The great God teaches you how to efficiently crawl huge amounts of data with Python crawlers.

How python can effectively crawl massive amounts of data

We all know that in the Internet age, the data is the most important, and if the data used well, it will create a lot of value space. But without a lot of data, how to create value? If your business can produce a lot of data every day, then the source of data is solved, but there is no data to do?? Ha haha, rely on reptiles to get AH!!!

Through the use of crawler technology to obtain large-scale Internet data, and then do market analysis, competitive product research, user analysis, business decision-making.

Perhaps for small white, crawler is a very difficult and technical threshold is high, but if mastered the correct method, in a short period of time can let you should be shipped freely. Let's share my study experience below.

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Learn the Python package first and implement the basic crawler process

Python crawler in a lot of packages: There are urllib, requests, BS4, Scrapy, Pyspider, beginners can start from the requests package and the XPath package to learn, requests package is mainly responsible for connecting the site, back to the Web, XPath is used to parse the Web page to extract data easily. Probably the process is to send the request first, then get the page and parse the page, and finally extract the storage content.

Mastering Anti-crawler technology

We usually encounter the problem of Web site IP, dynamic loading or a variety of strange verification code and useragent access restrictions in the process of crawling. We need to use Access frequency control, the use of proxy IP pool, packet capture, verification code of OCR and other means to solve.

Scrapy Construction of Engineered reptiles

In complex situations, you need to use the Scrapy framework. Scrapy is a very powerful reptile framework that makes it easy to build the request, as well as powerful selector to easily parse the response, with ultra-high performance, and to make crawlers engineered and modular.

Learn the basics of the database and deal with large-scale data storage

For example, MongoDB NoSQL database is used to store some unstructured data. There are also learning relational databases for MySQL or Oracle.

Implementation of concurrent crawling using distributed crawler

In the course of the crawler will encounter crawling large amounts of data, then the efficiency will be reduced. You can use distributed crawlers to solve this problem. Is the use of multi-threading principle to allow multiple crawlers to work at the same time, mainly using Scrapy + MongoDB + Redis three kinds of technology. Redis is primarily used to store the queue of Web pages to crawl, and MongoDB is to store the results.

If you even learn the distributed crawler Well, then you are basically a Daniel.

Welcome everyone to discuss below, if feel good, can share to others oh.

The great God teaches you how to efficiently crawl huge amounts of data with Python crawlers.