How to Build a Web Scraper?

In today‘s data-driven world, web scraping has become an essential tool for businesses across all industries. With the right techniques, web scraping allows you to extract large amounts of publicly available data from websites in an automated fashion. This data can then be analyzed to find insights and trends that would be impossible to detect manually.

In this comprehensive guide, we will walk through the key steps involved in building a web scraper from scratch using Python and JavaScript.

What is Web Scraping?

Web scraping refers to the automated collection of data from websites through scripts or bots. It works by sending HTTP requests to target sites, then extracting information from the HTML, XML or JSON response. The scraper locates and extracts specific pieces of data and stores them in a structured format.

Some common examples of web scraping include:

• Price monitoring – Track prices for products across e-commerce sites. This allows businesses to adjust their prices based on competition.
• Lead generation – Build marketing lists by scraping contact details from directories and yellow pages sites.
• News monitoring – Automatically aggregate news articles from different sources.
• Research – Gather data from public databases for analysis.
• SEO monitoring – Check rankings for target keywords across search engines.

Web scrapers can extract all kinds of data – text, images, documents, media files and more. The main benefit is the ability to gather large volumes of data that would take weeks or months to collect manually.

Is Web Scraping Legal?

The short answer – it depends.

Web scraping itself is legal in many jurisdictions. However, how you scrape and what you do with the extracted data is subject to laws and terms of use. Make sure to review the robots.txt and terms & conditions of any site before scraping. Avoid aggressive scraping that may overload servers.

Generally, scraping public data in a non-disruptive way for non-commercial use is permissible. The legal waters get murky when you use scrapers for commercial purposes without permission. Whenever in doubt, it‘s best to seek legal counsel.

Prerequisites for Building a Web Scraper

Before writing your first web scraper, you need a basic understanding of the following:

• HTTP – Web scraping relies on sending HTTP requests and processing responses. Familiarize yourself with request methods, response codes and common headers.
• HTML – Most of the data is extracted from HTML tags and attributes. Learn about page structure, tags, attributes and using browser inspect tools.
• CSS Selectors – For identifying specific HTML elements to extract data from. You‘ll be relying heavily on CSS ids, classes, attributes and hierarchies.
• JavaScript – Some sites load content dynamically via JS. You may need to reverse engineer where the data is coming from.
• APIs – Many sites offer APIs to access data. This is the preferred method over scraping.
• Cloud servers – Scrapers are often hosted on cloud servers to handle load. Know basics of cloud computing and services like AWS, Google Cloud, Azure.

These foundations will help you write robust scrapers and deal with complex sites. Let‘s now see how to put together a web scraper.

How to Build a Web Scraper in Python

Python is one of the most popular languages for web scraping due to its simplicity and vast libraries. We‘ll use two excellent Python modules – Requests and Beautiful Soup.

Step 1 – Send HTTP Requests

The first step is to send an HTTP request to the target page and fetch the raw HTML content. For this, we use the Requests module.

import requests

url = ‘http://example.com‘
response = requests.get(url)
html_content = response.text

We import Requests, specify the URL, use requests.get() to send a GET request and get the HTML response.

Requests also allows you to:

• Set custom headers like user agents
• Make POST and PUT requests with payloads
• Handle response codes, errors and exceptions

Step 2 – Parse HTML

Now that we have the raw HTML content, we need to parse it to extract relevant info. This is done using Beautiful Soup module.

We initialize Beautiful Soup with the HTML content and specify the parser:

from bs4 import BeautifulSoup

soup = BeautifulSoup(html_content, ‘html.parser‘) 

This converts the HTML into a navigable BeautifulSoup object that we can query to locate elements.

Step 3 – Extract Data

With the parsed HTML, we can now select elements and extract text or attributes as needed.

