Node.js web scraping tutorial

Editor’s note: This Node.js web scraping tutorial was last updated on 25 January 2022; all outdated information has been updated and a new section on the node-crawler package was added.

In this Node.js web scraping tutorial, we’ll demonstrate how to build a web crawler in Node.js to scrape websites and store the retrieved data in a Firebase database. Our web crawler will perform the web scraping and data transfer using Node.js worker threads.

Here’s what we’ll cover:

• What is a web crawler?
• What is web scraping in Node.js?
• What is a worker in Node.js?
• Communicating with worker threads
• How do I create a web crawler in Node.js?
• How do I scrape a website with Node.js?
• Crawling pages with node-crawler
• Using worker threads for web scraping in Node.js
• Is web scraping legal?

What is a web crawler?

A web crawler, often shortened to crawler or called a spiderbot, is a bot that systematically browses the internet typically for the purpose of web indexing. These internet bots can be used by search engines to improve the quality of search results for users.

What is web scraping in Node.js?

In addition to indexing the world wide web, crawling can also gather data. This is known as web scraping.

Use cases for web scraping include collecting prices from a retailer’s site or hotel listings from a travel site, scraping email directories for sales leads, and gathering information to train machine-learning models.

The process of web scraping can be quite taxing on the CPU depending on the site’s structure and complexity of data being extracted. You can use worker threads to optimize the CPU-intensive operations required to perform web scraping in Node.js.

Installation for Node.js web scraping

Launch a terminal and create a new directory for this tutorial:

$ mkdir worker-tutorial
$ cd worker-tutorial

Initialize the directory by running the following command:

$ yarn init -y

We also need the following packages to build the crawler:

We made a custom demo for .
No really. Click here to check it out.

Click here to see the full demo with network requests

• Axios, a promised based HTTP client for the browser and Node.js
• Cheerio, a lightweight implementation of jQuery which gives us access to the DOM on the server
• Firebase database, a cloud-hosted NoSQL database

If you’re not familiar with setting up a Firebase database, check out the documentation and follow steps 1 through 3 to get started.

Now, let’s install the packages listed above with the following command:

$ yarn add axios cheerio firebase-admin

What is a worker in Node.js?

Before we start building the crawler using workers, let’s go over some basics. You can create a test file, hello.js, in the root of the project to run the following snippets.

Registering a worker in Node.js

A worker can be initialized (registered) by importing the worker class from the worker_threads module like this:

// hello.js

const { Worker } = require('worker_threads');

new Worker("./worker.js");

Printing Hello World with workers in Node.js

Printing out Hello World with workers is as simple as running the snippet below:

// hello.js

const { Worker, isMainThread }  = require('worker_threads');
if(isMainThread){
    new Worker(__filename);
} else{
    console.log("Worker says: Hello World"); // prints 'Worker says: Hello World'
}

This snippet pulls in the worker class and the isMainThread object from the worker_threads module:

• isMainThread helps us know when we run either inside the main thread or a worker thread
• new Worker(__filename) registers a new worker with the __filename variable which, in this case, is hello.js

Communicating with worker threads in Node.js

When a new worker thread spawns, there is a messaging port that allows inter-thread communications. Below is a snippet that shows how to pass messages between workers (threads):

// hello.js

const { Worker, isMainThread, parentPort }  = require('worker_threads');

if (isMainThread) {
    const worker =  new Worker(__filename);
    worker.once('message', (message) => {
        console.log(message); // prints 'Worker thread: Hello!'
    });
    worker.postMessage('Main Thread: Hi!');
} else {
    parentPort.once('message', (message) => {
        console.log(message) // prints 'Main Thread: Hi!'
        parentPort.postMessage("Worker thread: Hello!");
    });
}

In the snippet above, we send a message to the parent thread using parentPort.postMessage() after initializing a worker thread. Then, we listen for a message from the parent thread using parentPort.once().

We also send a message to the worker thread using worker.postMessage() and listen for a message from the worker thread using worker.once().

Running the code produces the following output:

Main Thread: Hi!
Worker thread: Hello!

How do I create a web crawler with Node.js?

Let’s build a basic web crawler that uses Node workers to crawl and write to a database. The crawler will complete its task in the following order:

1. Fetch (request) HTML from the website
2. Extract the HTML from the response
3. Traverse the DOM and extract the table containing exchange rates
4. Format table elements (tbody, tr, and td) and extract exchange rate values
5. Store exchange rate values in an object and send it to a worker thread using worker.postMessage()
6. Accept message from parent thread in worker thread using parentPort.on()
7. Store message in Firestore (Firebase database)

Let’s create two new files in our project directory:

• main.js for the main thread
• dbWorker.js for the worker thread

The source code for this tutorial is available here on GitHub. Feel free to clone it, fork it, or submit an issue.

