Python Async Programming Tutorial – Complete Guide

Welcome to this exciting exploration of Python asynchronous programming. In this tutorial, we will unpack and demystify async programming, making it more accessible and engaging for learners of all levels. Be ready for lots of hands-on examples and practical illustrations that will pique your interest and deepen your understanding.

What is Python Async Programming?

Let’s kick off by defining Python async programming. This is a style of programming that is designed to handle tasks in a non-blocking manner. It allows you to write and manage programs that are able to do multiple things at the same time, similar to players participating in a well-coordinated team sport.

What is it Used For?

Async programming in Python is used when you need your applications to handle several tasks simultaneously, efficiently and without unnecessary blocking. This is very helpful in scenarios where tasks need to be executed simultaneously without waiting for each other to finish. Think of it as playing a strategic video game where the characters carry out multiple actions simultaneously.

Why Should I Learn it?

First, asynchronous programming allows for increased efficiency and performance in your Python applications, especially in I/O-bound and high-latency environments. Secondly, it’s an invaluable skill set in today’s multi-core, multi-thread world. As you deepen your programming skills, mastering async programming is a crucial milestone that will throw open wide the doors of possibilities for you as a developer.

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Getting Started With Async Programming in Python

To get started with async programming in Python, it’s important to understand the role of the “async” and “await” keywords. Let’s explore what they’re used for.

The async Keyword

The “async” keyword in Python is used to declare a function as a coroutine function. This is the first step in writing async code. Here’s a basic example:

async def my_function():  
    return "Hello, Zenva!"

The await Keyword

The “await” keyword is used in Python to wait for a coroutine to finish its execution. Let’s modify the previous function to see how it’s used.

import asyncio
async def my_function():  
    return "Hello, Zenva!"
    
await my_function()

Diving Deeper Into Async Programming

Now that we have a basic understanding of how to declare and use coroutine functions, let’s look at how we can run multiple coroutines simultaneously to see the power of async programming.

Running Coroutines Concurrently Using asyncio.gather()

Imagine we have two coroutines, and we need to run them concurrently. We can accomplish this using the asyncio.gather() function. Here’s an example:

import asyncio
async def first_function():
    print("First function starts")

async def second_function():
    print("Second function starts")

await asyncio.gather(
    first_function(),
    second_function()
)

The functions run concurrently, each printing their message without waiting for the other to finish.

Using Async Programming in a Practical Scenario

Let’s now simulate a longer running operation using asyncio.sleep() to really demonstrate the power of async programming in Python.

import asyncio
async def long_running_operation(x):
    print(f'Starting operation {x}')
    await asyncio.sleep(1)
    print(f'Finishing operation {x}')

await asyncio.gather(
    long_running_operation(1),
    long_running_operation(2),
    long_running_operation(3)
)

Even though each operation takes 1 second, the entire program only takes roughly 1 second to complete because they all run concurrently.

Understanding Task Scheduling in Python Async Programming

Essential to Python async programming is understanding how tasks are scheduled and managed. Python achieves this with the asyncio Task object.

Crafting an Asynchronous Task

Let’s change our long_running_operation function into a Task:

import asyncio
async def long_running_operation(x):
    print(f'Starting operation {x}')
    await asyncio.sleep(1)
    print(f'Finishing operation {x}')

# Declaring the Task
task = asyncio.create_task(long_running_operation(1))

# Running the Task
await task

In this example, the ‘asyncio.create_task()’ function wraps our coroutine in a Task object. It’s worth noting that tasks run immediately as soon as the event loop has time to execute them.

Using Tasks to Execute Coroutines Simultaneously

We can take advantage of Task objects to run multiple coroutines almost simultaneously. Let’s check out how.

import asyncio
async def long_running_operation(x):
    print(f'Starting operation {x}')
    await asyncio.sleep(1)
    print(f'Finishing operation {x}')

# Declaring the Tasks
task1 = asyncio.create_task(long_running_operation(1))
task2 = asyncio.create_task(long_running_operation(2))

# Running the Tasks
await task1
await task2

This code will start both tasks at nearly the same time. The event loop switches between them, managing their execution so they run concurrently.

Working with Concurrent Tasks

Asyncio offers various methods to work with tasks concurrently. Two popular ones are ‘wait()’ and ‘as_completed()’.

Using ‘asyncio.wait()’

The ‘asyncio.wait()’ function enables you to wait for several tasks at once. They will execute concurrently and the function will return when all tasks are done.

import asyncio
async def long_running_operation(x):
    print(f'Starting operation {x}')
    await asyncio.sleep(1)
    print(f'Finishing operation {x}')

tasks = [long_running_operation(i) for i in range(5)]

# Use 'asyncio.wait()' to run multiple tasks concurrently
await asyncio.wait(tasks)

Using ‘asyncio.as_completed()’

The ‘asyncio.as_completed()’ function allows you to process tasks as they complete, regardless of their order of completion. It’s particularly useful when dealing with tasks that may take variable times to complete.

import asyncio
async def long_running_operation(x):
    print(f'Starting operation {x}')
    await asyncio.sleep(x)
    print(f'Finishing operation {x}')

tasks = [long_running_operation(i) for i in range(1, 4)]

# Use 'asyncio.as_completed()' to process tasks as they complete
for task in asyncio.as_completed(tasks):
    result = await task
    print(f'Task with result {result} finished')

In this example, tasks start and finish according to the time they take for execution. The functions ‘asyncio.wait()’ and ‘asyncio.as_completed()’ are paramount tools in the arsenal of every Python async programmer.

Where to Go Next

As we wrap up this introduction to Python async programming, you might be wondering, “Where do I go from here?” The truth is, your journey is just beginning! To truly master Python and unlock its full potential, continued learning and practice are key.

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Conclusion

In conclusion, Python asynchronous programming is a powerful tool in every developer’s toolbelt. With async programming, you can transform your applications into highly performant, concurrent powerhouses capable of efficiently managing multiple tasks at once. As you dive deeper into Python, mastering async programming will unlock a new world of possibilities.

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