async-python-patterns

# 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

## 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:**
```python
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

```python
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

```python
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.exam