Systematically organize the characteristics and technical highlights of the scrapy framework

The Scrapy framework is a Python-based web crawler framework specifically used to obtain information from the Internet. It is efficient, flexible and scalable and can be used to crawl various types of data, such as web pages, images, audio, etc. This article will introduce the main features and technical highlights of the Scrapy framework and provide corresponding code examples.

1. Features

1. Asynchronous processing
   The Scrapy framework adopts asynchronous processing and is implemented through the Twisted framework. This method can greatly improve the efficiency of the crawler and save system resources. In Scrapy, each component handles requests asynchronously. These requests are added to the queue and executed only at the appropriate time.
2. Multi-threaded processing
   The components in the Scrapy framework use a multi-threaded model based on Twisted, which can handle multiple requests at the same time and improve efficiency.
3. Excellent request and response management
   The management of requests and responses in the Scrapy framework is very flexible. Requests and responses can be added, modified or deleted at any time as needed, which can achieve the depth, breadth and speed of the website. adjustments etc.
4. Data persistence
   The Scrapy framework provides a complete set of data persistence solutions, which can store the crawled data in the database, store it in a local file, or use other methods, such as FTP wait.
5. Easy to write plug-ins
   The Scrapy framework provides a plug-in mechanism that can easily extend the functions of the framework, such as adding custom download middleware, crawler middleware, etc.

2. Technical Highlights

1. Use selectors to process HTML
   The Scrapy framework has a built-in module based on XPath and CSS selectors, which can easily process HTML Documents are processed and parsed.

Sample code:

from scrapy.selector import Selector

# 获取HTML文本
html = '<div class="class1"><a href="http://www.baidu.com">baidu</a></div>'
sel = Selector(text=html)

# 使用CSS选择器提取数据
links = sel.css('div.class1 a::attr(href)').extract()

# 使用XPath选择器提取数据
links = sel.xpath('//div[@class="class1"]/a/@href').extract()

2. Use Item Pipeline to process data
   The Scrapy framework provides an Item Pipeline mechanism, which can easily process and process the crawled data. save. Item Pipeline consists of multiple components. Each component can modify the Item or pass the Item to the next component.

Sample code:

import pymongo

class MongoPipeline(object):
    def __init__(self):
        # 连接MongoDB数据库
        self.client = pymongo.MongoClient(host='localhost', port=27017)
        self.db = self.client['mydatabase']
        self.collection = self.db['mycollection']

    def process_item(self, item, spider):
        # 处理Item数据
        data = dict(item)
        self.collection.insert_one(data)
        return item

3. Use Downloader Middleware to process requests and responses
   The Scrapy framework provides a Downloader Middleware mechanism that can handle requests and responses by adding middleware . Middleware can modify request and response headers, request and response content, set proxies, etc.

Sample code:

from scrapy import signals

class MyDownloaderMiddleware(object):
    def process_request(self, request, spider):
        # 修改请求头部信息
        request.headers['User-Agent'] = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'

    def process_response(self, request, response, spider):
        # 处理响应内容
        return response

    def process_exception(self, request, exception, spider):
        # 处理异常
        pass

4. Use Spider Middleware to handle Spider
   The Scrapy framework provides a Spider Middleware mechanism, which can handle Spider by adding middleware. Middleware can modify Spider's requests and responses, add or delete Spider's processing functions, etc.

Sample code:

from scrapy import signals

class MySpiderMiddleware(object):
    def process_spider_input(self, response, spider):
        # 处理Spider的输入
        return response

    def process_spider_output(self, response, result, spider):
        # 处理Spider的输出
        return result

    def process_spider_exception(self, response, exception, spider):
        # 处理Spider的异常
        pass

In general, the Scrapy framework is efficient, flexible and scalable, can handle various types of data, and has powerful processing capabilities . By learning the characteristics and technical highlights of the Scrapy framework, you can better use and apply the Scrapy framework to crawl and process information.

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