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    About

    Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications...

    SKILL.md

    Async Python Patterns

    Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.

    When to Use This Skill

    • Building async web APIs (FastAPI, aiohttp, Sanic)
    • Implementing concurrent I/O operations (database, file, network)
    • Creating web scrapers with concurrent requests
    • Developing real-time applications (WebSocket servers, chat systems)
    • Processing multiple independent tasks simultaneously
    • Building microservices with async communication
    • Optimizing I/O-bound workloads
    • Implementing async background tasks and queues

    Sync vs Async Decision Guide

    Before adopting async, consider whether it's the right choice for your use case.

    Use Case Recommended Approach
    Many concurrent network/DB calls asyncio
    CPU-bound computation multiprocessing or thread pool
    Mixed I/O + CPU Offload CPU work with asyncio.to_thread()
    Simple scripts, few connections Sync (simpler, easier to debug)
    Web APIs with high concurrency Async frameworks (FastAPI, aiohttp)

    Key Rule: Stay fully sync or fully async within a call path. Mixing creates hidden blocking and complexity.

    Core Concepts

    1. Event Loop

    The event loop is the heart of asyncio, managing and scheduling asynchronous tasks.

    Key characteristics:

    • Single-threaded cooperative multitasking
    • Schedules coroutines for execution
    • Handles I/O operations without blocking
    • Manages callbacks and futures

    2. Coroutines

    Functions defined with async def that can be paused and resumed.

    Syntax:

    async def my_coroutine():
        result = await some_async_operation()
        return result
    

    3. Tasks

    Scheduled coroutines that run concurrently on the event loop.

    4. Futures

    Low-level objects representing eventual results of async operations.

    5. Async Context Managers

    Resources that support async with for proper cleanup.

    6. Async Iterators

    Objects that support async for for iterating over async data sources.

    Quick Start

    import asyncio
    
    async def main():
        print("Hello")
        await asyncio.sleep(1)
        print("World")
    
    # Python 3.7+
    asyncio.run(main())
    

    Fundamental Patterns

    Pattern 1: Basic Async/Await

    import asyncio
    
    async def fetch_data(url: str) -> dict:
        """Fetch data from URL asynchronously."""
        await asyncio.sleep(1)  # Simulate I/O
        return {"url": url, "data": "result"}
    
    async def main():
        result = await fetch_data("https://api.example.com")
        print(result)
    
    asyncio.run(main())
    

    Pattern 2: Concurrent Execution with gather()

    import asyncio
    from typing import List
    
    async def fetch_user(user_id: int) -> dict:
        """Fetch user data."""
        await asyncio.sleep(0.5)
        return {"id": user_id, "name": f"User {user_id}"}
    
    async def fetch_all_users(user_ids: List[int]) -> List[dict]:
        """Fetch multiple users concurrently."""
        tasks = [fetch_user(uid) for uid in user_ids]
        results = await asyncio.gather(*tasks)
        return results
    
    async def main():
        user_ids = [1, 2, 3, 4, 5]
        users = await fetch_all_users(user_ids)
        print(f"Fetched {len(users)} users")
    
    asyncio.run(main())
    

    Pattern 3: Task Creation and Management

    import asyncio
    
    async def background_task(name: str, delay: int):
        """Long-running background task."""
        print(f"{name} started")
        await asyncio.sleep(delay)
        print(f"{name} completed")
        return f"Result from {name}"
    
    async def main():
        # Create tasks
        task1 = asyncio.create_task(background_task("Task 1", 2))
        task2 = asyncio.create_task(background_task("Task 2", 1))
    
        # Do other work
        print("Main: doing other work")
        await asyncio.sleep(0.5)
    
        # Wait for tasks
        result1 = await task1
        result2 = await task2
    
        print(f"Results: {result1}, {result2}")
    
    asyncio.run(main())
    

    Pattern 4: Error Handling in Async Code

    import asyncio
    from typing import List, Optional
    
    async def risky_operation(item_id: int) -> dict:
        """Operation that might fail."""
        await asyncio.sleep(0.1)
        if item_id % 3 == 0:
            raise ValueError(f"Item {item_id} failed")
        return {"id": item_id, "status": "success"}
    
    async def safe_operation(item_id: int) -> Optional[dict]:
        """Wrapper with error handling."""
        try:
            return await risky_operation(item_id)
        except ValueError as e:
            print(f"Error: {e}")
            return None
    
    async def process_items(item_ids: List[int]):
        """Process multiple items with error handling."""
        tasks = [safe_operation(iid) for iid in item_ids]
        results = await asyncio.gather(*tasks, return_exceptions=True)
    
        # Filter out failures
        successful = [r for r in results if r is not None and not isinstance(r, Exception)]
        failed = [r for r in results if isinstance(r, Exception)]
    
        print(f"Success: {len(successful)}, Failed: {len(failed)}")
        return successful
    
    asyncio.run(process_items([1, 2, 3, 4, 5, 6]))
    

    Pattern 5: Timeout Handling

    import asyncio
    
    async def slow_operation(delay: int) -> str:
        """Operation that takes time."""
        await asyncio.sleep(delay)
        return f"Completed after {delay}s"
    
    async def with_timeout():
        """Execute operation with timeout."""
        try:
            result = await asyncio.wait_for(slow_operation(5), timeout=2.0)
            print(result)
        except asyncio.TimeoutError:
            print("Operation timed out")
    
    asyncio.run(with_timeout())
    

    Detailed worked examples and patterns

    Detailed sections (starting with ## Advanced Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.

    Common Pitfalls

    1. Forgetting await

    # Wrong - returns coroutine object, doesn't execute
    result = async_function()
    
    # Correct
    result = await async_function()
    

    2. Blocking the Event Loop

    # Wrong - blocks event loop
    import time
    async def bad():
        time.sleep(1)  # Blocks!
    
    # Correct
    async def good():
        await asyncio.sleep(1)  # Non-blocking
    

    3. Not Handling Cancellation

    async def cancelable_task():
        """Task that handles cancellation."""
        try:
            while True:
                await asyncio.sleep(1)
                print("Working...")
        except asyncio.CancelledError:
            print("Task cancelled, cleaning up...")
            # Perform cleanup
            raise  # Re-raise to propagate cancellation
    

    4. Mixing Sync and Async Code

    # Wrong - can't call async from sync directly
    def sync_function():
        result = await async_function()  # SyntaxError!
    
    # Correct
    def sync_function():
        result = asyncio.run(async_function())
    

    Testing Async Code

    import asyncio
    import pytest
    
    # Using pytest-asyncio
    @pytest.mark.asyncio
    async def test_async_function():
        """Test async function."""
        result = await fetch_data("https://api.example.com")
        assert result is not None
    
    @pytest.mark.asyncio
    async def test_with_timeout():
        """Test with timeout."""
        with pytest.raises(asyncio.TimeoutError):
            await asyncio.wait_for(slow_operation(5), timeout=1.0)
    
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