How do you monitor and update your scraping scripts and pipelines to adapt to website changes?

It would be a dream if sites were always the same, then the same script would be working all the time. However, this is not the case now. With the quick changes in technology, sites are being updated and changed many times, and so your script should adapt to these changes.

Pro Tip: Regularly review and update your scraping scripts in response to website changes to ensure the accuracy and reliability of your data collection.

As we know, website changes not only break scraping scripts but can also introduce changes in structure or data availability, which can affect downstream analytics and decision-making. Therefore, proactive monitoring is key. Implementing automated checks that detect changes in HTML structure (e.g., new or missing tags) and sending alerts can prevent data quality issues before they propagate. For example for e-commerce platforms that frequently update layouts, implementing automated visual diff tools like Puppeteer's screenshot comparison can detect DOM-level changes visually, alerting you to update your script.

One way to detect breaking changes in a website is to have robust, automated testing in place. For example, there should be a test run for each target website, perhaps each unique format from that website even, such that a breaking change would be detected as early as possible. The logic for detecting a failure would be up to the developer but, conceptually, a test would fail when the page changed to such a degree that the expected formatted output is no longer produced by the web scraper. In Python, a common language used for web scraping, the unittest module, from the standard library, can handle these types of testing. For more advanced testing, the pytest library can be used.

The most efficient solution is to have a website change monitoring solution that tracks if the website pattern is changed and that is what we do at Datahut. Typically the webscrapers are scheduled to run at a specific time and the monitoring solution usually finds it before the crawling schedule starts. Depending on the change we can either manually change it or the platform does the auto-correction like replacing the Xpath with the new value.

Plus, in addition to what is already mentioned, you will notice an error while scraping, or the data won't fit the same structure. This is the easiest way to notice it.

You can include simple code that looks for differences in the webpage's structure or checks if the data you expect to find at certain locations is still there. Depending on the importance of the website scraped, your script with the aforementioned code can be run periodically. It might just make sense to search for data based on unique identifiers like class names or ids that are less likely to change in the structure of the webpage to avoid this issue altogether.

Pro Tip: Use tools like Visualping or Distill.io to track changes in website content and receive alerts, helping you stay informed about any modifications that may affect your scraping.

Good practice is to keep a copy of the old script because, over time, you will face problems that you might have solved before, and then referring to your old script will be much easier to start over, especially when you are dealing with the same website.

Check out "Scrapy for Beginners" by Daryl McMurray for practical guidance on efficiently updating your scraping scripts and handling website changes effectively.

We use ChangeMiner to track changes on target websites and update our scraping scripts accordingly. By using modular functions, we can easily modify specific parts of the script without affecting the entire workflow. Likewise, you can also, - monitor target websites for changes in layout, structure, or content. Use tools like ChangeMiner or DiffNow to track modifications, - write modular and adaptable scripts that can be easily modified to accommodate changes and - regularly review and update XPath expressions or CSS selectors to match the new website structure.

Mostly, to change the pipeline, that means there is a core change in the data itself, which requires you to rethink the whole process, so consider using the old pipeline as a helper tool in your new one.

For a project, I used Apache Airflow to schedule our scraping pipelines and trigger automatic updates when changes are detected on target websites. This ensured that our data remains up-to-date and accurate. You also can, - use pipeline management tools like Apache Airflow or Luigi to automate the scheduling and execution of scraping tasks - maintain version control of your scraping scripts to track changes and easily revert to previous versions if necessary and - implement robust error handling mechanisms to detect and address issues that may arise due to website changes

Tools are getting updated a lot, so keeping up with new tools is essential now. With tools being updated quickly, old tools become outdated just as quickly.

Pro Tip: Implement data validation checks within your pipelines to ensure that the transformed data meets quality standards, allowing you to catch and correct issues caused by website changes early.

Finally, as scraping tools are frequently updated, websites' anti-scraping measures are also continuously evolving. Given the dynamic nature of this field, it's important to keep yourself informed about the latest tools and technologies.