IronTrack

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IronTrack is an experimental async backend sandbox designed for architectural exploration and architectural efficiency analysis.

The project serves as a laboratory for testing concurrency patterns, database optimization, and constraint-driven experiments within a workout tracking domain.

Note

Infrastructure Context: This project is intentionally developed and benchmarked on constrained bare-metal hardware (AMD FX-8320 / HDD).

This “high-latency” environment is a deliberate architectural choice. It amplifies performance bottlenecks—such as I/O wait times and serialization overhead—that are often masked by modern cloud infrastructure. This setup forces the evaluation of concurrency patterns and optimization techniques under realistic hardware limitations.

Warning

This is a learning-focused sandbox. It is not intended for production-driven business applications.

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Implemented Components

I. Architecture and Design Patterns

  • Layered Structure: Separation into domain (logic), db (models), config, and lib layers to ensure code maintainability.

  • Repository Pattern: Decouples business logic from SQLAlchemy operations using Advanced-Alchemy.

  • Schema Evolution: Database versioning and migrations via Alembic.

II. Performance & Optimization

  • Runtime & Proxy: Uses Granian (Rust-based ASGI) and Angie (reverse proxy).

  • Infrastructure Efficiency: Bypass TCP stack overhead via Unix Domain Sockets (UDS) for inter-container communication.

  • Containerization: Multi-stage Docker builds (Builder/Runner) with dependency optimization via uv.

  • Local Deployment: Orchestrated via docker-compose with automated migrations and service health checks.

  • Serialization: Fast JSON processing with msgspec to minimize response latency.

  • Connection Pooling: PgBouncer integration for efficient database transaction management.

III. Security & Administration

  • Auth System: Secure JWT (Ed25519) via HTTP-only cookies with token blacklisting and user caching.

  • Access Control (RBAC): Permission system supporting different user roles (e.g., superuser, trainer).

  • Unified CLI: Centralized command-line interface built with Typer for server management, migrations, and administrative tools.

IV. Engineering Standards (DX)

  • Type Safety: Strict static analysis with Mypy and runtime validation via Pydantic v2.

  • Testing: Integration suite using transactional rollbacks for clean and isolated tests.

  • Code Quality: Automated linting and formatting via Ruff and pre-commit.

  • Observability: Structured logging with structlog and request correlation IDs.

V. Data & Content

  • Automated Seeding: Built-in infrastructure to populate the database with a catalog of 1000+ exercises and reference directories.

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