Web Scraping with Ruby: A Simple Step-by-Step Guide

Web scraping with Ruby might not be the first language that comes to mind for data extraction – Python usually steals the spotlight here. However, Ruby's elegant syntax and powerful gems make it surprisingly effective. This guide walks you through building Ruby scrapers from your first HTTP request to production-ready systems that handle JavaScript rendering, proxy rotation, and anti-bot measures. We'll cover essential tools like HTTParty and Nokogiri, show practical code examples, and teach you how to avoid blocks and scale safely.

Zilvinas Tamulis

Dec 12, 2025

15 min read

Quick answer (TL;DR)

How Ruby scrapers work:

Send HTTP requests to fetch HTML from web pages
Parse the HTML document with Nokogiri to extract data
Save scraped data to CSV, JSON, or your database

Essential Ruby gems:

HTTParty – simple HTTP requests
Nokogiri – HTML parsing and CSS selectors
Mechanize – forms, sessions, and cookies
Ferrum/Selenium – JavaScript-heavy sites requiring headless browsers

When to build your own vs. use an API:

Build it yourself – good for learning, simple projects, complete control
Use a scraping API – good for production scale, JavaScript rendering, proxy rotation, avoiding blocks, and saving development time

DIY scrapers are great for learning and basic tasks. For serious web scraping use cases at scale, tools like Decodo's Web Scraping API handle the infrastructure (proxies, CAPTCHAs, rendering), making the process much less of a headache when debugging at 2 AM.

What is web scraping with Ruby?

Ruby is a programming language created by Yukihiro Matsumoto in the mid-1990s with a focus on developer happiness and readable code. It's best known for powering Ruby on Rails, a robust web application framework, but it's also a solid choice for web scraping thanks to its clean syntax and mature ecosystem of gems (Ruby's term for libraries).

Web scraping is the process of using Ruby to extract data from web pages automatically. Instead of manually copying information from websites, you write a Ruby script that fetches HTML documents, cleans them, and pulls out the specific data you need. You can extract anything – product prices, article text, contact information, or other structured data on the web.

The basic workflow is straightforward: your Ruby script sends an HTTP request to a target website, receives the HTML response, parses that HTML document to locate specific elements using CSS selectors or XPath, extracts the desired data, and saves it in a usable format like CSV or JSON.

How Ruby web scrapers work step-by-step

Building a Ruby web scraper follows a simple pattern that becomes second nature once you've done it a few times. Here's what a typical scraping workflow looks like:

Inspect the target webpage. Open your browser's developer tools and examine the HTML structure to identify which elements contain your desired data.
Send an HTTP request. Use a Ruby gem to fetch the HTML document from the web page.
Parse the HTML response. Load the HTML string into a parser that understands the document structure.
Extract data. Use CSS selectors or XPath to select HTML elements and pull out text, attributes, or links.
Clean and structure. Process the scraped data into a readable format, handling any inconsistencies or missing values.
Save the data. Export to a CSV file, JSON, database, or wherever you need it.

Once you've built the scraping logic for one page, you can loop through multiple pages, add error handling for failed requests, and schedule your Ruby script to run automatically. The hard part is understanding the target website's structure – the actual Ruby code can range from just a few dozen lines to a few hundred, based on website complexity.

Core Ruby gems for web scraping

Ruby's ecosystem offers several excellent web scraping libraries, each suited for different scenarios. Here are the essential ones every Ruby web scraper should know.

HTTParty

HTTParty makes performing HTTP requests super easy. It's perfect for basic GET and POST requests when you just need to fetch HTML.

require 'httparty'

response = HTTParty.get('https://example.com')
puts response.body  # The HTML document
puts response.code  # Status code (200, 404, etc.)

Use HTTParty when you need a lightweight HTTP client for straightforward requests without complex session handling.

Nokogiri

Nokogiri is the best choice for parsing HTML and XML documents in Ruby. It lets you navigate and extract data from HTML elements with ease, similar to Beautiful Soup in Python.

require 'nokogiri'

html = '<div class="product"><h2>Widget</h2><span>$19.99</span></div>'
doc = Nokogiri::HTML(html)

product_name = doc.css('.product h2').text
price = doc.css('.product span').text

puts "#{product_name}: #{price}"

Use Nokogiri when you need to parse and extract data from HTML documents (which is basically always).

