Web Scraping with Node-Fetch

The Fetch API’s introduction changed how JavaScript developers handle HTTP requests. Now, developers don’t need to use third-party packages to make an HTTP request. This is especially good news for front-end developers because the fetch function is only for browser use.

Backend developers used to need different third-party packages until node-fetch was introduced. Node-fetch brings the same fetch API functionality to the backend that browsers have. In this article, we’ll look at how you can use node-fetch for web scraping.

Prerequisites

Before you start, make sure you meet all the following requirements:

You’ll need Node.js installed on your machine. Download it from the official Node.js download page. This tutorial uses Node.js version 20.12.2 with npm 10.7.0, the latest LTS version at the time of writing.
You’ll need a code editor like VS Code or Atom installed on your machine.
You should have experience writing ES6 JavaScript, understanding promises, and async/await.

Understanding the node-fetch

The Fetch API provides an interface for making asynchronous HTTP requests in modern browsers. It is based on promises, making it a powerful tool for working with asynchronous code.

Node Fetch is a widely used lightweight module that brings the Fetch API to Node.js. It allows you to use the fetch function in Node.js as you would use window.fetch in native JavaScript but with a few differences.

One can connect to remote servers and fetch or post data from an external web server or API, making it a suitable tool for various tasks, including Node.js web scraping.

Read More: Web Scraping with Node.Js

How to use node-fetch?

To use node-fetch, the first step is to install it by executing the following command:

				
					 npm install node-fetch

Important Note: From version 3.0, node-fetch is an ESM-only module. This means you can no longer import it with require(). If you cannot switch to ESM, it’s advised to stay on version 2.0 instead of the latest one, in which case you can use the standard require() syntax.

				
					npm install node-fetch@2

Below is a simple code that sends an HTTP GET request to the GitHub homepage URL.

				
					import fetch from 'node-fetch';

const response = await fetch('<https://github.com/>');
const body = await response.text();

console.log(body);

Later in the following sections, you’ll learn in more detail how to use node-fetch for web scraping.

Basics of Cheerio

Cheerio is a lightweight library that allows you to extract data from web pages using CSS-style selectors. Cheerio can load HTML as a string and return an object for you to extract data using its built-in methods. But before you start scraping a website, it’s important to understand the basics of Cheerio. This includes loading markup, selecting elements using CSS-style selectors, and looping through a list of elements. In Cheerio, you can easily load an HTML or XML document using the load() function. This function takes a string containing the HTML as an argument and returns an object.

				
					import cheerio from 'cheerio';

const htmlMarkup = `<html>
  <head>
    <title>Cheerio Demo</title>
  </head>
  <body>
    <p>Hello, developers!</p>
  </body>
</html >`;

const $ = cheerio.load(htmlMarkup);

Cheerio makes it easy to select elements using CSS-style selectors. It allows you to target elements based on their tag, class, and attribute values. For example, to select all < p> tags in the document, you can use the following selector:

				
					import cheerio from 'cheerio';

const htmlMarkup = `
  <html>
    <head>
      <title>Cheerio Demo</title>
    </head>
    <body>
      <p>Hello, developers.</p>
      <p>Coding is fun.</p>
      <p>Code all day and night.</p>
    </body>
  </html>
`;

const $ = cheerio.load(htmlMarkup);

const $paragraphs = $('p');
console.log($paragraphs.text());

Use .each() and .map() methods to iterate over elements selected with Cheerio. The code below uses .each() to loop over the elements.

				
					import cheerio from "cheerio";

const htmlMarkup = `
  <body>
    <h3 class="country-list">List of Countries:</h3>
    <ul>
      <li class="country">India</li>
      <li class="country">Canada</li>
      <li class="country">UK</li>
      <li class="country">Australia</li>
    </ul>
  </body>`;

const $ = cheerio.load(htmlMarkup);

const listItems = $('li');
listItems.each((index, element) => {
  console.log($(element).text());
});

Similarly, you can perform many other operations like traversing the DOM, selecting elements using regular expressions, and filtering elements.

