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When to Use C++āš“ļøŽ

Before you write a single line of C++, ask yourself one question: Do I really need it?

C++ adds complexity. Use it only when it provides a meaningful impact.

The Golden Rule

Only use C++ for real performance bottlenecks that can't be solved with existing Python tools like NumPy or Numba.

šŸ§  The Decision-Making Flowchartāš“ļøŽ

Here's a simple flowchart to guide your decision:

graph LR
    A[Start] --> B{A good Python library exists?};
    B -- āœ… Yes --> C[Use it!];
    B -- āŒ No --> D{Is it a real performance bottleneck?};
    D -- āŒ No --> E[Stick with Python];
    D -- āœ… Yes --> F{Can it be optimized with NumPy/Numba?};
    F -- āœ… Yes --> G[Use NumPy/Numba];
    F -- āŒ No --> H[šŸš€ Write it in C++];

āœ… Good Use Casesāš“ļøŽ

When does C++ make sense? Here are a few examples:

Use Case Why C++? Example
Non-Maximum Suppression Tight loops over thousands of boxes nextcv.postprocessing.nms
Hungarian Algorithm Complex algorithm with O(nĀ³) complexity scipy.optimize.linear_sum_assignment
Low-Level Hardware Direct memory access for sensors/GPIO Interfacing with custom hardware

šŸš© Common Pitfallsāš“ļøŽ

Avoid these common traps:

Pitfall Description Better Approach
šŸƒā€ā™‚ļø "C++ is always faster!" Assuming C++ is a magic bullet. Profile first. Get concrete numbers.
šŸ”§ Reinventing the Wheel Writing C++ for a solved problem. Check SciPy, NumPy, and other libraries first.

āœ… Final Checklistāš“ļøŽ

Use this checklist before you start writing C++:

  • No good Python library exists for the task.
  • Profiling shows a clear, measurable bottleneck.
  • The bottleneck is a real issue in production.
  • C++ will provide a significant (e.g., >5x) speedup.
  • The team is comfortable maintaining the C++ code.

If you can't check all these boxes, stick with Python. šŸ