Mechanize

Mechanize combines HTTP requests with HTML parsing and adds session management, cookie handling, and the ability to submit forms. It's like a programmable browser without a GUI.

require 'mechanize'

agent = Mechanize.new
page = agent.get('https://example.com')

# Follow links, submit forms, maintain sessions
search_form = page.forms.first
search_form.q = 'ruby web scraping'
results = agent.submit(search_form)

Use Mechanize when you need to interact with forms, handle cookies, or maintain sessions across multiple requests. It shouldn't be confused with a headless browser, as it cannot handle JavaScript-heavy pages where content is loaded dynamically.

These three gems cover most of the simple web scraping scenarios. Combine HTTParty or Mechanize for fetching pages with Nokogiri for parsing, and you've got everything you need to build a capable Ruby web scraper.

Set up your Ruby environment

If you're entirely new to Ruby, you'll need to install it on your machine. The easiest way to install Ruby depends on your operating system:

macOS. Ruby comes pre-installed, but you'll want a newer version. Use Homebrew:

brew install ruby

This will install Ruby, but it will be a "keg-only" version, since it will try to avoid conflict with the existing Ruby installation. To use the latest installed version, add its bin directory to your PATH:

echo 'export PATH="/opt/homebrew/opt/ruby/bin:$PATH"' >> ~/.zshrc

Then reload your shell:

source ~/.zshrc

Windows. Download the RubyInstaller and run it. Follow the setup instructions.
Linux. Use your package manager:

sudo apt-get install ruby-full # Ubuntu/Debian
sudo yum install ruby # CentOS/Fedora

After installation, verify your Ruby version by opening your terminal and running:

ruby -v

You should see something like Ruby 3.2.0 or higher. If you see it, you're good to go.

Choose your Ruby IDE

While you can write Ruby in any text editor, VS Code is a solid choice with excellent Ruby support. Install the "Ruby LSP" extension by Shopify for syntax highlighting and IntelliSense.

Other popular options include RubyMine (if you're willing to pay) or good old Vim (if you're that kind of developer).

Test your setup

Create a simple Ruby script to make sure everything works. Make a file called test.rb:

puts "Hello from Ruby!"
puts "Ruby version: #{RUBY_VERSION}"

Run it from your terminal:

ruby test.rb

If you see a greeting and Ruby version printed, congratulations, you're ready to start scraping the web. Time to install some gems and make HTTP requests.

Build your first Ruby web scraper

Let's build a real web scraper. We'll scrape country information from a beginner-friendly practice site, extract some data, and save it to a CSV file. By the end of this section, you'll have a complete, functioning Ruby scraper you can modify for your own projects.

We're going to use Scrape This Site – a website specifically designed for learning web scraping.

Step 1: Inspect the page and define your data

Before writing any code, you need to understand the structure of the target webpage. Open the practice page in your browser and right-click on a country name, then select Inspect or Inspect Element.

You'll see the HTML structure. Each country is wrapped in a <div class="country"> with child elements containing the data:

Country name is in <h3 class="country-name">
Capital is in <span class="country-capital">
Population is in <span class="country-population">
Area is in <span class="country-area">

Keep these in mind, as these elements contain the information you'll be scraping.

Step 2: Set up your project

First, create a project folder manually, or through a terminal command:

mkdir ruby-scraper
cd ruby-scraper

Install the gems you'll need. Create a Gemfile (a plain text file without any extension) in your Ruby project:

source 'https://rubygems.org'

gem 'httparty'
gem 'nokogiri'

The source defines where to install the gems from. RubyGems is the leading choice, but it's possible to get them from alternate sources (such as internal company servers).

Then run the following command from your terminal to install all the gems:

bundle install

You can also install the gems individually:

gem install httparty
gem install nokogiri

Step 3: Fetch HTML with Ruby

To get data from the website, create a file called scraper.rb and fetch the HTML:

require 'httparty'

url = 'https://www.scrapethissite.com/pages/simple/'
response = HTTParty.get(url)

puts response.body  # print the full raw HTML

Run it with:

ruby scraper.rb

If you see a lot of scary HTML printed, then don't fear – it's a sign of success, and you've just performed your first HTTP request.

Step 4: Parse HTML with Nokogiri

Now for the fun part – extracting the data. Use Nokogiri to parse the HTML and CSS selectors to pinpoint the elements you want.

