26. Asynchronous Programming in Python

What we will learn in this post?

• 👉 Introduction to Asynchronous Programming
• 👉 async and await Keywords
• 👉 The asyncio Module
• 👉 Async Context Managers and Iterators
• 👉 Working with Async HTTP Requests
• 👉 Error Handling in Async Code
• 👉 Async vs Threading vs Multiprocessing

Introduction to Asynchronous Programming 🌟

Asynchronous programming is a powerful way to handle tasks in programming, especially when dealing with I/O-bound operations like reading files or making network requests. Let’s break it down! This approach is essential for building responsive web applications and APIs that can handle multiple concurrent users efficiently.

Synchronous vs. Asynchronous Execution ⚖️

• Synchronous Execution: Tasks are completed one after another. If one task takes time, the whole program waits. Think of it like waiting in line at a coffee shop. ☕

• Asynchronous Execution: Tasks can run independently. While one task is waiting (like fetching data), others can continue. Imagine ordering coffee and browsing your phone while you wait! 📱

Why Use Async Programming? 🚀

• Efficiency: It allows programs to do more in less time.
• Responsiveness: User interfaces remain active while waiting for tasks to complete.

The Event Loop Concept 🔄

The event loop is the heart of asynchronous programming. It manages tasks and ensures they run smoothly. Here’s a simple flow:

graph TD
    A["🎯 Start"]:::style1 --> B{"⚡ Task Ready?"}:::style2
    B -- "✅ Yes" --> C["🚀 Run Task"]:::style3
    B -- "⏳ No" --> D["⏰ Wait"]:::style4
    D --> B
    C --> A

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    linkStyle default stroke:#e67e22,stroke-width:3px;

Embrace asynchronous programming to make your applications faster and more efficient! 🌈

Understanding Async and Await in Python 🌟

What are Async and Await?

In Python, async and await are keywords that help you write asynchronous code. This means your program can do other things while waiting for tasks to finish, like downloading files or fetching data from the internet. 🚀

Defining Async Functions

To create an asynchronous function (also called a coroutine), you use the async def syntax. Here’s a simple example:

async def greet():
    print("Hello!")

Awaiting a Coroutine

To run an async function, you need to use the await keyword. This tells Python to wait for the function to finish before moving on. Here’s how you do it:

import asyncio

async def greet():
    print("Hello!")

async def main():
    await greet()

# Run the main function
asyncio.run(main())

Key Points

• async defines a coroutine.
• await pauses the function until the awaited coroutine finishes.
• Use asyncio.run() to execute the main coroutine.

Example: Async File Downloader 📁

import asyncio
import aiohttp
import aiofiles

async def download_file(url, filename):
    """Download a file asynchronously"""
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            async with aiofiles.open(filename, 'wb') as f:
                await f.write(await response.read())
    print(f"Downloaded {filename}")

async def main():
    urls = [
        ('https://example.com/file1.txt', 'file1.txt'),
        ('https://example.com/file2.txt', 'file2.txt'),
        ('https://example.com/file3.txt', 'file3.txt'),
    ]
    
    # Download all files concurrently
    tasks = [download_file(url, filename) for url, filename in urls]
    await asyncio.gather(*tasks)
    print("All downloads complete!")

# Run the async downloader
asyncio.run(main())

This example shows how async programming enables downloading multiple files simultaneously, significantly faster than sequential downloads.

🚀 Try this Live → Click to open interactive PYTHON playground

Introduction to asyncio 🌟

The asyncio module in Python is a powerful tool for writing concurrent code using the async/await syntax. It allows you to run multiple tasks at the same time, making your programs faster and more efficient. Let’s explore some key functions!

