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04.4 - Decorators

Theory 25 min Intermediate

What is a Decorator?​

A decorator is a function that wraps another function to add behavior without modifying the original code.

@decorator
def my_function():
    ...

# Equivalent to:
def my_function():
    ...
my_function = decorator(my_function)

Decorators implement the "Open/Closed Principle": open for extension, closed for modification.

Building a Decorator Step by Step​

# Step 1 — A simple wrapper
def my_decorator(func):
    def wrapper():
        print("Before the function")
        func()
        print("After the function")
    return wrapper

# Without @syntax
def say_hello():
    print("Hello!")

say_hello = my_decorator(say_hello)
say_hello()
# Before the function
# Hello!
# After the function

Using @functools.wraps​

Always use @functools.wraps to preserve the original function's metadata:

import functools

def my_decorator(func):
    @functools.wraps(func)   # preserves __name__, __doc__, etc.
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        result = func(*args, **kwargs)
        print(f"Done: {func.__name__}")
        return result
    return wrapper

@my_decorator
def add(a, b):
    """Add two numbers."""
    return a + b

add(3, 4)
# Calling add
# Done: add
# 7

add.__name__   # "add"  (not "wrapper" — thanks to @wraps)
add.__doc__    # "Add two numbers."

Practical Decorators​

1. Timing Decorator​

import functools
import time

def timeit(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = func(*args, **kwargs)
        elapsed = time.perf_counter() - start
        print(f"{func.__name__} took {elapsed:.4f}s")
        return result
    return wrapper

@timeit
def slow_operation(n):
    return sum(range(n))

slow_operation(10_000_000)
# slow_operation took 0.2341s

2. Retry Decorator​

import functools
import time

def retry(times=3, delay=1.0, exceptions=(Exception,)):
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            last_error = None
            for attempt in range(1, times + 1):
                try:
                    return func(*args, **kwargs)
                except exceptions as e:
                    last_error = e
                    print(f"Attempt {attempt}/{times} failed: {e}")
                    if attempt < times:
                        time.sleep(delay)
            raise last_error
        return wrapper
    return decorator

@retry(times=3, delay=0.5, exceptions=(ConnectionError,))
def fetch_data(url):
    # ...
    pass

3. Caching with functools.lru_cache​

from functools import lru_cache

@lru_cache(maxsize=128)
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

fibonacci(50)   # instant! (cached)

# Check cache stats
fibonacci.cache_info()
# CacheInfo(hits=48, misses=51, maxsize=128, currsize=51)

# Python 3.9+ shorthand
from functools import cache   # unlimited cache

@cache
def expensive(n):
    ...

4. Validation Decorator​

def require_positive(*arg_names):
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            import inspect
            sig = inspect.signature(func)
            bound = sig.bind(*args, **kwargs)
            bound.apply_defaults()
            for name in arg_names:
                if name in bound.arguments and bound.arguments[name] <= 0:
                    raise ValueError(f"'{name}' must be positive, got {bound.arguments[name]}")
            return func(*args, **kwargs)
        return wrapper
    return decorator

@require_positive("width", "height")
def area(width, height):
    return width * height

area(5, 10)    # 50
area(-1, 10)   # ValueError: 'width' must be positive

Decorator with Arguments (Decorator Factory)​

def repeat(n):
    """Run the function n times."""
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            for _ in range(n):
                result = func(*args, **kwargs)
            return result
        return wrapper
    return decorator

@repeat(3)
def greet(name):
    print(f"Hello, {name}!")

greet("Alice")
# Hello, Alice!
# Hello, Alice!
# Hello, Alice!

Stacking Decorators​

@timeit
@retry(times=3)
@lru_cache(maxsize=64)
def fetch_and_process(url):
    ...

# Execution order: timeit wraps retry wraps lru_cache wraps fetch_and_process
# Applied bottom-up, executed top-down

Class-Based Decorators​

class Singleton:
    """Ensure only one instance of a class is created."""
    _instances = {}

    def __call__(cls, *args, **kwargs):
        if cls not in Singleton._instances:
            Singleton._instances[cls] = super().__call__(*args, **kwargs)
        return Singleton._instances[cls]

@Singleton
class DatabaseConnection:
    def __init__(self):
        self.connected = False

Key Vocabulary​

TermDefinition
DecoratorA function that wraps another function to extend behavior
@syntaxSyntactic sugar: @dec before def f means f = dec(f)
@functools.wrapsPreserves the wrapped function's name, docstring, etc.
WrapperThe inner function inside a decorator
@lru_cacheMemoization decorator from functools
Decorator factoryA function that returns a decorator (for parameterized decorators)
Stacking decoratorsApplying multiple decorators to one function
Cross-cutting concernLogic applied to many functions (logging, timing, auth)

Summary​

  • A decorator is a function that takes a function and returns a modified version
  • Always use @functools.wraps(func) inside decorators to preserve metadata
  • Common built-in decorators: @lru_cache, @cache, @staticmethod, @classmethod, @property
  • Decorators with arguments use a factory pattern: function → decorator → wrapper
  • Stack multiple decorators — they apply bottom-up, execute top-down
  • Use decorators for cross-cutting concerns: logging, timing, retry, caching, validation