Update your scraper.rb:

require 'httparty'
require 'nokogiri'

url = 'https://www.scrapethissite.com/pages/simple/'
response = HTTParty.get(url)

# Parse the HTML document
doc = Nokogiri::HTML(response.body)

# Extract country data
countries = []

doc.css('.country').each do |country|
  name = country.css('.country-name').text.strip
  capital = country.css('.country-capital').text.strip
  population = country.css('.country-population').text.strip
  area = country.css('.country-area').text.strip
  
  countries << {
    name: name,
    capital: capital,
    population: population,
    area: area
  }
end

# Display the first 3 countries
countries.first(3).each do |country|
  puts "Country: #{country[:name]} \nCapital: #{country[:capital]}\nPopulation: #{country[:population]}\nArea: #{country[:area]}"
  puts "-----------------------------"
end

puts "Total countries scraped: #{countries.length}"

require 'httparty'
require 'nokogiri'

url = 'https://www.scrapethissite.com/pages/simple/'
response = HTTParty.get(url)

# Parse the HTML document
doc = Nokogiri::HTML(response.body)

# Extract country data
countries = []

doc.css('.country').each do |country|
  name = country.css('.country-name').text.strip
  capital = country.css('.country-capital').text.strip
  population = country.css('.country-population').text.strip
  area = country.css('.country-area').text.strip
  
  countries << {
    name: name,
    capital: capital,
    population: population,
    area: area
  }
end

# Display the first 3 countries
countries.first(3).each do |country|
  puts "Country: #{country[:name]} \nCapital: #{country[:capital]}\nPopulation: #{country[:population]}\nArea: #{country[:area]}"
  puts "-----------------------------"
end

puts "Total countries scraped: #{countries.length}"

The .css() method uses CSS selectors to find HTML elements – .text extracts the text content, and .strip removes any extra whitespaces. Run the script, and you'll see the first 3 countries' data printed to your terminal:

Country: Andorra 
Capital: Andorra la Vella
Population: 84000
Area: 468.0
-----------------------------
Country: United Arab Emirates 
Capital: Abu Dhabi
Population: 4975593
Area: 82880.0
-----------------------------
Country: Afghanistan 
Capital: Kabul
Population: 29121286
Area: 647500.0
-----------------------------
Total countries scraped: 250

If you want to see more than just the first 3 results, you can change the number in this line:

countries.first(3).each do |country|

Step 4: Save and reuse your data

Printing to the terminal is great for testing, but if you try to print all 250 results, it won't even fit in most terminal windows. It's also not great for readability or analysis. Let's save the scraped data to a CSV file so you can actually do something useful with it.

Luckily, Ruby has a built-in CSV library, so no additional gems are needed. Update your Ruby script:

require 'httparty'
require 'nokogiri'
require 'csv'

url = 'https://www.scrapethissite.com/pages/simple/'
response = HTTParty.get(url)

# Parse the HTML document
doc = Nokogiri::HTML(response.body)

# Extract country data
countries = []

doc.css('.country').each do |country|
  name = country.css('.country-name').text.strip
  capital = country.css('.country-capital').text.strip
  population = country.css('.country-population').text.strip
  area = country.css('.country-area').text.strip
  
  countries << {
    name: name,
    capital: capital,
    population: population,
    area: area
  }
end

# Write all results to a CSV file
CSV.open("countries.csv", "w", write_headers: true, headers: ["Name", "Capital", "Population", "Area"]) do |csv|
  countries.each do |country|
    csv << [country[:name], country[:capital], country[:population], country[:area]]
  end
end

puts "Scraped #{countries.length} countries and saved to file."

require 'httparty'
require 'nokogiri'
require 'csv'

url = 'https://www.scrapethissite.com/pages/simple/'
response = HTTParty.get(url)

# Parse the HTML document
doc = Nokogiri::HTML(response.body)

# Extract country data
countries = []

doc.css('.country').each do |country|
  name = country.css('.country-name').text.strip
  capital = country.css('.country-capital').text.strip
  population = country.css('.country-population').text.strip
  area = country.css('.country-area').text.strip
  
  countries << {
    name: name,
    capital: capital,
    population: population,
    area: area
  }
end

# Write all results to a CSV file
CSV.open("countries.csv", "w", write_headers: true, headers: ["Name", "Capital", "Population", "Area"]) do |csv|
  countries.each do |country|
    csv << [country[:name], country[:capital], country[:population], country[:area]]
  end
end

puts "Scraped #{countries.length} countries and saved to file."

