67. Decorators for Timing Functions

Decorators for Timing Functions: Measuring Execution Time

A decorator in Python is a function that wraps another function to modify its behavior. One useful application of decorators is to measure the execution time of functions. This can be useful for performance monitoring or profiling. Below is an example of how to create a decorator that measures the execution time of a function.

1. Basic Timing Decorator

This decorator will measure the time it takes for a function to execute and print the result.

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import time

# Timing decorator
def timing_decorator(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()  # Record start time
        result = func(*args, **kwargs)
        end_time = time.time()  # Record end time
        print(f"Execution time of {func.__name__}: {end_time - start_time:.4f} seconds")
        return result
    return wrapper

# Example function to measure
@timing_decorator
def slow_function():
    time.sleep(2)  # Simulating a time-consuming operation
    return "Done"

# Call the function
slow_function()

Output:

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Execution time of slow_function: 2.0002 seconds

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2. Decorator with Arguments

If you want to add functionality like logging the time for a list of functions or conditional execution, you can add parameters to the decorator.

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import time

# Timing decorator with an argument
def timing_decorator(log_time=False):
    def decorator(func):
        def wrapper(*args, **kwargs):
            start_time = time.time()
            result = func(*args, **kwargs)
            end_time = time.time()
            if log_time:
                print(f"Execution time of {func.__name__}: {end_time - start_time:.4f} seconds")
            return result
        return wrapper
    return decorator

# Example function
@timing_decorator(log_time=True)
def fast_function():
    time.sleep(1)

fast_function()

Output:

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Execution time of fast_function: 1.0001 seconds

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3. Timing Decorator for Multiple Functions

You can use a decorator to time multiple functions without repeating the timing logic.

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import time

def timing_decorator(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        print(f"{func.__name__} took {end_time - start_time:.4f} seconds")
        return result
    return wrapper

@timing_decorator
def function_one():
    time.sleep(0.5)

@timing_decorator
def function_two():
    time.sleep(1)

function_one()
function_two()

Output:

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function_one took 0.5003 seconds
function_two took 1.0002 seconds

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4. Using functools.wraps for Preserving Function Metadata

When using decorators, the metadata of the wrapped function (like its name and docstring) may be lost. To avoid this, use functools.wraps() to preserve the original function's attributes.

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import time
import functools

def timing_decorator(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        print(f"{func.__name__} took {end_time - start_time:.4f} seconds")
        return result
    return wrapper

@timing_decorator
def example_function():
    """This is an example function."""
    time.sleep(1)

example_function()
print(example_function.__name__)  # Output: example_function
print(example_function.__doc__)   # Output: This is an example function.

Output:

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example_function took 1.0001 seconds
example_function
This is an example function.

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5. Decorator for Asynchronous Functions

If your function is asynchronous, you can also use a timing decorator with asyncio.

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import asyncio
import time

def timing_decorator(func):
    async def wrapper(*args, **kwargs):
        start_time = time.time()
        result = await func(*args, **kwargs)
        end_time = time.time()
        print(f"{func.__name__} took {end_time - start_time:.4f} seconds")
        return result
    return wrapper

@timing_decorator
async def async_function():
    await asyncio.sleep(2)

# Running the async function
asyncio.run(async_function())

Output:

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async_function took 2.0001 seconds

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6. Using Time in Nanoseconds

To measure the execution time with more precision, you can use time.perf_counter() which provides a higher-resolution timer.

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import time

def timing_decorator(func):
    def wrapper(*args, **kwargs):
        start_time = time.perf_counter()
        result = func(*args, **kwargs)
        end_time = time.perf_counter()
        print(f"{func.__name__} took {end_time - start_time:.6f} seconds")
        return result
    return wrapper

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

slow_function()

Output:

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slow_function took 1.000123 seconds

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7. Timing Function with Return Value

The decorator can also return the function’s result after logging the execution time.

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import time

def timing_decorator(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        print(f"{func.__name__} took {end_time - start_time:.4f} seconds")
        return result
    return wrapper

@timing_decorator
def add(a, b):
    return a + b

print(add(3, 5))

Output:

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add took 0.0001 seconds
8

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8. Using Timing Decorators with Function Arguments

You can also pass arguments into the decorator, like logging the time for specific types of functions.

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import time

def timing_decorator(message):
    def decorator(func):
        def wrapper(*args, **kwargs):
            start_time = time.time()
            result = func(*args, **kwargs)
            end_time = time.time()
            print(f"{message}: {func.__name__} took {end_time - start_time:.4f} seconds")
            return result
        return wrapper
    return decorator

@timing_decorator("Execution Time")
def slow_function():
    time.sleep(3)

slow_function()

Output:

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Execution Time: slow_function took 3.0002 seconds

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9. Decorator for Profiling Multiple Functions

Use the decorator for profiling multiple functions in your code.

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import time

def timing_decorator(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        print(f"{func.__name__} execution time: {end_time - start_time:.4f} seconds")
        return result
    return wrapper

@timing_decorator
def long_function():
    time.sleep(2)

@timing_decorator
def short_function():
    time.sleep(1)

long_function()
short_function()

Output:

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long_function execution time: 2.0001 seconds
short_function execution time: 1.0002 seconds

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10. Combining Multiple Decorators

You can combine timing decorators with other decorators, for example, logging.

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import time

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

def timing_decorator(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        print(f"{func.__name__} execution time: {end_time - start_time:.4f} seconds")
        return result
    return wrapper

@log_decorator
@timing_decorator
def example_function():
    time.sleep(1)

example_function()

Output:

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Calling example_function
example_function execution time: 1.0001 seconds

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By using decorators to measure execution time, you can efficiently monitor the performance of your functions, especially when working with time-consuming tasks. The decorator pattern is a powerful tool in Python, and this technique helps keep the code clean and reusable.