5 Main Web Scraping Challenges & Solutions

Web scraping, the process of extracting publicly available data from websites, presents plenty of challenges, especially when conducted at scale. There are individual solutions for each challenge, yet many solutions work best when used in tandem or, in some cases, all at once.

Having unrestricted access to public data in real-time, regardless of location, is essential for successful data extraction at scale. The most prominent web scraping challenges include the following:

1. Getting blocked

2. Scalability

3. Dynamic content

4. Website structure changes

5. Infrastructure maintenance

Getting blocked

Websites use anti-bot mechanisms to improve user experience, protect servers from overload, and repulse undesirable non-organic traffic. Numerous strategies have been developed to detect whether the visiting device is a bot or an organic user (a person). From user behavior and HTTP request analysis to browser fingerprinting, webmasters set up more and more sophisticated detection measures.

Consequently, websites often block IP addresses that use automated data extraction tools. Websites often implement measures to manage incoming traffic, such as rate limiting (user traffic capacity control), IP blocking, browser fingerprinting, and CAPTCHAs.

Browser fingerprinting

Internet browsers provide information to websites upon every visit. This transaction allows a server to understand the nature of content destined for a user. The server determines language settings, layout preferences, and other parameters, as well as collects information about the browser, operating system, and device in use. The destination server can identify even minor details, such as browsing language and user agents.

Websites collect user information and attribute it to a specific digital fingerprint. Browser fingerprinting is the process of tracking users based on the following:

• HTTP headers – a browser sends request headers to the destination server.

• Transport Layer Security (TLS) version – TLS connection has its own fingerprint.

• webRTC.

• Custom JavaScript functions set up and executed by a website to gather information.

The process combines small data points into a larger set of parameters to create a unique digital fingerprint. The fingerprint accompanies users everywhere they go. Changing proxies, clearing browser history, and resetting cookies won’t affect the established fingerprint. 

Browser fingerprinting is the primary measure in identifying bots. Automated web scrapers must be adaptable to imitate organic user behavior.

Solution: To prevent browser fingerprinting from disturbing web scraping tasks, you can use a headless browser or one of the HTTP request libraries to manually construct a custom fingerprint. Combining user agents with a set of associated HTTP headers assures a browser sends successful HTTP(S) requests.

IP blocks

IP-based blocking is one of the most common methods of preventing web scrapers from collecting publicly available data. Exceeding the limit of actions allowed on a website will result in an IP ban. The typical threshold is sending too many HTTP(S) requests or having a suspicious browser configuration (for example, disabled JavaScript).

Other reasons for getting blocked are:

• Having a geographical location restricted by a particular website (geo-blocking). 

• Using an unsuitable proxy type – some websites can identify that an IP originates from a data center, therefore, a non-organic user, warranting an IP ban.

Solution: Use reliable proxies from trustworthy providers that operate at scale, including rotating IP addresses to appear as a number of different users. Also, simply controlling the scraping speed and getting familiar with the Robots Exclusion Protocol could be enough.

The ultimate solution to deal with anti-bot measures is to use advanced AI-based tools that combine all the methods described above into a single automated system. The system automates sending continuous requests, only returning a satisfactory result once the retrieval process has succeeded.

Oxylabs’ Web Unblocker is one such all-in-one tool, and you can try it for one week for free.

Scalability

To stay competitive, through price optimization or market analysis, businesses need to gather vast amounts of public data about customers and competitors from different sources and do it quickly. For small businesses building a highly scalable web scraping infrastructure is quite unrealistic due to the immense time, effort, and software/hardware expenses required.

Solution: Use an easily scalable web scraping solution that supports a high volume of requests, unlimited bandwidth, and retrieves data in seconds at high speed. Infrastructure as a service (IaaS) platforms provide various APIs you could integrate into your preexisting system.

You can try Oxylabs Scraper APIs for free with up to 5K results.

