So you’re looking to extract some data from the web to create an interesting data visualization and in search for the best ways to do it. Great! You’re not alone in this web scraping expedition as we’re here to help with our deep domain knowledge.
It’s important to find the best language for scraping since it doesn’t make sense to go with a tech stack that doesn’t yield the desired results or could drain your resources.
It’s said that the best programming language is the one you already know. This is true to an extent with web scraping too. If you have prior experience in programming, it won’t be a bad idea to find some pre-built resources that support web scraping in that language. Since you already have the know-how of that language, you’re likely to come to speed much faster while learning to scrape with it. You can consider this as a stepping stone.
When you start out with web scraping, you don’t really need to start from the scratch as there are many third party libraries dedicated to web scraping which you can easily master. To find a web scraping library for the language you know, you can do a simple google search like this:
“your language name web scraping library”
This should help you find one for sure. If it fails, you can always learn to scrape the web using the best language which we’ll find out in the later portion of this article.
If you’re new to programming, extracting data from the web via scraping can be your first step towards developing a passion for coding. Game and web development attract a lot of people into the tech industry and web scraping could be your eureka moment to be a coder.
Crawling and extracting data from websites involves a variety of problems – I/O mechanism, communication, multi-threading, task scheduling and deduplication are some. The language and framework you use will have a significant impact on your crawling efficiency as a whole.
Below are the things to look for from an ideal programming language for web scraping:
Many beginners overthink about the role of the programming language in the speed of web scraping. However, the processing speed is rarely the bottleneck here. Practically, the main factor that affects the speed is I/O (input/output) as web scraping is all about sending out requests and receiving the response. The communication with internet is the real bottleneck here. As you know, the speed of internet cannot match that of the processor inside your machine.
This doesn’t mean languages are insignificant; the speed of a language is mostly about the speed of development, ease of maintenance and the code readability.
Node.js is particularly great at crawling websites that use dynamic coding practices. Although it supports distributed crawling, the stability of communications is relatively weak and isn’t recommended for large scale projects.
C & C ++:
Although C and C++ offer great performance, the cost of developing a web scraping setup on these languages would be high. Hence, It is not recommended to create a crawler using C or C++ unless you are starting a company solely focused on web scraping.
PHP is perhaps the least favorable language to build a crawler program. The weak support for multi-threading and async is a big drawback and this could create many issues with task scheduling and queuing. PHP is not recommended for web scraping for the same reasons.
Python is the most popular language for web scraping. It’s more like an all-rounder and can handle most of the web crawling related processes smoothly.
Scrapy has some great features like support for XPath, enhanced performance owing to the Twisted library and a variety of debugging tools.
Beautiful soup is a Python library that’s designed for fast and highly efficient web scraping. Some of the notable features are Pythonic idioms for navigation, searching, and modifying a parse tree. Beautiful Soup can also convert incoming documents to Unicode and outgoing documents to UTF-8. Beautiful Soup works on popular Python parsers like lxml and html5lib, which allow you to try different parsing methodologies.
These highly evolved web scraping libraries make Python the best language for web scraping.
These libraries and frameworks can help you learn the basics of web scraping and could even cover small-scale use cases. However, if you’re looking to extract data from the web for business use cases, it’s better to go with a web scraping service that can take end-to-end ownership of the project. There are several reasons why an in-house crawling setup isn’t the best option, you can learn more about it here.
Now that you know the good and bad sides of different languages used for web scraping, it’s time to pick the right one for you and start scraping. It is however important to exercise caution and follow the best practices of web crawling like hitting the servers in a reasonable interval and scraping during the off-peak hours. Staying a good bot on the web is as important as getting data for your big data project.