Run it, and you'll get a countries.csv file with all your scraped data. Open it in Excel, import it into your database, or feed it to your AI as training data.

Want JSON instead? Just use Ruby's JSON library by replacing lines 3 and 28-33 with:

require 'json' # Line 3

File.open("countries.json", "w") do |file| # Line 28-33 (becomes 28-30)
  file.write(JSON.pretty_generate(countries))
end

You now have a complete, working Ruby web scraper. It fetches a web page, parses the HTML, extracts structured data, and saves it in a reusable format. This scraping logic is the foundation of scraping – you can adapt this pattern to scrape just about any static website on the internet.

Handling JavaScript-heavy websites in Ruby

When you scrape with HTTParty or Mechanize, you're only getting the initial HTML document the server sends. If a website loads its content dynamically with JavaScript after the page loads (like most modern single-page applications do), your scraper never sees that data because the JavaScript never executes. You're essentially trying to scrape a half-built page.

Let's see this in action. Try scraping the Scrape This Site's AJAX/JavaScript example page with our previous approach:

require 'httparty'
require 'nokogiri'

url = 'https://www.scrapethissite.com/pages/ajax-javascript/#2015'
response = HTTParty.get(url)
doc = Nokogiri::HTML(response.body)

# Try to find the film data
films = doc.css('.film')
puts "Films found: #{films.length}"  # Returns 0

You'll find zero films, even though the page displays them in your browser. That's because the film data is loaded via an AJAX request after the page loads – something our static scraper can't see.

When you need JavaScript rendering, you have two options:

Use a headless browser like Ferrum (Ruby binding for Chrome). It actually executes JavaScript like a real browser:


require 'ferrum'

browser = Ferrum::Browser.new
browser.go_to('https://www.scrapethissite.com/pages/ajax-javascript/#2015')

# Wait for the JS to finish loading
browser.network.wait_for_idle
browser.at_css('tr.film')

# Select all film rows
films = browser.css('tr.film')

films.each do |film|
  title = film.at_css('.film-title')&.text&.strip
  nominations = film.at_css('.film-nominations')&.text&.strip
  awards = film.at_css('.film-awards')&.text&.strip

  puts "Title: #{title}"
  puts "Nominations: #{nominations}"
  puts "Awards: #{awards}"
  puts "-----------------------------"
end

puts "Total movies found: #{films.size}"

browser.quit


require 'ferrum'

browser = Ferrum::Browser.new
browser.go_to('https://www.scrapethissite.com/pages/ajax-javascript/#2015')

# Wait for the JS to finish loading
browser.network.wait_for_idle
browser.at_css('tr.film')

# Select all film rows
films = browser.css('tr.film')

films.each do |film|
  title = film.at_css('.film-title')&.text&.strip
  nominations = film.at_css('.film-nominations')&.text&.strip
  awards = film.at_css('.film-awards')&.text&.strip

  puts "Title: #{title}"
  puts "Nominations: #{nominations}"
  puts "Awards: #{awards}"
  puts "-----------------------------"
end

puts "Total movies found: #{films.size}"

browser.quit

The key component here is browser.network.wait_for_idle – it ensures JS/AJAX finishes loading before selecting elements, allowing the script to see the full HTML with the required content.

You can also read more about browser automation approaches using Python in our guide on Playwright Web Scraping.

2. Use a scraping API with built-in JavaScript rendering. Services like Decodo handle all the headless browser functionality for you – just send your request and get back the fully rendered HTML. For JavaScript-heavy sites, this is the path of least resistance. Here's an example script with Decodo's Web Scraping API:


require 'httparty'
require 'nokogiri'
require 'json'

url = "https://scraper-api.decodo.com/v2/scrape"

payload = {
  url: "https://www.scrapethissite.com/pages/ajax-javascript/#2015",
  headless: "html"
}

headers = {
  "Accept" => "application/json",
  "Content-Type" => "application/json",
  "Authorization" => "Basic [Your Basic Auth Token]"
}

# Make the POST request
response = HTTParty.post(url, body: payload.to_json, headers: headers)

data = JSON.parse(response.body)

