How to Use Python cProfile async

Profiling asynchronous code in Python using cProfile can be a bit tricky because cProfile is primarily designed for profiling synchronous code. However, you can still profile asynchronous code with some modifications.

Here’s a general approach to profile asynchronous code using cProfile:

Import the necessary modules:

import cProfile
import asyncioCode language: Python (python)

Define your asynchronous function that you want to profile. For example:

async def my_async_function():
    # Your asynchronous code here
    await asyncio.sleep(1)Code language: Python (python)

Create a wrapper function that runs the asynchronous code within an event loop. This is necessary because cProfile expects synchronous code. We’ll use the asyncio.run() function for this purpose:

def profile_async_code():
    asyncio.run(my_async_function())Code language: Python (python)

Now, create a cProfile object and run the profiler on the wrapper function:

if __name__ == "__main__":
    profiler = cProfile.Profile()
    profiler.enable()
    
    profile_async_code()
    
    profiler.disable()
    profiler.print_stats(sort='cumulative')Code language: Python (python)

you run the script, it will profile the asynchronous code and print out the profiling statistics.

Keep in mind that profiling asynchronous code with cProfile may not give you the same level of detail and accuracy as profiling synchronous code, but it can still provide useful insights into the performance of your asynchronous code.

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