


Demystifying Monkey Patching
In the realm of programming, the term "monkey patching" often raises questions. What exactly does it entail? Is it akin to method/operator overloading or delegation?
Contrary to popular belief, monkey patching differs significantly from these concepts. Essentially, it involves the dynamic modification of attributes during runtime.
To illustrate, consider a class with a method called get_data. This method relies on external data sources, which can be problematic during unit testing. To address this, monkey patching allows you to replace get_data with a stub method that returns fixed data.
Since Python classes are mutable and methods are merely class attributes, monkey patching can be performed with ease. It extends to replacing classes and functions within a module using the same mechanism.
However, caution is advised when using this technique:
- Any other section of code aside from your test logic that references get_data will also call the modified method instead of the original.
- Existing variables or attributes referencing get_data will not be affected by the change and will continue to point to the original method.
In summation, monkey patching involves the dynamic alteration of attributes for testing or code alteration purposes, a practice that requires careful consideration.
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