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This article introduces the relevant knowledge of "what is the basic execution process of Python web crawler". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

A web crawler is a program that automatically crawls the content of a website on the Internet, also known as a web spider or a web robot. Large crawlers are widely used in search engines, data mining and other fields. Individual users or enterprises can also use crawlers to collect valuable data.

The basic execution process of a web crawler can summarize three processes: requesting data, parsing data, and saving data.

Request data

In addition to ordinary HTML, the requested data includes json data, string data, pictures, video, audio, and so on.

Parsing data

When a data is downloaded, the contents of the data are analyzed and the required data is extracted. The extracted data can be saved in a variety of formats, such as csv, json, pickle and so on.

Save data

Finally, the data is written to a file in a certain format (CSV, JSON), or stored in a database (MySQL, MongoDB). Save as one or more at the same time.

Usually, the data we want to get is not only in one page, but in multiple pages, which are related to each other, and one page may contain one or more links to other pages. After extracting the data from the current page, we should also extract some links from the page, and then crawl the linked page.

When designing a crawler program, we should also consider a series of problems, such as preventing repeated crawling of the same page (URL de-duplicating), web search strategy (depth first or breadth first, etc.), crawler access boundary restrictions and so on.

Developing a crawler program from scratch is a tedious task. in order to avoid wasting a lot of time making wheels, we can choose to use some excellent crawler frameworks in practical application, which can reduce development costs and improve program quality. allows us to focus on business logic (crawling valuable data)

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