Today we’re going to talk about asyncio that magical library that makes asynchronous programming in Python a breeze (or at least less of a headache). But before we dive into the details, let’s take a moment to appreciate how far we’ve come.
Remember when writing concurrent code meant creating multiple threads or processes and praying they wouldn’t collide? Well, asyncio is here to save us from that madness! With its elegant syntax and powerful features, it allows us to write asynchronous programs without sacrificing readability or maintainability. And the best part? It’s built into Python 3.5+!
So what exactly does asyncio do for us? Well, let’s start with a basic example:
# Import the necessary modules
import time # Import the time module for time-related functions
import asyncio # Import the asyncio module for asynchronous programming
# Define a coroutine function
async def my_coroutine():
print("Starting coroutine...") # Print a message to indicate the start of the coroutine
await asyncio.sleep(2) # Use the await keyword to pause the coroutine for 2 seconds
print("Coroutine finished!") # Print a message to indicate the end of the coroutine
# Get the event loop
loop = asyncio.get_event_loop() # Get the event loop for managing asynchronous tasks
# Create a list of tasks
tasks = [my_coroutine()] # Create a list of tasks, in this case only one task is defined
# Run the event loop until all tasks are completed
loop.run_until_complete(asyncio.gather(*tasks)) # Use the gather function to run all tasks in the list concurrently
In this example, we’re creating a coroutine (a function that returns an `awaitable`) and running it using the event loop provided by asyncio. The `asyncio.sleep()` function is what makes this asynchronous instead of blocking for 2 seconds like in traditional Python code, it yields control to other tasks until its time is up.
Asyncio also has a handy little feature called `to_thread`. This allows us to run our coroutines on a separate thread without having to worry about the GIL (Global Interpreter Lock) or synchronization issues. Here’s an example:
# Import necessary libraries
import time # Import the time library to use for time-related functions
import asyncio # Import the asyncio library for asynchronous programming
from concurrent.futures import ThreadPoolExecutor # Import the ThreadPoolExecutor from the concurrent.futures library for running coroutines on separate threads
# Define a coroutine function
async def my_coroutine():
print("Starting coroutine...")
await asyncio.sleep(2) # Use the asyncio.sleep function to pause execution for 2 seconds
print("Coroutine finished!")
# Get the event loop
loop = asyncio.get_event_loop()
# Create a ThreadPoolExecutor with a maximum of 10 workers
executor = ThreadPoolExecutor(max_workers=10)
# Create a list of tasks, in this case only one task is defined
tasks = [my_coroutine()]
# Run the tasks in the ThreadPoolExecutor using the run_in_executor function
# The __wrap__ function is used to wrap the coroutine in a future object
loop.run_in_executor(executor, tasks[0].__wrap__(asyncio.gather(*tasks)))
# Explanation:
# The script starts by importing the necessary libraries, including time, asyncio, and ThreadPoolExecutor.
# Next, a coroutine function named "my_coroutine" is defined. This function prints a message, waits for 2 seconds using the asyncio.sleep function, and then prints another message.
# The event loop is then retrieved using the asyncio.get_event_loop function.
# A ThreadPoolExecutor is created with a maximum of 10 workers.
# A list of tasks is created, with only one task defined in this case.
# Finally, the tasks are run in the ThreadPoolExecutor using the run_in_executor function, which takes in the executor and the wrapped coroutine as parameters. The __wrap__ function is used to wrap the coroutine in a future object, which allows it to be executed in a separate thread.
# This allows the coroutine to run asynchronously, meaning it yields control to other tasks until its time is up. The use of the ThreadPoolExecutor also ensures that the coroutine can run on a separate thread without worrying about the GIL or synchronization issues.
In this example, we’re using a `ThreadPoolExecutor` to run our coroutines on separate threads. The `__wrap__()` function is used to convert the `asyncio.gather()` call into an awaitable that can be passed to the executor. This allows us to take advantage of asyncio’s powerful features while still running our code in parallel using multiple threads!
With these tools at your disposal, you can write asynchronous programs that are both efficient and maintainable. And who knows? Maybe one day we’ll all be able to say goodbye to the madness of traditional concurrency forever!