Claude-skill-registry Asyncio Programming
Master asynchronous programming with asyncio, async/await, concurrent operations, and async frameworks
install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/asyncio-programming" ~/.claude/skills/majiayu000-claude-skill-registry-asyncio-programming && rm -rf "$T"
manifest:
skills/data/asyncio-programming/SKILL.mdsource content
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
- asyncio Docs - Official documentation
- aiohttp - Async HTTP client/server
- FastAPI - Modern async web framework
Learning Platforms
- Using Asyncio in Python - Real Python guide
- Python Concurrency - O'Reilly book
- Asyncio Recipes - Practical examples
Tools
- aiohttp - Async HTTP
- aiofiles - Async file I/O
- asyncpg - Async PostgreSQL
- aiodebug - Async debugging
Next Steps
After mastering asyncio, explore:
- Multiprocessing - CPU-bound parallelism
- Celery - Distributed task queue
- gRPC - Async RPC framework
- Kafka - Async event streaming