Uber Eats Scraper PPR in NodeJS and SQLite

Uber Eats Scraper PPR in NodeJS and SQLite

In the digital age, data is king. Businesses and developers alike are constantly seeking ways to harness the power of data to drive decision-making and innovation. One such avenue is web scraping, a technique used to extract information from websites. In this article, we will explore how to create an Uber Eats scraper using NodeJS and SQLite, providing a comprehensive guide to building a powerful data extraction tool.

Understanding Web Scraping

Web scraping is the process of automatically extracting data from websites. It involves fetching the HTML of a webpage and parsing it to extract the desired information. This technique is widely used for various purposes, including market research, price comparison, and data analysis.

However, web scraping must be done responsibly and ethically. It’s important to respect the terms of service of the website being scraped and ensure that the scraping process does not overload the server or violate any legal regulations.

Why Use NodeJS for Web Scraping?

NodeJS is a popular choice for web scraping due to its asynchronous nature and non-blocking I/O operations. This makes it highly efficient for handling multiple requests simultaneously, which is crucial when scraping large amounts of data from websites like Uber Eats.

Additionally, NodeJS has a rich ecosystem of libraries and tools that simplify the web scraping process. Libraries like Axios for HTTP requests and Cheerio for HTML parsing make it easy to build a robust scraper in NodeJS.

Setting Up the Environment

Before we dive into the code, let’s set up our development environment. First, ensure that you have NodeJS and npm (Node Package Manager) installed on your machine. You can download them from the official NodeJS website.

Next, create a new directory for your project and navigate into it using the terminal. Initialize a new NodeJS project by running the following command:

npm init -y

This will create a package.json file, which will manage your project’s dependencies.

Installing Required Packages

To build our Uber Eats scraper, we need to install a few packages. Run the following command to install Axios, Cheerio, and SQLite3:

npm install axios cheerio sqlite3

Axios will be used to make HTTP requests, Cheerio will help us parse the HTML, and SQLite3 will serve as our database to store the scraped data.

Building the Uber Eats Scraper

Now that we have our environment set up, let’s start building the scraper. Create a new file named scraper.js and open it in your preferred code editor.

First, import the required modules:

const axios = require('axios');
const cheerio = require('cheerio');
const sqlite3 = require('sqlite3').verbose();

Next, set up a connection to the SQLite database:

const db = new sqlite3.Database('./ubereats.db', (err) => {
  if (err) {
    console.error('Error opening database ' + err.message);
  } else {
    console.log('Connected to the SQLite database.');
  }
});

Create a table to store the scraped data:

db.run(`CREATE TABLE IF NOT EXISTS restaurants (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  name TEXT,
  address TEXT,
  rating REAL
)`);

Now, let’s write a function to scrape data from Uber Eats:

async function scrapeUberEats() {
  try {
    const response = await axios.get('https://www.ubereats.com/location');
    const $ = cheerio.load(response.data);

    $('div.restaurant').each((index, element) => {
      const name = $(element).find('h2.name').text();
      const address = $(element).find('p.address').text();
      const rating = parseFloat($(element).find('span.rating').text());

      db.run(`INSERT INTO restaurants (name, address, rating) VALUES (?, ?, ?)`, [name, address, rating], (err) => {
        if (err) {
          console.error('Error inserting data ' + err.message);
        }
      });
    });

    console.log('Scraping completed.');
  } catch (error) {
    console.error('Error fetching data ' + error.message);
  }
}

Finally, call the scrapeUberEats function to start the scraping process:

scrapeUberEats();

Conclusion

In this article, we explored how to build an Uber Eats scraper using NodeJS and SQLite. We covered the basics of web scraping, set up our development environment, and wrote a complete scraper to extract restaurant data from Uber Eats. By leveraging the power of NodeJS and SQLite, we created a robust and efficient tool for data extraction.

Remember, web scraping should always be done ethically and responsibly. Ensure that you comply with the website’s terms of service and legal regulations. With the knowledge gained from this article, you can now explore further enhancements to your scraper, such as adding more data fields, implementing error handling, or scheduling regular scraping tasks.