Calling a Method Before Every Other Method: A Guide to "Method Interception"
Have you ever needed to perform a specific action every time any method in your class is called? Imagine needing to log each function call, track performance metrics, or ensure certain conditions are met before any method execution. This is where the concept of "method interception" comes in handy.
The Problem: Running Code Before Any Method Call
Imagine you have a class called Calculator that performs basic arithmetic operations:
class Calculator:
def add(self, a, b):
return a + b
def subtract(self, a, b):
return a - b
Now, you want to track every method call in a log file. You could manually add logging statements to each method:
class Calculator:
def add(self, a, b):
with open("calculator_log.txt", "a") as log_file:
log_file.write(f"Calling add with arguments {a} and {b}\n")
return a + b
def subtract(self, a, b):
with open("calculator_log.txt", "a") as log_file:
log_file.write(f"Calling subtract with arguments {a} and {b}\n")
return a - b
This solution is repetitive and prone to errors if you forget to add logging to new methods. There must be a cleaner way to handle this!
The Solution: Using Decorators
Decorators are a powerful feature in Python that allow you to modify the behavior of functions or methods without directly changing their code. We can use decorators to intercept method calls and execute our desired code before the original method is called.
def log_call(func):
def wrapper(*args, **kwargs):
with open("calculator_log.txt", "a") as log_file:
log_file.write(f"Calling {func.__name__} with arguments {args} and {kwargs}\n")
return func(*args, **kwargs)
return wrapper
class Calculator:
@log_call
def add(self, a, b):
return a + b
@log_call
def subtract(self, a, b):
return a - b
In this code:
log_callis our decorator. It takes a function (func) as input and returns a wrapper function.- The wrapper function logs the method call and then calls the original method (
func) with the provided arguments. - We use
@log_callbefore each method in ourCalculatorclass to apply the decorator.
Now, every time you call Calculator.add or Calculator.subtract, the log_call decorator will ensure the call is logged before the actual method execution.
Going Beyond Logging
Decorators provide flexibility to implement diverse logic before any method call:
- Performance Tracking: Measure execution time of each method.
- Input Validation: Ensure method arguments meet certain criteria before execution.
- Authentication: Check user permissions before allowing method access.
- Caching: Cache method results to improve performance.
Key Points to Remember
- Decorator Scope: Decorators only affect the methods they are applied to.
- Parameter Handling: Ensure the decorator handles arguments correctly (especially for methods with
selfas the first argument). - Maintainability: Decorators make your code more modular and easier to manage.
Resources and Further Exploration
- Official Python Documentation on Decorators: https://docs.python.org/3/glossary.html#term-decorator
- Real-World Examples of Decorators: https://realpython.com/python-decorators/
By understanding and utilizing method interception with decorators, you can streamline your code, enhance functionality, and build robust and maintainable applications.
