Scraping with Cheerio Scraper

This scraping tutorial will go into the nitty gritty details of extracting data from https://apify.com/store using the apify/cheerio-scraper. If you arrived here from the Getting started with Apify scrapers, tutorial, great! You are ready to continue where we left off. If you haven't seen the Getting started yet, check it out, it will help you learn about Apify and scraping in general and set you up for this tutorial, because this one builds on topics and code examples discussed there.

Getting to know our tools

In the Getting started with Apify scrapers tutorial, we've confirmed that the scraper works as expected, so now it's time to add more data to the results.

To do that, we'll be using the Cheerio library. This may not sound familiar, so let me try again. Does jQuery library ring a bell? If it does you're in luck, because Cheerio is just jQuery that doesn't need an actual browser to run. Everything else is the same. All the functions you already know are there and even the familiar $ is used. If you still have no idea what either of those are, don't worry. We'll walk you through using them step by step.

To learn more about Cheerio, see the docs on their GitHub page.

Now that's out of the way, let's open one of the actor detail pages in the Store, for example the apify/web-scraper page and use our DevTools-Fu to scrape some data.

If you're wondering why we're using apify/web-scraper as an example instead of cheerio-scraper, it's only because we didn't want to triple the number of screenshots we needed to make. Lazy developers!

Quick recap

Before we start, let's do a quick recap of the data we chose to scrape:

  1. URL - The URL that goes directly to the actor's detail page.
  2. Unique identifier - Such as apify/web-scraper.
  3. Title - The title visible in the actor's detail page.
  4. Description - The actor's description.
  5. Last run date- When the actor was last run.
  6. Number of runs - How many times the actor was run.

data to scrape

We've already scraped number 1 and 2 in the Getting started with Apify scrapers tutorial, so let's get to the next one on the list: Title

Title

actor title

By using the element selector tool, we find out that the title is there under an <h1> tag, as titles should be. Maybe surprisingly, we find that there are actually two <h1> tags on the detail page. This should get us thinking. Is there any parent element that includes our <h1> tag, but not the other ones? Yes, there is! There is a <header> element that we can use to select only the heading we're interested in.

Remember that you can press CTRL+F (CMD+F) in the Elements tab of DevTools to open the search bar where you can quickly search for elements using their selectors. And always make sure to use the DevTools to verify your scraping process and assumptions. It's faster than changing the crawler code all the time.

To get the title we just need to find it using a header h1 selector, which selects all <h1> elements that have a <header> ancestor. And as we already know, there's only one.

// Using Cheerio.
return {
    title: $('header h1').text(),
};

Description

Getting the actor's description is a little more involved, but still pretty straightforward. We can't just simply search for a <p> tag, because there's a lot of them in the page. We need to narrow our search down a little. Using the DevTools we find that the actor description is nested within the <header> element too, same as the title. Sadly, we're still left with two <p> tags. To finally select only the description, we choose the <p> tag that has a class that starts with Text__Paragraph.

actor description selector

return {
    title: $('header h1').text(),
    description: $('header p[class^=Text__Paragraph]').text(),
};

Last run date

The DevTools tell us that the lastRunDate can be found in the second of the two <time> elements in the page.

actor last run date selector

return {
    title: $('header h1').text(),
    description: $('header p[class^=Text__Paragraph]').text(),
    lastRunDate: new Date(
        Number(
            $('time')
                .eq(1)
                .attr('datetime'),
        ),
    ),
};

It might look a little too complex at first glance, but let me walk you through it. We find all the <time> elements. There are two, so we grab the second one using the .eq(1) call (it's zero indexed) and then we read its datetime attribute, because that's where a unix timestamp is stored as a string.

But we would much rather see a readable date in our results, not a unix timestamp, so we need to convert it. Unfortunately the new Date() constructor will not accept a string, so we cast the string to a number using the Number() function before actually calling new Date(). Phew!

Run count

And so we're finishing up with the runCount. There's no specific element like <time>, so we need to create a complex selector and then do a transformation on the result.

return {
    title: $('header h1').text(),
    description: $('header p[class^=Text__Paragraph]').text(),
    lastRunDate: new Date(
        Number(
            $('time')
                .eq(1)
                .attr('datetime'),
        ),
    ),
    runCount: Number(
        $('ul.stats li:nth-of-type(3)')
            .text()
            .match(/\d+/)[0],
    ),
};

The ul.stats > li:nth-of-type(3) looks complicated, but it only reads that we're looking for a <ul class="stats ..."> element and within that element we're looking for the third <li> element. We grab its text, but we're only interested in the number of runs. So we parse the number out using a regular expression, but its type is still a string, so we finally convert the result to a number by wrapping it with a Number() call.

