asyncio-programming

majiayu000's avatarfrom majiayu000

Master asynchronous programming with asyncio, async/await, concurrent operations, and async frameworks

5stars🔀1forks📁View on GitHub🕐Updated Jan 11, 2026

When & Why to Use This Skill

This Claude skill provides a comprehensive guide to mastering asynchronous programming in Python using the asyncio library. It covers fundamental concepts like the event loop and coroutines, advanced task management, and practical applications in web development and high-performance I/O operations. Designed for developers looking to enhance software scalability, it offers deep insights into writing efficient, non-blocking code using the async/await paradigm.

Use Cases

  • Developing high-performance web scrapers that can handle thousands of concurrent network requests efficiently without blocking execution.
  • Building real-time communication platforms, such as WebSocket-based chat servers or live notification systems, using aiohttp and FastAPI.
  • Optimizing backend services by implementing non-blocking database queries and asynchronous file I/O to significantly improve system throughput.
  • Refactoring synchronous Python scripts into modern asynchronous patterns to reduce latency and improve resource utilization in data-intensive tasks.
nameAsyncio Programming
descriptionMaster asynchronous programming with asyncio, async/await, concurrent operations, and async frameworks
version"2.1.0"
sasmp_version"1.3.0"
bonded_agent05-async-concurrency
bond_typePRIMARY_BOND
retry_strategyexponential_backoff
loggingtrue
metricstask_completion_rate

Asyncio Programming

Overview

Master asynchronous programming in Python with asyncio. Learn to write concurrent code that efficiently handles I/O-bound operations, build async web applications, and understand the async/await paradigm.

Learning Objectives

  • Understand asynchronous programming concepts
  • Write async functions with async/await syntax
  • Manage concurrent operations with asyncio
  • Build async web applications
  • Handle async I/O operations efficiently
  • Debug and test async code

Core Topics

1. Async/Await Basics

  • Understanding coroutines
  • async/await syntax
  • Event loop fundamentals
  • Running async functions
  • Async vs sync execution
  • Common pitfalls

Code Example:

import asyncio
import time

# Synchronous version (slow)
def fetch_data_sync(url):
    print(f"Fetching {url}...")
    time.sleep(2)  # Simulating network delay
    return f"Data from {url}"

def main_sync():
    urls = ['url1', 'url2', 'url3']
    results = []
    for url in urls:
        data = fetch_data_sync(url)
        results.append(data)
    return results

# Takes 6 seconds (2 * 3)
start = time.time()
main_sync()
print(f"Sync took: {time.time() - start:.2f}s")

# Asynchronous version (fast)
async def fetch_data_async(url):
    print(f"Fetching {url}...")
    await asyncio.sleep(2)  # Non-blocking sleep
    return f"Data from {url}"

async def main_async():
    urls = ['url1', 'url2', 'url3']
    # Create tasks and run concurrently
    tasks = [fetch_data_async(url) for url in urls]
    results = await asyncio.gather(*tasks)
    return results

# Takes 2 seconds (concurrent execution)
start = time.time()
asyncio.run(main_async())
print(f"Async took: {time.time() - start:.2f}s")

2. Asyncio Tasks & Coroutines

  • Creating and managing tasks
  • asyncio.gather() vs asyncio.wait()
  • Task cancellation
  • Task groups (Python 3.11+)
  • Exception handling in tasks
  • Timeouts

Code Example:

import asyncio

async def process_item(item_id, delay):
    print(f"Processing item {item_id}")
    await asyncio.sleep(delay)
    if item_id == 3:
        raise ValueError(f"Item {item_id} failed!")
    return f"Result {item_id}"

async def main():
    # Method 1: gather (returns results in order)
    tasks = [
        process_item(1, 1),
        process_item(2, 2),
        process_item(3, 1),
    ]
    try:
        results = await asyncio.gather(*tasks, return_exceptions=True)
        for i, result in enumerate(results):
            if isinstance(result, Exception):
                print(f"Task {i} failed: {result}")
            else:
                print(f"Task {i} result: {result}")
    except Exception as e:
        print(f"Error: {e}")

    # Method 2: wait (returns done/pending sets)
    tasks = [
        asyncio.create_task(process_item(i, i))
        for i in range(1, 4)
    ]
    done, pending = await asyncio.wait(tasks, timeout=2.5)

    print(f"Completed: {len(done)}, Pending: {len(pending)}")

    # Cancel pending tasks
    for task in pending:
        task.cancel()

    # Method 3: Task groups (Python 3.11+)
    async with asyncio.TaskGroup() as tg:
        for i in range(1, 4):
            tg.create_task(process_item(i, 1))
    # All tasks completed or exception raised

asyncio.run(main())

