How can Python crawl massive amounts of data efficiently
We all know that in the Internet era, data is the most important, and if data is used well, it will create a lot of value space. But without a lot of data, how to create value? If your own business can generate a large amount of data every day, then the problem of the source of the data volume will be solved, but what if there is no data? ? Hahaha, rely on reptiles to get it! ! !
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, etc.
Perhaps for Xiaobai, crawling is a very difficult and technically demanding thing, but if you master the correct method, you can operate freely in a short period of time. Let me share my learning experience below.
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First learn the Python package and implement the basic crawler process
There are many crawlers in Python: urllib, requests, bs4, scrapy, pyspider, etc. Beginners can start learning from the requests package and Xpath package. The requests package is mainly responsible for connecting to websites and returning webpages, while Xpath is used to parse webpages for easy extraction data. The general process is probably to send a request first, then get the page and parse the page, and finally extract the stored content.
Master anti-reptile technology
During the crawling process, we generally encounter problems such as website blocking IP, dynamic loading or various strange verification codes and userAgent access restrictions. We need to use access frequency control, the use of proxy IP pools, packet capture, and OCR of verification codes to solve the problem.
scrapy builds engineered crawler
When you encounter complex situations, you need to use the scrapy framework. Scrapy is a very powerful crawler framework, which can easily build requests, as well as a powerful selector to easily parse responses, with super high performance, as well as engineering and modularization of crawlers.
Learn database basics 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 Mysql or Oracle.
Using distributed crawler to achieve concurrent crawling
In the process of crawling, it will encounter the situation of crawling massive data, and the efficiency will be reduced at this time. Distributed crawlers can be used to solve this problem. It is to use the principle of multi-threading to allow multiple crawlers to work at the same time, mainly using the three technologies of Scrapy + MongoDB + Redis. Redis is mainly used to store the queue of web pages to be crawled, and MongoDB is used to store the results.
If you even learn distributed crawler well, then you are basically a big cow.
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