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Optimize Python website access speed, use asynchronous framework, asynchronous IO and other technologies to achieve high concurrency
Overview
In today's Internet era, website access speed is one of the keys to user experience. In order to improve website performance and user satisfaction, optimizing website access speed is crucial. This article will introduce how to use Python's asynchronous framework and asynchronous IO technology to achieve high concurrency, thereby improving website access speed. Specifically involving data scraping and asynchronous processing of HTTP requests.
Asynchronous IO is a non-blocking IO mode that can continue to perform other tasks while waiting for the IO operation to complete, thereby improving the efficiency of the program. aiohttp is an HTTP framework based on asynchronous IO, which provides high-performance and scalable asynchronous processing capabilities.
import asyncio import aiohttp async def fetch(session, url): async with session.get(url) as response: return await response.text() async def main(): urls = [ 'https://www.example.com/page1', 'https://www.example.com/page2', 'https://www.example.com/page3' ] async with aiohttp.ClientSession() as session: tasks = [] for url in urls: tasks.append(fetch(session, url)) results = await asyncio.gather(*tasks) for result in results: print(result) if __name__ == '__main__': loop = asyncio.get_event_loop() loop.run_until_complete(main())
In the above code, use async with aiohttp.ClientSession() as session
to create an asynchronous HTTP session through The fetch
method initiates an asynchronous HTTP request. In the main
method, multiple asynchronous tasks are executed concurrently through asyncio.gather
to achieve high-concurrency data capture.
import asyncio import aiohttp async def fetch(session, url): async with session.get(url, timeout=10) as response: return await response.text() async def main(): urls = [ 'https://www.example.com/page1', 'https://www.example.com/page2', 'https://www.example.com/page3' ] connector = aiohttp.TCPConnector(limit=30) # 设置连接池大小为30 async with aiohttp.ClientSession(connector=connector) as session: tasks = [] for url in urls: tasks.append(fetch(session, url)) results = await asyncio.gather(*tasks) for result in results: print(result) if __name__ == '__main__': loop = asyncio.get_event_loop() loop.run_until_complete(main())
In the above code, we set the size of the connection pool through aiohttp.TCPConnector(limit=30)
is 30, and a 10-second timeout is set through the timeout
parameter. This can effectively control the concurrency and response time of HTTP requests and improve overall performance.
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