3. Async I/O Operations

  • Async file operations (aiofiles)
  • Async HTTP requests (aiohttp)
  • Async database operations (asyncpg, motor)
  • Async messaging (aio-pika)
  • Streams and protocols

Code Example:

import asyncio
import aiohttp
import aiofiles
from typing import List

# Async HTTP requests
async def fetch_url(session, url):
    async with session.get(url) as response:
        return await response.text()

async def fetch_multiple_urls(urls: List[str]):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_url(session, url) for url in urls]
        results = await asyncio.gather(*tasks)
        return results

# Async file operations
async def read_file_async(filepath):
    async with aiofiles.open(filepath, 'r') as f:
        content = await f.read()
        return content

async def write_file_async(filepath, content):
    async with aiofiles.open(filepath, 'w') as f:
        await f.write(content)

# Async database operations (example with asyncpg)
import asyncpg

async def fetch_users():
    conn = await asyncpg.connect(
        user='user',
        password='password',
        database='mydb',
        host='localhost'
    )
    try:
        rows = await conn.fetch('SELECT * FROM users')
        return rows
    finally:
        await conn.close()

# Usage
async def main():
    # Fetch URLs concurrently
    urls = [
        'https://api.example.com/data1',
        'https://api.example.com/data2',
        'https://api.example.com/data3',
    ]
    results = await fetch_multiple_urls(urls)

    # Read/write files
    content = await read_file_async('input.txt')
    await write_file_async('output.txt', content.upper())

    # Database operations
    users = await fetch_users()
    print(f"Found {len(users)} users")

asyncio.run(main())

4. Async Web Frameworks

  • FastAPI async routes
  • aiohttp web server
  • WebSocket handling
  • Background tasks
  • Middleware and dependencies

Code Example:

# FastAPI async example
from fastapi import FastAPI, BackgroundTasks
import asyncio

app = FastAPI()

# Async route
@app.get("/users/{user_id}")
async def get_user(user_id: int):
    # Async database call
    user = await fetch_user_from_db(user_id)
    return user

# Background task
async def send_notification(email: str, message: str):
    await asyncio.sleep(2)  # Simulate email sending
    print(f"Sent email to {email}: {message}")

@app.post("/orders/")
async def create_order(order_data: dict, background_tasks: BackgroundTasks):
    # Process order synchronously
    order_id = save_order(order_data)

    # Send notification in background
    background_tasks.add_task(
        send_notification,
        order_data['customer_email'],
        f"Order #{order_id} created"
    )

    return {"order_id": order_id}

# aiohttp web server
from aiohttp import web

async def handle_request(request):
    name = request.match_info.get('name', 'Anonymous')
    await asyncio.sleep(1)  # Async operation
    return web.json_response({'message': f'Hello {name}'})

app = web.Application()
app.add_routes([web.get('/{name}', handle_request)])

# WebSocket example
async def websocket_handler(request):
    ws = web.WebSocketResponse()
    await ws.prepare(request)

    async for msg in ws:
        if msg.type == web.WSMsgType.TEXT:
            await ws.send_str(f"Echo: {msg.data}")
        elif msg.type == web.WSMsgType.ERROR:
            print(f'Error: {ws.exception()}')

    return ws

app.add_routes([web.get('/ws', websocket_handler)])

Hands-On Practice

Project 1: Async Web Scraper

Build a concurrent web scraper with rate limiting.

Requirements:

  • Scrape multiple websites concurrently
  • Implement rate limiting
  • Handle errors gracefully
  • Save results to async database
  • Progress tracking
  • Retry failed requests

Key Skills: aiohttp, async I/O, error handling

Project 2: Real-time Chat Server

Create a WebSocket-based chat application.

Requirements:

  • WebSocket server with aiohttp
  • Multiple chat rooms
  • User authentication
  • Message broadcasting
  • Connection management
  • Message history persistence

Key Skills: WebSockets, async server, state management

Project 3: Async Task Queue

Build a distributed task processing system.

Requirements:

  • Task queue with Redis/RabbitMQ
  • Worker pool management
  • Task prioritization
  • Result caching
  • Progress monitoring
  • Graceful shutdown

Key Skills: Message queues, concurrent workers, cleanup

Assessment Criteria

  • Understand async/await semantics
  • Write efficient concurrent code
  • Handle async exceptions properly
  • Use asyncio tasks effectively
  • Build async web applications
  • Debug async code
  • Manage async resources (cleanup)

Resources

Official Documentation

Learning Platforms

Tools

Next Steps

After mastering asyncio, explore:

  • Multiprocessing - CPU-bound parallelism
  • Celery - Distributed task queue
  • gRPC - Async RPC framework
  • Kafka - Async event streaming