Beautiful Soup offers several options to find and select elements:

# Find by CSS class
soup.find(‘div‘, class_=‘product-listing‘)

# Find by tag name
soup.find(‘img‘) 

# Find by id
soup.find(id=‘sidebar‘)

# Find all occurrences 
products = soup.find_all(‘div‘, class_=‘product‘) 

# Use CSS selectors
products = soup.select(‘#main-content .product‘)

We can also traverse the parse tree:

# Go up to parent node
product_row = soup.find(‘div‘, class_=‘product‘)
product_container = product_row.parent

# Get child nodes
product_img = product_row.img

# Get siblings
next_row = product_row.next_sibling

Finally, we extract the text or attribute values:

page_title = soup.title.text

image_src = soup.find(‘img‘).get(‘src‘)

product_prices = []
for row in soup.find_all(‘div‘, class_=‘product‘):
  price = row.find(‘span‘, class_=‘price‘).text
  product_prices.append(price)

And that‘s really all there is to extracting data from HTML using Beautiful Soup in Python. The learning curve is quite short and capabilities quite extensive.

Step 4 – Store Scraped Data

For most scrapers, the end goal is to store extracted data somewhere for further processing. Here are some options for storage:

• CSV – Comma separated values file for tabular data
• JSON – Lightweight JSON format to serialize and store structured data
• Databases – Save directly to cloud databases like MongoDB or SQL
• Excel – Populate spreadsheets with Pandas dataframes
• API – Push to internal APIs to post-process and aggregate

Let‘s take a simple example of writing scraped data to a CSV file:

import csv 

# Open/create a CSV file 
with open(‘products.csv‘, ‘w‘) as csvfile:

  # Define column names 
  fieldnames = [‘title‘, ‘price‘, ‘stock‘]

  # Create CSV writer
  writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

  # Write column headers
  writer.writeheader()

  # Write scraped data as rows
  for product in products:
    writer.writerow({
      ‘title‘: product.title.text,
      ‘price‘: product.find(‘span‘, class_=‘price‘).text,
      ‘stock‘: product.stock_status.text
    })

This saves our scraped data in a structured CSV format for easy analysis and processing.

And we‘re done! With just a few lines of Python using Requests and Beautiful Soup, we were able to build a complete web scraper.

How to Build a Web Scraper in JavaScript

For JavaScript-based scrapers, we will use Axios for sending HTTP requests and Cheerio for parsing HTML.

Step 1 – Send HTTP Request

We use Axios to make the GET call and fetch the response:

const axios = require(‘axios‘);

const url = ‘http://example.com‘;

axios.get(url)
  .then(response => {
    // response handling
  })
  .catch(error => {
    // error handling
  });

Axios allows you to handle success and error scenarios easily. It also supports custom headers, POST requests and response timeouts.

Step 2 – Parse HTML

To parse the HTML, we use the Cheerio library – the JavaScript equivalent of Beautiful Soup.

Load the HTML into Cheerio:

const cheerio = require(‘cheerio‘);

const $ = cheerio.load(html);

This gives us a jQuery-like interface to query the document.

Step 3 – Extract Data

Cheerio uses the same CSS selectors for parsing and extracting data:

// By id
const siteHeader = $(‘#header‘);

// By class
const alertBanners = $(‘.alert‘);

// By tag
const images = $(‘img‘);

// Find 
const prices = $(‘.product‘).find(‘.price‘);

// Text
const pageTitle = $(‘h1‘).text();

// Attribute 
const imageSources = $(‘img‘).attr(‘src‘);

We can traverse and manipulate DOM elements like regular jQuery. Cheerio makes data extraction simple and intuitive.

Step 4 – Store Scraped Data

To save scraped data in Node.js, we can use the following libraries:

• fs – To write to files
• json2csv – To export JSON to CSV
• MongoDB – NoSQL database to store documents
• MySQL – Popular relational database

This example writes a JSON array to a file:

const fs = require(‘fs‘);

const products = // scraped array 

const json = JSON.stringify(products);

fs.writeFile(‘products.json‘, json, (err) => {
  if (err) throw err;

  console.log(‘File saved‘);  
}); 

And we have built a complete scraper with Axios + Cheerio! The process is quite similar to Python.

