Web scraping challenges and how to deal with them

Companies collect data to understand market trends, customer preferences, and the competitors actions. It is also possible to use scraping for prospecting, marketing analysis, etc. We talked more about the purpose of scraping in this article. 

Web scraping is not another method of gathering information, but a tactic for business development. Knowing just one data collection approach solves the problem in the short term, but all methods have their strengths and weaknesses. Recognizing this and finding new ways of scraping websites saves time and helps solve the problem more efficiently.

However, some web scraping challenges make this data collection difficult. 

Web Scraping Challenges

Bot access

The most popular scraping internet data challenge. Websites can decide whether they will give bots access to clean data. Some sites forbid automatic data collection. The reasons for the ban can be completely different. If you come across a website that prohibits collection through its robots.txt, follow fair play principles and ask the site owner for permission to collect data. Otherwise, it is better to look for an alternative site with similar information.

IP blocking

IP blocking is one of the rare methods of dealing with parsers. But it is also the easiest way. Blocking is triggered when the server detects a large number of requests from the same IP address or when a search robot makes several parallel requests. There is also IP blocking via geolocation. This is when the site is protected from attempts to collect data from specific locations. The website will either ban the IP completely or limit its access.

Captcha

Also one of the rare and difficult types of scraping challenges. CAPTCHA allows distinguishing a person from a robot. Logical tasks or input of characters are displayed for verification, which humans solve quickly and robots do not. Indeed, many CAPTCHA solvers are now implemented in bots for continuous data collection, although it slows down the process a bit.

Honeypot traps

Website owners put honeypot traps on pages to catch parsers. Traps can be links that ordinary people can’t see, but parsers can. When a parser falls into a trap, the website can use the information it receives to block bots. Some of the traps have a CSS style of “display: none” or a masked color to match the background color of the page. 

Slow or unstable load speed

Websites may be slow to load content or may not load at all when receiving a large number of access requests. In such a situation, you can refresh the page and wait for the site to recover. However, the parser will not know how to handle such a situation and data collection may be interrupted. 

Web page structure

Another website challenge you have to face when scraping. Designers may have their design standards when creating web pages, so page structures will vary. Websites also undergo periodic changes to improve user interaction or add new features. This often results in structural changes to the web page itself. Web parsers are created with page code elements in mind, so these changes make the codes more complex, which affects how the parsers work.

And because they are customized to a specific page design, they won’t work for the updated page. Sometimes even a minor change requires a new parser configuration.

Login requirement

Sometimes you have to log in first to get information. After you send your login credentials, the browser adds a cookie value to several requests that run on other sites. That way, the website knows that you are the same person who just logged in earlier. 

However, the login requirement is not a difficulty, but rather one of the stages of data collection. So when collecting data from websites, you need to make sure that cookies are sent with the requests.

Real-time data scraping

There are a myriad of instances where real-time data collection is important, such as price comparisons, inventory tracking, etc. Data can change instantly and generate huge revenue for companies. That’s why a parser needs to monitor sites and collect data around the clock. However, because parsers constantly monitor web pages, it always takes some time to query and provide data and any instability can lead to failures. 

Data from multiple sources

Sometimes the information can be on different sources. Some of the data will be on the website, some will be in the mobile app, and some will be in PDF format. The scraper works in such a way that it collects all the information from one service. In such a case, it is difficult to collect and group the information, some of it may be missing altogether. Plus it takes a lot of time.

Dynamic content

A minor problem, but worth mentioning. Websites use AJAX to update dynamic web content. For example, delayed loading of images or infinite scrolling and displaying additional information by clicking a button when AJAX is invoked. This is a convenient way for users to view more data on websites, but not for parsers.

How to avoid blocking

Beware of honeypot traps

Before you start collecting data, make sure that the link is set to the CSS properties “display: none” or “visibility: hidden”. If a link has one of these properties, avoid it.

It is also advisable to only follow links from reliable sources. While this does not give a complete guarantee, it will still allow you to better judge the security of the sites. 

Use a headless browser

A headless browser includes all the features of a website display. Because of the lack of a graphical interface, a command-line utility is used to interact with the headless browser. They are more flexible, faster than real browsers.  And since there’s no overhead for any user interface, these browsers are suitable for automatically stress-testing and cleaning web pages. And when using such a browser, you do not need to load the entire site. It can load the HTML part and collect the data.

Use captcha solving services

Captcha exists in many forms, but the point is the same – you need to solve a task to prove you’re human. And Captcha solvers automatically help you solve Captcha and improve your workflow. All you have to do is register, buy units, implement their Captcha submission API, and return the result as text.

There are two ways to solve such tasks. The first method is the Captcha solution services hire people to whom they send tasks to solve and forward the result. The second method is Optical Character Recognition (OCR). Artificial intelligence and Machine Learning determine Captcha content and its solution automatically.

Use Proxy Servers

A proxy server is an intermediate server between a user and a website. It has its own IP address. When a user requests access to a website through a proxy server, the website sends and receives data to the IP address of the proxy server, which forwards it to the user. Web parsers use proxies to make the traffic look like normal user traffic.

So, to avoid blocking the parser on the site, you can buy IP address pools and distribute them at randomly scheduled intervals. Using proxies is the easiest way to distribute them. These programs route requests through different IP addresses, masking the real IP address. 

Detect website changes

Before collecting data, it is best to run a thorough website test to avoid problems. Detect any changes and program the parser so that it does not stop working in the changed site structure. 

Set a real User Agent

The User Agent (UA) is a string in the request header that specifies the browser and operating system for the web server. If your user agent does not belong to one of the major browsers, some sites will block its requests. To avoid this problem, you need to specify the UA used for the parser. It is better to use your browser’s user agent since browser behavior is more likely to match the expectations of the user agent if you don’t change it too often.

Set random intervals in between your requests

More like an additional tip, not a solution to the problem. Do not overload the site with a large number of requests. It is better to make a time delay of a few seconds between requests. You can also save the URLs of the scanned pages so that you don’t have to come back to them again.

Respect the Robots.txt

Also advice, but for those who browse websites before collecting data. The robots.txt file contains information about which pages the web parser can crawl and which pages it cannot crawl. Good bots respect these rules and follow scanning and data collection methods. Be sure to check robots.txt before parsing. If the site is completely blocked by bots, it’s best to leave the site.

Don’t scrape during peak hours

It is better to collect data from sites during off-peak periods, so as not to interfere with the site work. It also plays a big role for the parser itself, as it will significantly increase the speed of data collection. 

Dealing with neural network-based site protection

Some sites are very concerned about their data and use different protections based on neural networks. These protections use any data about the user that can be obtained from the browser. This is how a neural network determines whether a user is a real or a bot. 

In a nutshell, we solve such problems by analyzing the script that collects user data to understand what kind of information the neural network gathers. We determine the mechanisms for identifying the automated software. Then we develop a script to replace the data. After the data is replaced the access to the site appears. Now you can collect any information you want.

This is only a small part of what we encounter in our work. But we wanted to share our experiences with the scraping challenges. If you want to know more about web scraping, you can write to us.

Conclusion

Businesses today depend on data and its quality. That’s why it’s important to choose experienced parsers who can help collect the right data. Even though scraping internet data is legal, there are many challenges. Various solutions can help achieve the goal. In any case, you should treat the site carefully and not overload it.