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

## 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:**

```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