In-depth analysis of the characteristics and advantages of the scrapy framework

The Scrapy framework is an open source Python crawler framework that can be used to create and manage applications that crawl data. It is one of the most popular crawler frameworks currently on the market. The Scrapy framework uses asynchronous IO for network requests, which can efficiently capture website data and has the advantages of scalability and stability.

This article will deeply analyze the characteristics and advantages of the Scrapy framework, and illustrate its efficient and stable operation through specific code examples.

1. Easy to learn

The Scrapy framework uses Python language, which is easy to learn and has a low entry barrier. At the same time, it also provides complete documentation and sample code to facilitate users to get started quickly. The following is a simple Scrapy crawler example that can be used to obtain the titles and links of popular questions on Zhihu:

import scrapy

class ZhihuSpider(scrapy.Spider):
    name = "zhihu" # 爬虫名
    start_urls = [
         'https://www.zhihu.com/hot'
    ] # 起始网站链接

    def parse(self, response):
        for question in response.css('.HotItem'):
            yield {
                'title': question.css('h2::text').get(),
                'link': question.css('a::attr(href)').get()
            }

In the above code, a crawler program named "zhihu" is defined by inheriting the scrapy.Spider class . The start_urls attribute is defined in the class, and the website links to be crawled are specified in a list. A parse() method is defined to parse the response and obtain the titles and links of popular questions through CSS selectors, and return the results as a dictionary yield.

2. Asynchronous IO

The Scrapy framework uses asynchronous IO for network requests. It can send multiple asynchronous requests at the same time and return all responses immediately. This method greatly improves the speed and efficiency of the crawler. The following is a simple Scrapy asynchronous request code example:

import asyncio
import aiohttp

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

async def main():
    urls = [
        'https://www.baidu.com',
        'https://www.google.com',
        'https://www.bing.com'
    ]
    tasks = []
    for url in urls:
        tasks.append(asyncio.ensure_future(fetch(url)))
    responses = await asyncio.gather(*tasks)
    print(responses)

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

In the above code, the asynchronous request method is implemented through the asyncio library and aiohttp library. A fetch() asynchronous function is defined for sending requests, and the aiohttp library is used to implement an asynchronous HTTP client. A main() asynchronous function is defined to process urls, the Future object returned by fetch() is added to the task list, and finally the asyncio.gather() function is used to obtain the return results of all tasks.

3. Extensibility

The Scrapy framework provides a wealth of extension interfaces and plug-ins. Users can easily add custom middleware, pipelines, downloaders, etc., thus Extend its functionality and performance. The following is an example of a simple Scrapy middleware:

from scrapy import signals

class MyMiddleware:
    @classmethod
    def from_crawler(cls, crawler):
        o = cls()
        crawler.signals.connect(o.spider_opened, signal=signals.spider_opened)
        crawler.signals.connect(o.spider_closed, signal=signals.spider_closed)
        return o

    def spider_opened(self, spider):
        spider.logger.info('常规中间件打开： %s', spider.name)

    def spider_closed(self, spider):
        spider.logger.info('常规中间件关闭： %s', spider.name)

    def process_request(self, request, spider):
        spider.logger.info('常规中间件请求： %s %s', request.method, request.url)
        return None

    def process_response(self, request, response, spider):
        spider.logger.info('常规中间件响应： %s %s', str(response.status), response.url)
        return response

    def process_exception(self, request, exception, spider):
        spider.logger.error('常规中间件异常： %s %s', exception, request.url)
        return None

In the above code, a MyMiddleware middleware class is defined. A special from_crawler() function is defined in the class to handle the signal connection of the crawler program. The spider_opened() and spider_closed() functions are defined to handle the crawler's opening and closing signals. The process_request() and process_response() functions are defined for processing request and response signals. The process_exception() function is defined to handle exception information.

4. Stability

The Scrapy framework is highly configurable and adjustable, and can adjust crawler details according to user needs, thus improving the stability and robustness of the Scrapy framework crawler. Great sex. The following is an example of Scrapy download delay and timeout configuration:

DOWNLOAD_DELAY = 3
DOWNLOAD_TIMEOUT = 5

In the above code, by setting the DOWNLOAD_DELAY parameter to 3, it means that you need to wait 3 seconds between each two downloads. By setting the DOWNLOAD_TIMEOUT parameter to 5, it means that if no response is received within 5 seconds, it will time out and exit.

Summary

The Scrapy framework is an efficient, scalable and stable Python crawler framework with the advantages of easy learning, asynchronous IO, scalability and stability. This article introduces the main features and advantages of the Scrapy framework through specific code examples. For users who want to develop efficient and stable crawler applications, the Scrapy framework is undoubtedly a good choice.

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