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Solving Circular Dependencies: A Journey to Better Architecture

Published at
1/15/2025
Categories
python
architecture
development
hypergraph
Author
d1d4c
Author
5 person written this
d1d4c
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Solving Circular Dependencies: A Journey to Better Architecture

After wrestling with circular dependencies in my personal project HyperGraph, I finally decided to tackle this technical debt head-on. The problem had been growing more apparent as the codebase expanded, making it increasingly difficult to maintain and test. Today, I want to share why I chose to implement a complete architectural overhaul and what this new implementation solves.

The Problem

When I first started developing HyperGraph, I focused on getting features working quickly. This led to some hasty architectural decisions that seemed fine at first but started causing problems as the project grew. The most significant issues were:

  1. Circular dependencies between core modules
  2. Tight coupling between components
  3. Difficult testing scenarios
  4. Complex initialization chains
  5. Poor separation of concerns

The breaking point came when I tried to implement a new plugin system and found myself in a dependency nightmare. The CLI module needed the plugin system, which needed the state service, which in turn required the CLI module. This circular dependency chain made it nearly impossible to maintain clean architecture.

The Solution

After some research and consideration, I decided to implement a comprehensive solution based on several modern patterns:

1. Interface-First Design

Instead of diving straight into implementations, I created a clean interfaces package that defines the contracts for all core components. This allows me to break circular dependencies by having modules depend on interfaces rather than concrete implementations.

2. Dependency Injection

I implemented a robust DI system that handles:

  • Service registration and resolution
  • Lifecycle management
  • Configuration injection
  • Lazy loading

This gives me much better control over component initialization and dependencies.

3. Lifecycle Management

I added a proper lifecycle management system that handles:

  • Component state transitions
  • Initialization chains
  • Resource cleanup
  • Error handling

4. Clean Package Structure

The new structure clearly separates:

hypergraph/
β”œβ”€β”€ core/
β”‚   β”œβ”€β”€ di/           # Dependency injection
β”‚   β”œβ”€β”€ interfaces/   # Core interfaces
β”‚   β”œβ”€β”€ lifecycle.py  # Lifecycle management
β”‚   └── implementations/
β”œβ”€β”€ cli/
β”‚   β”œβ”€β”€ interfaces/
β”‚   └── implementations/
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What This Solves

This new implementation solves several critical problems:

  1. Circular Dependencies: By depending on interfaces rather than implementations, I've eliminated all circular dependencies.

  2. Testing: Components are now easily mockable through their interfaces, making unit testing much simpler.

  3. Maintenance: Clear separation of concerns makes the code more maintainable and easier to understand.

  4. Flexibility: The plugin system can now be properly implemented without creating dependency cycles.

  5. Error Handling: Proper lifecycle management makes error handling more robust and predictable.

What It Enables

More exciting than what it solves is what it enables:

  1. Plugin Ecosystem: I can now create a proper plugin ecosystem without worrying about dependency issues.

  2. Feature Expansion: Adding new features is much cleaner as I can simply implement new interfaces.

  3. Better Testing: I can now write comprehensive tests without fighting with dependencies.

  4. State Management: The new architecture makes it possible to implement proper state management patterns.

Lessons Learned

The biggest lesson I've learned is that taking the time to design proper interfaces and architecture pays off enormously in the long run. While it might seem like overengineering at first, having clean separation of concerns and proper dependency management becomes crucial as a project grows.

I've also learned the importance of lifecycle management in a complex system. Having clear states and transitions makes the system much more predictable and easier to debug.

Going Forward

This new architecture gives me a solid foundation to build upon. I'm particularly excited about:

  • Implementing a comprehensive plugin system
  • Adding advanced state management features
  • Creating better testing infrastructure
  • Developing new CLI features

While it was a significant undertaking to refactor the entire codebase, the benefits are already clear. The code is more maintainable, testable, and extensible than ever before.

Most importantly, I can now focus on adding new features without fighting with architectural issues. Sometimes you have to take a step back to move forward.

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