Dealing with JavaScript Heavy Sites

Modern sites are increasingly shifting towards client-side JavaScript for rendering content. The initial HTML served may exclude the actual data, which is later injected via JS by the browser.

This poses a problem for scrapers, since we won‘t find the data we need in the initial HTML response.

Here are a couple of approaches to deal with JavaScript rendered sites:

• Browser Automation – Use Puppeteer (Node) or Selenium (Python) to drive an actual headless browser which will execute JS and allow scraping rendered HTML.
• API Reverse Engineering – Many sites fetch data from internal JSON APIs. Use proxy tools like Charles to inspect traffic and identify these endpoints.
• Rendering Service – Services like ScraperAPI and ProxyCrawl fetch via browsers and then expose APIs to deliver rendered HTML.

So in summary, if the data is not in the initial HTML, look for APIs or use a browser via automation tools or rendering services.

Overcoming Anti-Scraping Mechanisms

Large sites employ a variety of anti-scraping mechanisms to block bots and automation tools:

• Blocking IPs – Sites may blacklist your server IP if they detect scraping activity. Use proxies and rotation to avoid this.
• CAPTCHAs – Special puzzles to determine if request is from a human. Requires solving via automation or captcha solving services.
• User-agent Checks – Target sites may block requests with non-browser user agents. Configure scrapers to spoof real desktop and mobile browser agents.
• Speed Limits – Limits on concurrent connections or requests per second. Introduce delays between requests and use proxies to distribute load.
• Legal Threats – Cease and desist notices prohibiting all scraping activity. Seek legal counsel if you receive any such threats.
• Honeypots – Fake pages and elements designed to lure scrapers. Identify and filter out any honeypots return by your code.

With robust proxy rotation, keyword analysis and dealing with anti-scraping traps can help evade blocks. But if any legal concerns are raised, it‘s best to stop scraping those sites.

Web Scraping Best Practices

Here are some tips to ensure your web scraping activities are efficient, ethical and legal:

• Rigorously follow robots.txt directives and respect sites that forbid scraping.
• Limit request rate to avoid overloading target sites. Introduce delays if needed.
• Check Terms of Use – don‘t scrape sites that prohibit automated data collection.
• Avoid scraping data protected by copyright, licenses or with usage restrictions.
• Use proxies and randomized headers to distribute load and avoid blocks.
• Store extracted data securely to prevent unauthorized access.
• Seek permission whenever feasible before scraping commercial sites at scale.
• If asked to stop scraping a particular site, comply immediately.
• Use official APIs if available instead of scraping to access data.
• Scrape ethically and stay on the right side of anti-spam and hacking laws.

Adhering to these principles will keep your scrapers running smoothly and avoid potential legal troubles down the line.

Conclusion

In this detailed guide, we looked at what web scraping is, its common uses cases and legal standing. We covered the prerequisites for building a scraper and walked through a practical scraping example in both Python and JavaScript. Finally, we discussed how to overcome common challenges like JavaScript rendering and anti-scraping mechanisms.

The takeaways are:

• Web scraping is a powerful data harvesting technique to automate the extraction of large volumes of data from websites.
• Employing modules like Requests, Beautiful Soup, Axios and Cheerio make it easy to build full-featured scrapers with just a few lines of code.
• Deal with modern JavaScript heavy sites by identifying APIs or using browser automation tools like Selenium and Puppeteer.
• Overcome anti-scraping protections with robust proxies, randomization and appropriate delays.
• Follow ethical scraping practices and always respect site‘s terms of use.

Scraping opens up many possibilities for data-driven businesses. With this guide, you should be well equipped to start building scrapers tailored to your specific needs and extract exciting datasets from this vast web of information.