ch10s5_AsynchronousProgrammingWithAsyncio

Asynchronous programming enables your Python programs to handle **many tasks at once** without blocking.

Chapter 10: Advanced Topics — Asynchronous Programming with Asyncio

⚡ Asynchronous Programming with Asyncio — Efficient Concurrency in Python

Asynchronous programming enables your Python programs to handle many tasks at once without blocking.
Instead of waiting for one task to finish (like downloading a webpage or reading a file), asyncio allows your program to pause, perform other work, and come back when the result is ready.

🧩 1. Concurrency Models in Python

⚙️ Asyncio shines when you have many slow I/O operations — like API calls, database queries, or scraping hundreds of pages.

🧠 2. Understanding Asyncio Core Concepts

🧱 3. Basic Asyncio Example

import asyncio

async def greet():
    print("Hello...")
    await asyncio.sleep(1)
    print("World!")

# Modern way to run coroutines
asyncio.run(greet())

How it works:

• async def defines an asynchronous coroutine.
• await pauses execution without blocking the entire program.
• asyncio.run() starts and closes the event loop automatically.

⏱️ 4. Running Multiple Coroutines Concurrently

You can run multiple tasks at once using asyncio.create_task().

import asyncio

async def foo():
    await asyncio.sleep(2)
    print("Foo finished!")

async def bar():
    await asyncio.sleep(1)
    print("Bar finished!")

async def main():
    task1 = asyncio.create_task(foo())
    task2 = asyncio.create_task(bar())
    print("Tasks started...")
    await task1
    await task2

asyncio.run(main())

Output:

Tasks started...
Bar finished!
Foo finished!

⚡ Notice that both coroutines run “concurrently” — the shorter task finishes first.

🔄 5. Running Many Tasks Together with asyncio.gather()

gather() runs multiple coroutines concurrently and returns all their results.

import asyncio

async def countdown(name, delay):
    print(f"{name} started")
    await asyncio.sleep(delay)
    print(f"{name} finished")
    return f"{name} done"

async def main():
    results = await asyncio.gather(
        countdown("Task 1", 3),
        countdown("Task 2", 1),
        countdown("Task 3", 2),
    )
    print("Results:", results)

asyncio.run(main())

🌍 6. Real-World Example — Asynchronous Web Requests

Using aiohttp, you can fetch many URLs in parallel.

import aiohttp
import asyncio

async def fetch_url(session, url):
    async with session.get(url) as response:
        content = await response.text()
        print(f"Fetched {url} ({len(content)} bytes)")
        return content

async def main():
    urls = [
        "https://example.com",
        "https://python.org",
        "https://github.com",
    ]
    async with aiohttp.ClientSession() as session:
        results = await asyncio.gather(*(fetch_url(session, u) for u in urls))

asyncio.run(main())

⚙️ aiohttp enables thousands of concurrent requests efficiently — ideal for APIs, crawlers, and async web clients.

🧮 7. Using asyncio.as_completed() for Early Results

Instead of waiting for all tasks to finish, process results as they arrive.

import asyncio, aiohttp

async def fetch(url):
    async with aiohttp.ClientSession() as s:
        async with s.get(url) as r:
            await asyncio.sleep(0.5)
            return url, r.status

async def main():
    urls = ["https://example.com", "https://python.org", "https://github.com"]
    tasks = [asyncio.create_task(fetch(u)) for u in urls]

    for coro in asyncio.as_completed(tasks):
        url, status = await coro
        print(f"{url} -> {status}")

asyncio.run(main())

🚨 8. Error Handling in Async Code

Use try/except inside coroutines or wrap them when using gather().

import asyncio

async def risky_divide(x, y):
    try:
        await asyncio.sleep(1)
        return x / y
    except ZeroDivisionError:
        print("Division by zero!")
        return None

async def main():
    results = await asyncio.gather(
        risky_divide(10, 2),
        risky_divide(10, 0),
    )
    print("Results:", results)

asyncio.run(main())

🧩 9. Task Cancellation and Timeouts

Use asyncio.wait_for() to enforce time limits.

import asyncio

async def slow_operation():
    await asyncio.sleep(5)
    return "Done"

async def main():
    try:
        result = await asyncio.wait_for(slow_operation(), timeout=2)
        print(result)
    except asyncio.TimeoutError:
        print("Operation timed out!")

asyncio.run(main())

🏭 10. Producer–Consumer Pattern with asyncio.Queue

import asyncio
import random

async def producer(queue):
    for i in range(5):
        item = random.randint(1, 100)
        await queue.put(item)
        print(f"Produced: {item}")
        await asyncio.sleep(1)

async def consumer(queue):
    while True:
        item = await queue.get()
        print(f"Consumed: {item}")
        queue.task_done()

async def main():
    queue = asyncio.Queue()
    prod = asyncio.create_task(producer(queue))
    cons = asyncio.create_task(consumer(queue))
    await prod
    await queue.join()
    cons.cancel()

asyncio.run(main())

🧠 This pattern is common in streaming pipelines, event systems, and task schedulers.

⚡ 11. Async File I/O Example

While the built-in open() is synchronous, you can use aiofiles for non-blocking file operations.

pip install aiofiles

import aiofiles, asyncio

async def write_log():
    async with aiofiles.open("log.txt", "w") as f:
        await f.write("Async I/O in action!\n")

asyncio.run(write_log())

⚖️ 12. Comparison — Asyncio vs Threads vs Processes

🧭 13. Best Practices for Asyncio

✅ Always use asyncio.run() instead of manual loops.
✅ Never call blocking functions (time.sleep, requests.get) inside async code — use await asyncio.sleep() and aiohttp.
✅ Use asyncio.create_task() for concurrency, not for sequential execution.
✅ Wrap critical async calls in try/except.
✅ Use timeouts to avoid hanging tasks.
✅ Use gather() for batch operations and as_completed() for streaming.
✅ Consider frameworks like FastAPI, aiohttp, and Quart for async web apps.

🚀 14. Real-World Example — Concurrent API Requests with Timing

import aiohttp, asyncio, time

async def fetch(session, url):
    async with session.get(url) as resp:
        await asyncio.sleep(0.2)
        return url, resp.status

async def main():
    urls = [f"https://httpbin.org/delay/{i}" for i in range(1, 4)]
    start = time.perf_counter()

    async with aiohttp.ClientSession() as s:
        results = await asyncio.gather(*(fetch(s, u) for u in urls))

    end = time.perf_counter()
    print("Results:", results)
    print(f"Completed in {end - start:.2f}s")

asyncio.run(main())

🌟 Instead of taking 6 seconds (sequential), this finishes in ~2 seconds — true async concurrency!

🧠 Summary

Asyncio transforms how Python handles concurrency — unlocking high-performance, non-blocking systems perfect for modern APIs, scraping, and automation.