A decorator that profiles a method call and logs the profiling result in python

A decorator that profiles a method call and logs the profiling result in python

You can create a decorator in Python to profile a method call using the cProfile module, and then log the profiling results. Here's an example decorator that does this:

import cProfile import logging def profile_method(method): def wrapper(*args, **kwargs): profiler = cProfile.Profile() result = profiler.runcall(method, *args, **kwargs) profiler.print_stats(sort='cumulative') # You can customize the logging output as needed logging.info(f"Profiling results for {method.__name__}:") logging.info(profiler.get_stats()) return result return wrapper # Example usage: import time @profile_method def some_method(): for _ in range(1000000): pass if __name__ == "__main__": logging.basicConfig(level=logging.INFO) some_method()

In this example:

  1. We import the necessary modules: cProfile for profiling and logging for logging the results.

  2. We define a decorator profile_method that takes a method as its argument. Inside the decorator, we create a cProfile.Profile object, run the method using runcall, and then print the profiling statistics using print_stats.

  3. After printing the stats, we log the results using the logging module. You can customize the logging output format as needed.

  4. We provide an example method some_method that is decorated with @profile_method. When you call some_method, it will be profiled, and the results will be logged.

  5. Finally, we configure the logging level and call some_method to see the profiling results.

You can adjust the logging configuration and output format to suit your needs. This decorator allows you to easily profile any method by simply decorating it with @profile_method.

Examples

  1. How to create a Python decorator for method profiling?

    • Description: Users may search for a way to create a decorator in Python that can profile method calls for performance analysis.
    • Code Implementation:
      import time def profile_method(func): def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() print(f"Method {func.__name__} took {end_time - start_time} seconds to execute.") return result return wrapper # Usage example @profile_method def my_method(): # Your method code here pass

  2. Python decorator for method profiling with logging

    • Description: Users might want a Python decorator that profiles method calls and logs the profiling results for further analysis.
    • Code Implementation:
      import time import logging logging.basicConfig(filename='method_profiling.log', level=logging.INFO) def profile_method(func): def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() execution_time = end_time - start_time logging.info(f"Method {func.__name__} took {execution_time} seconds to execute.") return result return wrapper # Usage example @profile_method def my_method(): # Your method code here pass

  3. Python method profiling decorator with timestamp

    • Description: This query aims to find a Python decorator that profiles method calls and adds timestamps to the profiling results.
    • Code Implementation:
      import time def profile_method(func): def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() execution_time = end_time - start_time timestamp = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) print(f"[{timestamp}] Method {func.__name__} took {execution_time} seconds to execute.") return result return wrapper # Usage example @profile_method def my_method(): # Your method code here pass

  4. Python decorator to profile method performance

    • Description: Users may seek a Python decorator specifically designed to profile the performance of methods.
    • Code Implementation:
      import time def profile_method(func): def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() execution_time = end_time - start_time print(f"Method {func.__name__} took {execution_time} seconds to execute.") return result return wrapper # Usage example @profile_method def my_method(): # Your method code here pass

  5. Python decorator for method performance monitoring

    • Description: This query targets users interested in a Python decorator that monitors method performance for optimization purposes.
    • Code Implementation:
      import time def profile_method(func): def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() execution_time = end_time - start_time print(f"Method {func.__name__} took {execution_time} seconds to execute.") return result return wrapper # Usage example @profile_method def my_method(): # Your method code here pass

  6. Decorator to profile function execution time in Python

    • Description: Users may look for a decorator that can profile the execution time of functions in Python.
    • Code Implementation:
      import time def profile_method(func): def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() execution_time = end_time - start_time print(f"Method {func.__name__} took {execution_time} seconds to execute.") return result return wrapper # Usage example @profile_method def my_method(): # Your method code here pass

  7. Python decorator for method profiling with memory usage

    • Description: Users might want a Python decorator that not only profiles method calls but also records memory usage during execution.
    • Code Implementation: Note: This implementation requires additional memory profiling tools like memory_profiler.
      import time from memory_profiler import profile def profile_method(func): @profile def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() execution_time = end_time - start_time print(f"Method {func.__name__} took {execution_time} seconds to execute.") return result return wrapper # Usage example @profile_method def my_method(): # Your method code here pass

  8. Python decorator to profile method calls with CPU usage

    • Description: Users may search for a Python decorator capable of profiling method calls while also monitoring CPU usage.
    • Code Implementation: Note: This implementation requires additional CPU profiling tools like psutil.
      import time import psutil def profile_method(func): def wrapper(*args, **kwargs): start_time = time.time() process = psutil.Process() cpu_before = process.cpu_percent(interval=None) result = func(*args, **kwargs) end_time = time.time() cpu_after = process.cpu_percent(interval=None) execution_time = end_time - start_time print(f"Method {func.__name__} took {execution_time} seconds to execute.") print(f"CPU usage: {cpu_after - cpu_before}%") return result return wrapper # Usage example @profile_method def my_method(): # Your method code here pass

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