# Extract the HTML content
results = data["results"]
html = results.first["content"]  # HTML string

# Parse HTML with Nokogiri
doc = Nokogiri::HTML(html)

# Scrape the movies
films = doc.css('tr.film')
films.each do |film|
  title = film.at_css('.film-title')&.text&.strip
  nominations = film.at_css('.film-nominations')&.text&.strip
  awards = film.at_css('.film-awards')&.text&.strip

  puts " #{title} | Nominations: #{nominations} | Awards: #{awards}"
end

puts "Total movies found: #{films.size}"


require 'httparty'
require 'nokogiri'
require 'json'

url = "https://scraper-api.decodo.com/v2/scrape"

payload = {
  url: "https://www.scrapethissite.com/pages/ajax-javascript/#2015",
  headless: "html"
}

headers = {
  "Accept" => "application/json",
  "Content-Type" => "application/json",
  "Authorization" => "Basic [Your Basic Auth Token]"
}

# Make the POST request
response = HTTParty.post(url, body: payload.to_json, headers: headers)

data = JSON.parse(response.body)

# Extract the HTML content
results = data["results"]
html = results.first["content"]  # HTML string

# Parse HTML with Nokogiri
doc = Nokogiri::HTML(html)

# Scrape the movies
films = doc.css('tr.film')
films.each do |film|
  title = film.at_css('.film-title')&.text&.strip
  nominations = film.at_css('.film-nominations')&.text&.strip
  awards = film.at_css('.film-awards')&.text&.strip

  puts " #{title} | Nominations: #{nominations} | Awards: #{awards}"
end

puts "Total movies found: #{films.size}"

The script only uses HTTParty, Nokogiri, and JSON gems, but can perfectly retrieve dynamic content. In addition, it uses proxies and smart restriction handling, masking your identity online, allowing you to overcome CAPTCHAs, georestrictions, and IP blocks.

While its full potential can't be seen in an example scraping website, you'll find that in real-life scenarios, proxies, headers, and user-behavior simulation are necessary for efficient web scraping.

Stop building, start scraping

Get rendered HTML, rotating proxies, and CAPTCHA handling in one API call.

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Ruby vs. other web scraping stacks

Choosing a programming language for web scraping isn't about picking "the best" tool, but also selecting the right tool for your specific use case. Ruby is excellent for many scraping tasks, but so are Python and JavaScript. Let's look at when each makes sense so you can make an informed decision instead of starting a flame war in the comments.

Ruby vs. Python for web scraping

Let's address the elephant in the room: Python dominates the web scraping world. There's a reason for that, but it doesn't mean Ruby is a bad choice.

Python's advantages

Larger ecosystem. Beautiful Soup, Scrapy, Selenium, Playwright, and dozens of specialized scraping libraries.
More tutorials and Stack Overflow answers. When you're stuck at midnight, you'll find Python solutions faster.
Data science integration. If you're scraping data to feed directly into Pandas, NumPy, or machine learning pipelines, Python is the obvious choice.
Async scraping at scale. Scrapy and async libraries make building high-performance scrapers easier.

Ruby's advantages

Better syntax for readability. Ruby code often feels more natural to read and write, especially for developers coming from Rails.
Rails integration. If your scraper needs to feed data into a Rails app, staying in Ruby eliminates language switching.
Mechanize. Ruby's Mechanize gem is arguably more intuitive than Python's equivalent for session-based scraping.
Smaller, focused scripts. For quick automation tasks, Ruby's elegance shines.

If you're building a large-scale web scraping project from scratch, Python web scraping probably offers more tools and community support. If you're already working in a Ruby codebase or need to scrape data for a Rails application, Ruby is the natural fit. Don't rewrite your entire stack just because Python has more Medium articles about scraping.

Ruby vs. JavaScript scraping stacks

JavaScript brings a different flavor to web scraping, mainly because it has native access to browser automation tools.

JavaScript/Node.js advantages

Native browser control. Puppeteer and Playwright were built for JavaScript first, making browser automation feel natural.
Same language as the target sites. Many modern websites are built with React, Vue, or Angular, so understanding JavaScript helps you reverse-engineer how they work.
Real-time scraping. Node.js excels at handling multiple concurrent requests with its async nature.
Full-stack consistency. If your entire stack is JavaScript, staying in one language has benefits.

Ruby advantages

Simpler for HTTP-based scraping. For static HTML sites, Ruby's HTTParty and Nokogiri combo is cleaner than Node's various HTTP libraries.
Better error handling. Ruby's exception handling feels more straightforward than JavaScript's callback/promise chains.
Mature scraping patterns. Mechanize provides patterns that have been refined over the years.
Less dependency chaos. Ruby gems tend to be more stable than the npm ecosystem's constant churn.