Building a Simple Web Scraper

Let’s build a web scraper in Node.js using Node-fetch and Cheerio. We’ll target the Scrapingdog homepage and use these libraries to select HTML elements, retrieve data, and convert it into a useful format.

Let’s look at how to scrape data using Node-fetch and Cheerio.

Step 1: Initial Setup

To begin with, you need to create a folder for your Node.js web scraping project. You can do this by running the following command:

				
					mkdir nodejs-webscraper

You should now have an empty nodejs-webscraper directory. Now, enter the directory with:

				
					cd nodejs-webscraper

Now, initialize an npm project with:

				
					npm init -y

The nodejs-webscraper folder should now contain a package.json file that looks like this:

Now, create an index.js file in the root folder of your project.

Step 2: Install node-fetch and Cheerio

Install the dependencies required for the Node.js web scraper: cheerio and node-fetch.

				
					npm install node-fetch cheerio

Next, you need to specify the module format – we’ll use ES modules in this tutorial because it allow you to use modern JavaScript features, like await, that will come in handy later on. To specify the format, we’ll head over to the package.json and add the following field:

				
					"type": "module",

Let’s add it:

Then, include cheerio and node-fetch in your project:

				
					// index.js

import fetch from 'node-fetch';
import cheerio from 'cheerio';

Step 3: Download your target website

Now, use node-fetch to connect to your target website.

				
					const response = await fetch('<https://www.scrapingdog.com/>', {
    method: 'GET'
});

This code snippet fetches the HTML content of the website “Scrapingdog”. The fetch function returns a Promise, and await is used to pause execution until the Promise resolves. This ensures the response variable receives the response object before proceeding. Also, the request method is set to ‘GET’, the most common method for retrieving data from a server.

Step 4: Inspect the HTML page

If you visit the Scrapingdog homepage, you’ll see a list of dedicated scraper APIs for web scraping offered by Scrapingdog. Right-click on one of these HTML elements and select “Inspect”:

These APIs are organized into sections, each representing a specific scraper. Each section is contained within a <div> element with the class col. Within each section, you’ll find a title (using the <strong> element) and a description (using the <p> element).

Similarly, you can scrape additional data, such as:

Information about satisfied customers.
Insights into how customers use Scrapingdog.

Step 5: Select HTML elements with Cheerio

Cheerio loads HTML as a string and returns an object for data extraction using its built-in methods. It offers several ways to select HTML elements, including tag, class, and attribute value.

You can load the HTML using the load function. This function takes a string containing the HTML as an argument and returns an object. The resulting object is assigned to the variable $, which is a common convention used to refer to jQuery objects in JavaScript. In Cheerio, it acts as a reference to the parsed HTML document.

				
					const $ = cheerio.load(html);

Now you can manipulate the HTML by calling methods on the $ object provided by Cheerio. To select elements with class names, use the . (dot) followed by the class name selector. For example, if you want to get elements with the class name “hero-title”, use the selector .hero-title. In Cheerio, the .text() method is used to extract the text content of an HTML element.

				
					const title = $('.hero-title').text();

This is how you can select elements using class names. Similarly, you can use tags (like div or p) or attributes (like id) to select elements. Now, let’s learn how to use Cheerio to extract data from the desired HTML elements.

Step 6: Scrape data from a target webpage with Cheerio

You can expand the above logic to extract the desired data from a webpage. First, let’s see how to scrape all dedicated scraper APIs for web scraping.

The hero-title class holds the title. Each col class represents a section with details about a Scraper API. It contains a strong element (<strong>) for the API title and a paragraph (<p>) for the description.

Here’s the sample code snippet to target these selectors:

				
					const scraperAPIs = [];

const title = $('.hero-title').text().trim();

$('.col').each((index, element) => {
    const api = {};
    api.title = $(element).find('strong').text().trim();
    api.description = $(element).find('p').text().trim();
    scraperAPIs.push(api);
});

The .hero-title selector targets elements with the class hero-title, which likely contains subtitles. The .text() method extracts the text content of these elements.

