Using Python SAWS library to perform a practical technique of complication programming

Python's SAWS (Simple ASYNCHRONOUS Web Scraping) class library is a tool for concurrent programming, which allows programmers to easily grab data from multiple web pages.In practical applications, some practical skills can help programmers better use the SAWS class library for concurrent programming. 1. Use asynchronous programming mode: The SAWS class library is based on the idea of asynchronous programming, so it should make full use of its asynchronous characteristics when used.Define asynchronous functions through Async/Await keywords, and use the Asyncio library to manage concurrency. 2. Reasonable set of concurrent quantity: When programming is compiled, the number of concurrency should be rationally set according to the performance and network conditions of the computer to avoid excessive requests at the same time, resulting in a decline in performance or being limited by the server. 3. Use proxy IP: When large -scale data capture, the website may limit the IP, so it can be used to avoid being banned by using agency IP.The proxy IP can be set through the proxies parameter of the SAWS library. Below is a simple example code that demonstrates how to use the SAWS library for concurrent programming: python import asyncio from saws import Saw async def fetch_data(url): async with Saw() as saw: response = await saw.get(url) data = await response.text() print(f"Fetched data from {url}: {data[:50]}") async def main(): urls = ["http://example.com", "http://example.net", "http://example.org"] tasks = [fetch_data(url) for url in urls] await asyncio.gather(*tasks) if __name__ == "__main__": asyncio.run(main()) In the above sample code, we define a fetch_data function to obtain web data, and then use the asyncio.gather method in the main function to perform multiple fetch_data tasks at the same time. Through the reasonable utilization of the asynchronous characteristics and concurrency settings of the SAWS library, the efficiency of data capture can be effectively improved and efficient concurrent programming.Of course, it is necessary to adjust and optimize according to the specific application scenarios to obtain the best concurrent performance.