異步編程在Python中通過async和await關鍵字變得更加易用。它允許編寫非阻塞代碼以并發(fā)處理多項任務，尤其適用于I/O密集型操作。async def定義了一個可暫停和恢復的協(xié)程，而await用于等待任務完成而不阻塞整個程序。運行異步代碼需使用事件循環(huán)，推薦使用asyncio.run()啟動，并發(fā)執(zhí)行多個協(xié)程時可用asyncio.gather()。常見模式包括同時獲取多個URL數(shù)據(jù)、文件讀寫及網(wǎng)絡服務處理。注意事項包括：需使用支持異步的庫如aiohttp；CPU密集型任務不適用異步；避免混合同步與異步代碼；僅await可等待對象，且應使用asyncio.sleep()代替time.sleep()。掌握這些要點有助于編寫更高效清潔的代碼。

Asynchronous programming in Python has become much more approachable since the introduction of async and await keywords. These tools allow you to write non-blocking code that can handle many tasks concurrently — especially useful for I/O-bound operations like web requests, file handling, or network communication.

If you're new to async programming, it might look a bit different from standard synchronous code, but once you get the hang of it, it’s quite intuitive.

What async/await Actually Does

At its core, async def defines a coroutine — a special kind of function that can be paused and resumed. When you await something inside an async function, you're telling Python to pause execution until the awaited task completes, without blocking the entire program.

For example:

async def fetch_data():
    print("Start fetching")
    await asyncio.sleep(2)
    print("Done fetching")

Here, when await asyncio.sleep(2) is called, Python won’t sit idle for two seconds — it can go do other things (like run another coroutine) in the meantime.

How to Run Async Code Properly

You can't just call an async function like a normal one — doing so returns a coroutine object, not the result. You need an event loop to drive the execution.

In modern Python (3.7 ), the simplest way is using asyncio.run():

asyncio.run(fetch_data())

This handles setting up and tearing down the event loop for you. If you want to run multiple coroutines at once, use asyncio.gather():

asyncio.run(asyncio.gather(fetch_data(), fetch_data()))

This will execute both calls concurrently, rather than waiting for each to finish one after another.

Common Use Cases and Patterns

Async shines brightest when dealing with I/O-bound workloads. Here are a few common patterns:

• Web scraping or API calls: Fetching data from multiple URLs at once.
• File reading/writing: Especially when working with remote storage or large files.
• Network services: Handling multiple clients simultaneously without threads.

A typical async HTTP request using aiohttp looks like this:

import aiohttp

async def fetch_url(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.text()

Then you’d run it with:

asyncio.run(fetch_url("https://example.com"))

Things to Watch Out For

While async is powerful, it's not magic. A few gotchas to keep in mind:

• Not all libraries support async yet — you'll often need to find async-compatible versions (like aiohttp instead of requests).
• CPU-bound tasks don't benefit much from async — threading or multiprocessing might be better there.
• Mixing sync and async code can lead to confusion and performance issues if not handled carefully.

Also, remember:

• Only await things that are awaitable (coroutines, Tasks, Futures).
• Don’t block the event loop with time.sleep() — use await asyncio.sleep() instead.

So if you're working on network-heavy or I/O-heavy applications, learning how to use async/await properly can make your code faster and cleaner. It’s not overly complex once you understand the basics, but it does require thinking differently about how your functions interact and run.

基本上就這些。

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