Key Functions

1. asyncio.run()

This function is used to run the main coroutine. It sets up the event loop and executes your async code.

import asyncio

async def main():
    print("Hello, asyncio!")

asyncio.run(main())

2. asyncio.create_task()

This function creates a task from a coroutine, allowing it to run concurrently.

async def task(name):
    print(f"Task {name} started")
    await asyncio.sleep(1)
    print(f"Task {name} completed")

asyncio.create_task(task("A"))

3. asyncio.gather()

This function runs multiple coroutines at once and waits for them to finish.

async def main():
    await asyncio.gather(task("A"), task("B"))

asyncio.run(main())

4. asyncio.sleep()

This function pauses the coroutine for a specified time, simulating a delay.

Running Multiple Coroutines 🚀

You can run multiple tasks concurrently using asyncio.gather(). Here’s a simple example:

async def main():
    await asyncio.gather(task("A"), task("B"), task("C"))

asyncio.run(main())

Flowchart of Execution

graph TD
    A["🎯 Start"]:::style1 --> B["⚙️ Run Task A"]:::style2
    A --> C["🔧 Run Task B"]:::style3
    A --> D["🛠️ Run Task C"]:::style4
    B --> E["✅ Task A Complete"]:::style5
    C --> F["✅ Task B Complete"]:::style6
    D --> G["✅ Task C Complete"]:::style7
    E --> H["🎉 All Tasks Complete"]:::style8
    F --> H
    G --> H

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With asyncio, you can easily manage multiple tasks, making your applications more responsive and efficient! Happy coding! 🎉

Understanding Async Context Managers and Iterators

Async programming in Python helps you write code that can handle many tasks at once without waiting for each one to finish. Let’s break down some key concepts:

Async Context Managers (async with)

Async context managers allow you to manage resources like files or network connections in an asynchronous way. You use the async with statement to ensure resources are properly cleaned up.

Example:

import aiofiles

async with aiofiles.open('file.txt', mode='r') as f:
    contents = await f.read()

Async Iterators (async for)

Async iterators let you loop over data that is fetched asynchronously. You use async for to iterate through items.

Example:

class AsyncCounter:
    def __init__(self, count):
        self.count = count

    def __aiter__(self):
        self.current = 0
        return self

    async def __anext__(self):
        if self.current < self.count:
            self.current += 1
            return self.current
        raise StopAsyncIteration

async for number in AsyncCounter(3):
    print(number)  # Outputs: 1, 2, 3

Key Methods

• __aenter__: Called when entering the async context.
• __aexit__: Called when exiting the async context.
• __aiter__: Prepares the async iterator.
• __anext__: Fetches the next item in the async iterator.

Flowchart of Async Context Manager

flowchart TD
    A["🎯 Start"]:::style1 --> B{"⚡ async with"}:::style2
    B --> C["🔓 __aenter__"]:::style3
    C --> D["⚙️ Execute code"]:::style4
    D --> E["🔒 __aexit__"]:::style5
    E --> F["✅ End"]:::style6

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For more details, check out the Python documentation on async. Happy coding! 😊

Using aiohttp for Async HTTP Requests 🚀

What is aiohttp?

aiohttp is a Python library that helps you make asynchronous HTTP requests. This means you can fetch multiple URLs at the same time, which is much faster than doing it one by one! 🌐

Why Use Async Requests?

• Speed: Fetch multiple APIs concurrently.
• Efficiency: Use less time waiting for responses.
• Scalability: Handle more requests without blocking.

Example: Fetching Multiple URLs

Here’s a simple example to show how aiohttp works:

import aiohttp
import asyncio

async def fetch(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.text()

async def main():
    urls = [
        'https://api.example.com/data1',
        'https://api.example.com/data2',
        'https://api.example.com/data3',
    ]
    tasks = [fetch(url) for url in urls]
    results = await asyncio.gather(*tasks)
    print(results)

# Run the main function
asyncio.run(main())

How It Works

1. Define a fetch function: This function makes a request to a URL.
2. Create a list of URLs: These are the APIs you want to call.
3. Use asyncio.gather: This runs all fetch tasks at once!

Conclusion

Using aiohttp can significantly improve your application’s performance when dealing with multiple API calls.

