AOP is a programming paradigm designed to separate cross-cutting concerns from core business logic, while Python decorators are the natural way to implement AOP. The decorator uses a wrapper function to add common functions such as logging, performance timing, permission verification, etc. without modifying its definition. 1. Logging: Use the @log decorator to automatically print function call information; 2. Performance timing: Use the @timer decorator to measure execution time and output time; 3. Permission verification: Use the @requires_auth decorator to centrally manage user authentication logic; in addition, multiple decorators are executed in a bottom-to-up order, and the order should be reasonably arranged to ensure correct behavior, such as placing the logs on the outer layer and permission controls close to the function, while maintaining the single responsibilities of each decorator to improve maintainability.

In Python, decorators are a very natural and powerful way to implement Aspect-Oriented Programming (AOP). They allow you to add additional functions, such as logging, permission checking, performance monitoring, etc. without modifying the function or class definition.

What is the relationship between AOP and decorator?

The core idea of ??tangent-oriented programming is to separate cross-cutting concerns from business logic. For example, logging and transaction processing usually occur in multiple modules, but they are not part of the core business logic.

Python's decorator just solves this problem: it allows you to "wrape" a function or method, and execute some general logic before and after the call without having to hack into the original function. This mechanism is essentially a lightweight AOP implementation.

How to use decorator to implement common AOP scenarios

1. Logging

This is one of the most common AOP applications. You can add a unified log decorator to all functions that need to record call information:

 def log(func):
    def wrapper(*args, **kwargs):
        print(f"Calling function: {func.__name__}")
        return func(*args, **kwargs)
    Return wrapper

@log
def add(a, b):
    return ab

In this way, every call to add() will automatically print the log, and the function itself does not care about the existence of the log.

Tips: If you want the decorator to receive parameters, you can construct a three-layer structure using nested functions, or use functools.wraps to retain the original function meta information.

2. Performance Timing

Another typical application is measuring function execution time, which is helpful when debugging performance bottlenecks:

 import time

def timer(func):
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        duration = time.time() - start
        print(f"{func.__name__} took {duration:.4f}s")
        return result
    Return wrapper

@timer
def slow_function():
    time.sleep(1)

This decorator can monitor performance of any function without changing any business code.

3. Access Control

Permission control is a typical cross-cutting concern in a Web framework or API interface. Decorators can encapsulate this part of the logic well:

 def requires_auth(func):
    def wrapper(user, *args, **kwargs):
        if not user.is_authenticated:
            raise PermissionError("User not authenticated")
        return func(user, *args, **kwargs)
    Return wrapper

@requires_auth
def access_data(user):
    print(f"{user.name} is accessing data.")

In this way, we can centrally manage permission judgments to avoid writing similar if judgments in each function.

The combination and order of decorators is also important

When you add multiple decorators to a function, their execution order is from bottom to top (inside to outside). For example:

 @decorator1
@decorator2
def my_func():
    pass

Equivalent to:

 my_func = decorator1(decorator2(my_func))

Therefore, in actual use, pay attention to whether the order of the decorators meets expectations. This is especially important if you want multiple sections to take effect in a specific order.

Common practices include:

• Put the logs on the outermost layer to see the entire process
• Put permission control close to the function to prevent subsequent logic from being executed incorrectly

In addition, it is recommended to keep the decorator itself single and avoid one decorator doing too many things, which is more conducive to reuse and maintenance.

Basically that's it. Decorators are not a black magic, but a syntactic sugar provided by Python. Combining the characteristics of closures and higher-order functions, we can gracefully implement some of the capabilities of AOP. Using it well can make the code structure clearer and more convenient to maintain.

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