默认情况下，所有代码都是同步的。你可以使用使其异步定义函数，async def并使用来“调用”这些函数await。一个更正确的问题是“什么时候应该编写异步代码而不是同步代码？”。答案是“何时可以从中受益”。正如你指出的，在大多数情况下，你会受益。当你使用I / O操作时：

# Synchronous way:
download(url1)  # takes 5 sec.
download(url2)  # takes 5 sec.
# Total time: 10 sec.

# Asynchronous way:
await asyncio.gather(
    async_download(url1),  # takes 5 sec. 
    async_download(url2)   # takes 5 sec.
)
# Total time: only 5 sec. (+ little overhead for using asyncio)

当然，如果你创建了一个使用异步代码的函数，则该函数也应该是异步的（应定义为async def）。但是任何异步功能都可以自由使用同步代码。如果没有任何原因，将同步代码转换为异步是没有意义的：

# extract_links(url) should be async because it uses async func async_download() inside
async def extract_links(url):  

    # async_download() was created async to get benefit of I/O
    html = await async_download(url)  

    # parse() doesn't work with I/O, there's no sense to make it async
    links = parse(html)  

    return links

一个非常重要的事情是，任何长时间的同步操作（例如，> 50 ms，很难确切地说）都会冻结该时间的所有异步操作：

async def extract_links(url):
    data = await download(url)
    links = parse(data)
    # if search_in_very_big_file() takes much time to process,
    # all your running async funcs (somewhere else in code) will be frozen
    # you need to avoid this situation
    links_found = search_in_very_big_file(links)

你可以避免它在单独的进程中调用长时间运行的同步函数（并等待结果）：

executor = ProcessPoolExecutor(2)

async def extract_links(url):
    data = await download(url)
    links = parse(data)
    # Now your main process can handle another async functions while separate process running    
    links_found = await loop.run_in_executor(executor, search_in_very_big_file, links)

requests再举一个例子：何时需要在asyncio中使用。requests.get只是同步的长期运行函数，你不应该在异步代码中调用该函数（同样，避免冻结）。但是，由于I / O运行时间长，而不是因为计算时间长。在这种情况下，你可以使用ThreadPoolExecutor代替ProcessPoolExecutor以避免一些多处理开销：

executor = ThreadPoolExecutor(2)

async def download(url):
    response = await loop.run_in_executor(executor, requests.get, url)
    return response.text