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Fundamentals of Modular Architecture

· 4 min read
Pere Pages

In software development, managing dependencies and state is crucial for ensuring deterministic behavior: for any given input, we aim for a consistent output (This is a key reason behind the growing hype for functional programming, as it emphasizes immutability and stateless functions, simplifying the management of dependencies and state). However, as the number of inputs and modules increases, so does the complexity of handling them, potentially leading to a higher risk of bugs—unintended outcomes. To mitigate this, it's essential to minimize and manage dependencies effectively, acknowledging that they can introduce similar challenges by increasing system interconnectedness and potential failure points.

TL;DR: Key Concepts

  • Abstractions and APIs: Facilitate modular design and enable independence from specific implementations. Allow dynamic changes and testing in isolation.
  • Managing Dependencies: Critical for reducing complexity and potential points of failure. Involves minimizing and effectively managing connections between components.
  • Internal State Management: Ensuring consistent outcomes by maintaining predictable state behavior. Involves strategies for state isolation and controlled state changes.
  • Predictable Outcomes: Aim for deterministic outputs for given inputs to avoid bugs. Testing and interface contracts help anticipate and manage all possible states.
  • Modularity: Design components like "Brick" pieces with clear interfaces, allowing for easy assembly, modification, and reusability within the software architecture.
Complexity due to dependencies

Complexity tends to grow superlinearly or nonlinearly with the addition of dependencies. This means that each new dependency doesn't just add its own inherent complexity but can also increase the complexity of existing interactions and dependencies, leading to an overall complexity growth that is faster than linear but not necessarily exponential in all cases.

Complexity can indeed feel exponential in certain scenarios, especially in tightly coupled systems where each component interacts with many others in intricate ways. However, in well-designed systems that employ principles like modularity, encapsulation, and loose coupling, the growth of complexity can be mitigated. Techniques such as breaking down the system into smaller, manageable parts (microservices, for example) and using dependency injection to manage dependencies more effectively can help control and even reduce the rate at which complexity grows.

Thus, while the addition of dependencies undeniably increases complexity, the relationship is more nuanced than a simple exponential growth. It's influenced by the system's architecture and the strategies employed to manage dependencies and their interactions.

Quality assurance and testing

When testing in isolation, understanding our connections to dependencies is key. We need clear contracts, or interfaces, defining expected behavior and data models, helping us anticipate all possible states. Abstraction is vital, enabling us to remain independent from specific implementations and facilitating isolated testing and dynamic behavior changes. Being vigilant about potential unexpected states and providing appropriate feedback is paramount for robust software design.

The Brick analogy

Modular bricks

To extend the metaphor, consider software components as 'Brick' pieces. Each piece (or module) is designed with specific attachment points (interfaces and contracts) that allow it to connect seamlessly with other pieces. This modularity enables developers to assemble, rearrange, and disassemble these components, much like building with Brick, to form the desired software architecture. The key is to understand and design each piece not just in isolation but also in how it fits into the larger picture, ensuring that adding, removing, or modifying components doesn’t compromise the overall structure but rather enhances it, embodying the vision of what we aim to create.

The Brick shape and pins: Input parameters and dependencies

Input parameters and dependencies can be likened to the pins on Brick pieces that allow them to connect. This analogy effectively illustrates how these elements enable the integration and interaction between different software components. Here's how you might express this concept:

  • Input Parameters: Represent the specific connection points on a Brick piece. Just as the shape and size of the pins determine which pieces can connect, input parameters define how functions or components interact with each other, specifying the kind of data they can receive.

  • Dependencies: Are akin to the overall design of the Brick piece, including the pins and sockets. Dependencies determine how a component fits into the larger structure of the application. They are the necessary connections a component needs to function properly, similar to how certain Brick pieces need to connect to specific others to build a coherent structure.

This analogy underscores the importance of designing software components (or "Brick pieces") with careful consideration of their interaction points (input parameters and dependencies), ensuring that they can easily connect, communicate, and function together within the larger application architecture.