Every day, users from all over the world produce immeasurable amounts of data online. Retrieving this data programmatically requires a great deal of time and resources. As you can imagine, a manual approach can't work. That's why you need to rely on a large-scale web scraping process.
Implementing such a process isn't easy. There are so many challenges to face that you may feel discouraged. Yet, there are a lot of solutions! Here, you'll learn everything you need to know to get started with large-scale web scraping.
Let's get into it!
What is Large-Scale Web Scraping?
Performing web scraping on a large scale means building an automatic process that can crawl and scrape millions of web pages. It also involves running several web scrapers on one or more websites simultaneously.
- The first one involves scraping thousands of web pages from a large website, such as Amazon, LinkedIn, or Transfermarkt.
- The second one includes crawling and extracting content from thousands of different small websites at once.
In both cases, large-scale web scraping's all about building a robust infrastructure to extract data from the web. This requires an advanced system, and you'll soon see what you need to build one.
Let's now better understand what large-scale is through a few examples.
Examples of Large-Scale Scraping
Let's imagine you want to extract data from each product in an Amazon category. This category includes 20,000 pages, containing 20 articles each. That'd mean 400,000 pages to crawl and scrape. Or in other words, that'd mean performing 400,000 HTTP GET requests.
Now, suppose each webpage takes 2.5 seconds to load in the browser. That'd mean spending 400,000*2.5 seconds, which is 1,000,000 seconds. This corresponds to more than 11 days, and it's simply the time it takes to load all the pages. Extracting the data from each one and saving it would take much longer.
It isn't possible to manually extract all the data of an entire product category from Amazon. That's where a large-scale scraping system comes into play!
By making the GET requests on the server to parse the HTML content directly, you can reduce each request to a few hundred milliseconds. Also, you can run the scraping process in parallel, extracting data from several web pages per second.
So, a large-scale scraping system would allow you to achieve the same result in a few hours and without any human work. This may seem easy, but large-scale web scraping involves some challenges you can't avoid. Let's dig deeper into them.
Challenges in Large-Scale Scraping
Let's see the three most important challenges of scraping at scale.
Whether scraping the same website or many sites, getting a page from a server takes time. Also, if the web page uses AJAX, you may need a headless browser. These open a browser behind the scenes. But waiting for a page to fully load in the browser can take several seconds.
2. Websites Changing Their Structure
Web scraping involves selecting particular DOM elements and extracting data from them. Yet, the structure of a web page is likely to change over time. This requires you to update the logic of your web scrapers.
3. Anti-Scraping Techniques
The value of most websites lies in their data. Although it's publicly accessible, they don't want competitors to steal it. This is why they implement techniques to identify bots and prevent undesired requests. Learn more on how to avoid being blocked while scraping.
What Do You Need To Perform Web Scraping at a Large Scale?
Now, let's see what you need or need to know to set up a large-scale web scraping process. This will include tools, methodologies, and insightful lessons.
1. Build a Continuous Scraping Process With Scheduled Tasks
Many small scrapers are better than using one large scraper crawling several pages. Let's assume you design a small scraper for each type of webpage on a website. You could launch these scrapers in parallel and extract data from different sections simultaneously.
Also, each scraper can scrape several pages in parallel behind the scenes. This way, you could achieve a double level of parallelism.
Of course, such an approach to web scraping requires an orchestration system. That's because you don't want your scrapers to crawl a webpage twice at the same time. That'd mean a waste of time and resources.
One way to avoid this is to write to the database the URLs of the pages scraped and the current timestamp. Doing so can avoid scraping the same page twice in a short time.
2. Premium Web Proxies
Several sites log the IP related to each request received. When the same request comes from the same IP too many times in a limited time interval, the IP gets blocked.
As you can imagine, this represents a problem for your web scraper. Especially, if they have to scrape thousands of web pages from the same website.
To avoid your IP from being exposed and blocked, you can use a proxy server. Such a server's an intermediary between your scraper and the server of your target website.
Most web proxies online are free, but these are generally not reliable and fast solutions. This is why your large-scale scraping system should rely on premium proxies. Note that ZenRows offers an excellent premium proxy service.
Premium web proxies offer several features, including rotating IPs. This gives you a fresh IP each time you perform a request. So you do have to worry if the IP used by your scraper gets banned or blacklisted. Premium web proxies also allow your scrapers to be anonymous and untraceable.
3. Advanced Data Storage Systems
Scraping thousands of web pages means extracting a lot of data. This data can be divided into two categories: raw and processed. In both cases, you need to store them somewhere.
The raw data can be the HTML documents crawled by your scrapers. Keeping track of this information's useful for future scraping iterations. When it comes to raw data, you can choose one of the many cloud storage services available. These allow you to have virtually unlimited storage space, but they come with a cost.
Your scraper's likely to extract only a small part of the data contained in a webpage HTML document. Then, such data's converted into new formats. This is the other type of data, the processed data.
Such data are generally stored in database rows or aggregated in human-readable formats.
When it comes to processed data, the best solution's to save it in a database. This can be both a relational or NoSQL database.
4. Technologies to Bypass Antibot Detection
More and more websites have been adopting antibot strategies. That's especially true since many CDN (Cloud Delivery Network) services now offer built-in antibot systems.
Usually, these antibot systems involve completing challenges that only a human can. For example, this is how CAPTCHA work. They typically ask you to select pictures of a particular object or animal.
Such antibot methods prevent non-human automated systems from accessing and navigating a website. So, these technologies could represent an obstacle to your web scrapers. At first glance, they might seem impossible to overcome. But they aren't.
In detail, you can bypass the Cloudflare anti-bot system. Similarly, you can bypass the Akamai anti-bot technologies. Keep in mind that bypassing these systems isn't easy. Plus, the current workaround you're exploiting may not work in the future.
Don't forget that Antibot protection's just one of the anti-scraping protection systems. Your large-scale web scraping process may have to deal with several of them. That's why we wrote a list of ways to avoid being blocked while web scraping.
5. Keep Your Scrapers Up To Date
Technology's constantly evolving. As a result, websites, security policies, protection systems, and libraries change. For this reason, keeping your scrapers up to date's crucial. Yet, knowing what to change isn't easy.
To make this web scraping with scale easier, you should implement a logging system. This will tell you if everything's working as expected or if something went wrong. Logging will help you understand how to update your scrapers in case they no longer work, and ZenRows allows you to log everything easily.
What Are Some Tools for Large-Scale Web Scraping?
If you want to perform web scraping with scale, you must be in control of your process. There are so many challenges to face that you're likely to need a system customized to your needs. Building such a custom application's difficult for all the reasons you saw early.
Luckily, you don't have to start from scratch. You could use the most popular web scraping libraries and build a large-scale process. To get all the elements such a process should have, you'd need to subscribe, adopt and integrate several different services… which takes time and money.
Or you could use an all-in-one API-based solution such as ZenRows. In this case, a single solution would get you access to premium proxies, anti-bot protection, and CAPTCHAs bypass systems, as well as everything you need to perform web scraping. Join ZenRows for free.
Here, you've learned everything you should know about performing web scraping on a large scale. As shown above, large-scale web scraping comes with several challenges, but they all have a solution.
To set scraping at scale, you need a few steps, and here you saw what you need to achieve that.
- What is large-scale web scraping.
- What challenges it involves.
- The building block of a reliable large-scale web scraping system.
- Why you should adopt an API-based solution to run web scraping with scale.
Thanks for reading! We hope that you found this guide helpful. You can sign up for free, try ZenRows, and let us know any questions, comments, or suggestions.