Well, it means you can write code that doesn’t block. That’s right, no more waiting for slow functions to finish! You might be thinking “Sweet, I can finally stop drinking coffee while my program runs.”
But hold on a second there, cowboy. Asynchronous programming is not without its challenges. In fact, it’s downright confusing at times. But no need to get all worked up, bro, for we are here to help you navigate the treacherous waters of asyncio in Python!
Before anything else: why would you want to use asynchronous code? Well, let me tell ya, there are a few reasons. For one thing, it can make your program run faster by allowing multiple tasks to be executed at once. This is especially useful for I/O-bound operations like reading from files or making network requests.
Another reason to use asynchronous code is that it can improve the responsiveness of your application. Instead of blocking on a slow function, you can continue processing other tasks while waiting for the results to come back. This can make your program feel more snappy and less sluggish.
So how do we write asynchronous code in Python? Well, first we need to import the asyncio module:
# Import the asyncio module to enable asynchronous programming
import asyncio
# Define a function that will simulate a slow task
async def slow_function():
# Use the asyncio.sleep() function to simulate a delay of 2 seconds
await asyncio.sleep(2)
# Print a message to indicate that the task is complete
print("Slow task complete!")
# Define a function that will run the slow task asynchronously
async def main():
# Use the asyncio.create_task() function to create a task for the slow function
task = asyncio.create_task(slow_function())
# Print a message to indicate that the task has been started
print("Task started!")
# Do other tasks while waiting for the slow task to complete
# In this case, we will print a message every second for 5 seconds
for i in range(5):
# Use the asyncio.sleep() function to simulate a delay of 1 second
await asyncio.sleep(1)
# Print a message to indicate that another task has been completed
print("Another task completed!")
# Wait for the slow task to complete before moving on
await task
# Use the asyncio.run() function to run the main function
asyncio.run(main())
# Output:
# Task started!
# Another task completed!
# Another task completed!
# Another task completed!
# Another task completed!
# Another task completed!
# Slow task complete!
# Explanation:
# The asyncio module allows us to write asynchronous code in Python.
# The slow_function() function is defined to simulate a slow task that takes 2 seconds to complete.
# The main() function is defined to run the slow task asynchronously using the asyncio.create_task() function.
# The asyncio.sleep() function is used to simulate delays in the code.
# The asyncio.run() function is used to run the main function and execute the asynchronous tasks.
# This allows us to continue processing other tasks while waiting for the slow task to complete, making our program feel more responsive.
Now that we have our trusty sidekick by our side, let’s take a look at some examples!
Example 1: Reading from a file
Let’s say you want to read data from a text file and print it out. Normally, this would involve reading the entire contents of the file into memory, which can be slow for large files. But with asyncio, we can read the file line by line asynchronously! Here’s how:
# This script is using asyncio to read data from a text file asynchronously, line by line.
# Import the necessary libraries
import asyncio
# Define the main function as asynchronous
async def main():
# Set the filename to be read
filename = 'example.txt'
# Open the file and assign it to the variable 'f'
with open(filename) as f:
# Open a connection using asyncio, specifying the event loop and setting the connection to be readable but not writable
lines = asyncio.open_connection(loop=asyncio.get_event_loop(), readable=True, writable=False)
# Open a connection to the server, specifying the address and port number
reader, writer = await asyncio.open_connection('localhost', 80) # replace 'localhost' and port number with your server address
# Create an empty response variable
response = b''
# Create a loop to read each line of the file asynchronously
while True:
# Read a line from the file and assign it to the variable 'data'
data = await lines.readline()
# If there is no data, break out of the loop
if not data:
break
# Print the decoded data to the console
print(data.decode())
# Run the main function asynchronously
loop.run_until_complete(main())
In this example, we’re using the `asyncio.open_connection` function to create a connection to our server (replace ‘localhost’ and port number with your server address). We then use a while loop to read data from the file line by line asynchronously. This allows us to print out each line without having to wait for the entire file to be loaded into memory!
Example 2: Making network requests
Let’s say you want to make multiple network requests at once and process their results in parallel. Normally, this would involve making one request after another, which can take a long time if there are many requests to be made. But with asyncio, we can make all the requests asynchronously and then wait for them to finish! Here’s how:
# This script uses asyncio to make multiple network requests at once and process their results in parallel.
# Import the necessary libraries
import json
import asyncio
import requests
# Define the main function as asynchronous
async def main():
# Define the URLs for the requests
url1 = 'https://example.com/data1'
url2 = 'https://example.com/data2'
# Use asyncio's run_in_executor method to make the requests asynchronously
# The requests are executed in parallel using the None executor
# The requests.get method is wrapped in a lambda function to be executed later
# The results are stored in a tasks variable
tasks = await asyncio.gather(
asyncio.get_event_loop().run_in_executor(None, lambda: requests.get(url1)),
asyncio.get_event_loop().run_in_executor(None, lambda: requests.get(url2))
)
# Use asyncio's shield method to prevent the tasks from being cancelled
# The results of the tasks are stored in response1 and response2 variables
response1 = await asyncio.shield(tasks[0])
response2 = await asyncio.shield(tasks[1])
# Convert the response content to JSON format and store it in data1 and data2 variables
data1 = json.loads(response1.content)
data2 = json.loads(response2.content)
# Process the results here!
# This is where you can manipulate the data1 and data2 variables to get the desired output
# Create an event loop and run the main function until it is complete
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
In this example, we’re using `asyncio.get_event_loop().run_in_executor(None, lambda: requests.get(url))` to make a network request asynchronously in the background. We then use `await asyncio.shield(tasks[0])` and `await asyncio.shield(tasks[1])` to wait for both tasks to finish before continuing with our code!
Remember, asynchronous programming can be confusing at times, but it’s worth learning if you want your programs to run faster and feel more responsive!