Imagine you’re building a house of cards. Each card depends on others for support, creating a delicate but stable structure. Now imagine two cards needed to support each other simultaneously, it would be physically impossible to build.

This is exactly the problem we face with dependencies in software development.

🔁 A Real-World Example

In this scenario:

  • AuthenticationService needs UserService to verify user permissions
  • UserService needs ProfileService to get user details
  • ProfileService needs AuthenticationService to access secure data

This creates an impossible situation: none of these services can be initialized because each depends on another that isn’t yet created. It’s like trying to solve the classic chicken-and-egg problem.

In small applications, these cycles might be obvious. But in large systems with hundreds of dependencies, detecting them becomes increasingly difficult.

This is where SparkDI’s DFS-based detection comes into play.

🧭 What You’ll Learn in This Article

  • How DFS helps detect circular dependencies
  • SparkDI’s implementation details
  • Real-world examples and edge cases
  • Future improvements and optimizations

🧠 What is DFS and Why Use It?

Think of DFS (Depth-First Search) as an explorer in a maze. Instead of checking every nearby path, the explorer picks one and follows it as deep as possible before backtracking.

This characteristic makes DFS particularly well-suited for cycle detection in dependency graphs.

🔎 Simple Visualization

  1. Start at a root node (e.g., ServiceA)
  2. Follow dependencies (A → B → C → D)
  3. Track visited nodes using a stack
  4. If we encounter a node already in the stack → Cycle detected

🧩 Key Concepts

1. Visit Stack

  • Used to detect cycles
  • Tracks the current exploration path
  • Cleared when backtracking

2. Visited Set

  • Tracks all fully explored nodes
  • Prevents re-exploring branches

3. Backtracking

  • Returns to previous nodes
  • Removes nodes from stack (but not visited)
  • Ensures all paths are explored

🧱 Core DFS Logic in SparkDI

The Stack (stack: Set<ObjectIdentifier>)

var stack: Set<ObjectIdentifier> = []

• Acts as the current path tracker • Like breadcrumbs in a maze:


stack.insert(currentId) // Enter node  
stack.remove(currentId) // Exit node

Example of stack during traversal:

Visit ServiceA → stack = [A]
Visit ServiceB → stack = [A, B]
Visit ServiceC → stack = [A, B, C]
If C → A: Cycle detected!