection, validation, and export of metrics.
- FlextTracingService: Coordinates distributed tracing and context propagation.
Health and Alerts
- FlextHealthService: Conducts health monitoring with dependency validation.
- FlextAlertService: Handles alert processing, severity levels (INFO, WARNING, ERROR, CRITICAL), and routing.
Sources: src/flext_observability/README.md:31-50, examples/01_functional.py:84-89
Data Flow and Implementation Patterns
Railway-Oriented Programming (ROP)
All operations within the library utilize the p.Result[T] or r[T] pattern. This ensures that every function returns a result object indicating success or failure, rather than throwing unmanaged exceptions.
Factory Pattern
The FlextObservabilityMasterFactory serves as the central point for creating domain entities. It ensures that all created entities are immediately validated against domain rules before being returned to the caller.
Sources: src/flext_observability/README.md:55-57, tests/README.md:41-43
Integration and Infrastructure
Dependency Injection
The system integrates with flext-core using the FlextContainer for dependency injection. This allows services to be easily swapped or mocked during testing.
External Standards
The project is designed to be compatible with:
- OpenTelemetry: For industry-standard telemetry (future integration).
- Prometheus: For metrics export and collection.
- Structured Logging: JSON-based logging with support for Correlation IDs.
Sources: pyproject.toml:22-30, src/README.md:58-63, src/flext_observability/README.md:66-70
Design Principles
The architecture adheres to several key engineering standards:
- SOLID Principles: Specifically demonstrated through specialized observability contracts and dependency inversion.
- Type Safety: Strict MyPy adoption and 100% type coverage for public APIs.
- SSOT (Single Source of Truth): Authoritative identity and configuration management.
- Zero-Tolerance Quality: Mandatory high test coverage (95%+) and strict linting.
Sources: src/README.md:49-56, AGENTS.md:28-32, 108-112, examples/README.md:24-27
Conclusion
The architecture of FLEXT Observability provides a robust, layered framework for monitoring complex distributed systems. By combining Clean Architecture with Railway-Oriented Programming and Domain-Driven Design, it ensures that observability data is validated, type-safe, and consistently handled across the entire FLEXT ecosystem. This structured approach allows for seamless integration with modern monitoring stacks while maintaining high internal code quality.