How do I scrape a website with Node.js?

In the main thread (main.js), we will scrape the IBAN website for the current exchange rates of popular currencies against the US dollar. We will then import axios and use it to fetch the HTML from the site using a simple GET request.

We will also use cheerio to traverse the DOM and extract data from the table element. To know the exact elements to extract, we will open the IBAN website in our browser and load dev tools:

From the image above, we can see the table element with the classes:

table table-bordered table-hover downloads. 

This will be a great starting point and we can feed that into our cheerio root element selector:

// main.js

const axios = require('axios');
const cheerio = require('cheerio');
const url = "https://www.iban.com/exchange-rates";

fetchData(url).then( (res) => {
    const html = res.data;
    const $ = cheerio.load(html);
    const statsTable = $('.table.table-bordered.table-hover.downloads > tbody > tr');
    statsTable.each(function() {
        let title = $(this).find('td').text();
        console.log(title);
    });
})

async function fetchData(url){
    console.log("Crawling data...")
    // make http call to url
    let response = await axios(url).catch((err) => console.log(err));

    if(response.status !== 200){
        console.log("Error occurred while fetching data");
        return;
    }
    return response;
}

Running the code above with Node will give the following output:

Going forward, we will update the main.js file so we can properly format our output and send it to our worker thread.

Updating the main thread

To properly format our output, we must get rid of white space and tabs since we will store the final output in JSON. Let’s update the main.js file accordingly:

// main.js
[...]
let workDir = __dirname+"/dbWorker.js";

const mainFunc = async () => {
  const url = "https://www.iban.com/exchange-rates";
  // fetch html data from iban website
  let res = await fetchData(url);
  if(!res.data){
    console.log("Invalid data Obj");
    return;
  }
  const html = res.data;
  let dataObj = new Object();
  // mount html page to the root element
  const $ = cheerio.load(html);

  let dataObj = new Object();
  const statsTable = $('.table.table-bordered.table-hover.downloads > tbody > tr');
  //loop through all table rows and get table data
  statsTable.each(function() {
    let title = $(this).find('td').text(); // get the text in all the td elements
    let newStr = title.split("\t"); // convert text (string) into an array
    newStr.shift(); // strip off empty array element at index 0
    formatStr(newStr, dataObj); // format array string and store in an object
  });

  return dataObj;
}

mainFunc().then((res) => {
    // start worker
    const worker = new Worker(workDir); 
    console.log("Sending crawled data to dbWorker...");
    // send formatted data to worker thread 
    worker.postMessage(res);
    // listen to message from worker thread
    worker.on("message", (message) => {
        console.log(message)
    });
});

[...]

function formatStr(arr, dataObj){
    // regex to match all the words before the first digit
    let regExp = /[^A-Z]*(^\D+)/ 
    let newArr = arr[0].split(regExp); // split array element 0 using the regExp rule
    dataObj[newArr[1]] = newArr[2]; // store object 
}

In the snippet above, we are doing more than data formatting; after the mainFunc() resolves, we pass the formatted data to the worker thread for storage.

Using worker threads for web scraping in Node.js

In this worker thread, we will initialize Firebase and listen for the crawled data from the main thread. When the data arrives, we will store it in the database and send a message back to the main thread to confirm that data storage was successful.

The snippet that takes care of the aforementioned operations can be seen below:

// dbWorker.js

const { parentPort } = require('worker_threads');
const admin = require("firebase-admin");

//firebase credentials
let firebaseConfig = {
    apiKey: "XXXXXXXXXXXX-XXX-XXX",
    authDomain: "XXXXXXXXXXXX-XXX-XXX",
    databaseURL: "XXXXXXXXXXXX-XXX-XXX",
    projectId: "XXXXXXXXXXXX-XXX-XXX",
    storageBucket: "XXXXXXXXXXXX-XXX-XXX",
    messagingSenderId: "XXXXXXXXXXXX-XXX-XXX",
    appId: "XXXXXXXXXXXX-XXX-XXX"
};

