Asynchronous Context Managers: A New Feature in Python 3.5

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Are you tired of waiting for your code to finish running before moving on to the next task? Introduced in Python 3.5, these bad boys allow us to write more efficient and concurrent code by handling multiple tasks at once without blocking the main thread. Let’s jump right into how they work and why you should care about them. First, let’s define what an asynchronous context manager is. It’s a type of decorator that wraps around your existing functions or classes to make them run in parallel with other tasks. This means that instead of waiting for one task to finish before moving on to the next, we can handle multiple requests at once without any noticeable delay. Here’s an example:

# Import the necessary libraries
import asyncio # Importing the asyncio library for asynchronous programming
from contextlib import asynccontextmanager # Importing the asynccontextmanager function from the contextlib library

# Define a decorator function that allows for asynchronous execution
@asynccontextmanager # Decorator that allows for asynchronous execution
def my_asynchronous_function():
    # Perform any necessary setup here
    yield # Yield statement to indicate where the code should pause and allow other tasks to run
    # Perform any necessary cleanup after the code has finished executing

# Example of using the asynchronous function
async def main():
    await my_asynchronous_function() # Await statement to indicate that the code should wait for the asynchronous function to finish before moving on
    print("Done!") # Print statement to indicate that the code has finished executing

# Call the main function to start the asynchronous execution
main()

In this code snippet, we’ve defined a function called `my_asynchronous_function`. This function is decorated with the `@asynccontextmanager` decorator to make it run asynchronously. The yield statement inside the function tells Python that we want to execute any code within the context of this function in parallel with other tasks. To use our new asynchronous function, we can call it from another async function like so:

# Using the `async` keyword to define an asynchronous function
async def main():
    # Using the `await` keyword to wait for the completion of an asynchronous function
    await my_asynchronous_function()
    # Printing a message after the asynchronous function has completed
    print("Done!")

The `await` keyword tells Python to wait for the result of the `my_asynchronous_function` call before moving on. This allows us to handle multiple tasks at once without any noticeable delay. So, why should you care about asynchronous context managers? Well, they can significantly improve your application’s performance and scalability by handling more concurrent requests with less overhead. They also allow for better resource management since we can clean up after ourselves inside the `yield` statement.

Now some best practices for writing asynchronous code with asyncio in Python. Here are some tips to follow:

1. Use the `async` keyword on all functions that return an awaitable object, such as coroutines or futures. This tells Python that this function can be called from within another async function and will yield control back to the main event loop when it’s done executing. 2. Avoid using global variables in your code, since they can cause issues with concurrency and resource management. Instead, use local variables or pass data between functions as arguments. 3. Use `asyncio`’s built-in tasks and events to manage the flow of execution within your application. This allows you to handle multiple requests at once without any noticeable delay. 4. Avoid using blocking I/O operations in your code, since they can cause issues with concurrency and resource management. Instead, use non-blocking I/O operations or asyncio’s built-in `asyncio.open_connection()` function to handle network connections asynchronously. 5. Use the `await` keyword sparingly in your code, since it can cause issues with performance if used excessively. Instead, use coroutines and futures to manage multiple tasks at once without blocking the main event loop. By following these best practices, you’ll be able to write more efficient and concurrent asynchronous code using asyncio in Python.

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