Building a Price Comparison Engine: Web Scraping with Java and Node.js in 2026

Introduction: Why Price Comparison Websites Matter in 2026

In 2026, consumers rely on price comparison tools more than ever. With inflation and supply chain shifts, shoppers compare prices across multiple retailers before buying. As part of the CST 3130 coursework, you'll build a price comparison website that scrapes product data from several sources using Java, stores it in a SQL database, and serves it via a Node.js REST API. This tutorial walks you through the key components, from multithreaded scraping to frontend display, without giving away the full assignment solution.

Project Overview and Key Requirements

Your price comparison website must allow users to search for products and see prices from different websites. You'll need to scrape data using Java with Spring and Hibernate, store it in MySQL, and build a REST API with Node.js. The frontend uses HTML, CSS, and JavaScript. You'll also create a video demonstration and submit a project report. The assignment emphasizes code quality, unit testing, and the quantity of scraped data.

1. Database Design with SQL

Start by designing your database schema. For a price comparison site, you'll need tables for products, retailers, prices, and categories. Use MySQL or MariaDB. Here's an example SQL snippet:

CREATE TABLE products (
  id INT AUTO_INCREMENT PRIMARY KEY,
  name VARCHAR(255) NOT NULL,
  description TEXT,
  category_id INT,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE retailers (
  id INT AUTO_INCREMENT PRIMARY KEY,
  name VARCHAR(100) NOT NULL,
  base_url VARCHAR(255)
);

CREATE TABLE prices (
  id INT AUTO_INCREMENT PRIMARY KEY,
  product_id INT,
  retailer_id INT,
  price DECIMAL(10,2),
  url VARCHAR(500),
  scraped_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
  FOREIGN KEY (product_id) REFERENCES products(id),
  FOREIGN KEY (retailer_id) REFERENCES retailers(id)
);

This schema allows you to store multiple prices per product from different retailers. Include indexes on frequently queried columns like product_id and price to improve performance.

2. Multithreaded Web Scraping with Java

Use Java with Spring Boot and Hibernate to scrape data. The assignment requires multithreading to download data from multiple websites simultaneously. You can use ExecutorService to manage a thread pool. For example:

ExecutorService executor = Executors.newFixedThreadPool(5);
List<Future<List<Product>>> futures = new ArrayList<>();
for (String url : targetUrls) {
    Callable<List<Product>> scraper = new ScraperTask(url, productRepository);
    futures.add(executor.submit(scraper));
}
executor.shutdown();

Each ScraperTask parses HTML using Jsoup or a similar library, extracts product names, prices, and URLs, then saves them via Hibernate. Remember to handle errors gracefully and respect robots.txt.

3. Building the REST API with Node.js

The backend API must be in Node.js. Use Express to create endpoints for searching products and retrieving price comparisons. Your API should return JSON. Example endpoint:

app.get('/api/products', async (req, res) => {
    const { query } = req.query;
    const products = await db.query('SELECT * FROM products WHERE name LIKE ?', ['%' + query + '%']);
    res.json(products);
});

Connect to your MySQL database using a library like mysql2 or an ORM like Sequelize. Make sure to implement pagination and error handling.

4. Frontend: Displaying Data with JavaScript

The frontend is pure HTML, CSS, and JavaScript. Use fetch API to call your Node.js backend and display results. For a modern feel, you can use a lightweight framework like Vue.js (if allowed) or vanilla JS. Example:

async function searchProducts(query) {
    const response = await fetch(`/api/products?query=${encodeURIComponent(query)}`);
    const products = await response.json();
    // Render products in a table or card layout
}

Include sorting and filtering options. The user should be able to click a link to go to the original product page.

5. Unit Testing and Code Quality

Write unit tests for your Java scraper and Node.js API. Use JUnit for Java and Mocha/Jest for Node.js. Test edge cases like empty results, network errors, and malformed HTML. Code quality is part of the mark—use consistent formatting, meaningful variable names, and comments where necessary.

6. Video Demonstration and Report

Your final submission must include a 5-minute video demonstrating the website's functionality. Show the scraping process, database content, API responses, and frontend interaction. The project report should include screenshots, database diagram, and test documentation.

Trend Example: Price Comparison for AI Gadgets

Imagine you're comparing prices for the latest AI-powered smart glasses in 2026. Your scraper collects data from tech retailers like Amazon, Best Buy, and Newegg. The multithreaded approach ensures you get prices quickly. The Node.js API serves this data to your frontend, where users can sort by price or retailer. This real-world scenario mirrors the coursework requirements and demonstrates the value of your project.

Conclusion

Building a price comparison website is a great way to learn full-stack development with big data scraping. Focus on clean architecture, efficient scraping, and a user-friendly interface. Good luck with your CST 3130 coursework!