Golang Scraper: Why Go and the Best Libraries to Use for Parsing

Python is the most popular language among web developers. That is why, we have initially focused on a list of libraries and frameworks that facilitate the development of custom web parsers: Python Web Scraping Libraries.

Parsers and parsing in general will remain a significant area of activity within any business for quite a long time. This is because data scraping is used for various tasks, including competitor analysis, search of untapped niches, creating market snapshots, price monitoring etc. Learn more about parsing here.

However, let's talk about a more interesting Python alternative - the Go language, also known as Golang web scraping.

Introduction to Golang

Golang is a cross-platform programming language with strong typing, developed by Google to efficiently replace C++ and C. The latter are often used for writing multi-threaded desktop and server applications capable of managing memory allocated for computational operations.

However, C and C++ have a number of unsolvable problems, including long-lasting program compilation, weak dependency control, a large number of language subsets, poor documentation etc.

All of these issues are addressed by the Go language. But that's more about Golang's features, though. Why was the language specifically developed when there is C++ and C? After all, it looks like a new language that may become unnecessary in the future, emerges instead of solving a specific practical problem in the market.

Here is what you can create with Golang scraping:

• Network libraries and databases that require multi-threading and high performance;
• Web applications and services, including those with microservices architecture;
• High-performance mobile and desktop applications;
• Utilities and data processing scripts, including parsers;
• Games and programs to work with graphics;
• Blockchain solutions etc.

However, each niche may have its own nuances and specific use cases for Golang.

Golang web scraper should not be used for creating prototypes (MVP models, where rapid development is paramount, as there are not many ready-made frameworks for Go yet), machine learning systems (there are no ready libraries for this purpose in the language) and for organizing certain APIs (there would be too many unnecessary transactions).

The Go language will become the best solution for distributed programming due to its built-in low-level network libraries that support parallel web requests.

Why Do You Need Go Scraping?

Due to its high-quality multithreading and integrated parallelism, Go is ideal for working with websites and parsing.

Furthermore, the standard language library already includes tools for analyzing HTML structures (documents) and HTTP headers.

If needed, you can expand and enhance custom solutions with external libraries. We’ll discuss them further. But why should you parse in Go instead of Python? Both languages are generally suitable for this task, but Go has several advantages:

• In certain cases, Go is nearly 40 times faster than Python (specific tests for comparison), while in practical parsing tasks, Go consistently demonstrates a two-fold advantage (a real case sample).
• Go is a compiled language, so it doesn't require virtual machines or any other environment to run a code;
• Go applications consume minimal computational resources (this means that they are more energy-efficient and functional). You can implement more with Go rather than with Python on the same server.

Despite these advantages, Go's syntax is as simple as Python's. There are special solutions for waste collection, testing, profiling, race condition tracking, detecting outdated constructions and automated document generation from code comments.

Best Go Web Scraping Libraries

The list of the most popular libraries, frameworks and automation tools for the Go programming language to work with parsing is provided below. Just in case, here is some information about the difference between web crawling and web parsing.

Colly

Colly, an interesting fusion of the words "Collie" (like the dog breed "collie") and "Call" (“request”), is a powerful framework for accelerating the development of program parsers, web crawlers and scanners of any complexity. It has been in development since 2017, has a well-established community and is distributed as an open-source software. Developers also earn from professional data scraping services (on demand).

Features:

• It comes with a ready-made API implementation and tools to analyze the DOM structure;
• Colly makes it possible to work both with synchronous and asynchronous scraping (including handling parallel requests);
• There is a built-in extension system, letting you easily add missing functionality or write your own module without modifying the framework's core codebase;
• Developers provide a large number of examples of ready-made Colly-based parser implementations, which can be used as a starting point for customization or for learning/understanding the framework's principles;
• Colly is capable of handling over 1,000 requests per second on a single CPU core;
• Built-in support for proxy usage (ProxyFunc);
• Colly cannot work with dynamic content (e.g., involving Ajax or JavaScript technologies). To provide support, you'll need an independent headless browser (e.g., through the chromedp or Selenium extension).