If you're scraping JavaScript-heavy pages that require extensive browser interaction, Node.js with Puppeteer or our guide on web scraping with JavaScript is worth considering. For traditional server-rendered sites or REST API scraping, Ruby is more straightforward.

Choose based on your existing stack, team expertise, and the specific sites you're scraping – not because someone on Reddit said their favorite language is "obviously superior."

Scaling web scraping with Ruby in production

Network requests can fail, servers can time out, and websites can become unavailable. Your scraper should handle these conditions without crashing. Here's what you need to add when scaling from prototype to production:

Retry logic. Network requests fail, no matter what you do. Add automatic retries with exponential backoff (wait 2s, then 4s, then 8s), so temporary failures don't kill your entire process.
Proper logging. Replace puts with a real logger that writes to files. When your scraper breaks, you'll need logs to figure out what happened.
Rate limiting. Add random delays between requests (2-5 seconds). Scraping too fast gets you blocked. Humans don't click 100 pages per minute.
Error handling. Websites change their HTML structure. Use safe navigation and fallback values so missing elements don't crash your entire Ruby script.
Scheduling. Use cron jobs, schedulers, or AWS Lambda to run your scraper automatically at scheduled intervals.
Monitoring. Track success rates, response times, and error patterns. You need to know when your scraper breaks before your boss does.

Lastly, the most significant production challenge isn't any of these – it's avoiding getting blocked. That's where proxies become essential.

Use proxy rotation to avoid blocks

Send a few hundred requests from your own IP address and watch how fast websites block you. Anti-bot systems are intelligent – they track request patterns, timing, and IP addresses. When one IP hammers their servers, they shut it down.

Proxies solve this by routing your HTTP requests through different IP addresses. Instead of looking like one bot making 1,000 requests, you look like 1,000 different users making one request each. It's the difference between suspicious behavior and regular traffic.

Of course, there are a few types. It's recommended to use residential proxies, which are particularly effective because they're real IP addresses from real internet service providers. Datacenter proxies, on the other hand, are faster and cheaper, but easier to detect and block. ISP proxies are the perfect middle ground between both, while mobile proxies are the most effective, but also come at the highest cost.

Here's a simple example that scrapes through Decodo's residential proxy network to verify your connection details:

require 'httparty'
require 'json'

# Decodo proxy configuration
proxy_host = 'gate.decodo.com'
proxy_port = 7000
proxy_user = 'username'
proxy_pass = 'password'

# Scrape through the proxy
response = HTTParty.get(
  'https://ip.decodo.com/',
  http_proxyaddr: proxy_host,
  http_proxyport: proxy_port,
  http_proxyuser: proxy_user,
  http_proxypass: proxy_pass
)

# Parse JSON response
data = JSON.parse(response.body)

ip = data['proxy']['ip']
country = data['country']['name']
city = data['city']['name']

puts "IP: #{ip}"
puts "Country: #{country}"
puts "City: #{city}"

require 'httparty'
require 'json'

# Decodo proxy configuration
proxy_host = 'gate.decodo.com'
proxy_port = 7000
proxy_user = 'username'
proxy_pass = 'password'

# Scrape through the proxy
response = HTTParty.get(
  'https://ip.decodo.com/',
  http_proxyaddr: proxy_host,
  http_proxyport: proxy_port,
  http_proxyuser: proxy_user,
  http_proxypass: proxy_pass
)

# Parse JSON response
data = JSON.parse(response.body)

ip = data['proxy']['ip']
country = data['country']['name']
city = data['city']['name']

puts "IP: #{ip}"
puts "Country: #{country}"
puts "City: #{city}"

Run this, and you'll see a different IP address than your own each time you run it – that's the proxy in action. Each request can route through a different residential IP anywhere in the world, making your Ruby web scraper virtually unblockable at scale.

Start web scraping with Ruby today

You've learned the fundamentals of web scraping with Ruby – from HTTParty and Nokogiri basics to production scaling with Decodo proxies. Start simple, build your first scraper, and expand as you need more complexity. Ruby might not be everyone's first choice when it comes to web scraping, but its elegant syntax and ease of use get the job done perfectly.

Your Ruby scraper, production-ready

Skip the infrastructure headaches and scale to millions of requests with Decodo's Web Scraping API.

