# Source: https://docs.apify.com/academy/scraping-basics-javascript/legacy/crawling/headless-browser.md

# Headless browsers

**Learn how to scrape the web with a headless browser using only a few lines of code. Chrome, Firefox, Safari, Edge - all are supported.**

Archived course

This is an archive of our old course. Check out our new [Web scraping basics for JavaScript devs](https://docs.apify.com/academy/scraping-basics-javascript.md) course instead! We plan to completely retire this old course in a few months.

***

A headless browser is a browser that runs without a user interface (UI). This means that it's normally controlled by automated scripts. Headless browsers are very popular in scraping because they can help you render JavaScript or programmatically behave like a human user to prevent blocking. The two most popular libraries for controlling headless browsers are [Puppeteer](https://pptr.dev/) and [Playwright](https://playwright.dev/). **Crawlee** supports both.

## Building a Playwright scraper

> Our focus will be on Playwright, which boasts additional features and better documentation. Notably, it originates from the same team responsible for Puppeteer.

Crawlee has a built-in support for building Playwright scrapers. Let's reuse code of the Cheerio scraper from the previous lesson. It'll take us just a few changes to turn it into a full headless scraper.

First, we must install Playwright into our project. It's not included in Crawlee, because it's quite large as it bundles all the browsers.


```
npm install playwright
```


After Playwright installs, we can proceed with updating the scraper code. Let's create a new file called `browser.js` and put the new code there. As always, the comments in the example describe changes in the code. Everything else is the same as before.


```
// First, import PlaywrightCrawler instead of CheerioCrawler
import { PlaywrightCrawler, Dataset } from 'crawlee';

const crawler = new PlaywrightCrawler({
    // Second, tell the browser to run with visible UI,
    // so that we can see what's going on.
    headless: false,
    // Third, replace $ with parseWithCheerio function.
    requestHandler: async ({ parseWithCheerio, request, enqueueLinks }) => {
        console.log(`Fetching URL: ${request.url}`);

        if (request.label === 'start-url') {
            await enqueueLinks({
                selector: 'a.product-item__title',
            });
            return;
        }

        // Fourth, parse the browser's page with Cheerio.
        const $ = await parseWithCheerio();

        const title = $('h1').text().trim();
        const vendor = $('a.product-meta__vendor').text().trim();
        const price = $('span.price').contents()[2].nodeValue;
        const reviewCount = parseInt($('span.rating__caption').text(), 10);
        const description = $('div[class*="description"] div.rte').text().trim();

        await Dataset.pushData({
            title,
            vendor,
            price,
            reviewCount,
            description,
        });
    },
});

await crawler.addRequests([{
    url: 'https://warehouse-theme-metal.myshopify.com/collections/sales',
    label: 'start-url',
}]);

await crawler.run();
```


tip

The `parseWithCheerio` function is available even in `CheerioCrawler` and all the other Crawlee crawlers. If you think you'll often switch up the crawlers, you can use it to further reduce the number of needed line changes.

When you run the code with `node browser.js`, you'll see a browser window open and then the individual pages getting scraped, each in a new browser tab.

That's it. In 4 lines of code, we transformed our crawler from a static HTTP crawler to a headless browser crawler. The crawler now runs the same as before, but uses a Chromium browser instead of plain HTTP requests. This isn't possible without Crawlee.

Using Playwright in combination with Cheerio like this is only one of many ways how you can utilize Playwright (and Puppeteer) with Crawlee. In the advanced courses of the Academy, we will go deeper into using headless browsers for scraping and web automation (RPA) use cases.

## Running in headless mode

We said that headless browsers didn't have a UI, but while scraping with the above scraper code, you could definitely see the browser. That's because we added the `headless: false` option. This is useful for debugging and seeing what's going on in the browser. Once your scraper is complete, you can remove the line and the crawler will run without a UI.

You can also switch between headless and headful (with UI) using the [CRAWLEE_HEADLESS](https://crawlee.dev/docs/guides/configuration#crawlee_headless) environment variable. This allows you to change the mode without touching your code.

* MacOS/Linux
* Windows CMD
* Windows Powershell


```
CRAWLEE_HEADLESS=1 node browser.js
```



```
set CRAWLEE_HEADLESS=1 && node browser.js
```



```
$env:CRAWLEE_HEADLESS=1; & node browser.js
```


## Dynamically loaded data

One of the important benefits of using a browser is that it allows you to extract data that's dynamically loaded, such as data that's only fetched after a user scrolls or interacts with the page. In our case, it's the "**You may also like**" section of the product detail pages. Those products aren't available in the initial HTML, but the browser loads them later using an API.

![headless-dynamic-data.png](/assets/images/headless-dynamic-data-556e6fe0874146dbff6ccef48365ed66.png)

tip

We discuss dynamic data at length in the [How to scrape dynamic pages](https://docs.apify.com/academy/node-js/dealing-with-dynamic-pages.md) tutorial, and we also have a special lesson dedicated to it in our [Puppeteer & Playwright course](https://docs.apify.com/academy/puppeteer-playwright/page/waiting.md).

If we added an appropriate selector to our original `CheerioCrawler` code, it would not extract the information, but a browser automatically fetches and renders this extra data.

Let's add this new extractor to our code. It collects the names of the recommended products.


```
// ...
const recommendedProducts = $('.product-recommendations a.product-item__title')
    .map((i, el) => $(el).text().trim())
    .toArray();
// ...
await Dataset.pushData({
    // ...
    recommendedProducts,
});
```


And here's the complete, runnable code:


```
import { PlaywrightCrawler, Dataset } from 'crawlee';

const crawler = new PlaywrightCrawler({
    // We removed the headless: false option to hide the browser windows.
    requestHandler: async ({ parseWithCheerio, request, enqueueLinks }) => {
        console.log(`Fetching URL: ${request.url}`);

        if (request.label === 'start-url') {
            await enqueueLinks({
                selector: 'a.product-item__title',
            });
            return;
        }

        // Fourth, parse the browser's page with Cheerio.
        const $ = await parseWithCheerio();

        const title = $('h1').text().trim();
        const vendor = $('a.product-meta__vendor').text().trim();
        const price = $('span.price').contents()[2].nodeValue;
        const reviewCount = parseInt($('span.rating__caption').text(), 10);
        const description = $('div[class*="description"] div.rte').text().trim();
        // We added one more extractor to get all the recommended products.
        const recommendedProducts = $('.product-recommendations a.product-item__title')
            .map((i, el) => $(el).text().trim())
            .toArray();

        await Dataset.pushData({
            title,
            vendor,
            price,
            reviewCount,
            description,
            // And we saved the extracted product names.
            recommendedProducts,
        });
    },
});

await crawler.addRequests([{
    url: 'https://warehouse-theme-metal.myshopify.com/collections/sales',
    label: 'start-url',
}]);

await crawler.run();
```


When you run the code, you'll find the recommended product names correctly extracted in the dataset files. If you tried the same with our earlier `CheerioCrawler` code, you would find the `recommendedProducts` array empty in your results. That's because Cheerio can't make the API call to retrieve the additional data, like a browser can.

## Next up

We learned how to scrape with Cheerio and Playwright, but how do we export the data for further processing? Let's learn that in the [next and final lesson](https://docs.apify.com/academy/scraping-basics-javascript/legacy/crawling/exporting-data.md) of the Basics of crawling section.