Advantages:

• Simple installation and deployment process;
• Free to use for commercial purposes and projects;
• Clean API;
• High-speed performance;
• Control over delays and parallel requests for each domain;
• Automatic handling of cookies and sessions;
• Built-in caching system;
• Conversion of third-party encodings to Unicode;
• Distributed data parsing support;
• Tools for analyzing robots.txt files;
• Extension system;
• Google App Engine support.

GoQuery

GoQuery is a library for the Go programming language that provides the same syntax for working with the DOM structure as jQuery. Just like its prototype, it can be used not only for scripting and creating interfaces but also for convenient data parsing.

GoQuery is based on the standard Go net/html package and on the third-party cascadia library used for processing CSS selectors.

Features:

• GoQuery's syntax is similar to that used in the jQuery library;
• It comes with an open source code and can be used for commercial purposes;
• Since the net/html parser returns nodes rather than a full-fledged DOM tree, jQuery's state manipulation functions (such as height(), css(), detach()) have been excluded;
• Detailed documentation and API are provided;
• It's essential for HTML documents to have UTF-8 encoding (must-have conversion will be required for other encodings);
• GoQuery cannot work with dynamic content. A headless browser (e.g., webloop) and a JavaScript parser (e.g., otto) will be required.

Advantages:

• GoQuery uses the same license as the Go language (distributed on a free basis);
• The library makes it possible to work with web page structures as conveniently as with jQuery (which is one of the most popular libraries for web development);
• It significantly reduces the code volume when writing parsers;
• Easy installation and minimal dependencies.

Ferret

Ferret is a ready-made system for data extraction from web pages (a parsing software). This is open-source software, the prevailing amount of code in which is written in the Go language (an alternative implementation in Python is called PyFer). Ferret can also be used for testing user interfaces, generating databases for the machine learning process, analytics and similar tasks. Developers pay special attention to data abstraction achieved through a specific declarative language called FQL.

Features:

• This is not a framework but a full-fledged parser ready for work;
• The command-line interface (CLI) is used as a primary interface. If needed, you can develop your own web interface or standalone software;
• There is a ready-made server implementation with a web interface;
• Data is extracted from web pages not through direct connections but via the browser's API (CDP driver for Google Chrome). For this very reason, Ferret can effectively handle any dynamic content with JavaScript code;
• It has proxy support, but with its own nuances. For example, when working through the Chrome driver, a new browser has to be launched under a new proxy.

Advantages:

• It offers a direct HTTP request mode and the ability to integrate with a headless browser;
• Both a browser and Ferret can operate within virtual machines (Docker containers, and there is even a ready containerized version for Ferret);
• The program is fully prepared for its intended use;
• There is a ready-made server implementation and a web interface;
• It comes with highly detailed documentation and manuals for quick setup.

Gocrawly

Gocrawly is a lightweight web crawler (scanner) written in the Go programming language. It depends on other related libraries like goquery (mentioned earlier), purell and robotstxt.go (for complying with rules from the robots.txt file).

Features:

• Complete control over the list of web addresses in the queue for traversal (scanning);
• The HTML document syntax is analyzed using the goquery library, which is largely similar to jQuery;
• The parser analyzes rules from robots.txt files and adheres to them;
• You can configure the logic for logging actions;
• Parallel request support (based on goroutines provided in the Go language);
• Gocrawly cannot work with JavaScript pages/websites. Only direct HTML parsing is possible.

Advantages:

• Minimalistic web scanner with a simple syntax and a well-thought-out framework structure;
• With Gocrawly, you can build a full-fledged indexing system with custom caching;
• Parallel scanning of a large number of hosts is supported here and all threats will work independently;
• The Crawler.Run() method can be extended and supplemented with the necessary functions;
• If you don't set a maximum number of visits for a web resource, the scanner will only stop when it has crawled all its URL addresses (so, don't forget to limit the scanning to at least one domain).

Soup

Soup is a web parser implemented as an analogue to the BeautifulSoup parser written in Python. This isі currently a small project (compared to BeautifulSoup) maintained by a single developer from India, Anas Khan.