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About the author

Zilvinas Tamulis

Technical Copywriter

A technical writer with over 4 years of experience, Žilvinas blends his studies in Multimedia & Computer Design with practical expertise in creating user manuals, guides, and technical documentation. His work includes developing web projects used by hundreds daily, drawing from hands-on experience with JavaScript, PHP, and Python.

Connect with Žilvinas via LinkedIn

All information on Decodo Blog is provided on an as is basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Decodo Blog or any third-party websites that may belinked therein.

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DATA COLLECTION

🐍 Python Web Scraping: In-Depth Guide 2025

Welcome to 2025, the year of the snake – and what better way to celebrate than by mastering Python, the ultimate "snake" in the tech world! If you’re new to web scraping, don’t worry – this guide starts from the basics, guiding you step-by-step on collecting data from websites. Whether you’re curious about automating simple tasks or diving into more significant projects, Python makes it easy and fun to start. Let’s slither into the world of web scraping and see how powerful this tool can be!

Zilvinas Tamulis

Feb 28, 2025

15 min read

DATA COLLECTION

PYTHON

How to Choose the Best Language for Web Scraping

Psst! Come closer to hear a secret: collecting publicly accessible data can skyrocket your business to the next level. If you unlock and gather valuable info, you can easily monitor brand reputation, compare prices, test links, analyze competitors, and much more.

While the benefits sound legit, collecting data manually can quickly become a pain in the neck. But what if we told you that it’s possible to enjoy all the advantages without any need to sweat? With automated data scraping, it’s more than possible to do so.

However, there’s one lil’ thing you may wanna know about before starting your web scraping journey. And it’s how to choose the best programming language to build a scraper for your specific projects.

James Keenan

Feb 17, 2022

7 min read

Frequently asked questions

Is Ruby good for web scraping if I am a beginner?

Ruby is a solid choice for beginners thanks to its clean, readable syntax that feels almost like writing English. You can get a basic scraper running in just a few lines of code, which makes it less intimidating than some other languages. That said, Python tends to have more scraping-specific resources and tutorials available, but if you're already comfortable with Ruby or prefer its style, you'll be productive quickly.

Which Ruby gems should I learn first for web scraping?

Start with HTTParty for making HTTP requests – it simplifies fetching web pages with minimal setup. Pair that with Nokogiri, which parses HTML and lets you extract specific elements using CSS selectors or XPath. These two gems cover most basic scraping needs. Once you're comfortable, look into Mechanize if you need to interact with forms, follow links automatically, or maintain sessions with cookies. Choose Ferrum if you need headless browser capabilities.

How do I avoid getting blocked when scraping with Ruby?

Getting blocked usually happens when websites detect automated behavior. To stay under the radar, rotate your user agent strings so your requests look like they're coming from different browsers. Add random delays between requests (a few seconds works for most sites) to mimic human browsing patterns. For larger projects or protected sites, use residential proxies that route your requests through real user IPs, making them much more challenging to detect and block.

Can I mix Ruby with other languages or AI tools when scraping?

Ruby plays well with other languages and tools. You can call external APIs directly from your Ruby scripts, which means you can send scraped data to Python-based machine learning models, Node.js services, or AI platforms without any hassle.

Web Scraping with Ruby: A Simple Step-by-Step Guide

Quick answer (TL;DR)

What is web scraping with Ruby?

How Ruby web scrapers work step-by-step

Core Ruby gems for web scraping

HTTParty

Nokogiri

Mechanize

Set up your Ruby environment

Choose your Ruby IDE

Test your setup

Build your first Ruby web scraper

Step 1: Inspect the page and define your data

Step 2: Set up your project

Step 3: Fetch HTML with Ruby

Step 4: Parse HTML with Nokogiri

Step 4: Save and reuse your data

Handling JavaScript-heavy websites in Ruby

Ruby vs. other web scraping stacks

Ruby vs. Python for web scraping

Python's advantages

Ruby's advantages

Ruby vs. JavaScript scraping stacks

JavaScript/Node.js advantages

Ruby advantages

Scaling web scraping with Ruby in production

Use proxy rotation to avoid blocks

Start web scraping with Ruby today

Related articles

Frequently asked questions

Is Ruby good for web scraping if I am a beginner?

Which Ruby gems should I learn first for web scraping?

How do I avoid getting blocked when scraping with Ruby?

Can I mix Ruby with other languages or AI tools when scraping?