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📜 异步使用效果 (1)

📅 最后修改于: 2023-12-03 15:25:33.559000 🧑 作者: Mango

异步使用效果

Asynchronous programming is a programming paradigm that allows the execution of tasks in a non-blocking way. This can be particularly useful in situations where the processing time of a task is unpredictable or when multiple tasks need to be executed concurrently.

Advantages of Asynchronous Programming

Non-blocking execution allows the program to continue executing other tasks while waiting for I/O operations to complete.
Asynchronous programming can improve the responsiveness of a program, especially in user interface scenarios.
It can help reduce the resource requirements of a program by avoiding unnecessary resource allocations.

Common Use Cases

Network communication: Asynchronous programming is commonly used in network communication because it can help minimize the time spent waiting for data to arrive or be transmitted.
User interface responsiveness: In user interface scenarios, asynchronous programming can help improve the responsiveness of the user interface by allowing non-blocking execution of long-running tasks.
Data processing: Asynchronous programming can be useful in data processing scenarios where multiple tasks need to be executed concurrently, such as processing a large dataset.

Implementations of Asynchronous Programming

In Python, asynchronous programming can be implemented using several different frameworks and libraries, including:

asyncio: A library for writing asynchronous code using coroutines, which are functions that can be paused and resumed.
Twisted: A framework for building networked applications using asynchronous programming.
Tornado: A web framework that includes support for asynchronous programming.

Example Code

Here is an example of using asyncio to write a simple program that fetches multiple web pages concurrently:

import asyncio
import aiohttp

async def fetch(session, url):
    async with session.get(url) as response:
        return await response.text()

async def fetch_all(urls):
    async with aiohttp.ClientSession() as session:
        tasks = []
        for url in urls:
            task = asyncio.ensure_future(fetch(session, url))
            tasks.append(task)
        results = await asyncio.gather(*tasks)
        return results

async def main():
    urls = [
        'https://www.python.org',
        'https://www.google.com',
        'https://www.baidu.com',
    ]
    results = await fetch_all(urls)
    for result in results:
        print(result)

if __name__ == '__main__':
    loop = asyncio.get_event_loop()
    loop.run_until_complete(main())

In this code, we first define a function called fetch which takes a ClientSession object and a URL and returns the text content of the response. The fetch_all function takes a list of URLs and uses asyncio.gather to execute multiple fetch calls concurrently. Finally, we define a main function that creates a list of URLs and calls the fetch_all function to retrieve their contents.

When we run this code, we see that it fetches the contents of all three URLs concurrently:

<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" ...
<!doctype html>
<html itemscope="" itemtype="http://schema.org/WebPage" lang="en">
<head> ...
<!DOCTYPE html>
<html lang="zh-CN">
<head>
 ...

Conclusion

Asynchronous programming can be a powerful tool for improving the performance and responsiveness of your programs. By allowing tasks to be executed in a non-blocking way, you can make your programs more efficient and more user-friendly.