Wrapping it up

And there we have it! All the data we needed in a single object. For the sake of completeness, let's add the properties we parsed from the URL earlier and we're good to go.

const { url } = request;

// ...

const uniqueIdentifier = url.split('/').slice(-2).join('/');

return {
    url,
    uniqueIdentifier,
    title: $('header h1').text(),
    description: $('header p[class^=Text__Paragraph]').text(),
    lastRunDate: new Date(
        Number(
            $('time')
                .eq(1)
                .attr('datetime'),
        ),
    ),
    runCount: Number(
        $('ul.stats li:nth-of-type(3)')
            .text()
            .match(/\d+/)[0],
    ),
};

All we need to do now is add this to our pageFunction:

async function pageFunction(context) {
    const { request, log, skipLinks, $ } = context; // $ is Cheerio
    if (request.userData.label === 'START') {
        log.info('Store opened!');
        // Do some stuff later.
    }
    if (request.userData.label === 'DETAIL') {
        const { url } = request;
        log.info(`Scraping ${url}`);
        await skipLinks();

        // Do some scraping.
        const uniqueIdentifier = url.split('/').slice(-2).join('/');

        return {
            url,
            uniqueIdentifier,
            title: $('header h1').text(),
            description: $('header p[class^=Text__Paragraph]').text(),
            lastRunDate: new Date(
                Number(
                    $('time')
                        .eq(1)
                        .attr('datetime'),
                ),
            ),
            runCount: Number(
                $('ul.stats li:nth-of-type(3)')
                    .text()
                    .match(/\d+/)[0],
            ),
        };
    }
}

Test run 3

As always, try hitting that Save & Run button and visit the Dataset preview of clean items. You should see a nice table of all the attributes correctly scraped. You nailed it!

Pagination

Pagination is just a term that represents "going to the next page of results". You may have noticed that we did not actually scrape all the actors, just the first page of results. That's because to load the rest of the actors, one needs to click the orange Show more button at the very bottom of the list. This is pagination.

This is a typical JavaScript pagination, sometimes called infinite scroll. Other pages may use links that take you to the next page. If you encounter those, just make a Pseudo URL for those links and they will be automatically enqueued to the request queue. Use a label to let the scraper know what kind of URL it's processing.

If you paid close attention, you may now see a problem. How do we click a button in the page when we're working with Cheerio? We don't have a browser to do it and we only have the HTML of the page to work with. So the simple answer is that we can't click a button. Does that mean that we cannot get the data at all? Usually not, but it requires some clever DevTools-Fu.

Analyzing the page

While with apify/web-scraper and apify/puppeteer-scraper, we could get away with simply clicking a button, with apify/cheerio-scraper we need to dig a little deeper into the page's architecture. For this, we will use the Network tab of the Chrome DevTools.

It's a very powerful tool with a lot of features, so if you're not familiar with it, please see this tutorial: https://developers.google.com/web/tools/chrome-devtools/network/ which explains everything much better than we ever could.

We want to know what happens when we click the Show more button, so we open the DevTools Network tab and clear it. Then we click the Show more button and wait for incoming requests to appear in the list.

inspect-network

Now, this is interesting. It seems that we've only received two images after clicking the button and no additional data. This means that the data about actors must already be available in the page and the Show more button only displays it. This is good news.

Finding the actors

Now that we know the information we seek is already in the page, we just need to find it. The first actor in the store is apify/web-scraper so let's try using the search tool in the Elements tab to find some reference to it. The first few hits do not provide any interesting information, but in the end, we find our goldmine. There is a <script> tag, with the ID __NEXT_DATA__ that seems to hold a lot of information about apify/web-scraper. In DevTools, you can right click an element and click Store as global variable to make this element available in the Console.

find-data

A temp1 variable is now added to your console. We're mostly interested in its contents and we can get that using the temp1.textContent property. You can see that it's a rather large JSON string. How do we know? The type attribute of the <script> element says application/json. But working with a string would be very cumbersome, so we need to parse it.

const data = JSON.parse(temp1.textContent);

After entering the above command into the console, we can inspect the data variable and see that all the information we need is there, in the data.props.pageProps.items array. Great!

inspect-data

It's obvious that all the information we set to scrape is available in this one data object, so you might already be wondering, can I just make one request to the store to get this JSON and then parse it out and be done with it in a single request? Yes you can! And that's the power of clever page analysis.

Using the data to enqueue all actor details

We don't really need to go to all the actor details now, but for the sake of practice, let's imagine we only found actor names such as cheerio-scraper and their owners, such as apify in the data. We will use this information to construct URLs that will take us to the actor detail pages and enqueue those URLs into the request queue.

// We're not in DevTools anymore, so we use Cheerio to get the data.
const dataJson = $('#__NEXT_DATA__').text();
const data = JSON.parse(dataJson);

for (const item of data.props.pageProps.items) {
    const { name, username } = item;
    const actorDetailUrl = `https://apify.com/${username}/${name}`;
    await context.enqueueRequest({
        url: actorDetailUrl,
        userData: {
            label: 'DETAIL', // Don't forget the label.
        }
    });
}

We iterate through the items we found, build actor detail URLs from the available properties and then enqueue those URLs into the request queue. We need to specify the label too, otherwise our page function wouldn't know how to route those requests.

If you're wondering how we know the structure of the URL, see the Getting started with Apify Scrapers tutorial again.