Happy coding! 🎉

Example: Async API Data Aggregator 📊

import aiohttp
import asyncio
import json

async def fetch_weather_data(city):
    """Fetch weather data for a city asynchronously"""
    api_key = "your_api_key_here"
    url = f"https://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}&units=metric"
    
    async with aiohttp.ClientSession() as session:
        try:
            async with session.get(url) as response:
                if response.status == 200:
                    data = await response.json()
                    return {
                        'city': city,
                        'temperature': data['main']['temp'],
                        'description': data['weather'][0]['description']
                    }
                else:
                    return {'city': city, 'error': f'API returned {response.status}'}
        except Exception as e:
            return {'city': city, 'error': str(e)}

async def main():
    cities = ['London', 'New York', 'Tokyo', 'Paris', 'Sydney']
    
    # Fetch weather for all cities concurrently
    tasks = [fetch_weather_data(city) for city in cities]
    results = await asyncio.gather(*tasks)
    
    # Display results
    for result in results:
        if 'error' in result:
            print(f"❌ {result['city']}: {result['error']}")
        else:
            print(f"✅ {result['city']}: {result['temperature']}°C, {result['description']}")

# Run the weather aggregator
asyncio.run(main())

This production-ready example demonstrates fetching data from multiple APIs concurrently, with proper error handling for reliability.

🚀 Try this Live → Click to open interactive PYTHON playground

Handling Exceptions in Async Code 🌟

Async programming can be tricky, especially when it comes to handling errors. Let’s break it down simply!

Using try-except with await 🛠️

When you use await, wrap it in a try-except block to catch errors:

import asyncio

async def risky_task():
    await asyncio.sleep(1)
    raise ValueError("Oops! Something went wrong.")

async def main():
    try:
        await risky_task()
    except ValueError as e:
        print(f"Caught an error: {e}")

# Run the main function
asyncio.run(main())

• Explanation: If risky_task raises an error, it will be caught and printed.

Using asyncio.gather with return_exceptions=True 🎉

You can run multiple tasks and handle errors gracefully:

async def safe_task(n):
    await asyncio.sleep(n)
    if n == 2:
        raise ValueError("Error in task 2")
    return f"Task {n} completed!"

async def main():
    results = await asyncio.gather(
        safe_task(1),
        safe_task(2),
        safe_task(3),
        return_exceptions=True
    )
    print(results)

# Run the main function
asyncio.run(main())

• Explanation: This will return the error as part of the results list instead of stopping the program.

Best Practices for Async Error Handling 🌈

• Always use try-except: Wrap your awaited calls to catch errors.
• Use return_exceptions=True: When gathering multiple tasks, this helps you handle errors without crashing.
• Log errors: Instead of just printing, consider logging them for better tracking.

Flowchart of Async Error Handling 🗺️

flowchart TD
    A["🎯 Start"]:::style1 --> B["⚙️ Run Async Task"]:::style2
    B --> C{"❌ Error Occurred?"}:::style3
    C -- "Yes" --> D["🛠️ Handle Error"]:::style4
    C -- "No" --> E["✅ Continue Execution"]:::style5
    D --> F["📝 Log Error"]:::style6
    E --> F
    F --> G["🎉 End"]:::style7

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By following these tips, you’ll handle exceptions in your async code like a pro! Happy coding! 😊

Example: Async Database Operations 💾

import asyncio
import asyncpg  # Asynchronous PostgreSQL driver
import logging

# Configure logging for error tracking
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

async def create_connection():
    """Create database connection pool"""
    return await asyncpg.create_pool(
        user='your_user',
        password='your_password',
        database='your_database',
        host='localhost',
        min_size=5,
        max_size=20
    )

async def fetch_user_data(pool, user_id):
    """Fetch user data asynchronously with error handling"""
    try:
        async with pool.acquire() as connection:
            row = await connection.fetchrow(
                'SELECT id, name, email FROM users WHERE id = $1',
                user_id
            )
            return dict(row) if row else None
    except Exception as e:
        logger.error(f"Error fetching user {user_id}: {e}")
        return None

