Web scraping can be a powerful tool for businesses to extract data from the web. Still, it can also be challenging due to the legal and technical barriers data owners set to protect their information.
To ensure successful web scraping, it is important to be aware of the common challenges and best practices to avoid them. With knowledge of these challenges and best practices, businesses can successfully and safely scrape public data from the web.
To stay ahead of these issues and avoid them altogether, here are the ten most common web scraping challenges and the best practices to prevent them.
1. Unstructured data
Webpages are often unstructured by design, meaning that data is consistently presented in different places or formats across multiple pages. This creates a significant challenge for web scraping because the data needs to be extracted from different places on different pages. To overcome this issue, use intelligent navigation techniques to identify the website’s structure and extract data from the appropriate source without errors.
2. Anonymity
Anonymity is a major challenge for web scraping. To scrape data without being blocked, users must rely on various techniques such as using multiple IP addresses, rotating proxies, and using browser fingerprinting.
By making it difficult to identify the source of requests, these techniques allow web scrapers to stay anonymous while extracting data from websites. A good rule of thumb is to use proxy services like scrapingant.com. This allows you to seamlessly do web scraping without getting blocked.
3. Accuracy of the scraped results
Scraping results must often meet a certain level of accuracy; otherwise, the data points will be useless. To make sure scraped results are accurate, web scrapers should use algorithms to parse and structure the data they crawl precisely. They can also progress through multiple stages of testing and manual verification to ensure the accuracy of their results.
4. Rate Limiting
Some websites employ rate limiting, a technique that can be used to prevent abuse from bots. Rate limiting ensures that scrapers do not visit the same page or API more times than allowed within an established time frame.
To avoid this challenge, you must understand the particular server’s limit and structure your web scraping process accordingly to ensure it does not exceed this limit. You can also implement technology such as proxy rotation to distribute and spread the traffic over multiple proxies to avoid blocking or bans by the website server from excessive requests.
5. Dynamic webpages and AJAX requests
Dynamic webpages often use AJAX requests when they are loading new content. This means that when a web scraper visits the page, it will not find any of this content, as the response sent back from the server does not contain it.
To scrape these pages, you must simulate an AJAX request to retrieve the content dynamically without visiting the actual webpage itself. This can be done using tools such as Selenium to automate browser requests to retrieve otherwise inaccessible content.
6. Blocked by robots.txt
In Robots.txt, robots are instructed not to access certain parts of a website. While generally used to prevent malicious crawlers from scraping a website, they can also be misused by web owners or developers and block legitimate crawlers from accessing the site.
To solve this issue, always look at robots.txt before you start crawling to check for any restrictions that could limit what data you retrieve.
7. Bypassing HTTP basic authentication
HTTP basic authentication is when a website or web service restricts access to resources, forcing visitors to authenticate themselves with a username and password.
This can present problems for scraping, as the web scraper may not have valid credentials to access the necessary data. To solve this challenge, use custom-built browser middleware that can handle complex authentication requirements by automatically entering site credentials.
8. Dealing with broken links and databases
Broken links and missing databases can be major issues when scraping the web. These problems can happen for various reasons, ranging from servers being taken down to website structure changes.
To detect broken links, use crawlers to scan websites regularly and track any changes that may cause errors. Additionally, it’s essential to ensure that your scraper actively monitors the sources you’re attempting to scrape so you can adjust your strategy if needed.
9. Coping with dynamic websites
Due to advanced technologies and JavaScript-based libraries, dynamic websites are often more difficult to scrape. These sites usually load content asynchronously, and creating an efficient scraper for them can be complex.
To combat these issues, bots can detect dynamic elements on the page to render a complete version of the DOM tree with all necessary data and then parse it accordingly. This method will ensure that your web scrapers collect the correct information without manually going through each website’s source code.
10. Extracting data from CAPTCHAs and JavaScript-rendered elements
In certain conditions, CAPTCHAs and JavaScript-rendered elements can present a challenge for web scraping bots. These elements often detect automated access and block content from being scraped.
The best way to handle this type of issue is by using a combination of technologies designed to bypass CAPTCHA tests and decode JavaScript elements on any given website. This should help you gather the desired information while respecting the website’s anti-scraping protection features.
Conclusion
Data scraping is becoming a necessity for all businesses. However, web scraping projects can be intimidating. In addition, making mistakes can cost you money and time once you start.
So before you start picking apart the internet, take some time to review these ten tips for avoiding common scraper pitfalls.
Cover Photo by Emile Perron on Unsplash