Plugging it into the pageFunction

We've got the general algorithm ready, so all that's left is to integrate it into our earlier pageFunction. Remember the // Do some stuff later comment? Let's replace it.

async function pageFunction(context) {
    const { request, log, skipLinks, $ } = context;
    if (request.userData.label === 'START') {
        log.info('Store opened!');

        const dataJson = $('#__NEXT_DATA__').text();
        const data = JSON.parse(dataJson);

        for (const item of data.props.pageProps.items) {
            const { name, username } = item;
            const actorDetailUrl = `https://apify.com/${username}/${name}`;
            await context.enqueueRequest({
                url: actorDetailUrl,
                userData: {
                    label: 'DETAIL',
                }
            });
        }
    }
    if (request.userData.label === 'DETAIL') {
        const { url } = request;
        log.info(`Scraping ${url}`);
        await skipLinks();

        // Do some scraping.
        const uniqueIdentifier = url.split('/').slice(-2).join('/');

        return {
            url,
            uniqueIdentifier,
            title: $('header h1').text(),
            description: $('header p[class^=Text__Paragraph]').text(),
            lastRunDate: new Date(
                Number(
                    $('time')
                        .eq(1)
                        .attr('datetime'),
                ),
            ),
            runCount: Number(
                $('ul.stats li:nth-of-type(3)')
                    .text()
                    .match(/\d+/)[0],
            ),
        };
    }
}

That's it! You can now remove the Max pages per run limit, Save & Run your task and watch the scraper scrape all of the actors' data. After it succeeds, open the Dataset again and see the clean items. You should have a table of all the actor's details in front of you. If you do, great job! You've successfully scraped the Apify Store. And if not, no worries, just go through the code examples again, it's probably just some typo.

There's an important caveat. The way we implemented pagination here is in no way a generic system that you can easily use with other websites. Cheerio is fast (and that means it's cheap), but it's not easy. Sometimes there's just no way to get all results with Cheerio only and other times it takes hours of research. Keep this in mind when choosing the right scraper for your job. But don't get discouraged. Often times, the only thing you will ever need is to define a correct Pseudo URL. So do your research first before giving up on Cheerio Scraper.

Downloading the scraped data

You already know the DATASET tab of the run console since this is where we've always previewed our data. Notice that at the bottom, there is a table with multiple data formats, such as JSON, CSV or an Excel sheet, and to the right, there are options to download the scraping results in any of those formats. Go ahead and try it.

If you prefer working with an API, you can find an example in the API tab of the run console: Get dataset items.

Items and Clean items

There are two types of data available for download. Items and Clean items. The Items will always include a record for each pageFunction invocation, even if you did not return any results. The record also includes hidden fields such as #debug, where you can find various information that can help you with debugging your scrapers.

Clean items, on the other hand, include only the data you returned from the pageFunction. If you're only interested in the data you scraped, this format is what you will be using most of the time.

Bonus: Making your code neater

You may have noticed that the pageFunction gets quite bulky. To make better sense of your code and have an easier time maintaining or extending your task, feel free to define other functions inside the pageFunction that encapsulate all the different logic. You can, for example, define a function for each of the different pages:

async function pageFunction(context) {
    switch (context.request.userData.label) {
        case 'START': return handleStart(context);
        case 'DETAIL': return handleDetail(context);
    }

    async function handleStart({ log, waitFor, $ }) {
        log.info('Store opened!');

        const dataJson = $('#__NEXT_DATA__').text();
        const data = JSON.parse(dataJson);

        for (const item of data.props.pageProps.items) {
            const { name, username } = item;
            const actorDetailUrl = `https://apify.com/${username}/${name}`;
            await context.enqueueRequest({
                url: actorDetailUrl,
                userData: {
                    label: 'DETAIL',
                }
            });
        }
    }

    async function handleDetail({ request, log, skipLinks, $ }) {
        const { url } = request;
        log.info(`Scraping ${url}`);
        await skipLinks();

        // Do some scraping.
        const uniqueIdentifier = url.split('/').slice(-2).join('/');

        return {
            url,
            uniqueIdentifier,
            title: $('header h1').text(),
            description: $('header p[class^=Text__Paragraph]').text(),
            lastRunDate: new Date(
                Number(
                    $('time')
                        .eq(1)
                        .attr('datetime'),
                ),
            ),
            runCount: Number(
                $('ul.stats li:nth-of-type(3)')
                    .text()
                    .match(/\d+/)[0],
            ),
        };
    }
}

If you're confused by the functions being declared below their executions, it's called hoisting and it's a feature of JavaScript. It helps you put what matters on top, if you so desire.

Final word

Thank you for reading this whole tutorial! Really! It's important to us that our users have the best information available to them so that they can use Apify easily and effectively. We're glad that you made it all the way here and congratulations on creating your first scraping task. We hope that you liked the tutorial and if there's anything you'd like to ask, do it on Stack Overflow!

Finally, apify/cheerio-scraper is just an actor and writing your own actors is a breeze with the Apify SDK. It's a bit more complex and involved than writing a simple pageFunction, but it allows you to fine-tune all the details of your scraper to your liking. Perhaps some other time, when you're in the mood for yet another tutorial, visit the Getting Started. We think you'd like it!