Large Scale Web Scraping: Challenges and Solutions

large scale web scraping
Share post:
Share on facebook
Share on linkedin
Share on twitter
Share on email

Web data has immense value for various businesses. It can help predict future trends, monitor competitors, and introduce various business strategies, such as dynamic pricing for e-commerce companies.

 

Collecting large-scale data comes with challenges, which may not appear with regular data extraction. Web scraper maintenance, proxy management, avoiding anti-scraping mechanisms and other challenges can become an issue or even stop operations if the team is unprepared for them. 

 

The good news is that all these challenges have solutions that you will soon learn about.

Table of Contents
Get datacenter proxies now

Forget confusing implementations as we automatically rotate shared datacenter proxies to hide your identity.

What Is Considered As Large-Scale Web Scraping?

Large-scale web scraping is considered when running multiple web scrapers simultaneously on one or a few websites. It’s an automated process that requires certain frameworks to collect large amounts of data from various sources.

 

For example, having more than one web scraper gather data from multiple pages of an e-commerce site will be considered as large-scale scraping. E-commerce marketplaces may have hundreds of pages with thousands or even millions of listings on them, so collecting this information manually wouldn’t be efficient.

 

Scraping at scale causes challenges that you may manage to avoid with regular data extraction, for example, when gathering web data with one scraper. However, scraping at scale is the only way to automatically collect large volumes of data. 

What Are the Challenges of Large Scale Web Scraping?

Web scraping at large scale comes with challenges

Most large-scale web scraping challenges are universally common, so they can be predicted and avoided. However, this requires preparation, such as getting reliable proxies, CAPTCHA-solving services, purchasing space at data warehouses, etc. 

 

We’ll look into the most common challenges and suggest how they can be solved, so you can successfully run uninterrupted large-scale scraping operations.

1. Data Accuracy

When scraping at scale, it’s important to ensure the gathered data is accurate. Otherwise, you’ll be wasting your time gathering random data from thousands of sites, which will have no use to you as you won’t be able to put it into a structured format.

 

Following these steps can help you solve this challenge:

 

  • Specify what data you need. Setting certain requirements for your scrapers will help gather accurate data. For that, you should know in advance what data you’re after. This will also help you verify the data quality later.
  • Define testing criteria. For example, you can set your scraper to find duplicates, hieroglyphic symbols, or empty data and remove them before storing the information in the database. This will help you store clean and accurate data that can later be used more efficiently.
  • Testing for quality assurance. Start testing as soon as possible to be sure everything works as planned. Begin by scraping at a smaller scale and manually check the gathered data to see if everything’s in place.
  • Constantly monitor your scrapers. This step is essential and will be covered in more detail in the following paragraphs. Scraper monitoring can help avoid significant issues with data quality.

 

Accurate and fresh data is very valuable, and, essentially, having validated data is the goal of any web scraping project. That’s why it’s important to set your goals and prepare before you start scraping.

2. Dynamic Website Structures

Scraping HTML websites is relatively easy. However, more and more websites use Javascript or Ajax techniques to load content dynamically. This is used for better user experience or to increase site speed. Data gathering from such websites at scale requires a robust framework and complex libraries.

 

Another issue is constantly changing website content and structure. If one day your scrapers work fine on a specific website, the next day you may get completely different data from the same page, or your scrapers may stop working on that website altogether. This may happen because a website has new features, updated content, or a new structure.

 

Solving this challenge requires a team of developers who would constantly monitor and update web scrapers. Only then can you ensure your gathered data is accurate and scrapers function properly.

3. Anti-scraping Technologies

Anti-scraping technologies are among the most common large-scale web scraping challenges

CAPTCHAs, honeypot traps, and IP blocking are very common anti-scraping technologies employed by many websites. These can cause serious scraping issues, as you never know when you may run into them. CAPTCHAs can seriously slow down your web scrapers, while honeypot traps will send them into an infinite loop of sending nowhere-leading requests.

 

The best solution for overcoming anti-scraping measures is proxies. Using proxies will help you avoid IP blocking. In general, large-scale scraping is unimaginable without proxies. Datacenter proxies are fast and great for gathering data at scale. Proxies can be rotated with every request or at set intervals, which will help your scrapers overcome anti-scraping techniques used by target websites.

 

Another solution may be CAPTCHA-solving services. If your web scrapers often run into CAPTCHAs, getting a tool for solving them will help your web scraping projects run smoothly.

4. Slow Loading

Slow loading websites can seriously affect your scraping operations. Since large-scale web scraping means gathering data from many websites, slow sites can significantly extend your work or even break your local machine. 

 

One of the reasons for slow loading may be too many requests coming to the website. Some sites may not be able to process heavy loads, and therefore it takes longer for them to respond. A solution to this issue is reducing the number of requests you send to the website. 

 

In general, web scraping at scale should not intervene with regular site operation. One of the ways to do it is to moderate the number of your requests. If you notice that a site is slow to respond, respect the website and reduce your scraping speed.

Get datacenter proxies now

Forget confusing implementations as we automatically rotate shared datacenter proxies to hide your identity.

5. Scraper Maintenance

As previously mentioned, web scrapers require constant maintenance. This is required to ensure your tools work properly and return quality data. Complex websites with changing structures, anti-scraping mechanisms, and other factors can break your scrapers. 

 

To avoid breakage, maintain your scrapers. This requires a team of developers who can make sure your tools are up to date and web scrapers can always return real-time data in high quality.

6. Proxy Management

We already mentioned that proxies are essential for large-scale data scraping operations. They can help bypass geo-location restrictions, avoid IP blocks, and enhance anonymity. 

 

However, proxies require management to ensure they do their job. Different targets may require different types of proxies, so you should consider whether to use residential or datacenter proxies. Each type has its benefits, and when you choose correctly, your proxy management will become much easier. 

 

Picking a reliable proxy service provider is also important. Free proxies may not be the best choice for large-scale scraping because they often come from questionable sources and aren’t as resilient as paid proxies.

7. Data Storage

When gathering large amounts of data, it has to be stored somewhere. Cloud storage is a good solution for keeping all the scraped data in one place. Other options are cloud-hosted databases or relational databases. 

 

Depending on your web scraping architecture, you can create a data queue and write it directly to the database while the scrapers are running.

 

It’s important to calculate how much data you plan on storing and if you’ll need access to it in real time. Another feature to consider is whether you plan on scaling your web scraping projects in the future. In that case, you should ensure that your data warehouse can offer additional space to suit your growing needs.

Conclusion

Large-scale data has many use cases and can bring a lot of value to various businesses. More and more companies want to get their hands on valuable data, and those who manage to deliver large-scale data can also benefit greatly. However, collecting this data comes with challenges. 

 

It’s important to prepare for large-scale gathering operations by learning about the potential issues. Ensuring data accuracy, dynamic and complex websites, anti-scraping technologies, slow website loading, scraper maintenance, proxy management, and data storage are among the most common challenges. These issues can be solved with the right choice of proxies and a team of experts.

Choose Razorproxy

Use shared rotating or dedicated datacenter proxies and scale your business with no session, request, location and target limitations.

More To Explore
content automation guide

Content Automation: What You Should Know

Content automation may seem like a buzzword that has been making the rounds due to artificial intelligence and machine learning. But it actually has been