Web scraping, the process of extracting required data from web sources, is an essential tool; however, it is a technique fraught with challenges.
See below the most common web scraping challenges and practical solutions to address them. We cover everything from navigating web scraping ethics to overcoming technical barriers such as dynamic content and anti-scraping measures.
What are the major web scraping challenges?
There are many technical challenges that web scrapers face due to the barriers set by data owners or website owners to distinguish between humans and bots, and limit non-human access to their information. Web scraping challenges can be divided into two distinct categories:
1. Challenges arising from target websites:
- Dynamic content
- Website structure changes
- Anti-scraping techniques (CAPTCHA blockers, Robots.txt, IP blockers, Honeypots, and browser fingerprinting)
2. Challenges inherent to web scraping tools:
- Scalability
- Legal and ethical issues
- Infrastructure maintenance
1. Dynamic web content
Dynamic web content poses a significant challenge for web scrapers, as it fundamentally alters how information is delivered and displayed on a webpage.
Unlike static sites, where all the content is in the initial HTML file, dynamic sites build the page on the fly, often in response to user behavior. Technologies like AJAX (Asynchronous JavaScript and XML) are at the core of dynamic websites.
The challenge for web scrapers
The primary issue is that standard scraping tools are not web browsers. They see the initial HTML shell, which might contain placeholders, loading animations, and <script> tags, but often lacks the actual data you want to extract. These simple tools do not execute JavaScript.
The solution: Headless browsers
To overcome these challenges, web scrapers need to evolve from simple HTML parsers to tools that can fully render a webpage just like a human’s browser.
A headless browser is a web browser without a graphical user interface (GUI). It runs in the background but has all the capabilities of a standard browser, including a powerful JavaScript engine.
Tools like Selenium, Puppeteer, and Playwright enable you to programmatically control browsers (such as Chrome, Firefox, or WebKit). By using these advanced tools, you can build web scrapers that can interact with complex, dynamic websites and access content that would be completely invisible to simpler web scraping methods.
2. Website structure changes
Websites are continually being improved. These alterations can affect a site’s layout, design, or underlying code.
The impact of a minor change
For example, if a developer decides to change the price element’s class from price to current-price for better clarity, the scraper’s instructions will fail:
- The scraper will no longer be able to find the price. It might return an error, an empty value, or worse, it could accidentally grab the wrong piece of data that happens to be in a similar location.
- Because these changes can occur at any time and without warning, the scraper’s code is constantly in need of potential adjustments.
The solution: Building adaptable parsers
Instead of relying on highly specific and fragile selectors, developers can write smarter ones. For instance, instead of looking for a <span> with the exact class price, an adaptable parser might look for a <span> that is located next to the text “Price:” or one that contains a dollar sign ($).
Automated checks can run periodically to validate the scraped data. Suppose the price field suddenly starts returning empty values for all products. In that case, the system can automatically alert the developer that the website structure has likely changed and the parser needs to be updated.
3. Anti-scraping techniques
Many websites employ anti-scraping technologies to prevent or hinder web scraping activities. The following points provide an overview of some of the most common anti-bot measures encountered in the web scraping process:
3.1 CAPTCHA blockers
Websites use CAPTCHA when they suspect a visitor might be a bot. This is common on web pages for user registration, login forms, comment sections, and during checkout processes for high-demand items.
The challenge for web scrapers
Overly aggressive CAPTCHA implementations can block “good bots,” such as the Google bot that crawls the web to index pages for search results. If Google’s crawler is blocked, a website’s pages may not be properly indexed, which can negatively impact its SEO practices and search engine ranking.
The solution: Implementing a CAPTCHA solver
To navigate this obstacle, scrapers must be equipped with a mechanism to solve these challenges. While effective, using a CAPTCHA-solving service adds another layer of complexity and financial cost to the web scraping project, as these services typically charge per CAPTCHA solved.
3.2 Robots.txt
The robots.txt file is a fundamental aspect of the web’s ecosystem, acting as a guide for automated bots. While it’s listed as a challenge, it’s more of an ethical and legal guideline than a technical barrier. Robots.txt files indicate whether the content is crawlable or not, and specify a crawl limit to prevent network congestion.
The challenge for scrapers
The challenge presented by robots.txt is not a technical one. A scraper can be programmed to ignore the file and crawl the entire website anyway, simply. However, doing so is a clear violation of the website’s stated terms of service.
Ignoring robots.txt can lead to the website quickly identifying and permanently blocking your scraper’s IP address.
The solution: Seeking legitimate access
The correct approach is to find an officially sanctioned way to get the web data. The best alternative is to see if the website offers an API for data access. If no public API is available, the next step is direct communication. You can reach out to the website owner or data owner, explaining who you are and what you intend to do with the data.
3.3 IP blocking
IP blocking (also known as IP banning) is one of the most common and fundamental anti-scraping measures employed by websites. When a website’s server detects unusually high traffic from a single IP address, it flags it as suspicious. Once your IP is blocked, any further requests from your scraper will be rejected.