Features:

• The project is very simple and lightweight (it's still far from being as feature-rich as its Python prototype);
• However, the Soup library can be used for parsing (structure analysis) HTML pages;
• Find and FindAll methods are used similarly to BeautifulSoup;
• Unlike BeautifulSoup, the library does not support XPath or CSS selector options.

Advantages:

• Soup can significantly speed up the development of a custom parser;
• It offers minimal code and familiar methods (especially for those who have previously programmed in Python);
• There are enough usage samples and configurations available out there;
• It supports the opportunity of working with cookies and HTTP headers.

Hakrawler

Hakrawler is another lightweight web parser written in Golang. The author presents the library as a simpler implementation of Gocolly for collecting URL addresses and locations of JavaScript files.

Features:

• A full-featured crawler ready for work;
• A very straightforward command-line syntax;
• Proxy support;
• Multithreaded scanning;
• Works with HTTPS website versions (with TLS/SSL encryption certificates);
• Hakrawler cannot parse dynamic content;
• It is impossible to save specific content from a page (only discovered URL addresses are saved).

Advantages:

• Docker containers support;
• Hakrawler is available in the Kali Linux repository (it can be installed using the apt console command);
• Data is exported in the JSON format (easily convertible to any other tabular format);
• You can limit the number of parallel scanning threads.

Geziyor

Geziyor is an extremely fast web scraping platform with a wide range of built-in features. Unlike most competitors, Geziyor supports JavaScript (it can parse dynamic content loaded with JavaScript and Ajax).

Features:

• Separate caching system (to disk, memory or a specialized database);
• Built-in metrics to evaluate performance;
• Automatic conversion of server responses to UTF-8;
• Processing of cookies and robots.txt files;
• Requires an installed Chrome browser for JavaScript rendering (chromedp/Chrome DevTools Protocol).

Advantages:

• High performance, with over 5000 requests per second;
• Full JavaScript code rendering;
• Fine-tuned parallelism settings (per domain or globally);
• Customizable delay settings (random or constant values);
• Clear control over proxy lists based on custom logic (plus, proxies can be set globally for all requests). HTTP, HTTPS and SOCKS5 proxies are supported.
• Supports various data export formats (custom format, JSON, CSV).

Dataflow kit (DFK)

Dataflow Kit is a web scraper with a graphical interface that operates on a "point-and-click" principle. DFK makes it possible to extract data from any website and web pages - you just need to open a page and use your mouse to pick the element from which you wish to parse the info.

Features:

• The Dataflow Kit scraper consists of three components: a data retrieval service (responsible for loading web pages), an analysis service (a layer for syntactic analysis of HTML code) and an export system (for saving data in the required format);
• If the web page content cannot be parsed using standard methods, a JavaScript processing system is activated (via the Google Chrome browser's API/driver);
• The structure of the future data table is determined directly in the parsing task;
• Dataflow Kit is a ready-to-use software complex. For maximum efficiency, it is recommended to run it within a virtualized environment like Docker (recommended).

Advantages:

• Support for parsing pages with dynamic content (JavaScript);
• Working with pages that have infinite scrolling;
• Opportunity to authenticate on websites with secure login forms;
• Cookies and session management;
• Crawling all discovered links;
• Proxy support;
• Various data export formats (JSON, CSV, XLS, XML). Intermediate data is stored in DiskV or MongoDB databases;
• Dataflow Kit is effective when working with a large number of pages and parallel threads - up to 4 million URLs in 7 hours;
• A web interface is provided for creating scraping tasks (a utility for selecting the desired elements on pages using the mouse pointer).

Conclusion

We have presented some of the most popular solutions for data parsing from web pages written in the Golang programming language.

Google has clearly put a lot of effort into Go web scraping, as it demonstrates significantly better performance and speed in specific tasks when compared to Python, while maintaining a similarly simple syntax.

However, due to the language's lower availability, there are not as many ready-made libraries available for it as there are for Python. The existing solutions might not be as powerful as desired. Nevertheless, there are some interesting comprehensive solutions like Colly, Ferret, Geziyor or Dataflow Kit.

The work of such web scraping programs, even written in the Go language and running within a high-performance server, will still require a large pool of proxy addresses. You can accelerate the data collection process and parallelize threads with them without the risk of being blocked.

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