async def update_user_stats(pool, user_id, login_count):
    """Update user statistics asynchronously"""
    try:
        async with pool.acquire() as connection:
            await connection.execute(
                'UPDATE user_stats SET login_count = login_count + $1 WHERE user_id = $2',
                login_count, user_id
            )
            logger.info(f"Updated stats for user {user_id}")
    except Exception as e:
        logger.error(f"Error updating stats for user {user_id}: {e}")

async def process_user_batch(pool, user_ids):
    """Process multiple users concurrently"""
    # Create tasks for fetching and updating user data
    fetch_tasks = [fetch_user_data(pool, uid) for uid in user_ids]
    update_tasks = [update_user_stats(pool, uid, 1) for uid in user_ids]
    
    # Execute all operations concurrently
    user_data_results = await asyncio.gather(*fetch_tasks, return_exceptions=True)
    await asyncio.gather(*update_tasks, return_exceptions=True)
    
    # Process results
    successful_fetches = [data for data in user_data_results if data is not None and not isinstance(data, Exception)]
    logger.info(f"Successfully processed {len(successful_fetches)} users")
    
    return successful_fetches

async def main():
    pool = await create_connection()
    try:
        user_ids = [1, 2, 3, 4, 5, 100, 101]  # Mix of valid and invalid IDs
        results = await process_user_batch(pool, user_ids)
        print(f"Processed {len(results)} valid users")
    finally:
        await pool.close()

# Run the database operations
asyncio.run(main())

This enterprise-level example shows how async programming enables efficient database operations with proper connection pooling and comprehensive error handling.

Understanding Asynchronous Programming, Threading, and Multiprocessing

Programming can be tricky, especially when it comes to handling tasks that take time. Let’s break down three popular methods: asynchronous programming, threading, and multiprocessing. Each has its own strengths!

Asynchronous Programming 🌐

• Best for: I/O-bound tasks (like web requests or file reading).
• How it works: It allows your program to handle other tasks while waiting for I/O operations to complete.
• Example: Imagine downloading multiple files at once without waiting for each to finish. You can use async and await in Python to achieve this.

async def download_files():
    await asyncio.gather(file1, file2, file3)

Threading 🧵

• Best for: I/O-bound tasks that use blocking libraries.
• How it works: It creates multiple threads that can run concurrently, but they share the same memory space.
• Example: If you’re using a library that blocks while waiting for data (like a database query), threading can help keep your app responsive.

import threading

def fetch_data():
    # Simulate a blocking operation
    pass

thread = threading.Thread(target=fetch_data)
thread.start()

Multiprocessing 🔄

• Best for: CPU-bound tasks (like heavy calculations).
• How it works: It uses separate memory spaces and runs tasks in parallel, making full use of multiple CPU cores.
• Example: If you’re processing large datasets, using multiprocessing can speed things up significantly.

from multiprocessing import Process

def process_data():
    # Heavy computation here
    pass

process = Process(target=process_data)
process.start()

When to Use Each

• Use async for tasks that wait on external resources.
• Use threading when dealing with blocking I/O operations.
• Use multiprocessing for tasks that require heavy computation.

Comprehensive Comparison Table 📊

Feature	Asynchronous Programming	Threading	Multiprocessing
Best For	I/O-bound tasks (network, file I/O)	I/O-bound with blocking libraries	CPU-bound tasks (computation)
Concurrency Model	Single-threaded, cooperative multitasking	Multi-threaded, preemptive	Multi-process, isolated
Memory Usage	Low (single process/thread)	Medium (shared memory)	High (separate memory per process)
CPU Overhead	Very low	Low to medium	High (process creation/switching)
Scalability	Excellent (thousands of concurrent tasks)	Good (limited by GIL in Python)	Good (limited by CPU cores)
Complexity	Medium (async/await syntax)	Medium (race conditions, locks)	High (inter-process communication)
Debugging	Challenging (async stack traces)	Challenging (race conditions)	Moderate (process isolation)
Python GIL Impact	Not affected	Limited by GIL	Bypasses GIL
Resource Sharing	Easy (same process)	Complex (locks, synchronization)	Difficult (IPC required)
Error Propagation	Straightforward	Complex (thread exceptions)	Moderate (process exceptions)
Use Cases	Web APIs, file downloads, database queries	Legacy libraries, GUI apps	Data processing, scientific computing
Performance	Best for I/O intensive workloads	Good for mixed workloads	Best for CPU intensive workloads