The solution: Using proxies to mask identity
A proxy is an intermediary server that sits between your scraper and the target website. When you send a request through a proxy, the website sees the request coming from the proxy’s IP address, not your own IP address. Two powerful types of proxies for this purpose:
- Rotating proxies: Your web scraping tool is configured to use this pool, and with each new request (or after a set number of requests), it automatically rotates to a different IP address. This distributes your requests across multiple IP addresses, so no single one exceeds the website’s rate limits.
- Residential proxies: The IP addresses in a residential proxy pool belong to real, consumer-grade internet connections provided by Internet Service Providers (ISPs) to homeowners. Since the traffic originates from a legitimate residential IP address, it is almost impossible for a website to distinguish a scraper’s request from that of a genuine human user.
3.4 Honeypot traps
Honeypots are computer systems designed to lure hackers and prevent them from accessing websites. A honeypot trap typically appears like a legitimate part of the website and contains data that an attacker may target.
If a crawling bot attempts to extract the content of a honeypot trap, it will enter an infinite loop of requests and fail to extract any further data.
Why bots fall for it
A human user interacts with the rendered, visual version of a website and would never see or click on this hidden link. However, many simple scrapers don’t render the page visually.
They work by parsing the raw HTML source code and programmatically extracting all the links (<a href=”…”> tags) they find. Since the honeypot link exists in the HTML, the naive bot will see it and follow it, just like any other legitimate link.
Solution
Instead of just parsing the raw HTML, use a headless browser, such as Selenium, Puppeteer, or Playwright. Additionally, by defining specific, predictable locations for the links you want to follow, you can reduce the chance of your scraper stumbling upon a honeypot link that has been intentionally placed in an obscure part of the HTML.
3.4 Browser fingerprinting
Browser fingerprinting is a method used by websites to gather information about their visitors through their IP addresses. Whenever you access a website, your device issues a request for connection to the site to load its content. This allows the website to retrieve and store data transmitted by your browser regarding your device.
Websites can accumulate extensive details about a user’s device, enabling them to customize suggestions for their visitors using browser fingerprinting. For instance, the target website can extract data about your user agents, HTTP header, language settings, and installed plugins.
Source: AmIUnique
The challenge for scrapers
Browser fingerprinting poses a significant challenge because scrapers, by default, have very strange and inconsistent fingerprints.
- Generic fingerprints: A basic scraper using a simple library will send a very minimal set of headers and have no plugins, screen resolution, or other “human” attributes.
- Inconsistent fingerprints: A scraper might use rotating proxies, causing its IP address to appear from Germany on one request and Japan on the next.
The solution: Blending in
Utilize headless browsers such as Selenium, Puppeteer, or Playwright. These are real browser engines that generate a much more complete and believable fingerprint out of the box compared to simple HTTP libraries.
You can also maintain a list of standard, real-world User-Agent strings and rotate them for different sessions. Ensure that the HTTP headers sent are also consistent with those of a real browser.
4. Scalability
You might need to scrape a large amount of web data from multiple websites to gain insights into the pricing intelligence, market research, and customer preferences. As the amount of data to be scraped increases, you need a highly scalable web scraper to make multiple parallel requests.
The solution: Asynchronous and parallel requests
You need to use a web scraper designed to handle asynchronous requests to enhance speed and gather large quantities of data more quickly.
Asynchronous data scraping is a technique that allows a scraper to send multiple requests to different websites without waiting for each one to respond before sending the next.
For instance, if one website is slow to respond, an asynchronous scraper can continue to send and process requests to other, faster websites in the meantime.
5. Ethical and legal issues
Web scraping is not an illegal act in itself, provided the extracted data is not used for unethical purposes. In many legal cases where businesses used web crawlers to extract competitors’ public data, judges did not find a legitimate reason to rule against the crawlers, even though crawling was frowned upon by the data owners.
For example, in the case of eBay vs. Bidder’s Edge, an auction data aggregator that used a proxy to crawl eBay’s data, the judge did not find Bidder’s Edge guilty of breaking federal hacking laws.2
However, if using the scraped data causes either direct or indirect copyright infringement, then web scraping would be deemed illegal, as seen in the case of Facebook vs. Power Ventures.3
6. Infrastructure maintenance
To maintain optimal server performance, it’s essential to regularly upgrade or expand resources such as storage to accommodate increasing data volumes and the complexities of web scraping. You must continuously update your web scraping infrastructure to keep pace with evolving demands.
The main challenge: A constantly moving target
As a business’s data requirements increase, so does the demand on its infrastructure. This necessitates constant upgrades to server capacity, storage solutions, and network bandwidth to handle the ever-growing volume of data being collected, processed, and stored.
Building and managing a scraping infrastructure requires a wide range of technical skills. This includes server administration, network management, database optimization, and the specialized knowledge needed to bypass anti-bot mechanisms.
The solution: Outsourcing the complexity
When outsourcing your web scraping requirements, ensure the service provider offers built-in features such as a proxy rotator and data parser. Additionally, the provider should provide easy scalability options and regularly update their infrastructure to meet changing needs.
External Links
- 1. Detection and Classification of Web Robots with Honeypots.
- 2. EBay v. Bidder's Edge - Wikipedia. Contributors to Wikimedia projects
- 3. Facebook, Inc. v. Power Ventures, Inc. - Wikipedia. Contributors to Wikimedia projects
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