The .col selector targets elements with the class col, representing individual sections with information about different Scraper APIs. Here, the .each() method iterates over each selected element. Within this loop, .find('strong') selects the <strong> element within each .col, typically containing the API title. Similarly, .find('p') selects the <p> element containing the API description.

The .find method helps us to further filter out the selected elements based on any selector. It searches for matching descendant elements at any level below the selected element.

The result of extracted data would be:

Similarly, you can extract the data on how customers use Scrapingdog. For this, you have to extract data from each < div> element within the class landing-overview. Then iterate through each < div> and retrieve the title from the < h3> tag and the description from the < p> tag.

Here’s the code snippet:

				
					const customerUsageData = [];

$('.landing-overview div').each((index, element) => {
    const titleElement = $(element).find('h3');
    const title = titleElement.text().trim();

    if (titleElement.length && title.length > 0) {
        const descriptionElement = $(element).find('p');
        const description = descriptionElement.text().trim();

        customerUsageData.push({ title, description });
    }
});

This code snippet extracts data from each <div> element within the class landing-overview. It iterates through each <div> and retrieves the title from the <h3> tag and the description from the <p> tag. If both the title and description are found, they are pushed into the “customerUsageData” array.

The result is:

Lastly, you can extract the data of satisfied customers of scrapingdog. For this, you have to fetch the absolute paths of images (< img> elements) that are descendants of < div> element with the class interstitial-companies.

Here’s the code snippet:

				
					const satisfiedCustomersData = [];

$('div.interstitial-companies img').each((index, element) => {
    const relativePath = $(element).attr('src');
    const absolutePath = new URL(relativePath, url).href;
    satisfiedCustomersData.push(absolutePath);
});

The result is:

Congrats! You have successfully extracted the desired data from the Scrapingdog home page.

Step 7: Convert the extracted data to JSON

JSON is one of the best data formats for storing scraped data on your local machine. You can use the built-in fs module in Node.js. It allows you to interact with the computer’s file system. Here’s how you can modify the code to write the data to a JSON file:

				
					const data = {
  customerUsage: customerUsageData,
  scraperAPI: scraperAPIData,
  satisfiedCustomers: satisfiedCustomersData
};

const jsonData = JSON.stringify(data); // Convert the object to JSON format

fs.writeFile('data.json', jsonData, () => {
  console.log('Data written to the file.');
});

First, you need to create a JavaScript object to hold all the scraped data. Then, you can convert that object into JSON format using JSON.stringify(). The writeFile function takes three arguments: the filename, the data you want to write, and an optional function (called a callback) that gets executed after the writing is complete.

Here’s the JSON data stored in a data.json file:

Great! Now the data has been stored in the JSON file.

Putting it all together

This is what our complete Node.js web scraper looks like:

				
					import fetch from 'node-fetch';
import cheerio from 'cheerio';
import fs from 'fs';

async function customerUsage(url) {
    const response = await fetch(url);
    const html = await response.text();
    const $ = cheerio.load(html);
    const customerUsageData = [];

    $('.landing-overview div').each((index, element) => {
        const titleElement = $(element).find('h3');
        const title = titleElement.text().trim();

        if (titleElement.length && title.length > 0) {
            const descriptionElement = $(element).find('p');
            const description = descriptionElement.text().trim();

            customerUsageData.push({ title, description });
        }
    });

    return customerUsageData;
}

async function scraperAPI(url) {
    const response = await fetch(url);
    const html = await response.text();
    const $ = cheerio.load(html);
    const scraperAPIs = [];

    const title = $('.hero-title').text().trim();
    $('.col').each((index, element) => {
        const api = {};
        api.title = $(element).find('strong').text().trim();
        api.description = $(element).find('p').text().trim();
        scraperAPIs.push(api);
    });

    return { title, scraperAPIs };
}

async function satisfiedCustomers(url) {
    const response = await fetch(url);
    const html = await response.text();
    const $ = cheerio.load(html);

    const satisfiedCustomersData = [];