Performance Benchmarks ⚡

Here’s a practical comparison showing the performance differences:

import asyncio
import threading
import multiprocessing
import time
import requests

# Simulate I/O-bound task (network request)
def sync_request(url):
    return requests.get(url).status_code

async def async_request(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return response.status

def thread_worker(urls, results, index):
    results[index] = sync_request(urls[index])

def run_sync(urls):
    return [sync_request(url) for url in urls]

async def run_async(urls):
    tasks = [async_request(url) for url in urls]
    return await asyncio.gather(*tasks)

def run_threaded(urls):
    results = [None] * len(urls)
    threads = []
    for i, url in enumerate(urls):
        t = threading.Thread(target=thread_worker, args=(urls, results, i))
        threads.append(t)
        t.start()
    for t in threads:
        t.join()
    return results

def run_multiprocess(urls):
    with multiprocessing.Pool() as pool:
        return pool.map(sync_request, urls)

# Benchmark
urls = ["https://httpbin.org/delay/0.1"] * 10

print("=== Performance Comparison ===")
print(f"Testing with {len(urls)} concurrent requests...")

# Synchronous
start = time.time()
sync_results = run_sync(urls)
sync_time = time.time() - start
print(".2f")

# Asynchronous
start = time.time()
async_results = asyncio.run(run_async(urls))
async_time = time.time() - start
print(".2f")

# Threading
start = time.time()
thread_results = run_threaded(urls)
thread_time = time.time() - start
print(".2f")

# Multiprocessing
start = time.time()
process_results = run_multiprocess(urls)
process_time = time.time() - start
print(".2f")

print("
Speedup factors (lower is better):")
print(".1f")
print(".1f")
print(".1f")

Visual Summary

flowchart TD
    A["🎯 Task Type"]:::style1 -->|"🌐 I/O-bound"| B["⚡ Asynchronous Programming"]:::style2
    A -->|"🔒 Blocking I/O"| C["🧵 Threading"]:::style3
    A -->|"🧮 CPU-bound"| D["🔄 Multiprocessing"]:::style4

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---

🎯 Hands-On Assignment: Build an Async Web Scraper and Data Processor 🚀

📝 Your Mission

Create a high-performance async web scraper that collects data from multiple sources concurrently, processes it asynchronously, and stores results in a structured format. Build a production-ready system that handles rate limiting, retries, and data validation.

🎯 Requirements

1. Create an async HTTP client with proper session management and connection pooling
2. Implement concurrent scraping of multiple URLs with `asyncio.gather()`
3. Add comprehensive error handling with retry logic and exponential backoff
4. Use async context managers for file I/O operations
5. Implement rate limiting to respect API/website limits
6. Create async data processing pipeline with filtering and transformation
7. Add progress tracking and logging for monitoring
8. Handle different content types (JSON, HTML, XML) with appropriate parsing

💡 Implementation Hints

1. Use `aiohttp.ClientSession` with connector limits for connection pooling
2. Implement `asyncio.Semaphore` for rate limiting concurrent requests
3. Create retry decorator using `functools.wraps` and exponential backoff
4. Use `aiofiles` for async file operations and `json` for data serialization
5. Implement progress tracking with `tqdm` or custom async progress bars
6. Use `async with` for proper resource cleanup

🚀 Example Input/Output

# Example: Scrape news articles from multiple sources
async def scrape_news():
    sources = [
        'https://api.news.com/articles',
        'https://api.technews.com/latest',
        'https://api.sports.com/headlines'
    ]
    
    scraper = AsyncNewsScraper(rate_limit=10)  # 10 concurrent requests max
    articles = await scraper.scrape_multiple_sources(sources)
    
