33 Web Scraping Tools for Developers
Updated: Aug 24, 2022
Web scraping is a very powerful technique that can be used to extract data from the internet. This practice has become one of the most popular ways for companies to gather information about their competitors, customers, and the market in general.
When it comes to web scraping, Python is the most popular language among the many tools, libraries, and frameworks available for scraping web data. But there are some strong contenders that have been gaining in popularity over the years.
Web scraping libraries for Python developers
Python is an excellent tool for web scraping because it offers a wide range of libraries that make it easy to get started with this task.
We’ve identified 13 Python libraries that help with web scraping, and we are going to discuss each of them one by one.
Requests is the most popular python library for fetching URLs. It is also used by many web scraping tools, which means that if you want to skip reinventing the wheel and save yourself some time, you should start with requests.
It is easy to use and allows you to fetch the URL of any webpage. If you are a beginner learning web scraping, start with requests. It is the first step in developing a web scraper.
BeautifulSoup is a Python library for extracting data from HTML or XML documents. The developer can use beautifulsoup for navigating, searching, and modifying the parse tree. Beautiful Soup supports the default HTML parser in Python’s standard library, but it also supports several third-party Python parsers. The combination of requests and BeautifulSoup works great for simple web scrapers.
See a nice tutorial here on how to scrape data using requests and BeautifulSoup
lxml is the most feature-rich and easy-to-use library for processing XML and HTML using the Python programming language. You can use lxml with BeautifulSoup by replacing the default parser with the lxml parser. Lxml is a relatively fast library however - it has a dependency on the external C libraries.
See a tutorial here on how to scrape data with Python and lxml
Unlike lxml, which has C dependencies, Html5lib is a pure-python library for parsing HTML. It is designed to conform to the WHATWG HTML specification, as is implemented by all major web browsers. This means it parses the data the same way modern browsers do. Since it uses an HTML5 parsing algorithm, it even fixes lots of broken HTML and adds several missing tags to complete the text and make it look like an HTML doc. Compared to lxml - html5lib is slow, the speed difference is noticeable on large-scale web scraping jobs. however, for small use cases - it is fast enough.
Here is a good comparison of html5lib and lxml
Scrapy is a mature framework for web scraping and web crawling. Scrapy provides an asynchronous mechanism that processes multiple requests in parallel. If you’re building web scrapers internally using an open-source technology - scrapy is a good choice. The learning curve of scrapy is a little high - but it comes with batteries included that help handle most common web scraping problems.
See a nice tutorial on how to use Scrapy to scrape Amazon
See a tutorial on using the Requests-HTML here
Selenium requires more system resources compared to alternatives. This could mean spending a lot of money on servers and other resources for large projects.
Selenium is notoriously slow.
It is easier to detect Selenium-based web scraping
Playwright is a type of headless browser like Puppeteer and Selenium used for web scraping. Playwright gained popularity recently as more developers started replacing selenium with playwright for web scraping tasks. It is a library based on Node.js that allows fast and efficient data extraction.
Playwright outperformed Selenium in several ways:
The execution speed for Playwright is faster and more reliable than Selenium.
It has a lower learning curve
11. Chomp JS
Here is an old tutorial on how to use PhantomJS with BeautifulSoup and Selenium
Pandas is a data manipulation library of Python. It would not come to your mind a web scraping tool. However - Pandas works great extracting Tabular data from websites. See a tutorial here.
4. Simple web crawler for node.js
Nightmare is a high-level browser automation library built by the awesome people Segment. Nightmare and Cheerio together is an amazing combination to scrape data, especially in cases where you need to mimic the user actions to get to the data. Nightmare was originally developed for automating tasks across websites that don't have APIs but is most often used for web scraping these days.
Osmosis is an HTML/XML parser written in Node Js. It uses native libxml C bindings under the hood and has support for CSS selectors and Xpaths. The biggest advantage is the faster parsing, faster searching, and minimal system resource usage. This will come in handy when data is extracted from websites on a very large scale.
Web Scraping Libraries for Java Developers
Heritrix is a web scraper library written in Java. It is known for its high extensibility and is designed for web archiving. You can interface with a web browser and control it using Heritrix. The crawler behind the internet archive is built using Heritrix.
2. Web Harvest
Web-Harvest is an open-source web scraping framework written in Java. It can collect useful data from specified pages. It uses XSLT, XQuery, and Regular Expressions for parsing and XML manipulations. Custom Java libraries could easily supplement it to augment its extraction capabilities. It is one of the oldest web scraping tools.
3. Apache Nutch
Separating crawling from scraping is an industry best practice for large use cases. Nutch is a highly extensible, highly scalable, matured, production-ready Web crawler. You can save the crawler output into a database and then pass it on to Solr and use other libraries to get information out of it.
Gecco is also a lightweight web crawler built in Java language. Gecco framework is known for its remarkable scalability. They achieve it using distributed crawling using Redis at the backend. Gecco supports Asynchronous Ajax requests and has ways to manage proxies.
StormCrawler is a mature open-source web scraping library written mostly in Java. It is used for building low-latency, scalable and optimized web scraping solutions in Java. Stormcrawler is the preferred choice of Java developers over the other alternatives because of its scalability and extendability. The learning curve for stormcrawler is relatively low if you’ve worked with Jave before.
jsoup is a Java library for scraping and parsing HTML. jsoup implements the WHATWG HTML5 specification and parses HTML to the same DOM as modern browsers do. Jsoup provides a very convenient API for fetching URLs and extracting and manipulating data using the HTML5 DOM methods and CSS selectors.
Web Scraping libraries in other programming languages
1. Nokogiri - Ruby
Nokogiri is a web scraping library written in Ruby programming language. It helps extract data from XML and HTML documents. Nokogiri is built on top of libxml2 and libxslt and supports using CSS and XPath selectors. However, for enterprise use cases, Nokogiri is not really preferred as it lacks functionalities provided by other mature frameworks. Also, Ruby's ecosystem of machine learning and ML tools is minimal, and people prefer Python as the absolute master in those.
2. Colly - Golang
Colly is a web scraping tool written in Golang. It is a good choice for golang developers and has a following of 17.2K developers on Github. Colly is built in a way that is scalable and fast. Using a single core, it can support more than a thousand requests per second. Another useful feature is the automatic cookie and session handling. If you want to enhance the capabilities of Colly - use extensions already available or build your own.
3. rvest - R
There are plenty of web scraping tools out there, and they're all great. But we've narrowed the list down to the top 33 and made a handy infographic so that you can easily compare them all at once.
Use this guide to help you find the one that's right for your project!
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