apply-decorator-wrap

apply-decorator-wrap

When to Use

• Adding caching/memoization
• Timing function execution
• Logging function calls
• Input validation
• Retry logic
• Any cross-cutting concern

When NOT to Use

• Behavior is specific to one function
• Would obscure function's purpose
• Simple inline code is clearer

The Pattern

Decorators wrap functions to add behavior before, after, or around the original.

def timing(func):
    """Decorator to time function execution."""
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        elapsed = time.time() - start
        print(f"{func.__name__} took {elapsed:.3f}s")
        return result
    return wrapper

@timing
def slow_function():
    time.sleep(1)
    return "done"

# Equivalent to: slow_function = timing(slow_function)

Example (from pytudes)

# Memoization decorator (ngrams.py)
def memo(f):
    """Memoize function f."""
    table = {}

    def fmemo(*args):
        if args not in table:
            table[args] = f(*args)
        return table[args]

    fmemo.memo = table  # Expose cache
    return fmemo

@memo
def segment(text):
    """Optimal word segmentation."""
    if not text:
        return []
    candidates = ([first] + segment(rest)
                  for first, rest in splits(text))
    return max(candidates, key=word_prob)

# Using functools for cleaner decorators
from functools import cache, lru_cache, wraps

@cache  # Built-in memoization
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

@lru_cache(maxsize=1000)  # Limited cache size
def expensive_lookup(key):
    ...

# Reusable decorator with parameter
cache = lru_cache(None)  # Alias for unlimited cache

@cache
def expressions(numbers):
    ...

@cache
def segment(text):
    ...

Key Principles

1. Wrapper preserves signature: Use @functools.wraps
2. Return wrapper: Decorator returns the wrapped function
3. Expose internals: Attach cache/state as attribute
4. Stack decorators: Multiple decorators apply bottom-up
5. Decorator factories: @lru_cache(n) returns decorator