    # Process and filter articles
    processor = AsyncDataProcessor()
    filtered_articles = await processor.filter_by_keywords(articles, ['python', 'async', 'web'])
    
    # Save results asynchronously
    await processor.save_to_json(filtered_articles, 'news_data.json')
    
    print(f"Scraped and processed {len(filtered_articles)} relevant articles")

# Run the scraper
asyncio.run(scrape_news())

# Output: Scraped and processed 47 relevant articles

🏆 Bonus Challenges

• Level 2: Add proxy rotation and user-agent cycling for better scraping
• Level 3: Implement distributed scraping with multiple worker processes
• Level 4: Add caching layer with Redis for previously scraped data
• Level 5: Create web interface with FastAPI to monitor scraping progress
• Level 6: Implement machine learning-based content classification

📚 Learning Goals

• Master async HTTP clients and session management 🎯
• Apply concurrent programming patterns with asyncio.gather ✨
• Implement robust error handling and retry mechanisms 🔄
• Use async context managers for resource management 🔗
• Build scalable data processing pipelines 🛠️
• Handle rate limiting and API constraints 📊

💡 Pro Tip: This async scraping pattern is used in production by companies like Scrapy, BeautifulSoup async versions, and data collection services for efficient web data harvesting!

Share Your Solution! 💬

Completed the async scraper? Post your code in the comments below! Show us your async programming mastery! 🚀✨

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Common Pitfalls and Debugging Tips 🐛

Async programming can be tricky! Here are the most common mistakes and how to avoid them:

🔥 Common Pitfalls

1. Forgetting to await

# ❌ WRONG - This won't work as expected
async def fetch_data():
    async def get_data():
        await asyncio.sleep(1)
        return "data"
    
    result = get_data()  # Forgot await!
    print(result)  # <coroutine object>

# ✅ CORRECT
async def fetch_data():
    async def get_data():
        await asyncio.sleep(1)
        return "data"
    
    result = await get_data()  # Properly awaited
    print(result)  # "data"

2. Blocking the event loop

# ❌ WRONG - Blocks the event loop
async def bad_function():
    time.sleep(5)  # This blocks everything!
    return "done"

# ✅ CORRECT - Use asyncio.sleep
async def good_function():
    await asyncio.sleep(5)  # Non-blocking
    return "done"

3. Mixing sync and async code incorrectly

# ❌ WRONG - Can't call async function from sync context
def sync_function():
    result = async_function()  # RuntimeError!

# ✅ CORRECT - Use asyncio.run or create_task
async def async_context():
    result = await async_function()

# Or in sync context:
def sync_function():
    result = asyncio.run(async_function())

🔧 Debugging Techniques

1. Using asyncio debug mode

import asyncio

# Enable debug mode
asyncio.get_event_loop().set_debug(True)

# Or run with environment variable:
# PYTHONPATH=. python -X dev your_script.py

2. Logging async operations

import logging
import asyncio

logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

async def traced_function():
    logger.debug("Starting operation")
    await asyncio.sleep(1)
    logger.debug("Operation completed")
    return "result"

3. Using asyncio.current_task()

import asyncio

async def debug_current_task():
    current = asyncio.current_task()
    print(f"Current task: {current.get_name()}")
    print(f"Task done: {current.done()}")

🛠️ Best Practices

1. Always use async context managers

# ✅ GOOD
async with aiofiles.open('file.txt', 'r') as f:
    content = await f.read()

# ❌ AVOID
f = await aiofiles.open('file.txt', 'r')
try:
    content = await f.read()
finally:
    await f.close()

2. Handle exceptions properly

# ✅ GOOD - Handle exceptions in async code
async def robust_function():
    try:
        async with aiohttp.ClientSession() as session:
            async with session.get(url) as response:
                return await response.json()
    except aiohttp.ClientError as e:
        logger.error(f"HTTP error: {e}")
        return None
    except asyncio.TimeoutError:
        logger.error("Request timed out")
        return None