    $('div.interstitial-companies img').each((index, element) => {
        const relativePath = $(element).attr('src');
        const absolutePath = new URL(relativePath, url).href;
        satisfiedCustomersData.push(absolutePath);
    });

    return satisfiedCustomersData;
}

async function writeDataToJsonFile() {
    const url = '<https://www.scrapingdog.com/>';
    const customerUsageData = await customerUsage(url);
    const scraperAPIData = await scraperAPI(url);
    const satisfiedCustomersData = await satisfiedCustomers(url);

    const data = {
        customerUsage: customerUsageData,
        scraperAPI: scraperAPIData,
        satisfiedCustomers: satisfiedCustomersData
    };

    fs.writeFile('data.json', JSON.stringify(data, null, 2), (err) => {
        if (err) {
            console.error('Error writing JSON file:', err);
        } else {
            console.log('Data has been written to data.json');
        }
    });
}

writeDataToJsonFile();

As shown here, Node.js web scraping allows you to download and parse HTML web pages, extract their data, and convert it into a structured JSON format – all with the help of libraries like Cheerio and node-fetch.

Finally, launch your web scraper with:

				
					npm run start

Handling Exceptions and Errors

Your requests can sometimes fail for a variety of reasons, including small errors within the fetch() function, internet connectivity issues, server errors, and so on. You need a way to handle or detect these errors.

You can handle runtime exceptions by adding catch() at the end of the promise chain. Let’s add a simple catch() function to the code:

				
					fetch(url)
  .then(response => response.json())
  .then(data => console.log('Data:', data))
  .catch(error => console.error('Error:', error));

It is not recommended to ignore and print errors, but instead, have a mechanism to handle them effectively. One way to ensure failed requests throw an error is to check the HTTP status of the server’s response.

If the status code doesn’t indicate success (codes outside the 2xx range), you can throw an error, and .catch() will catch it. You can use the ok field of Response objects, which equals true if the status code is in the 2xx range.

				
					fetch(url)
  .then(res => {
    if (res.ok) {
      return res.json();
    } else {
      throw new Error(`The HTTP status of the response: ${res.status} (${res.statusText})`);
    }
  })
  .then(data => {
    console.log('Data:', data);
  })
  .catch(error => {
    console.error('Error:', error);
  });

Now, let’s try to implement this approach in one of the functions we created above.

				
					async function customerUsage(url) {
  return fetch(url)
    .then(response => {
      if (!response.ok) {
        throw new Error('Network response was not ok');
      }
      return response.text();
    })
    .then(html => {
      const $ = cheerio.load(html);
      const customerUsageData = [];

      $('.landing-overview div').each((index, element) => {
        const titleElement = $(element).find('h3');
        const title = titleElement.text().trim();

        if (titleElement.length && title.length > 0) {
          const descriptionElement = $(element).find('p');
          const description = descriptionElement.text().trim();

          customerUsageData.push({ title, description });
        }
      });

      return customerUsageData;
    })
    .catch(error => {
      console.error('There was a problem with the fetch operation:', error);
    });
}

The code uses fetch(URL) to make an asynchronous HTTP request to the specified URL. It returns a Promise that resolves to the response. The .then() method is chained to handle the response. Inside this block, the status code is checked using response.ok. If the status code isn’t in the 200-299 range (success), an error is thrown.

Following the .then() block, the .catch() method catches any errors during the fetch or subsequent Promise chain. If an error occurs, this block logs it to the console using console.error().

Avoiding Blocks

Note that, websites often implement anti-scraping measures to prevent unauthorized data collection. A common method involves blocking requests that lack a valid User-Agent HTTP header and the use of proxies. To bypass these anti-bot mechanisms, ensure you use a real user agent and proxies.

For example, Node-Fetch includes the following user-agent in the request it sends:

				
					"User-Agent": "node-fetch"

This user agent identifies requests made by the Node-Fetch library, which makes it easy for websites to block scraping.

