Debugging Decorators: Common Pitfalls to Avoid

Debugging Decorators: Common Pitfalls to Avoid

Have you ever spent hours staring at your Python code, wondering why your decorator isn’t working as expected? Maybe your function mysteriously returns None, or the metadata gets lost, leaving you scratching your head. You’re not alone—decorators are powerful tools, but they come with subtle traps that can trip up even experienced developers.

In this article, we’ll break down the most common decorator pitfalls, explain why they happen, and show you how to avoid them. By the end, you’ll be able to write and debug decorators with confidence.

1. The Silent Mistake: Forgetting to Return the Wrapped Function

The Problem

One of the most frequent decorator mistakes is forgetting to return the wrapped function. Here’s what that looks like: 

def my_decorator(func):  
    def wrapper(*args, **kwargs):  
        print("Something is happening before the function is called.")  
        func(*args, **kwargs)  
        print("Something is happening after the function is called.")  
    # Oops! Forgot to return wrapper

If you apply this decorator, your decorated function will return None—even if the original function returns a value.

The Fix

Always ensure your decorator returns the wrapper: 

def my_decorator(func):  
    def wrapper(*args, **kwargs):  
        print("Something is happening before the function is called.")  
        result = func(*args, **kwargs)  # Capture the result  
        print("Something is happening after the function is called.")  
        return result  # Return it  
    return wrapper  # Don't forget this!

Pro Tip: If you’re modifying behavior but not returning anything (e.g., logging decorators), make sure the wrapper still returns func(*args, **kwargs).

2. Lost Identity: Forgetting @functools.wraps

The Problem

Decorators can unintentionally erase a function’s metadata (__name__, __doc__, etc.). For example: 

@my_decorator  
def greet():  
    """Returns a friendly greeting."""  
    return "Hello!"  

print(greet.__name__)  # Outputs 'wrapper' instead of 'greet'  
print(greet.__doc__)   # Returns None instead of the docstring

This makes debugging harder and breaks tools that rely on function metadata (like documentation generators).

The Fix

Use @functools.wraps to preserve the original function’s identity: 

import functools  

def my_decorator(func):  
    @functools.wraps(func)  # Preserves metadata  
    def wrapper(*args, **kwargs):  
        # Your decorator logic  
        return func(*args, **kwargs)  
    return wrapper

Now, greet.__name__ and greet.__doc__ will work as expected.

3. Decorators That Break Function Signatures

The Problem

Some decorators modify function arguments in ways that make them incompatible with static type checkers (like mypy) or IDE autocompletion.

For example: 

def debug_args(func):  
    def wrapper(*args, **kwargs):  
        print(f"Args: {args}, Kwargs: {kwargs}")  
        return func(*args, **kwargs)  
    return wrapper  

@debug_args  
def add(a: int, b: int) -> int:  
    return a + b  

# Type hints are now "lost" to the outside world

The Fix

Use typing and functools to maintain type safety: 

from typing import Callable, TypeVar  

T = TypeVar('T')  

def debug_args(func: Callable[..., T]) -> Callable[..., T]:  
    @functools.wraps(func)  
    def wrapper(*args, **kwargs) -> T:  
        print(f"Args: {args}, Kwargs: {kwargs}")  
        return func(*args, **kwargs)  
    return wrapper

This ensures type checkers and IDEs still recognize the function signature.

4. Stacking Decorators in the Wrong Order

The Problem

The order in which you apply decorators matters. Consider: 

@decorator1  
@decorator2  
def my_function():  
    pass

This is equivalent to: 

my_function = decorator1(decorator2(my_function))

If decorator1 depends on behavior added by decorator2, reversing them could break your code.

The Fix

Think of decorators as "layers" applied from bottom to top. Test decorator interactions carefully.

5. Accidentally Decorating Twice

The Problem

If a decorator doesn’t check whether a function is already decorated, you might accidentally apply it multiple times: 

@my_decorator  
@my_decorator  # Double decoration!  
def my_func():  
    pass

This can lead to unexpected behavior (e.g., duplicate logging, slowed performance).

The Fix

Add a check (or use a decorator registry pattern if needed).

Final Thoughts

Debugging decorators doesn’t have to be painful. By remembering to:
✅ Always return the wrapper
✅ Use @functools.wraps
✅ Preserve function signatures
✅ Mind the decorator order
✅ Avoid double-decoration

…you’ll save hours of frustration.

Have you encountered other decorator gotchas? Share your debugging tips below! 🚀

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