// Initialize Firebase
admin.initializeApp(firebaseConfig);
let db = admin.firestore();
// get current data in DD-MM-YYYY format
let date = new Date();
let currDate = `${date.getDate()}-${date.getMonth()}-${date.getFullYear()}`;
// recieve crawled data from main thread
parentPort.once("message", (message) => {
    console.log("Recieved data from mainWorker...");
    // store data gotten from main thread in database
    db.collection("Rates").doc(currDate).set({
        rates: JSON.stringify(message)
    }).then(() => {
        // send data back to main thread if operation was successful
        parentPort.postMessage("Data saved successfully");
    })
    .catch((err) => console.log(err))    
});

Running main.js (which encompasses dbWorker.js) with Node will give the following output:

You can now check your Firebase database and see the following crawled data:

Crawling pages with node-crawler

The method we implemented above utilizes two different packages (Axios and Cheerios) to fetch and traverse webpages.

With node-crawler only, we can perform those functions easily. node-crawler uses Cheerio under the hood and comes with extra functionalities that allow you to customize the way you crawl and scrape websites.

You can specify options like the maximum number of requests that can be carried out at a time (maxConnections), the minimum time allowed between requests (rateLimit), the number of retries allowed if a request fails, and the priority of each request.

Clearly, node-crawler has a lot to offer. Let’s take a look at how its code works.

Installing node-crawler

In your project directory, run the following command:

npm install crawler

In a file named crawler.js, add the following code:

const Crawler = require('crawler');
const crawlerInstance = new Crawler({
    maxConnections: 10,

    callback: (error, res, done) => {
        if (error) {
            console.log(error);
        } else {
            const $ = res.$;
            const statsTable = 
            $('.table.table-bordered.table-hover.downloads > tbody > tr');
            statsTable.each(function() {
                let title = $(this).find('td').text();
                console.log(title);
            });
        }
        done();
    }
});

crawlerInstance.queue('https://www.iban.com/exchange-rates');

Here, we use one package—node-crawler—to fetch a webpage and traverse its DOM. We import its package into our project and create an instance of it named crawlerInstance.

The maxConnection option specifies the number of tasks to perform at a time. In this case, we set it to 10. Next, we create a callback function that carries out after a web page is fetched. The line const $ = res.$ makes Cheerio available in the just fetched webpage.

Fetching data with node-crawler

Next, similar to what we did before, we traverse the IBAN exchange rate page, grab the data on the table, and display them in our console.

The queue function is responsible for fetching the data of webpages, a task performed by Axios in our previous example.

To fetch data from multiple webpages at once, add all the URLs to queue like this:

crawlerInstance.queue(['https://www.iban.com/exchange-rates','http://www.facebook.com']);

By default, node-crawler uses the callback function created when instantiating it (the global callback). To create a custom callback function for a particular task, simply add it to the queue request:

crawlerInstance.queue([{
    uri: 'http://www.facebook.com',

    callback: (error, res, done) => {
        if (error) {
            console.log(error);
        } else {
            console.log('res.body.length');
        }
        done();
    }
}]);

Adding bottlenecks with node-crawler

As mentioned above, one of the advantages of using node-crawler is that it lets you customize your web-scraping tasks and add bottlenecks to them.

Now, you might wonder why you’d need to purposefully add bottlenecks to your tasks. Well, websites tend to have anticrawler mechanisms that can detect and block your requests if they all execute at once.

With node-crawler’s rateLimit, time gaps can be added between requests, to ensure that they don’t execute at the same time.

As mentioned earlier, maxConnection can also add bottlenecks to your tasks by limiting the number of queries that can at the same time. Here’s how to use both options:

const crawlerInstance = new Crawler({
    rateLimit: 2000,
    maxConnections: 1,
    callback: (error, res, done) => {
        if (error) {
            console.log(error);
        } else {
            const $ = res.$;
            console.log($('body').text());
        }
        done();
    }
});

With rateLimit set to 2000, there will be a 2-second gap between requests.

Is it legal to use a web scraper?

Although web scraping can be fun, it can also be against the law if you use data to commit copyright infringement. It is generally advised that you read the terms and conditions of the site you intend to crawl to know their data crawling policy beforehand.

You can learn more about web crawling policy before undertaking your own Node.js web scraping project.

The use of worker threads does not guarantee your application will be faster but can present that mirage if used efficiently because it frees up the main thread by making CPU-intensive tasks less cumbersome on the main thread.

Conclusion

In this tutorial, we learned how to build a web crawler that scrapes currency exchange rates and saves them to a database. We also learned how to use worker threads to run these operations.

The source code for each of the following snippets is available on GitHub. Feel free to clone it, fork it, or submit an issue.

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