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!