Welcome to this intriguing journey of exploring Python async IO! Whether you are a beginner or an experienced coder, this tutorial will help you understand this complex yet indispensable concept in Python programming. Our goal is to make this journey engaging and valuable, and by the end of this text, you’ll have a robust understanding of async IO and its practical applications.
Table of contents
What is Python async IO?
Async IO, standing for asynchronous input/output, is a concurrent programming design within Python. It’s a module that has functions for working with asynchronous IO, using coroutines and streams. It lets your programs handle more tasks at once, boosting overall performance.
What is It Used For?
Python’s async IO is primarily utilized to write single-threaded concurrent code using coroutines and multiplexing IO access over sockets and other resources. In simpler terms, it’s heavily used for writing code that handles high IO tasks like web scraping, reading and writing to databases, or managing websockets.
Why Should You Learn It?
Learning async IO opens up a plethora of opportunities for implementing efficient and high-performance applications. It’s a powerful tool for any Python programmer aiming to enhance their code’s performance while dealing with high IO tasks. This understanding is especially vital for managing high traffic websites or data-intensive applications.
So why wait? Let’s dive into the depths of asynchronous IO in Python!
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Part 2: Basic Syntax and Understanding Async/Await
Python uses two essential keywords, ‘async’ and ‘await’, to establish the functionality of async IO. We’ll start by emphasizing these to set a strong foundation.
Async
The ‘async’ keyword in Python denotes a function or IO operation as asynchronous, meaning it’ll return a coroutine object which can then be scheduled with an ‘event loop.
async def function_name():
print('Hello from the async function')Await
The ‘await’ keyword, on the other hand, is used to wait for a coroutine to complete. It can only be used inside an ‘async def’ function.
async def function_name():
print('Start')
await asyncio.sleep(1)
print('End')Both snippets won’t show the expected output if run in a typical Python environment. That’s because these are coroutines that need to be scheduled to run in an event loop, which brings us to the next part of our tutorial.
Part 3: Understanding Event Loop
The event loop is the fundamental part of asynchronous IO in Python. It’s a loop that can register, execute and cancel calls. It also supports the execution of coroutines directly.
Creating an Event Loop
In order to use async IO in python, an event loop must be created to manage and handle all asynchronous tasks. Here’s how you can do it:
import asyncio loop = asyncio.get_event_loop()
Scheduling a Coroutine
Scheduling a coroutine in meant for the event loop can be done by the following function:
coroutine = function_name() loop.run_until_complete(coroutine) loop.close()
Running Multiple Coroutines
You can also run multiple coroutines concurrently using ‘asyncio.gather()’:
async def f1(): pass async def f2(): pass loop = asyncio.get_event_loop() loop.run_until_complete(asyncio.gather(f1(), f2())) loop.close()
This tutorial was meant to be an easy-to-follow guide on Python async IO. Moving forward, you can now experiment with different async functions and take your python application performance to the next level!
Handling IO Operations
Handling IO operations in Python using async IO can drastically boost the overall performance of your applications. To demonstrate this, let’s look at a basic example of reading a file using traditional IO and then do the same using async IO.
Traditional IO
The traditional method of reading a file in Python goes something like this:
def read_file():
with open('file.txt', 'r') as f:
print(f.read())
read_file()Async IO
A similar operation using async IO:
import asyncio
async def read_file():
with open('file.txt', 'r') as f:
print(await f.read())
loop = asyncio.get_event_loop()
loop.run_until_complete(read_file())
loop.close()In async IO, the read operation will not block the entire program and will only take up as much time as required to complete the operation, making the overall code more efficient.
Working with Requests
Another important use-case of async IO is handling network requests. Let’s look at how we can make two requests asynchronously:
Traditional Requests
Firstly, the traditional way of making requests:
import requests
def fetch_data(url):
response = requests.get(url)
print(response.text)
fetch_data('http://example.com')
fetch_data('http://example2.com')Async Requests
And now the async way of making the same requests:
import aiohttp
import asyncio
async def fetch_data(session, url):
async with session.get(url) as response:
return await response.text()
async def main():
async with aiohttp.ClientSession() as session:
data1 = await fetch_data(session, 'http://example.com')
data2 = await fetch_data(session, 'http://example2.com')
print(data1)
print(data2)
loop = asyncio.get_event_loop()
loop.run_until_complete(main())In the async version, both requests are executed concurrently, reducing the overall wait time and significantly improving the performance of your program when dealing with IO operations.
Working with Databases
Lastly, let’s look at how we can interact with a database asynchronously:
import asyncio
import aiomysql
loop = asyncio.get_event_loop()
async def get_name(id):
conn = await aiomysql.connect(host='localhost', user='root', password='password', db='mydb', loop=loop)
cur = await conn.cursor()
await cur.execute('SELECT name FROM employees WHERE id=%s', (id,))
print(await r.fetchone())
cur.close()
conn.close()
loop.run_until_complete(get_name(1))Again, this code snippet becomes significantly more efficient when you have to handle tons of requests or operations concurrently. So, with a good understanding of async IO, you’re now ready to make your Python programs a lot more efficient!
Where To Go Next?
Now that you have acquired the basics of Python async IO, it’s important to keep the momentum going! With Python being highly in demand—particularly in data science—expanding your Python skillset can open doors to endless opportunities. That’s where we would recommend taking the next step with our Python Mini-Degree.
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Conclusion
Once you have conquered the intricacies of Python async IO, you are on the right path to becoming a proficient concurrent programmer. As you venture into this territory, you’ll cultivate a pragmatic approach to enhancing your programs’ performance and throughput. So, don’t let this momentum fade away. Continually seek to grasp and master complex programming intricacies, and you’ll soon find yourself on par with the best in the industry.
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