README.md

Memory Management and RAII (Python)

This module introduces deterministic cleanup in Python through context managers.

Learning Metadata

• Difficulty: Advanced.
• Estimated Time: 45-60 minutes.
• Prerequisites: 01-foundations/scope-and-lifetime-basics, 03-advanced/structs-and-classes.
• Cross-Language Lens: Contrast deterministic cleanup in C++ with IDisposable, defer, and context-manager style resource handling.

Quick Run

python example/main.py

Topics Covered

• Automatic cleanup with with.
• Implementing __enter__ and __exit__.
• Guarding methods after a resource has been released.
• Distinguishing garbage collection from deterministic scope cleanup.

Common Pitfalls

• Assuming cleanup will happen at a predictable time without a context manager.
• Forgetting to release resources on early returns.
• Reusing an object after it has already been closed.
• Using __del__ as the primary cleanup strategy.

Cross-Language Notes

• Python maps this idea to context managers, try/finally, and explicit close methods rather than destructor-based RAII.
• Compared with C++, object lifetime and resource cleanup are related but not identical concerns.
• The learner goal is to make release points obvious and reliable.

Exercise Focus

• exercises/01.py: owned integer buffer with deterministic cleanup.
• exercises/02.py: scope guard that proves nested cleanup order.

Exercise Specs

1. exercises/01.py

• Input: integer n, then n integers.
• Output: sum and reversed sequence.
• Edge cases: n <= 0; invalid integer input.

2. exercises/02.py

• Input: none.
• Output: enter/exit logs proving automatic cleanup.
• Edge cases: nested scopes; final active counter must return to zero.

Checkpoint

• I can explain why with gives deterministic cleanup.
• I can write a small context manager.
• I can prevent work on a closed resource.
• I completed exercises/01.py.
• I completed exercises/02.py.