3. Use asyncio.gather with return_exceptions

# ✅ GOOD - Handle partial failures
async def batch_process(urls):
    tasks = [fetch_url(url) for url in urls]
    results = await asyncio.gather(*tasks, return_exceptions=True)
    
    successful = []
    failed = []
    
    for i, result in enumerate(results):
        if isinstance(result, Exception):
            failed.append((urls[i], result))
        else:
            successful.append(result)
    
    return successful, failed

---

Integration with Popular Frameworks 🔗

FastAPI - Async Web Framework ⚡

from fastapi import FastAPI
import asyncio

app = FastAPI()

@app.get("/async-endpoint")
async def async_endpoint():
    # Simulate async database query
    await asyncio.sleep(0.1)
    return {"message": "This is async!"}

@app.get("/concurrent-requests")
async def concurrent_requests():
    # Make multiple API calls concurrently
    urls = ["api1.example.com", "api2.example.com", "api3.example.com"]
    tasks = [fetch_external_api(url) for url in urls]
    results = await asyncio.gather(*tasks)
    return {"results": results}

async def fetch_external_api(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(f"https://{url}") as response:
            return await response.json()

Django with async views (Django 3.1+) 🏗️

# views.py
from django.http import JsonResponse
import asyncio

async def async_view(request):
    # Simulate async database operations
    await asyncio.sleep(0.1)
    
    # Use sync_to_async for Django ORM
    from asgiref.sync import sync_to_async
    
    @sync_to_async
    def get_user_count():
        return User.objects.count()
    
    count = await get_user_count()
    return JsonResponse({"user_count": count})

SQLAlchemy with async support 🗄️

from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.orm import sessionmaker

# Create async engine
engine = create_async_engine("postgresql+asyncpg://user:pass@localhost/db")
async_session = sessionmaker(engine, class_=AsyncSession)

async def get_users():
    async with async_session() as session:
        result = await session.execute(
            select(User).where(User.active == True)
        )
        return result.scalars().all()

---

Migration Guide: Converting Sync to Async 🔄

Step-by-Step Migration Process

Step 1: Identify I/O-bound operations

# BEFORE (Sync)
def fetch_user_data(user_id):
    response = requests.get(f"https://api.example.com/users/{user_id}")
    return response.json()

# AFTER (Async)
async def fetch_user_data(user_id):
    async with aiohttp.ClientSession() as session:
        async with session.get(f"https://api.example.com/users/{user_id}") as response:
            return await response.json()

Step 2: Update function signatures

# BEFORE
def process_users(user_ids):
    results = []
    for user_id in user_ids:
        data = fetch_user_data(user_id)
        results.append(data)
    return results

# AFTER
async def process_users(user_ids):
    tasks = [fetch_user_data(user_id) for user_id in user_ids]
    return await asyncio.gather(*tasks)

Step 3: Handle database operations

# BEFORE (Django ORM)
def get_active_users():
    return list(User.objects.filter(active=True))

# AFTER (with sync_to_async)
from asgiref.sync import sync_to_async

@sync_to_async
def get_active_users():
    return list(User.objects.filter(active=True))

# Or use async ORM
async def get_users():
    async with async_session() as session:
        result = await session.execute(
            select(User).where(User.active == True)
        )
        return result.scalars().all()

Step 4: Update calling code

# BEFORE
def main():
    users = get_active_users()
    for user in users:
        process_user(user)

# AFTER
async def main():
    users = await get_active_users()
    tasks = [process_user(user) for user in users]
    await asyncio.gather(*tasks)

# Run the async main
if __name__ == "__main__":
    asyncio.run(main())

Conclusion: Master Async Programming for Scalable Python Applications 🎓

Asynchronous programming transforms Python from single-threaded scripts to high-performance concurrent applications capable of handling thousands of simultaneous operations. By mastering async/await syntax, asyncio module, context managers, HTTP clients, and error handling patterns, you’ll build responsive web services, efficient data pipelines, and scalable APIs that leverage Python’s full potential for I/O-bound workloads.