Setting a fake user-agent in Node-Fetch is simple. You can create an options object with the desired user-agent string in the headers parameter and pass it to the fetch request.

				
					const options = {
  method: "GET",
  headers: {
    'User-Agent': 'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/38.0.2125.111 Safari/537.36'
  }
};

Here’s the code:

				
					import fetch from 'node-fetch';

(async () => {
    const url = '<http://httpbin.org/headers>';
    const options = {
        method: 'GET',
        headers: {
            'User-Agent': 'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/38.0.2125.111 Safari/537.36'
        }
    };

    try {
        const response = await fetch(url, options);
        const data = await response.json();
        console.log(data);

    } catch (error) {
        console.log('error', error);
    }

})();

Using proxies with the Node-fetch library allows you to distribute your requests over multiple IP addresses, making it harder for websites to detect and block your web scrapers.

However, Node-fetch does not natively support proxies. There is a workaround, though: you need to integrate a proxy server using the HTTPS-proxy-agent library. Install it using the command below:

				
					npm install https-proxy-agent

Once installed, you need to create a proxy agent using HttpsProxyAgent and pass it into your fetch request using the agent parameter.

				
					import fetch from 'node-fetch';
import { HttpsProxyAgent } from 'https-proxy-agent';

(async () => {
    const targetUrl = '<https://httpbin.org/ip?json>';
    const proxyUrl = '<http://20.210.113.32:80>';

    const agent = new HttpsProxyAgent(proxyUrl);
    const response = await fetch(targetUrl, { agent });
    const data = await response.text();

    console.log(data);
})();

Depending on your specific needs, setting up a Node-fetch proxy may not be enough to avoid being blocked. If you make too many requests from the same IP address, you could get blocked or banned.

One way to avoid this is by rotating proxies, which means each request will appear to come from a different IP, making it difficult for websites to detect. However, manually rotating proxies has its drawbacks. You will need to create and maintain an updated list yourself.

Also, when you are setting user-agents to your scraper, the same principle applies!

Scrapingdog simplifies web scraping. You no longer need to manually configure proxy rotation using open-source libraries which are less updated. Scrapingdog handles all of this for you with its user-friendly web scraping API. It takes care of rotating proxies, headless browsers, and CAPTCHAs, making the entire process effortless.

Conclusion

Node-fetch is great for simple web scraping tasks, however, it gets difficult to scrape when single Page Applications use Javascript. One of the benefits of using node-fetch is that it’s more efficient than using a headless browser. Scraping the web is challenging given that anti-scraping mechanisms are getting smart. It is a tedious task, to bypass it. Scrapingdog can allow you to web scrape without getting blocked in the long run.

Additional Resources

Web Scraping with Scrapingdog

Scrape the web without the hassle of getting blocked

My name is Manthan Koolwal and I am the founder of scrapingdog.com. I love creating scraper and seamless data pipelines.

Manthan Koolwal

Web Scraping with Scrapingdog

Scrape the web without the hassle of getting blocked

Recent Blogs

How To Scrape Flipkart Data using Python

Flipkart is an Indian ecommerce brand and has a huge data for different types of products. In this guide, we have extracted data from it using Python.

2024-12-03

How to Scrape Instagram using Python

In this tutorial, we have scraped Instagram using Python, further to scale the process we have used Scrapingdog's API to get get data without any blockage.

2024-11-29

BIG NEWS: Scrapingdog is collaborating with Serpdog.

Products

Resources

Web Scraping with Node-Fetch

Table of Contents

Prerequisites

Understanding the node-fetch

How to use node-fetch?

Basics of Cheerio

Building a Simple Web Scraper

Step 1: Initial Setup

Step 2: Install node-fetch and Cheerio

Step 3: Download your target website

Step 4: Inspect the HTML page

Step 5: Select HTML elements with Cheerio

Step 6: Scrape data from a target webpage with Cheerio

Step 7: Convert the extracted data to JSON

Putting it all together

Handling Exceptions and Errors

Avoiding Blocks

Conclusion

Additional Resources

Web Scraping with Scrapingdog

Web Scraping with Scrapingdog

Recent Blogs

Try Scrapingdog for Free!

Product

Scrapingdog vs Competitors

Learn Web Scraping

Company