Dynamic content

Many websites apply asynchronous JavaScript and XML – AJAX – to load data dynamically (infinite downward scroll). In turn, the initial HTML response does not contain all the desired data.

AJAX allows web applications to send and extract data from a server asynchronously. It, therefore, updates web pages without reloading or refreshing. Dynamic content enhances user experience but also creates a bottleneck when web scraping.

Web developers employ delayed JavaScript-based rendering to cut off non-organic traffic and reduce server load. This technique loads a web page with a delay. Websites use it to check whether the web client (browser) can render hidden JavaScript content. The website flags the visitor as a bot if the client cannot accomplish this requirement and won’t fulfill any subsequent request.

Web scrapers need to simulate user interactions, handle asynchronous requests, and extract public data from dynamically generated content.

Solution: Build or acquire web scraping tools capable of rendering content hidden in JavaScript elements. Check how scraping dynamic JavaScript websites works with a custom-built Python script.

Most things you can do manually, including web scraping, can also be done with certain frameworks. Three tools, Playwright, Puppeteer, and Selenium, are often pitted against each other when it comes to web automation.

Website structure changes

Websites undergo periodic structural changes to improve design, layout, and features, leading to a better user experience. However, such changes can significantly complicate the web scraping process.

Data parsers are built according to specific web page layouts. Any changes that impact parameters defined in the parser would require adjustments from the scraper’s side. Otherwise, you will likely end up with an incomplete data set or crashed web scraper.

Extracting relevant data from HTML pages can be challenging due to inconsistent markup, irregular data formats, or nested structures. Scrapers need to employ robust parsing techniques using libraries like Beautiful Soup or regular expressions to extract the desired data accurately.

Solution: Use a parser designed for a specific target that could be hand-adjusted to accommodate changes when they happen.

Alternatively, AI-based parsers can adapt to website changes to a certain degree. The trained model recognizes prices, descriptions, or anything it was taught to do, even after layout changes. An AI-based parser has a collection of website layouts based on HTML tags usage, their relative positions, and other attributes to determine newly encountered code elements when dealing with a new target website.

Data parsing is a native feature of Oxylabs Scraper APIs. You can use a dedicated parser for the most popular targets or set up your own custom parser to accommodate a desirable website.

For the most popular product pages, Oxylabs AI-based Adaptive Parser, a feature of E-Commerce Scraper API, can return structured data from Google Shopping, Amazon, and more.

Infrastructure maintenance

High-volume web data extraction requires a reliable infrastructure to handle scalability, reliability, high speed, and facilitate maintenance. Managing proxies, handling request failures, avoiding detection, and maintaining code updates are the ever-present challenges.

When monitoring public data, a lot can change in just a day, if not within hours. Considering thousands of target websites on the internet, companies must constantly update the data they use in real-time and at scale. For business decisions, data accuracy is paramount.

However, it’s really difficult. For instance, you may not have access to tools that automatically sort (parse) the collected public data. As a result, time is wasted going through the already obsolete data by the time you finish sorting and analyzing it.

The main question is should you build your own web data collection infrastructure or outsource to a third-party provider?

Solution: Various programming languages have sprawling web scraping networks with multiple libraries that support HTTP(S) requests and data parsing out of the box. Alternatively, a third-party solution could be used to alleviate some of the more time-consuming steps and processes. The main benefits of dedicated scraping APIs such as Oxylabs Web Scraper API, when compared to Playwright, Puppeteer, Selenium, and various standalone web scraping libraries, are:

• Automation of web scraping processes

• Ease of scalability 

• Considerably less coding

• High return rates per successful requests

• Built-in tools (proxy rotators, job schedulers, parsers, crawlers)

The web data extraction architecture is highly customizable, allowing a purely hands-on approach or completely automated solutions, as well as a combination of both.

The decision should be made based on the requirements for scalability and the availability of in-house resources (devs, software, hardware) in accordance with their capabilities to ensure the flow of the action chain pictured below.