Asyncio in python - Full Tutorial

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Asyncio is a library in Python that provides infrastructure for writing asynchronous programs, enabling concurrent code execution without the need for multi-threading or multi-processing. It uses event loops to run asynchronous tasks, which allows programs to perform I/O-bound tasks (like web requests, file handling, etc.) without blocking the execution of other code.

Key Concepts of asyncio:
Coroutines: The core building blocks of asyncio. A coroutine is a function defined using async def. Coroutines are used to perform non-blocking tasks. They are paused using the await keyword, allowing other tasks to run while waiting for some I/O to complete.

Example:

python

import asyncio

async def my_coroutine():
print("Start")
print("End")

Example:

python

Tasks: These are used to run multiple coroutines concurrently. A task schedules a coroutine to run within an event loop, allowing many coroutines to execute asynchronously.

Example:

python

async def task(name, duration):
print(f'Task {name} completed')

async def main():
task("A", 2),
task("B", 1)
)

In this example, tasks A and B are run concurrently, and the program doesn't wait for one task to finish before starting the other.

await: This is used to "pause" the execution of a coroutine until the awaited task completes. It's a way to yield control back to the event loop, allowing other coroutines to run.

Example:

python

async def fetch_data():
data = await some_async_function()
return data
Benefits of asyncio:
Non-blocking I/O: asyncio allows for efficient I/O-bound task handling (e.g., network requests, file I/O) without blocking the main execution thread.
Concurrency without Threads: Instead of using threads or processes, asyncio uses a single thread with cooperative multitasking, reducing overhead.
Resource-efficient: It uses fewer system resources (like memory and CPU) compared to multi-threading or multi-processing, making it ideal for I/O-bound operations.
Common Use Cases:
Network requests: Running multiple web requests concurrently without waiting for each to complete before starting the next.
File I/O: Efficient reading/writing of files while other tasks continue running.
Web Scraping: Fetching data from many websites at the same time using asynchronous HTTP requests.
Example with asyncio and HTTP requests:
python

import asyncio
import aiohttp # aiohttp is an async HTTP client

async def fetch_url(session, url):

async def main():
async with aiohttp.ClientSession() as session:
tasks = [fetch_url(session, url) for url in urls]
print(results)

In this example, multiple URLs are fetched concurrently using asyncio and aiohttp, demonstrating efficient handling of I/O-bound tasks like network requests.

Conclusion:
asyncio provides an elegant way to write non-blocking, concurrent Python code, making it ideal for applications that involve I/O-bound operations, such as web servers, network clients, or real-time data processing tasks. @TechWithMachines
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